| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
The Jane and Jerry Weintraub Center for Reconstructive Biotechnology, Division of Advanced Prosthodontics, Biomaterials and Hospital Dentistry, UCLA School of Dentistry, Box 951668, CHS B3-082, Los Angeles, California 90095-1668, USA;
* corresponding author, ichiron{at}dent.ucla.edu
(I) Introduction (II) Principle of Microarray Technologies (III) Critical Issues on the Methods (A) FABRICATION OF A CDNA MICROARRAY (B) PROBE PREPARATION AND MICROARRAY HYBRIDIZATION (IV) Technical Challenges and Alternative Methods (A) ALTERNATIVE TECHNIQUES FOR MICROARRAY FABRICATION (B) MODIFIED METHODS FOR PROBE PREPARATION FROM A SMALL AMOUNT OF SAMPLE (V) Data Interpretation and Validation (A) IMAGE SCANNING (B) MEASUREMENT AND NORMALIZATION OF SIGNAL INTENSITIES (VI) Data Management Strategies (A) SELECTION OF DIFFERENTIALLY EXPRESSED GENES (B) DATA VISUALIZATION AND EXPLORATION (C) BIOLOGICAL VALIDITY OF MICROARRAY DATA (VII) Designs, Applications, and Weaknesses of the Microarray (VIII) Microarray Application to Oral Biology and Medicine (IX) Use of Microarrays in Tissue Engineering, Phenotype Analysis, and Monitoring (X) Future Directions of Genome-wide Biology REFERENCES
| Abstract |
|---|
 Top Next |
|---|
Key words. DNA microarray, oligonucleotide, gene expression, oral biology, post-genomics
| (I) Introduction |
|---|
|
TopPreviousNext |
|---|
). Publication of an entire supplemental issue of Nature Genetics (Phimister, 1999), some monographs (Schena, 1999, 2000), and the increasing number of commercially available services (Table) also reflect the rapid growth of interest in microarray technology.
|
|
| (II) Principle of Microarray Technologies |
|---|
|
TopPreviousNext |
|---|
). [With the recent progress in technology, the size of the filter array has also been reduced from macro to micro. Although the term "microarray" was originally used to describe solid-support DNA microarrays, it has recently been used to include some filter arrays (Chen et al., 1998; Han et al., 2000; Lyakhovich et al., 2000). To prevent confusion, we use the term "microarray" to describe only solid-support microarrays in this article. Also, the terms "target" and "probe" have been used to denote immobilized DNA on a support and labeled cDNA synthesized from an RNA sample, respectively, by the group of developers (Shalon et al., 1996). Although some researchers have used these terms to denote the opposite meanings, in this article we define these terms using the same definitions as the developers' to prevent confusion.] After hybridization, the fluorescent signals of two probes bound to individual spots are detected with a confocal laser scanner. The separately scanned images from each of the two probes are then combined and pseudocolored by means of computer software, and the expression ratios of two probes are calculated. Based on these systematic procedures, microarrays make possible large-scale gene expression monitoring in a parallel fashion.
|
Currently, there are two different formats of microarray-based technologies dependent on the target nucleic acid components, i.e., the oligonucleotide array and the cDNA microarray. The oligonucleotide type of array consists of oligonucleotide targets, generally less than 25 mer in length (Shoemaker et al., 1996; Fambrough et al., 1999; Lipshutz et al., 1999), which are generated in situ on a solid surface by light-directed synthesis (GeneChip®, Affymetrix, Inc., Santa Clara, CA, USA) (Fodor et al., 1991; Hacia et al., 1996). Synthetic linkers modified with photochemically removable protecting groups are attached to the glass substrate. Light is then directed through a photolithographic mask to specific areas on the surface to produce localized photodeprotection. Hydroxyl-protected deoxynucleotides are incubated with the surface so that chemical coupling occurs at the sites that have been illuminated in the preceding step. By repetition of these procedures with new masks, hundreds of thousands of oligonucleotides can be synthesized in a very small area (Fodor et al., 1991; Lipshutz et al., 1999). Alternatively, oligonucleotide arrays can be constructed by the spotting of pre-synthesized oligonucleotides onto the solid surface (Yershov et al., 1996; Marshall and Hodgson, 1998; Ramsay, 1998).
Because oligonucleotide arrays are designed and synthesized based on sequence information, physical intermediates such as cloning and polymerase chain-reaction (PCR) are not required. Specific sequences, which are non-overlapping if possible or minimally overlapping if necessary, can be designed to increase the hybridization sensitivity, even through their shorter sequences (Lipshutz et al., 1999). In contrast, the cDNA microarray is fabricated by the printing of cloned and amplified cDNAs onto the solid surface. The advantages of the cDNA microarray compared with the oligonucleotide array have been thought to include less susceptibility and higher specificity due to the longer sequences of the targets (Bilban et al., 2000). However, cDNA may contain repetitive sequences that are often observed in various genes, or similar sequences that are found in family member genes. These non-specific sequences may affect the sensitivity of the cDNA microarray.
To date, it is still unclear which method is more sensitive. There are no scientific data in which the cDNA microarray and the oligonucleotide array are directly compared. There is a tendency that the cDNA microarray is used for the screening of steady-state mRNA expression levels and the oligonucleotide array is applied when more precise analysis, including the detection of single nucleotide polymorphisms, is required (Wang et al., 1998; Sapolsky et al., 1999; Lindblad-Toh et al., 2000).
| (III) Critical Issues on the Methods |
|---|
|
TopPreviousNext |
|---|
). The coated surface provides attachment sites for the target cDNA so that it remains bound to the glass surface during hybridization and washing. Two types of arraying techniques have been used for application of target cDNA to the glass surface. First, in passive dispensing, in which the target is loaded into a spotting pin by capillary action, a small volume of the target is transferred to the glass surface by physical contact between the pin and the solid surface. The second method is based on drop-on-demand delivery, which is achieved by the adaptation of ink- or bubble-jet technology (Schena et al., 1998; Okamoto et al., 2000). To date, the passive dispensing method with spotting pins has been used more widely than ink-jet technology. By means of a robotic arrayer, approximately 75,000 genes can be printed on a standard 1 x 3 inch microscope (Eisen and Brown, 1999).
|
). Particular attention must be paid to the blocking process. An unnecessarily long period of blocking can cause washing-off of the spotted DNA. This phenomenon is called "comet tails" (Eisen and Brown, 1999) (Fig. 4B). The final step in post-processing is the denaturation of the printed target cDNA by heating of the array with boiling water for 2-3 min. After post-processing, individual spots on the microarray are usually invisible. Based on our experience, post-processed microarrays can be stored at room temperature for at least six months to probably several years.
|
| (IV) Technical Challenges and Alternative Methods |
|---|
|
TopPreviousNext |
|---|
(B) MODIFIED METHODS FOR PROBE PREPARATION FROM A SMALL AMOUNT OF SAMPLE
One of the technical challenges is to obtain the distinction of hybridization signals. As described above, DNA microarray hybridization requires relatively large amounts of RNA for probe cDNA synthesis and labeling. However, when only a small amount of sample tissue or a limited number of cells is available, it is difficult for enough RNA to be prepared. Microarray hybridization with cDNA probes synthesized from insufficient RNA results in inadequate or undetectable signal intensities. Therefore, to maintain or improve the fluorescent signal with a concomitant reduction of starting RNA, investigators have applied modified methods for probe preparation and labeling for microarray technology.
In vitro transcription (IVT), also known as the antisense RNA (aRNA) or complementary RNA (cRNA) amplification technique, was originally developed for gene expression analysis of single cells and extremely small amounts of tissue sample (Kacharmina et al., 1999). cDNA synthesis from mRNA is carried out with a specially designed oligo(dT) primer [oligo(dT)24-T7]. This primer contains the bacteriophage T7 RNA promoter sequence. The cDNA is made double-stranded by conventional techniques. Synthesized double-stranded cDNA containing the T7 RNA promoter can then be utilized as a template for aRNA synthesis by the T7 RNA polymerase. The original protocol recommended repeating the amplification procedure to produce a greater concentration of aRNA. By repetition of the procedure for two rounds, aRNA is amplified 106-fold greater than the starting material (Eberwine et al., 1992). This amplified aRNA can be used in microarray assessment, as well as in other methods for gene expression analysis, such as reverse transcriptase (RT)-PCR. For microarray hybridization, aRNA amplification is carried out in the presence of biotinylated UTP or CTP. The biotinylated aRNA probe can be hybridized to the microarray and stained with streptavidin-phycoerythrein before or after hybridization to the microarray (Coller et al., 2000). The detailed protocol for this method is available on the Whitehead/MIT Genome Center's Molecular Pattern Recognition Web site (http://waldo.wi.mit.edu/MPR/index.html). Alternatively, conventional cDNA synthesis and labeling can also be applied to the amplified aRNA (http://cmgm.stanford.edu/pbrown/).
Some studies have demonstrated the effectiveness of the aRNA amplification technique for microarray probe preparation. The improved signal intensities by aRNA amplification with 2.5 µg of starting total RNA have been documented by application to oligonucleotide array hybridization (Mahadevappa and Warrington, 1999). It has also been demonstrated that two rounds of aRNA amplification from 0.01 µg of starting total RNA and Cy dye-labeling can produce enough signal for microarray analysis (Wang et al., 2000).
By integrating aRNA amplification with the whole-cell patch electrode technique, one can analyze the gene expression in a single cell (VanGelder et al., 1990; Eberwine et al., 1992). It is likely that this approach can be applied to microarray technology. In addition, when aRNA amplification is performed in situ (in situ transcription; IST) on a fixed tissue section or microdissected tissue (Tecott et al., 1988; Zangger et al., 1989), the aRNA can be separately amplified in histologically normal and abnormal areas. Therefore, a more accurate comparison of gene expression in histologically different areas within the same tissue section is possible (Bowtell, 1999; Kacharmina et al., 1999). In fact, the RNA expression patterns in large- and small-sized neurons harvested independently from a fixed tissue section by laser-capture microdissection can be analyzed by aRNA amplification and DNA microarray (Luo et al., 1999). The results successfully have demonstrated the differential expression of genes in small and large neurons, as well as the usefulness of the integration of aRNA amplification with the microarray system. A combination of aRNA amplification and immunohistochemical staining may make possible a comparison of gene expression profiles between immunologically positive and negative areas in the same tissue section.
As another method for increasing the fluorescent signal intensities, amino-allyl reverse transcription (AA-RT) can be used for probe preparation (http://cmgm.stanford.edu/pbrown/). Briefly, cDNA is synthesized from total RNA or mRNA in the presence of amino-allyl dUTP (aa-dUTP, Sigma, St. Louis, MO, USA) instead of Cy3- or Cy5-dUTP. The aa-dUTPs incorporated into the synthesized cDNA are coupled with Cy3 or Cy5 monofunctional dye (Amersham Pharmacia Biotech, Inc.). Before the two labeled samples are pooled, aa-dUTP is quenched by the addition of hydroxylamine. Based on our findings, this technique results in an increase in fluorescent signal intensities compared with the direct fluorescent dye incorporation method, and reduces the required starting total RNA concentration to less than 10 µg. It is suspected that the enhancement of sensitivity by AA-RT is due to an increase in the reverse transcription rate compared with reverse transcription in the presence of Cy-dUTP.
Although the aRNA amplification technique is a very effective method for reducing the necessary starting RNA concentration, it does involve a long, complex protocol and the use of additional materials, such as the oligo(dT)24-T7 primer and the IVT kit (Ambion, Austin, TX, USA). In contrast, although AA-RT is less effective, this method is simpler and easier than aRNA amplification and has been the primary protocol used in our laboratory.
| (V) Data Interpretation and Validation |
|---|
|
TopPreviousNext |
|---|
). Repeated scanning of the microarray also causes a decrease in fluorescent signal intensity, particularly for Cy5 (van Hal et al., 2000). For an accurate comparison of two samples, the scanned signal intensities of Cy3 and Cy5 should be at the same level. Because the starting RNA volumes of the two samples may not be exactly the same in most cases, the signal intensities of Cy3 and Cy5 must be adjusted to be as close as possible, with the use of sets of positive control genes. Although normalization of signal intensities between two samples is usually performed after scanning, adjustment of scanning level makes the normalization process easier.
|
). Also, when a large-scale microarray including thousands of genes is used, the distribution of the Cy3/Cy5 ratios likely shows a "bell-shaped curve". In these instances, the general normalization method is a useful tool.
|
). In these cases, discrepancies between two samples should be normalized with sets of housekeeping genes. The distribution of the Cy3/Cy5 ratios is an important factor to be considered in the choice of a normalization strategy. In addition, when normalization is carried out with selected housekeeping genes, it is essential for as many control genes as possible to be used, particularly in a comparison between dissimilar samples. Our study revealed that the transcript levels of housekeeping genes were divergent from sample to sample (Figs. 7A, 7B). Inappropriate normalization may affect the results of selection of differentially expressed genes. After the normalization process, the Cy3/Cy5 ratio for each individual spot is calculated against the normalized signal intensities.
|
). Yellow spots indicate evenly expressed genes in both test and reference samples. Red and green spots denote up-regulated and down-regulated genes in the test compared with the reference sample, respectively. | (VI) Data Management Strategies |
|---|
|
TopPreviousNext |
|---|
). Like the normalization process for signal intensities, distribution of the expression ratios is an essential factor for choice of the appropriate gene selection strategy. In addition, ± 2 or ± 3 standard deviations of expression ratios is also used for selection of the differentially expressed genes (DeRisi et al., 1996; Behr et al., 1999; Karpf et al., 1999).
(B) DATA VISUALIZATION AND EXPLORATION
To visualize and explore microarray expression data, investigators have applied several methods (Ermolaeva et al., 1998; Bittner et al., 1999; Gaasterland and Bekiranov, 2000; Young, 2000). Scatter-plot analysis can identify outlying genes whose expression levels are different between the test and reference (Coller et al., 2000; Sudarsanam et al., 2000). If one reference (e.g., one time-point) is used as a baseline, the scatter-plot comparisons of one reference with several test samples generate a Pearson correlation coefficient for each comparison (Khan et al., 1998; Voehringer et al., 2000).
For identification of the sets of regulated genes, several clustering methods have been applied for microarray data. K-means cluster, clustergram, and self-organizing maps with a software program make clustering of genes through several time points possible, due to the similarity of their expression patterns (Eisen et al., 1998; Tamayo et al., 1999; White et al., 1999; Soukas et al., 2000; Zhao et al., 2000). Clustering analysis of sample-sample correlation can also be performed by the dendrogram method (Khan et al., 1998; Scherf et al., 2000). In this technique, samples are clustered based on their gene expression profiles or their sensitivity to the stimuli, such as a drug, by measurement of the metric distance of one Pearson correlation coefficient. Additionally, by the addition of a second dimension of clustering, such as gene clusters, to the dendrogram, a double dendrogram can be displayed (Perou et al., 1999; Alizadeh et al., 2000). As another means of cluster analysis, genes can be classified into several categories based on their biological functions (Ferea et al., 1999; Iyer et al., 1999; Ly et al., 2000). In addition, some investigators combine several clustering methods and/or other techniques to elucidate and explore the comprehensive and complex transcriptional regulation mechanisms and functional interactions of genes. As mentioned above, individual clustering techniques provide different information. Investigators, therefore, should choose or combine the appropriate methods for their purpose.
The chromosomal display technique of the yeast genome has been applied to microarray data for visualization of the chromosomal locations of differentially expressed genes by histone H4 depletion (Wyrick et al., 1999). The ProbeBrowser software (http://molepi.stanford.edu/free_software.html), which integrates microarray data with the genomic positions of the hybridization targets and displays corresponding open reading frames annotations, has been used for the microarray analysis of Bacille Calmette-Guerin vaccines (Behr et al., 1999). These methods can visualize the relationship between differentially expressed gene and genomic region. To determine the genetic network architecture, investigators have applied a combination of K-means clustering and sequence motif searching to the microarray results at the several time points throughout the yeast cell cycle (Tavazoie et al., 1999). The results indicate a significant correlation between gene expression patterns and sequence motifs.
(C) BIOLOGICAL VALIDITY OF MICROARRAY DATA
In some studies, microarray analysis was performed in tandem with another traditional gene expression assessment assay as a means of ensuring the reliability of the microarray data. Northern blotting and RT-PCR have generally been used for comparison. Consistencies or at least similar tendencies were demonstrated in the results of these experiments (DeRisi et al., 1996; Chu et al., 1998; Mochii et al., 1999; Wilson et al., 1999; Aharoni et al., 2000; Coller et al., 2000; Feng et al., 2000; Pendurthi et al., 2000; Soukas et al., 2000; Yoshioka et al., 2000; Zhao et al., 2000). In another study, the in situ hybridization positive cells were captured by laser microdissection, and the corresponding gene expression was tested by microarray. Two independent experiments validated the microarray data (Luo et al., 1999). Thus, the high reliability of the microarray data has been documented.
| (VII) Designs, Applications, and Weaknesses of the Microarray |
|---|
|
TopPreviousNext |
|---|
The second category of microarrays includes those designed for a specific purpose. We call this the "aim-specific microarray (ASMA)". A microarray in this category is constructed with selected genes of interest, or genes that are significant to a certain disease. An ASMA fabricated with 96 inflammatory-related genes has been used for evaluation of the mRNA expression levels in samples from rheumatoid arthritis patients (Hellar et al., 1997). Another ASMA with 148 target genes, including metabolic enzymes, DNA repair enzymes, stress proteins, and cytokines, has been generated for analysis of the genetic response to toxicants (Bartosiewicz et al., 2000). Several studies have reported analyses combining the microarray and other differential display techniques (Welford et al., 1998; Yang et al., 1999; Liau et al., 2000). For instance, 26 differential immuno-absorption products of human glioblastoma (GBM) and normal brain tissues are used for construction of an ASMA for monitoring transcript levels in tumorous and non-tumorous brain specimens (Liau et al., 2000).
Microarray construction with a cDNA library derived from a specific tissue, which is called "tissue-specific microarray", has also been proposed. Microarrays with rat heart cDNA libraries are fabricated for examination of the gene expression profile in response to myocardial infarction (Sehl et al., 2000; Stanton et al., 2000). In addition, others have fabricated microarrays with genomic DNA. A microarray constructed with clones from chromosome 20 is used for analysis of the DNA copy number variation in breast cancer (Pinkel et al., 1998). With this approach, the result has demonstrated chromosome 20 aberrations in breast cancer. Linkage-disequilibrium mapping in combination with a chromosome-11 microarray analysis has successfully achieved gene-mapping without marker-by-marker genotyping (Cheung et al., 1998).
Various types of ASMAs, such as apoptosis, malignant tumor, and cytokines, are now commercially available from several manufacturers. In general, an ASMA can be constructed on a smaller scale. An advantage of the smaller microarrays is that it is possible for the time and cost for microarray fabrication to be reduced, the RNA sample volume to be minimized, and a high quality of target DNA to be maintained. Although there are more limited data acquired with an ASMA, it is a valuable tool for investigators to use in achieving specific objectives. Researchers should therefore choose the design of the microarray with due consideration given to their purpose.
Today, with the rapid progress in sequencing the human genome, we have entered the "post-genome" era. Whereas the concept of functional genomics was once futuristic, it is now a reality. The research after extended genome projects is moving from data-poor science to data-rich science. This is reflected in a recent Nature "Insight" (Dhand, 2000) that focused on functional genomics, including global gene expression analysis by microarray technology (Lockhart and Winzeler, 2000), proteomics or large-scale analyses of proteins (Pandey and Mann, 2000), and computational biology (Eisenberg et al., 2000). Some researchers have even attempted to analyze DNA-protein interactions using a double-stranded DNA array (Bulyk et al., 1999), or the differential-display proteomics assay using a protein chip (Pandey and Mann, 2000). Future post-genomic research includes functional genomics, global expression monitoring for genes and proteins, and gene network analyses that combine several genetic analysis techniques (Thieffry, 1999).
Against the advantages, several weaknesses of microarray technology have also been pointed out. These include high cost and time consumption, necessity of special devices, and difficulty of data interchange between individual microarrays. Moreover, it is also difficult for the expression levels between individual targets to be compared in the same RNA sample, because of different hybridization rates due to variations of melting temperature depending on sequence and length of target gene fragments (Duggan et al., 1999).
| (VIII) Microarray Application to Oral Biology and Medicine |
|---|
|
TopPreviousNext |
|---|
In the areas of dental, oral, and maxillofacial research, great effort has been devoted to elucidating the molecular and genetic bases of normal and abnormal conditions. Although most human diseases are assumed to have multifactorial etiologies, they often include a genetic contribution (Townsend et al., 1998; Hart et al., 2000). Typically, congenital and developmental malformation, infections, and malignant diseases are associated with a particular genetic background (Mitchel, 1997; Miller et al., 1998; Tucker and Sharpe, 1998; Weiss et al., 1998; Hodge et al., 2000; Schwartz, 2000; Scully et al., 2000a,b). Molecular and genetic information related to these diseases has accumulated and has been applied for developing clinical tools for diagnosis, prognosis, and treatment (Garlick and Fenjves, 1996; Shillitoe, 1998; Tralongo et al., 1999; Komiya et al., 2000). However, the complex mechanisms of the genetic pathways specific to oral and maxillofacial tissues in both physiological and pathophysiological events are not yet fully understood.
It has been suggested that the neural-crest-derived craniofacial tissues may react differently to tissue remodeling, wound healing, and/or aging processes. In particular, craniofacial bones, which lack cartilage precursor tissue, develop through intramembranous ossification, whereas lumbar and extremity bones undergo endochondral ossification. Once these bones of different embryonic origin are generated, however, a similar remodeling process is thought to take place. We have characterized the differential expression patterns of the extracellular matrix (ECM)-related genes between adult female mouse calvaria and humerus bones with a custom ASMA constructed with ECM-related genes (Fig. 8). The gene expression pattern was generally similar; however, calvaria was associated with the elevated expression (> two-fold) of col1a1, col1a2, col9a1, col19a1, and osteonectin.
|
The gene expression profiles of nearly 300 Saccharomyces cerevisiae deletion mutants were evaluated by DNA microarray with genomic DNA hybridization (Hughes et al., 2000). The results demonstrated widespread aneuploidy in those mutants. In combination with comparative genomic hybridization for surveying DNA copy-number variation across the whole genome in normal and tumor cells, the microarray analysis has successfully identified gene amplifications and deletions throughout the genome (Pollack et al., 1999). These types of genomic analyses would be useful for elucidating the pathological mechanisms of congenital and developmental abnormalities, such as cleft lip and palate, and mandibular prognathism. Adaptation of microarray technology makes it possible for investigators to analyze the genetic pathways and dynamic interactions of genes in various diseases, e.g., oral mucosal disease including pre-malignant and malignant tumors, periodontal disease, endodontic disease, temporomandibular joint disorders, and cystic diseases, as well as normal and abnormal development of oral and craniofacial structures.
| (IX) Use of Microarrays in Tissue Engineering, Phenotype Analysis, and Monitoring |
|---|
|
TopPreviousNext |
|---|
Gene expression analyses have been used for the phenotypic assessment of various tissues, as well as engineered tissue (Khorramizadeh et al., 1999; Kim et al., 1999; Nishimura et al., 2000). However, classic gene expression techniques, such as Northern or Southern blot analysis, are limited in their ability to evaluate the expression patterns of multiple genes. In fact, in a recent study, Northern blot analysis failed to document differential gene expression (Delany et al., 2000). Comprehensive monitoring of gene expression by microarray technologies is essential for the identification of a cell or tissue phenotype and for determination of their activities.
The contributions of the senescent cell type and telomerase expression in dermal fibroblasts to the morphology and phenotype of skin were evaluated by means of a cDNA microarray (Funk et al., 2000). The results have demonstrated a higher expression of matrix degradation-related genes, such as tPA, uPA, stromelysins-1 and -2, and cathepsin O, and a lower expression of ECM genes, such as 
1(I), 1(III) collagens, and integrin {alpha}1, in senescent and telomerase-expressing fibroblasts compared with young fibroblasts. Gene expression during cardiac growth and myocardial infarction development by a cDNA has demonstrated specific molecular characteristics of those phenotypes (Sehl et al., 2000; Stanton et al., 2000). Surprisingly, the result suggests that osteoblast-specific factor 2, also known as type II Cbfa1, which is thought to be a specific gene in bone and tooth formation (D'Souza et al., 1999; Ducy et al., 1999), might be an important gene for the development of myocardial infarction (Stanton et al., 2000). An aging study of human fibroblasts with the use of oligonucleotide arrays demonstrated that one-third of differentially expressed genes are involved in the maintenance and remodeling of the ECM (Ly et al., 2000).
The gene expression patterns in a p53-transfected human colon cancer cell line were assessed by oligonucleotide arrays (Zhao et al., 2000). The results revealed that the genes of cytoskeletal molecules, growth factors, ECMs, and cell adhesion proteins are significantly more frequent in p53-regulated genes than those of other categories. These studies demonstrate the usefulness of comprehensive gene expression profiling by means of microarray technologies, as well as the importance of ECM-related genes for cell and tissue phenotype analysis.
There is a relatively high incidence of tissue defects in the oral and maxillofacial area. Tissue engineering, therefore, will be a very important area of study in the next decade. It may be valuable for investigators, using comprehensive gene expression analysis, to determine how close engineered tissues are to the original tissue. Microarray-based technologies can lead us to a higher level of understanding of the biology of the oral and maxillofacial area.
| (X) Future Directions of Genome-wide Biology |
|---|
|
TopPreviousNext |
|---|
| Acknowledgments |
|---|
| REFERENCES |
|---|
|
Top |
|---|
Adams JC, Watt FM (1993). Regulation of development and differentiation by the extracellular matrix. Development 117:1183–1198.[Medline]
Aharoni A, Keizer LCP, Bouwmeester HJ, Sun Z, Huerta MA, Verhoeven HA, et al. (2000). Identification of the SAAT gene involved in strawberry flavor biogenesis by use of DNA microarrays. Plant Cell 12:647–661.
Alizadeh AA, Eisen MB, Davis ER, Ma C, Lossos IS, Rosenwald A, et al. (2000). Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403:503–511.[Medline]
Ashkenas J, Muschler J, Bissell MJ (1996). The extracellular matrix in epithelial biology: shared molecules and common themes in distant phyla. Dev Biol 180:443–444.
Bartosiewicz M, Trounstine M, Barker D, Johnston R, Buckpitt A (2000). Development of toxicological gene array and quantitative assessment of this technology. Arch Biochem Biophys 376:66–73.[Medline]
Behr MA, Wilson MA, Gill WP, Salamon H, Schoolnik GK, Rane S, et al. (1999). Comparative genomics of BCG vaccines by whole-genome DNA microarray. Science 284:1520–1523.
Bilban M, Head S, Desoye G, Quaranta V (2000). DNA microarrays: a novel approach to investigate genomics in trophoblast invasion—a review. Placenta 21(Suppl A):S99–S105.
Bittner M, Meltzer P, Trent J (1999). Data analysis and integration: of steps and arrows. Nat Genet 22:213–215.[Medline]
Bowtell DD (1999). Options available—from start to finish—for obtaining expression data by microarray. Nat Genet 21(1 Suppl):25–32.[Medline]
Bulyk ML, Gentalen E, Lockhart DJ, Church GM (1999). Quantifying DNA-protein interactions by double-stranded DNA arrays. Nat Biotechnol 17:573–577.[Medline]
Chen JJ, Wu R, Yang PC, Huang JY, Sher YP, Han MH, et al. (1998). Profiling expression patterns and isolating differentially expressed genes by cDNA microarray system with colorimetry detection. Genomics 51:313–324.[Medline]
Chetverin AB, Kramer FR (1993). Sequencing of pools of nucleic acids on oligonucleotide arrays. Biosystems 30:215–231.[Medline]
Cheung VG, Gregg JP, Gogolin-Ewens KJ, Bandong J, Stanley CA, Baker L, et al. (1998). Linkage-disequilibrium mapping without genotyping. Nat Genet 18:225–230.[Medline]
Cheung VG, Morley M, Aguilar F, Massimi A, Kucherlapati R, Childs G (1999). Making and reading microarrays. Nat Genet 21(1 Suppl):15–19.[Medline]
Chu S, DeRisi J, Eisen M, Mulholland J, Botstein D, Brown PO, et al. (1998). The transcriptional program of sporulation in budding yeast. Science 282:699–705.
Coller HA, Grandori C, Tamayo P, Colbert T, Lander ES, Eisenman RN, et al. (2000). Expression analysis with oligonucleotide microarrays reveals that MYC regulates genes involved in growth, cell cycle, signaling, and adhesion. Proc Natl Acad Sci USA 97:3260–3265.
D'Souza RN, Åberg T, Gaikwad J, Cavender A, Owen M, Karsenty G, et al. (1999). Cbfa1 is required for epithelial-mesenchymal interactions regulating tooth development in mice. Development 126:2911–2920.[Abstract]
Delany AM, Amling M, Priemel M, Howe C, Baron R, Canalis E (2000). Osteopenia and decreased bone formation in osteonectin-deficient mice. J Clin Invest 105:91–923.
DeRisi J, Penland L, Brown PO, Bittner ML, Meltzer PS, Ray M, et al. (1996). Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nat Genet 14:457–460.[Medline]
Dhand R, editor (2000). Nature insight: functional genomics. Nature 405:819–865.
Ducy P, Starbuck M, Priemel M, Shen J, Pinero G, Geoffroy V, et al. (1999). A Cbfa1-dependent genetic pathway controls bone formation beyond embryonic development. Genes Dev 13:1025–1036.
Duggan DJ, Bittner M, Chen Y, Meltzer P, Trent JM (1999). Expression profiling using cDNA microarrays. Nat Genet 21(1 Suppl):10–14.[Medline]
Eberwine J, Yeh H, Miyashiro K, Cao Y, Nair S, Finnell R, et al. (1992). Analysis of gene expression in single live neurons. Proc Natl Acad Sci USA 89:3010–3014.
Eisen MB, Spellman PT, Brown PO, Botstein D (1998). Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95:14863–14868.
Eisen MB, Brown PO (1999). DNA arrays for analysis of gene expression. Meth Enzymol 303:179–205.[Medline]
Eisenberg D, Marcotte EM, Xenarios I, Teates TO (2000). Protein function in the post-genomic era. Nature 405:823–826.[Medline]
Ermolaeva O, Rastogi M, Pruitt KD, Schuler GD, Bittner ML, Chen Y, et al. (1998). Data management and analysis for gene expression arrays. Nat Genet 20:19–23.[Medline]
Fambrough D, McClure K, Kazlauskas A, Lander E (1999). Diverse signaling pathways activated by growth factor receptors induce broadly overlapping, rather than independent, sets of genes. Cell 97:727–741.[Medline]
Favis R, Day JP, Gerry N, Phelan C, Narod S, Barany F (2000). Universal DNA array detection of small insertions and deletions in BRCA1 and BRCA2. Nat Biotechnol 18:561–564.[Medline]
Feng Y, Yang JH, Huang H, Kennedy SP, Turi TG, Thompson JF, et al. (1999). Transcriptional profile of mechanically induced genes in human vascular smooth muscle cells. Circ Res 85:1118–1123.
Feng X, Jiang Y, Meltzer P, Yen PM (2000). Thyroid hormone regulation of hepatic genes in vivo detected by complementary DNA microarray. Mol Endocrinol 14:947–955.
Ferea TL, Botstein D, Brown PO, Rosenzweig RF (1999). Systematic changes in gene expression patterns following adaptive evolution in yeast. Proc Natl Acad Sci USA 96:9721–9726.
Fodor SPA, Read JL, Pirrung MC, Stryer L, Lu AT, Solus D (1991). Light-directed, spartially addressable parallel chemical synthesis. Science 251:767–773.
Fodor SPA, Rava RP, Huang XC, Pease AC, Holmes CP, Adams CL (1993). Multiplexed biochemical assays with biological chips. Nature 364:555–556.[Medline]
Funk WD, Wang CK, Shelton DN, Harley CB, Pagon GD, Hoeffler WK (2000). Telomerase expression restores dermal integrity to in vitro-aged fibroblasts in a reconstituted skin model. Exp Cell Res 258:270–278.[Medline]
Gaasterland T, Bekiranov S (2000). Making the most of microarray data. Nat Genet 24:204–206.[Medline]
Garlick JA, Fenjves ES (1996). Keratinocyte gene transfer and gene therapy. Crit Rev Oral Biol Med 7:204–221.
Geiss GK, Bumgarner RE, An MC, Agy MB, van't Wout AB, Hammersmark E, et al. (2000). Large-scale monitoring of host cell gene expression during HIV-1 infection using cDNA microarrays. Virology 266:8–16.[Medline]
