Hybridization analyses of arrayed cDNA libraries

Non-radioactive In Vivo Labeling of RNA with 4-Thiouracil

RNA molecules and their expression dynamics play essential roles in the establishment of complex cellular phenotypes and/or in the rapid cellular adaption to environmental changes. Accordingly, analyzing RNA expression remains an important step to understand the molecular basis controlling the formation of cellular phenotypes, cellular homeostasis or disease progression. Steady-state RNA levels in the cells are controlled by the sum of highly dynamic molecular processes contributing to RNA expression and can be classified in transcription, maturation and degradation. The main goal of analyzing RNA dynamics is to disentangle the individual contribution of these molecular processes to the life cycle of a given RNA under different physiological conditions. In the recent years, the use of nonradioactive nucleotide/nucleoside analogs and improved chemistry, in combination with time-dependent and high-throughput analysis, have greatly expanded our understanding of RNA metabolism across various cell types, organisms, and growth conditions.In this chapter, we describe a step-by-step protocol allowing pulse labeling of RNA with the nonradioactive nucleotide analog, 4-thiouracil , in the eukaryotic model organism Saccharomyces cerevisiae and the model archaeon Haloferax volcanii .

The epigenetic basis of cellular heterogeneity

Single-cell sequencing-based methods for profiling gene transcript levels have revealed substantial heterogeneity in expression levels among morphologically indistinguishable cells. This variability has important functional implications for tissue biology and disease states such as cancer. Mapping of epigenomic information such as chromatin accessibility, nucleosome positioning, histone tail modifications and enhancer-promoter interactions in both bulk-cell and single-cell samples has shown that these characteristics of chromatin state contribute to expression or repression of associated genes. Advances in single-cell epigenomic profiling methods are enabling high-resolution mapping of chromatin states in individual cells. Recent studies using these techniques provide evidence that variations in different aspects of chromatin organization collectively define gene expression heterogeneity among otherwise highly similar cells.

Multiplex Analysis Using cDNA Transcriptomic Profiling

DNA microarrays contain microscopic DNA spots attached to a solid surface. Each spot contains picomolar levels of a specific DNA probe sequence and hybridization to the corresponding gene products can be detected and quantitated through the use of fluorescently labeled target DNA. In this format, DNA microarrays can be used to measure the expression level of thousands of genes in a single experiment. Here, we present a method to detect the mRNA transcriptional changes in neuronal precursor cells following differentiation using high density cDNA microarrays.

A history of microarrays in biomedicine

The fundamental strategy of the current postgenomic era or the era of functional genomics is to expand the scale of biologic research from studying single genes or proteins to studying all genes or proteins simultaneously using a systematic approach. As recently developed methods for obtaining genome-wide mRNA expression data, oligonucleotide and DNA microarrays are particularly powerful in the context of knowing the entire genome sequence and can provide a global view of changes in gene expression patterns in response to physiologic alterations or manipulation of transcriptional regulators. In biomedical research, such an approach will ultimately determine biologic behavior of both normal and diseased tissues, which may provide insights into disease mechanisms and identify novel markers and candidates for diagnostic, prognostic and therapeutic intervention. However, microarray technology is still in a continuous state of evolution and development, and it may take time to implement microarrays as a routine medical device. Many limitations exist and many challenges remain to be achieved to help inclusion of microarrays in clinical medicine. In this review, a brief history of microarrays in biomedical research is provided, including experimental overview, limitations, challenges and future developments.

DNA sequencing methods in human genetics and disease research

DNA sequencing has revolutionized biological and medical research, and is poised to have a similar impact in medicine. This tool is just one of a number of developments in our capability to identify, quantitate and functionally characterize the components of the biological networks keeping us healthy or making us sick, but in many respects it has played the leading role in this process. The new technologies do, however, also provide a bridge between genotype and phenotype, both in man and model (as well as all other) organisms, revolutionize the identification of elements involved in a multitude of human diseases or other phenotypes, and generate a wealth of medically relevant information on every single person, as the basis of a truly personalized medicine of the future.

Overview of DNA microarrays: types, applications, and their future

This unit provides an overview of DNA microarrays. Microarrays are a technology in which thousands of nucleic acids are bound to a surface and are used to measure the relative concentration of nucleic acid sequences in a mixture via hybridization and subsequent detection of the hybridization events. This overview first discusses the history of microarrays and the antecedent technologies that led to their development. This is followed by discussion of the methods of manufacture of microarrays and the most common biological applications. The unit ends with a brief description of the limitations of microarrays and discusses how microarrays are being rapidly replaced by DNA sequencing technologies.

Single-molecule direct RNA sequencing without cDNA synthesis

Methods for in-depth genome-wide characterization of transcriptomes and quantification of transcript levels using various microarray and next-generation sequencing technologies have emerged as valuable tools for understanding cellular physiology and human disease biology and have begun to be utilized in various clinical diagnostic applications. Current methods, however, typically require RNA to be converted to complementary DNA prior to measurements. This step has been shown to introduce many biases and artifacts. In order to best characterize the 'true' transcriptome, the single-molecule direct RNA sequencing (DRS) technology was developed. This review focuses on the underlying principles behind the DRS, sample preparation steps, and the current and novel avenues of research and applications DRS offers.

Transcriptome profiling using single-molecule direct RNA sequencing

Methods for in-depth characterization of transcriptomes and quantification of transcript levels have emerged as valuable tools for understanding cellular physiology and human disease biology, and have begun to be utilized in various clinical diagnostic applications. Today, current methods utilized by the scientific community typically require RNA to be converted to cDNA prior to comprehensive measurements. However, this cDNA conversion process has been shown to introduce many biases and artifacts that interfere with the proper characterization and quantitation of transcripts. We have developed a direct RNA sequencing (DRS) approach, in which, unlike other technologies, RNA is sequenced directly without prior conversion to cDNA. The benefits of DRS include the ability to use minute quantities (e.g. on the order of several femtomoles) of RNA with minimal sample preparation, the ability to analyze short RNAs which pose unique challenges for analysis using cDNA-based approaches, and the ability to perform these analyses in a low-cost and high-throughput manner. Here, we describe the strategies and procedures we employ to prepare various RNA species for analysis with DRS.

FragIdent--automatic identification and characterisation of cDNA-fragments

BACKGROUND

Many genetic studies and functional assays are based on cDNA fragments. After the generation of cDNA fragments from an mRNA sample, their content is at first unknown and must be assigned by sequencing reactions or hybridisation experiments. Even in characterised libraries, a considerable number of clones are wrongly annotated. Furthermore, mix-ups can happen in the laboratory. It is therefore essential to the relevance of experimental results to confirm or determine the identity of the employed cDNA fragments. However, the manual approach for the characterisation of these fragments using BLAST web interfaces is not suited for larger number of sequences and so far, no user-friendly software is publicly available.

RESULTS

Here we present the development of FragIdent, an application for the automatic identification of open reading frames (ORFs) within cDNA-fragments. The software performs BLAST analyses to identify the genes represented by the sequences and suggests primers to complete the sequencing of the whole insert. Gene-specific information as well as the protein domains encoded by the cDNA fragment are retrieved from Internet-based databases and included in the output. The application features an intuitive graphical interface and is designed for researchers without any bioinformatics skills. It is suited for projects comprising up to several hundred different clones.

CONCLUSION

We used FragIdent to identify 84 cDNA clones from a yeast two-hybrid experiment. Furthermore, we identified 131 protein domains within our analysed clones. The source code is freely available from our homepage at http://compbio.charite.de/genetik/FragIdent/.

A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome

The functional complexity of the human transcriptome is not yet fully elucidated. We report a high-throughput sequence of the human transcriptome from a human embryonic kidney and a B cell line. We used shotgun sequencing of transcripts to generate randomly distributed reads. Of these, 50% mapped to unique genomic locations, of which 80% corresponded to known exons. We found that 66% of the polyadenylated transcriptome mapped to known genes and 34% to nonannotated genomic regions. On the basis of known transcripts, RNA-Seq can detect 25% more genes than can microarrays. A global survey of messenger RNA splicing events identified 94,241 splice junctions (4096 of which were previously unidentified) and showed that exon skipping is the most prevalent form of alternative splicing.

Expression profiling of drug response--from genes to pathways

Understanding individual response to a drug -what determines its efficacy and tolerability -is the major bottleneck in current drug development and clinical trials. Intracellular response and metabolism, for example through cytochrome P-450 enzymes, may either enhance or decrease the effect of different drugs, dependent on the genetic variant. Microarrays offer the potential to screen the genetic composition of the individual patient However, experiments are «noisy» and must be accompanied by solid and robust data analysis. Furthermore, recent research aims at the combination of high-throughput data with methods of mathematical modeling, enabling problem-oriented assistance in the drug discovery process. This article will discuss state-of-the-art DNA array technology platforms and the basic elements of data analysis and bioinformatics research in drug discovery. Enhancing single-gene analysis, we will present a new method for interpreting gene expression changes in the context of entire pathways. Furthermore, we will introduce the concept of systems biology as a new paradigm for drug development and highlight our recent research - the development of a modeling and simulation platform for biomedical applications. We discuss the potentials of systems biology for modeling the drug response of the individual patient.

Toxicogenomics of kojic acid on gene expression profiling of a375 human malignant melanoma cells

Kojic acid is a natural product and normally used as a food additive and preservative, a skin-whitening agent in cosmetics, a plant growth regulator and a chemical intermediate. Using DNA microarray technology, the overall biological effects of kojic acid on the gene expression profiling of a human skin A375 malignant melanoma cells were examined. After treatment with kojic acid, a total of 361 differentially expressed genes were distinctively changed with 136 up-regulated genes and 225 down-regulated genes. We used the bioinformatics tool to search the gene ontology and category classification of differentially expressed genes that provided the useful information of expressed genes belonging to cellular component, molecular function and biological process in regulation of melanogenesis. Seven down-regulated genes of APOBEC1, ARHGEF16, CD22, FGFR3, GALNT1, UNC5C and ZNF146 that were typically validated by the real-time quantitative PCR (RT-qPCR) analysis technology showed to be the tumor suppressor genes in melanoma cancer cells. Thus, microarray technology coupled with RT-qPCR offered a high throughput method to explore the number of differentially expressed genes responding to kojic acid and their biological functions, and led to more understanding of kojic acid effects on skin cancer therapy and related side effects. Moreover, the differentially expressed genes may become useful markers of skin malignant melanoma for further diagnostic and therapeutic applications.

Quantitative comparison of EST libraries requires compensation for systematic biases in cDNA generation

Background

Publicly accessible EST libraries contain valuable information that can be utilized for studies of tissue-specific gene expression and processing of individual genes. This information is, however, confounded by multiple systematic effects arising from the procedures used to generate these libraries.

Results

We used alignment of ESTs against a reference set of transcripts to estimate the size distributions of the cDNA inserts and sampled mRNA transcripts in individual EST libraries and show how these measurements can be used to inform quantitative comparisons of libraries. While significant attention has been paid to the effects of normalization and substraction, we also find significant biases in transcript sampling introduced by the combined procedures of reverse transcription and selection of cDNA clones for sequencing. Using examples drawn from studies of mRNA 3'-processing (cleavage and polyadenylation), we demonstrate effects of the transcript sampling bias, and provide a method for identifying libraries that can be safely compared without bias. All data sets, supplemental data, and software are available at our supplemental web site [1].

Conclusion

The biases we characterize in the transcript sampling of EST libraries represent a significant and heretofore under-appreciated source of false positive candidates for tissue-, cell type-, or developmental stage-specific activity or processing of genes. Uncorrected, quantitative comparison of dissimilar EST libraries will likely result in the identification of statistically significant, but biologically meaningless changes.

A conceptual and practical overview of cDNA microarray technology: implications for basic and clinical sciences

cDNA microarray is an innovative technology that facilitates the analysis of the expression of thousands of genes simultaneously. The utilization of this methodology, which is rapidly evolving, requires a combination of expertise from the biological, mathematical and statistical sciences. In this review, we attempt to provide an overview of the principles of cDNA microarray technology, the practical concerns of the analytical processing of the data obtained, the correlation of this methodology with other data analysis methods such as immunohistochemistry in tissue microarrays, and the cDNA microarray application in distinct areas of the basic and clinical sciences.

Miniaturization in functional genomics and proteomics

Proteins are the key components of the cellular machinery responsible for processing changes that are ordered by genomic information. Analysis of most human proteins and nucleic acids is important in order to decode the complex networks that are likely to underlie many common diseases. Significant improvements in current technology are also required to dissect the regulatory processes in high-throughtput and with low cost. Miniaturization of biological assays is an important prerequisite to achieve these goals in the near future.

Classification of oligonucleotide fingerprints: application for microbial community and gene expression analyses

MOTIVATION

Oligonucleotide fingerprinting of ribosomal RNA genes (OFRG) is a procedure that sorts rRNA gene (rDNA) clones into taxonomic groups through a series of hybridization experiments. The hybridization signals are classified into three discrete values 0, 1 and N, where 0 and 1, respectively, specify negative and positive hybridization events and N designates an uncertain assignment. This study examined various approaches for classifying the values including Bayesian classification with normally distributed signal data, Bayesian classification with the exponentially distributed data, and with gamma distributed data, along with tree-based classification. All classification data were clustered using the unweighted pair group method with arithmetic mean.

RESULTS

The performance of each classification/clustering procedure was compared with results from known reference data. Comparisons indicated that the approach using the Bayesian classification with normal densities followed by tree clustering out-performed all others. The paper includes a discussion of how this Bayesian approach may be useful for the analysis of gene expression data.

Gene expression profiling: from microarrays to medicine

With the mapping of the human genome comes the ability to identify genes of interest in specific diseases and the pathways involved therein. Laboratory technology has evolved in parallel, providing us with the ability to assay thousands of these genes at once, a technique known as microarray analysis. The main #x003Fion that this type of technology raises is how we can apply this powerful technology to clinical medicine. Recently, advances in data analysis, as well as standardization of the technology, have allowed us to examine this #x003Fion, and indeed a few clinical trials currently being performed include microarrays as part of their protocol. In this review we outline the microarray technique and describe these types of studies in further detail.

DNA microarrays — techniques and applications in microbial systems

Genome projects produce a huge amount of sequence information. As a result, the focus of genomics research is turning toward deduction of functional information about newly discovered genes. Thus structural genomics paves the way for a new discipline called functional genomics by providing the information required for microarray manufacture. Microarray technology is the result of automation and miniaturization in the detection of differential gene expression. By using this technology one can make a parallel analysis of RNA abundance and DNA homology for thousands of genes in a single experiment. Over the past several years, this unique technology has been used to explore hundreds transcriptional patterns and genome differences for a variety of microbial species. Applications of microarrays extend beyond the boundaries of basic biology into diagnostics, environmental monitoring, pharmacology, toxicology and biotechnology. We describe comprehensive nature of DNA microarray technology with emphasis on fabrication of DNA microarrays and application of this technology in biological environment with primary accent on microbial systems.

A comparison of oligonucleotide and cDNA-based microarray systems

Large-scale public data mining will become more common as public release of microarray data sets becomes a corequisite for publication. Therefore, there is an urgent need to clarify whether data from different microarray platforms are comparable. To assess the compatibility of microarray data, results were compared from the two main types of high-throughput microarray expression technologies, namely, an oligonucleotide-based and a cDNA-based platform, using RNA obtained from complex tissue (human colonic mucosa) of five individuals. From 715 sequence-verified genes represented on both platforms, 64% of the genes matched in "present" or "absent" calls made by both platforms. Calls were influenced by spurious signals caused by Alu repeats in cDNA clones, clone annotation errors, or matched probes that were designed to different regions of the gene; however, these factors could not completely account for the level of call discordance observed. Expression levels in sequence-verified, platform-overlapping genes were not related, as demonstrated by weakly positive rank order correlation. This study demonstrates that there is only moderate overlap in the results from the two array systems. This fact should be carefully considered when performing large-scale analyses on data originating from different microarray platforms.

Multiclass classification of microarray data with repeated measurements: application to cancer

Prediction of the diagnostic category of a tissue sample from its gene-expression profile and selection of relevant genes for class prediction have important applications in cancer research. Uncorrelated shrunken centroid and error-weighted, uncorrelated shrunken centroid algorithms have been developed that are applicable to microarray data with any number of classes.

Prediction of the diagnostic category of a tissue sample from its gene-expression profile and selection of relevant genes for class prediction have important applications in cancer research. We have developed the uncorrelated shrunken centroid (USC) and error-weighted, uncorrelated shrunken centroid (EWUSC) algorithms that are applicable to microarray data with any number of classes. We show that removing highly correlated genes typically improves classification results using a small set of genes.

A nonredundant human protein chip for antibody screening and serum profiling

There is burgeoning interest in protein microarrays, but a source of thousands of nonredundant, purified proteins was not previously available. Here we show a glass chip containing 2413 nonredundant purified human fusion proteins on a polymer surface, where densities up to 1600 proteins/cm(2) on a microscope slide can be realized. In addition, the polymer coating of the glass slide enables screening of protein interactions under nondenaturing conditions. Such screenings require only 200-microl sample volumes, illustrating their potential for high-throughput applications. Here we demonstrate two applications: the characterization of antibody binding, specificity, and cross-reactivity; and profiling the antibody repertoire in body fluids, such as serum from patients with autoimmune diseases. For the first application, we have incubated these protein chips with anti-RGSHis(6), anti-GAPDH, and anti-HSP90beta antibodies. In an initial proof of principle study for the second application, we have screened serum from alopecia and arthritis patients. With analysis of large sample numbers, identification of disease-associated proteins to generate novel diagnostic markers may be possible.

Gene expression profiles in different stages of mouse spermatogenic cells during spermatogenesis

During spermatogenesis, diploid stem cells differentiate, undergo meiosis and spermiogenesis, and transform into haploid spermatozoa. Various factors have been demonstrated to regulate this marvelous process of differentiation, but the expression of only a few genes specifically involved in spermatogenesis has been studied. In the present study, different types of spermatogenic cells were isolated from Balb/c mice testes of different ages using the velocity sedimentation method, and we determined the expression profiles of 1176 known mouse genes in six different types of mouse spermatogenic cells (primitive type A spermatogonia, type B spermatogonia, preleptotene spermatocytes, pachytene spermatocytes, round spermatids, and elongating spermatids) using Atlas cDNA arrays. Of the 1176 genes on the Atlas Mouse 1.2 cDNA Expression Arrays, we detected 181 genes in primitive type A spermatogonia, 256 in type B spermatogonia, 221 in preleptotene spermatocytes, 160 in pachytene spermatocytes, 141 in round spermatids, and 126 in elongating spermatids. A number of genes were detected as differential expression (up-regulation or down-regulation). Fourteen of the differentially expressed genes have been further confirmed by reverse transcription-polymerase chain reaction for their expression characterizations in different types of spermatogenic cells. These results provide more information for further studies into spermatogenesis-related genes and may lead to the identification of genes with potential relevance to spermatogenesis.

Molecular genetics in the pediatric intensive care unit

Molecular genetics and genomics have become highly relevant tools in the field of pediatric critical care medicine. The most immediate examples involve the use of molecular genetics as routine diagnostic tests (eg, detection of herpes simplex virus by PCR). Perhaps the most exciting and promising examples involve the research efforts based in these fields, which hold the potential to gain novel insight regarding the complex mechanisms involved in critical illness. Ultimately, the generation of more comprehensive and fundamental knowledge can lead to the design of more effective and specific therapeutic strategies.

Construction of a 'unigene' cDNA clone set by oligonucleotide fingerprinting allows access to 25 000 potential sugar beet genes

Access to the complete gene inventory of an organism is crucial to understanding physiological processes like development, differentiation, pathogenesis, or adaptation to the environment. Transcripts from many active genes are present at low copy numbers. Therefore, procedures that rely on random EST sequencing or on normalisation and subtraction methods have to produce massively redundant data to get access to low-abundance genes. Here, we present an improved oligonucleotide fingerprinting (ofp) approach to the genome of sugar beet (Beta vulgaris), a plant for which practically no molecular information has been available. To identify distinct genes and to provide a representative 'unigene' cDNA set for sugar beet, 159 936 cDNA clones were processed utilizing large-scale, high-throughput data generation and analysis methods. Data analysis yielded 30 444 ofp clusters reflecting the number of different genes in the original cDNA sample. A sample of 10 961 cDNA clones, each representing a different cluster, were selected for sequencing. Standard sequence analysis confirmed that 89% of these EST sequences did represent different genes. These results indicate that the full set of 30 444 ofp clusters represent up to 25 000 genes. We conclude that the ofp analysis pipeline is an accurate and effective way to construct large representative 'unigene' sets for any plant of interest with no requirement for prior molecular sequence data.

Historical background and anticipated developments

Expression profiling using DNA arrays is often believed to have appeared during the second half of the 1990s, and to be based exclusively on nonisotopic methods. In fact, the first article describing the application of cDNA arrays to expression analysis was published in 1992, relied on radioactive labeling, and was a new development of "high-density" membranes used until then essentially for efficient screening of libraries. Several papers described the use of this technology for simultaneous expression measurement of thousands of genes at the time when the first glass microarrays were published. Simultaneously, oligonucleotide chips, originally developed for resequencing and mutation detection applications, were shown to be capable of expression measurement as well. The three approaches have developed over the years and still coexist, as each of them has specific advantages (and drawbacks); the major issues have become those of data quality, data analysis and storage (ideally in a common public database). Meanwhile, the technology continues to evolve. The most obvious trend is a shift towards using arrays of relatively long oligonucleotides that combine most of the advantages of very long (cDNA) and very short (25-mer) DNA segments. The search for better detection methods, ideally without labeling of the sample, is continuing, although it seems difficult to reach the required sensitivity. New materials for microarray manufacture and new implementations of existing methods have appeared. In addition, the field is progressively becoming segmented into high gene number, low volume (research) applications on the one hand, and low gene number, high throughput (diagnostic) uses on the other.

Analysis of bacterial community composition by oligonucleotide fingerprinting of rRNA genes

One of the first steps in characterizing an ecosystem is to describe the organisms inhabiting it. For microbial studies, experimental limitations have hindered the ability to depict diverse communities. Here we describe oligonucleotide fingerprinting of rRNA genes (OFRG), a method that permits identification of arrayed rRNA genes (rDNA) through a series of hybridization experiments using small DNA probes. To demonstrate this strategy, we examined the bacteria inhabiting two different soils. Analysis of 1,536 rDNA clones revealed 766 clusters grouped into five major taxa: Bacillus, Actinobacteria, Proteobacteria, and two undefined assemblages. Soil-specific taxa were identified and then independently confirmed through cluster-specific PCR of the original soil DNA. Near-species-level resolution was obtained by this analysis as clones with average sequence identities of 97% were grouped in the same cluster. A comparison of these OFRG results with the results obtained in a denaturing gradient gel electrophoresis analysis of the same two soils demonstrated the significance of this methodological advance. OFRG provides a cost-effective means to extensively analyze microbial communities and should have applications in medicine, biotechnology, and ecosystem studies.

cDNA macroarray analysis of gene expression in synoviocytes stimulated with TNFalpha

Gene expression of synoviocytes stimulated with tumor necrosis factor-alpha (TNFalpha) was studied by macroarray analysis to elucidate the cellular response and identify new biological functions of known and unknown genes. 10035 cDNA clones were used to make cDNA macroarrays of representative genes. Synoviocytes expressed large amounts of fibronectin and collagen mRNA. Statistical analysis of the macroarray data revealed 26 genes, including six new genes, which underwent significant alteration of gene expression in response to TNFalpha stimulation. These findings suggest that the synoviocyte response to TNFalpha stimulation forms the basis of development of various aspects of the pathophysiology of rheumatoid arthritis.

The promise of microarray technology in melanoma care

BACKGROUND

Genetic aberration is responsible for the development of neoplastic potential in a number of malignancies. These DNA alterations result in significant changes in gene expression that may now be measured and catalogued. The microarray technique screens and identifies expressed genes that may be responsible for tumorigenesis.

METHODS

The authors review the application of the microarray technique in malignant melanoma.

RESULTS

Candidate melanoma suppressor genes have been identified in melanoma cell lines using this technique. Furthermore, molecular classification using gene expression profiling may improve the accuracy of the staging system for determining prognosis.

CONCLUSIONS

The microarray technique is in its initial development for clinical application in a variety of tumor models. Melanoma is an ideal system to study the genetic changes associated with the stepwise progression of malignancy. It may be possible to efficiently screen the entire human genome to identify the particular aberrations in gene expression responsible for tumorigenesis in melanoma.

Functional genomics, gene arrays, and the future of pathology

The human genome project has attracted a great deal of attention in recent years among the general public as well as the scientific community. Although it is likely to be a number of years before many of the expected benefits of the genomics revolution are realized, the impact of these scientific breakthroughs on diagnostic pathology is likely to become apparent relatively quickly. In particular, gene array technology, which allows gene expression measurements of thousands of genes in parallel, provides a powerful tool for pathologists seeking new markers for diagnosis. Several recent studies demonstrate how the gene array approach can not only recognize markers for known categories of neoplasia but also lead to recognition of different categories not previously appreciated. Although this approach shows great potential, the successful application of gene arrays to diagnostic problems will require thoughtful interpretation, just as immunochemical technologies require careful planning and analysis.

Statistical evaluation of differential expression on cDNA nylon arrays with replicated experiments

In this paper we focus on the detection of differentially expressed genes according to changes in hybridization signals using statistical tests. These tests were applied to 14 208 zebrafish cDNA clones that were immobilized on a nylon support and hybridized with radioactively labeled target mRNA from wild-type and lithium-treated zebrafish embryos. The methods were evaluated with respect to 16 control clones that correspond to eight different genes which are known to be involved in dorso-ventral axis specification. Moreover, 4608 Arabidopsis thaliana clones on the same array were used to judge statistical significance of expression changes and to control the false positive rates of the test decisions. Utilizing this special array design we show that differential expression of a high proportion of cDNA clones (15/16) and the respective genes (7/8) were identified, with a false positive error of <5% using the constant control data. Furthermore, we investigated the influence of the number of repetitions of experiments on the accuracy of the procedures with experimental and simulated data. Our results suggest that the detection of differential expression with repeated hybridization experiments is an accurate and sensitive way of identifying even small expression changes (1:1.5) of a large number of genes in parallel.

The impact of molecular biology on the practice of pediatric critical care medicine

Molecular biology is increasingly affecting all areas of clinical medicine, including pediatric critical care medicine. Recent advances in genomics will allow for a more in-depth understanding of disease processes that are relevant to the pediatric intensivist, such as sepsis, the acute respiratory distress syndrome, and multiple organ dysfunction syndrome. In turn, understanding critical illness at the genomic level may allow for more effective stratification of patient subclasses and targeted, patient-specific therapy. The related fields of pharmacogenomics and pharmacogenetics hold the promise of improved drug development and the tailoring of drug therapy based on the individual's drug metabolism profile. Therapeutic strategies aimed at modulating host inflammatory responses remain viable but will need to take into account the inherent redundancy of the host inflammatory response and the heterogenous responses between individual patients. Thus, "immuno-phenotyping" of critically ill patients will allow for more rational immune-modulating therapies, either in the form of inhibiting or enhancing specific immune/inflammatory responses. The host also contains powerful, broad cytoprotective mechanisms that could potentially be harnessed as a strategy for organ and tissue protection in many forms of critical illness. Finally, prospects for gene therapy, although quite challenging at present, may be applicable to the intensive care unit in the near future. With these rapid advancements in molecular biology, it is imperative that all pediatric critical care practitioners become, at least, familiar with the field and its related technology. Hopefully, clinician-scientists involved in pediatric critical care will also shape the direction of these future prospects.

Expression profiling of cancer-related genes in human keratinocytes following non-lethal ultraviolet B irradiation

Ultraviolet B irradiation initiates and promotes skin cancers, photo-aging, and immune suppression. In order to elucidate the effect of these processes at the level of gene expression, we used cDNA microarray technology to examine the effect of ultraviolet B irradiation on 588 cancer-related genes in human keratinocytes at 1, 6, and 24 h post-irradiation with a mildly cytotoxic dose of ultraviolet B (170 mJ/cm(2)). The viability of the irradiated keratinocytes was 75% at 24 h post-irradiation. Various cytokeratins and transcription factors were up-regulated within 1 h post-irradiation. After 6 h, expression of a variety of genes related to growth regulation (e.g. p21(WAF1), notch 4, and smoothened), apoptosis (e.g. caspase 10, hTRIP, and CRAF1), DNA repair (ERCC1, XRCC1), cytokines (e.g. IL-6, IL-13, TGF-beta, and endothelin 2), and cell adhesion (e.g. RhoE, and RhoGDI) were altered in human keratinocytes. These data suggest the changes in a cascade of gene expression in human keratinocytes occurring within 24 h after UVB exposure. Although the roles of these cellular genes after UVB-irradiation remain to be elucidated, microarray analysis may provide a new view of gene expression in epidermal keratinocytes following UVB exposure.

Improved statistical inference from DNA microarray data using analysis of variance and a Bayesian statistical framework. Analysis of global gene expression in Escherichia coli K12

We describe statistical methods based on the t test that can be conveniently used on high density array data to test for statistically significant differences between treatments. These t tests employ either the observed variance among replicates within treatments or a Bayesian estimate of the variance among replicates within treatments based on a prior estimate obtained from a local estimate of the standard deviation. The Bayesian prior allows statistical inference to be made from microarray data even when experiments are only replicated at nominal levels. We apply these new statistical tests to a data set that examined differential gene expression patterns in IHF(+) and IHF(-) Escherichia coli cells (Arfin, S. M., Long, A. D., Ito, E. T., Tolleri, L., Riehle, M. M., Paegle, E. S., and Hatfield, G. W. (2000) J. Biol. Chem. 275, 29672-29684). These analyses identify a more biologically reasonable set of candidate genes than those identified using statistical tests not incorporating a Bayesian prior. We also show that statistical tests based on analysis of variance and a Bayesian prior identify genes that are up- or down-regulated following an experimental manipulation more reliably than approaches based only on a t test or fold change. All the described tests are implemented in a simple-to-use web interface called Cyber-T that is located on the University of California at Irvine genomics web site.

DNA microarray analysis of complex biologic processes

DNA microarrays, or gene chips, allow surveys of gene expression, (i.e., mRNA expression) in a highly parallel and comprehensive manner. The pattern of gene expression produced, known as the expression profile, depicts the subset of gene transcripts expressed in a cell or tissue. At its most fundamental level, the expression profile can address qualitatively which genes are expressed in disease states. However, with the aid of bioinformatics tools such as cluster analysis, self-organizing maps, and principle component analysis, more sophisticated questions can be answered. Microarrays can be used to characterize the functions of novel genes, identify genes in a biologic pathway, analyze genetic variation, and identify therapeutic drug targets. Moreover, the expression profile can be used as a tissue or disease "fingerprint." This review details the fabrication of arrays, data management tools, and applications of microarrays to the field of renal research and the future of clinical practice.

Extracting information from cDNA arrays

High-density DNA arrays allow measurements of gene expression levels (messenger RNA abundance) for thousands of genes simultaneously. We analyze arrays with spotted cDNA used in monitoring of expression profiles. A dilution series of a mouse liver probe is deployed to quantify the reproducibility of expression measurements. Saturation effects limit the accessible signal range at high intensities. Additive noise and outshining from neighboring spots dominate at low intensities. For repeated measurements on the same filter and filter-to-filter comparisons correlation coefficients of 0.98 are found. Next we consider the clustering of gene expression time series from stimulated human fibroblasts which aims at finding co-regulated genes. We analyze how preprocessing, the distance measure, and the clustering algorithm affect the resulting clusters. Finally we discuss algorithms for the identification of transcription factor binding sites from clusters of co-regulated genes. (c) 2001 American Institute of Physics.

Microarray analysis of genes differentially expressed in HepG2 cells cultured in simulated microgravity: preliminary report

Developed at NASA, the rotary cell culture system (RCCS) allows the creation of unique microgravity environment of low shear force, high-mass transfer, and enables three-dimensional (3D) cell culture of dissimilar cell types. Recently we demonstrated that a simulated microgravity is conducive for maintaining long-term cultures of functional hepatocytes and promote 3D cell assembly. Using deoxyribonucleic acid (DNA) microarray technology, it is now possible to measure the levels of thousands of different messenger ribonucleic acids (mRNAs) in a single hybridization step. This technique is particularly powerful for comparing gene expression in the same tissue under different environmental conditions. The aim of this research was to analyze gene expression of hepatoblastoma cell line (HepG2) during early stage of 3D-cell assembly in simulated microgravity. For this, mRNA from HepG2 cultured in the RCCS was analyzed by deoxyribonucleic acid microarray. Analyses of HepG2 mRNA by using 6K glass DNA microarray revealed changes in expression of 95 genes (overexpression of 85 genes and downregulation of 10 genes). Our preliminary results indicated that simulated microgravity modifies the expression of several genes and that microarray technology may provide new understanding of the fundamental biological questions of how gravity affects the development and function of individual cells.

Tissue gene expression analysis using arrayed normalized cDNA libraries

We have used oligonucleotide-fingerprinting data on 60,000 cDNA clones from two different mouse embryonic stages to establish a normalized cDNA clone set. The normalized set of 5,376 clones represents different clusters and therefore, in almost all cases, different genes. The inserts of the cDNA clones were amplified by PCR and spotted on glass slides. The resulting arrays were hybridized with mRNA probes prepared from six different adult mouse tissues. Expression profiles were analyzed by hierarchical clustering techniques. We have chosen radioactive detection because it combines robustness with sensitivity and allows the comparison of multiple normalized experiments. Sensitive detection combined with highly effective clustering algorithms allowed the identification of tissue-specific expression profiles and the detection of genes specifically expressed in the tissues investigated. The obtained results are publicly available (http://www.rzpd.de) and can be used by other researchers as a digital expression reference.

Global analysis of gene expression in cells of the immune system II. Cell-free translation products and high-density filter hybridization data

We have developed an experimental system for linking information on cell-free transcription and translation products from cDNA clones with data obtained from hybridization signals from complex probes. The work described in this paper consists of two distinct processes, one being the construction of a system of clonal addresses and the other the identification of expressed genes involved in the studied processes. We describe the use of this system to identify genes involved in thymus development. Complex probes from fetal thymuses (GD15, 17 and newborn) of Balb/c mice were used to identify genes, which are up- or downregulated during the process of differentiation. The full set of information is available in the Clone-base of the Basel Institute for Immunology and will be retrievable from the website of the collaborating laboratories.

Genomics and proteomics: the new millennium of drug discovery and development

One of the most pressing issues facing the pharmaceutical and biotechnology industry is the tremendous dropout rate of lead drug candidates. Over the last two decades, several new genomic technologies have been developed in hopes of addressing the issues of target identification and lead candidate optimization. Gene expression microarray is one of these technologies and this review describes the four main formats, which are currently available: (a) cDNA; (b) oligonucleotide; (c) electrokinetic; and (d) fiberoptic. Many of these formats have been developed with the goal of screening large numbers of genes. Recently, a high-throughput array format has been developed where a large number of samples can be assayed using arrays in parallel. In addition, focusing on gene expression may be only one avenue in preventing lead candidate failure. Proteomics or the study of protein expression may also play a role. Two-dimensional polyacrylamide gel electrophoresis (2-DE) coupled with mass spectroscopy has been the most widely accepted format to study protein expression. However, protein microarrays are now being developed and modified to a high-throughput screening format. Examples of several gene and protein expression studies as they apply to drug discovery and development are reviewed. These studies often result in large data sets. Examples of how several statistical methods (principal components analysis [PCA], clustering methods, Shannon entropy, etc.) have been applied to these data sets are also described. These newer genomic and proteomic technologies and their analysis and visualization methods have the potential to make the drug discovery and development process less costly and more efficient by aiding to select better target and lead candidates.

An algorithm for clustering cDNA fingerprints

Clustering large data sets is a central challenge in gene expression analysis. The hybridization of synthetic oligonucleotides to arrayed cDNAs yields a fingerprint for each cDNA clone. Cluster analysis of these fingerprints can identify clones corresponding to the same gene. We have developed a novel algorithm for cluster analysis that is based on graph theoretic techniques. Unlike other methods, it does not assume that the clusters are hierarchically structured and does not require prior knowledge on the number of clusters. In tests with simulated libraries the algorithm outperformed the Greedy method and demonstrated high speed and robustness to high error rate. Good solution quality was also obtained in a blind test on real cDNA fingerprints.

Genomics, gene expression and DNA arrays

Experimental genomics in combination with the growing body of sequence information promise to revolutionize the way cells and cellular processes are studied. Information on genomic sequence can be used experimentally with high-density DNA arrays that allow complex mixtures of RNA and DNA to be interrogated in a parallel and quantitative fashion. DNA arrays can be used for many different purposes, most prominently to measure levels of gene expression (messenger RNA abundance) for tens of thousands of genes simultaneously. Measurements of gene expression and other applications of arrays embody much of what is implied by the term 'genomics'; they are broad in scope, large in scale, and take advantage of all available sequence information for experimental design and data interpretation in pursuit of biological understanding.

Selective transcriptional regulations in the human liver cell by hepatitis B viral X protein

The hepatitis B viral X protein (HBx) is known as a transcription factor and potential oncogene. To gain a better view of the effect of HBx on the transcriptional regulation in the human liver cell, we constructed a HepG2 cell line stably expressing HBx (HepG2-HBx), and performed cDNA microarray analysis on 588 cellular cDNAs comparing with untransformed control cells. Two genes (IGFR-2, RhoA) of oncogenes, one gene (p55CDC) of cell cycle regulators, three genes (thrombin receptor, MLK-3, MacMARCKS) of intracellular transducers, one gene (HSP27) of stress response proteins, two genes (FAST kinase, Bak) of apoptosis response proteins, one gene (p21(WAF)) of transcription factors were highly up-regulated; one gene (transcription elongation factor SII) of transcription factors and two genes (monocyte chemotactic protein 1, T-lymphocyte-secreted protein I-309) of growth factors were highly down-regulated. These results showed selective transcriptional regulation by HBx in the human liver cell.

Translational control of viral gene expression in eukaryotes

As obligate intracellular parasites, viruses rely exclusively on the translational machinery of the host cell for the synthesis of viral proteins. This relationship has imposed numerous challenges on both the infecting virus and the host cell. Importantly, viruses must compete with the endogenous transcripts of the host cell for the translation of viral mRNA. Eukaryotic viruses have thus evolved diverse mechanisms to ensure translational efficiency of viral mRNA above and beyond that of cellular mRNA. Mechanisms that facilitate the efficient and selective translation of viral mRNA may be inherent in the structure of the viral nucleic acid itself and can involve the recruitment and/or modification of specific host factors. These processes serve to redirect the translation apparatus to favor viral transcripts, and they often come at the expense of the host cell. Accordingly, eukaryotic cells have developed antiviral countermeasures to target the translational machinery and disrupt protein synthesis during the course of virus infection. Not to be outdone, many viruses have answered these countermeasures with their own mechanisms to disrupt cellular antiviral pathways, thereby ensuring the uncompromised translation of virion proteins. Here we review the varied and complex translational programs employed by eukaryotic viruses. We discuss how these translational strategies have been incorporated into the virus life cycle and examine how such programming contributes to the pathogenesis of the host cell.

Protein arrays for gene expression and molecular interaction screening

The array format has revolutionised biomedical experimentation and diagnostics, enabling ordered high-throughput analysis. During the past decade, classic solid phase substrates, such as microtitre plates, membrane filters and microscopic slides, were turned into high-density, chip-like structures. The concept of the arrayed library was central to this development which now extends from DNA to protein. The new and versatile protein array technology allows high-throughput screening for gene expression and molecular interactions. As a major platform for functional genomics, it is already on its way into medical diagnostics.

DNA microarray analyses of genes regulated during the differentiation of embryonic stem cells

Embryonic stem (ES) cells are derived from the inner cell mass of blastocysts, and in response to retinoic acid (RA) are induced to differentiate to form some of the first distinguishable cell types of early mammalian development. This makes ES cells an attractive model system for studying the initial developmental decisions that occur during embryogenesis and the molecular genetics and associated mechanisms underlying these decisions. Additionally, ES cells are of significant interest to those characterizing various gene functions utilizing transgenic and gene-targeting techniques. With the advent of DNA microarray technology, which allows for the study of expression patterns of a large number of genes simultaneously within a cell type, there is an efficient means of gaining critical insights to the expression, regulation, and function of genes involved in mammalian development for which information is not currently available. To this end, we have utilized Clontech's Atlas Mouse cDNA Expression Arrays to examine the expression of 588 known regulatory genes in D3 ES cells and their RA-induced differentiated progeny. We report that nearly 50% of the regulatory genes are expressed in D3 and/or D3-differentiated cells. Of these genes, the steady-state levels of 18 are down-regulated and 61 are up-regulated by a factor of 2.5-fold or greater. These changes in gene expression are highly reproducible and represent changes in the expression of a variety of molecular markers, including: transcription factors, growth factors and their receptors, cytoskeletal and extracellular matrix proteins, cell surface antigens, and intracellular signal transduction modulators and effectors.