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TOPICAL REVIEW |
1 Department of Physiology 2 Department of Biotechnology and Bioengineering Center, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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Abstract |
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(Received 25 June 2003;
accepted after revision 25 September 2003;
first published online 26 September 2003)
Corresponding author M. Liang: Department of Physiology, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA. Email: mliang{at}mcw.edu
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Introduction |
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TopAbstractIntroductionSummaryReferences |
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Changes in gene expression are often results of interactions between genetic materials and environmental stimuli, which underlie the regulation of various physiological responses, especially those with long-term implications. While the microarray technology has been touted as an integrative tool that provides insights into regulatory pathways, the physiology community has been slow in acceptance of these techniques because of early failure in generating useful data and the lack of a cohesive theoretical framework in which experiments can be analysed. With recent advance in both technology and analysis, it is appropriate to re-examine the use of microarray technology in the physiology laboratory.
In this review, we will address the question of how integrative physiologists can take advantage of these high-throughput gene expression profiling techniques in order to better understand organ function. The authors will present reasoning and evidence that these techniques, although appearing very ‘molecular’, are in fact particularly suitable and powerful for studying integrative physiology.
An overview of the DNA microarray technology
DNA microarray technology involves hybridizing target molecules derived from mRNA samples to probe molecules that have been immobilized as thousands of microspots on a solid support. The fluorescence or radioactivity incorporated in the target molecules can then be quantified in each microspot to reflect the number of target molecules corresponding to the genes represented by the probes in that spot. Figure 1 depicts the basic process of typical cDNA microarray techniques that have been utilized in our laboratories (Kita et al. 2002; Liang et al. 2002, 2003b; Yuan et al. 2003), which are derived from the prototype described by Brown and colleagues (Schena et al. 1995; Brown Laboratory homepage). Basically, thousands of cDNA clones are individually amplified by PCR and printed on glass slides using a robotic arrayer to produce microarrays of DNA probes. mRNAs from two samples (e.g. control and treated) are reverse-transcribed to cDNA and labelled with Cy3 and Cy5 fluorescent dyes, respectively. The labelled cDNAs are pooled and hybridized to a microarray. After unbound labelled cDNA is washed off the slide, fluorescence intensities of Cy3 and Cy5 are quantified in each spot using a laser confocal scanner. Fluorescence intensity data are then analysed to yield log-transformed ratios between Cy3 and Cy5 in each spot, which, with spotted DNA probes in excess, reflect the relative mRNA expression levels of the corresponding gene in the control and treated samples.
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The DNA microarray as a molecular tool for studying integrative physiology
Concept. Integrative physiology is traditionally characterized by its focus on the study of whole organisms, especially whole animals. It emphasizes the interaction between multiple functional pathways and regulatory mechanisms with overlapping, complementary, or opposing effects in the context of intact organisms. Its ultimate goal is to understand quantitatively how an organism works as a whole. However, because of the formidable complexity of an intact organism, especially in mammalian model animals commonly used by physiologists, integrative physiology can become a victim of the so-called ‘black box’ phenomenon: the inability to interpret the mechanistic link between the input (such as an experimental manipulation) and the output (such as the functional alterations observed). In addition, the lack of appropriate tools may force integrative physiologists to abandon their emphasis on the global network of regulation or, worse yet, to yield themselves to the temptation of explaining the entire network based on the small parts that they are able to study.
Revolutionary progress in genomics along with an array of new tools, such as those for high throughput gene expression profiling, have brought new prospects and opportunities to integrative physiology. As schematized in Fig. 2, these new tools are beginning to make a multidimensional integration of physiology possible. At the top of this multidimensional integration is the traditional integrative physiology that categorizes, measures, and integrates the functions of an organism. An excellent example is the cardiovascular model established by Guyton and associates more than three decades ago (Guyton et al. 1972). This first dimension of integrative physiology is termed ‘physiome’ by some (Physiome Project website). At the bottom is the genome sequence, which is or soon will be available for many species including human, mouse and rat (Rat Genome Project website; Rat Genome Database website). High throughput assays can then be used to categorize and measure on a global scale genome sequence variations, mRNA expression, and protein expression, modification, and interaction, as well as small regulatory molecules. The results of these measurements will constitute what Vidal described as ‘a biological atlas’ (Vidal, 2001). These will then become the substance for a second dimension of integration that links the genome to transcriptome, proteome and function. The integration could and should also be carried out in yet another dimension that is across different environmental conditions that influence the organism as well as across various developmental stages.
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Approach. Can this actually be done in the foreseeable future? Obviously, an enormous amount of detail needs to be worked out, and new technologies and tools need to be developed if this is to be done with any level of efficiency. Already, a number of attempts, primarily involving the use of DNA microarray, have been made in this direction with various degrees of success. These studies were mostly performed in simpler model organisms and typically investigated only one dimension of the ‘biological atlas’. However, they provided examples of how high throughput expression profiling techniques such as DNA microarray can be used to assist the integration of physiology. The following section of this article will focus on analysing in some details the approaches used in several of these studies and how the results might be expanded.
Hughes and colleagues (Hughes et al. 2000) used DNA microarrays to produce a compendium of genome-wide expression profiles in yeast corresponding to 300 mutations and chemical treatments. They argued that these reference expression profiles could serve as ‘fingerprints’ to identify functions of unknown open reading frames (ORFs), which are segments of DNA potentially encoding proteins. If the mutation of an unknown ORF resulted in an expression profile that matches reference expression profiles associated with mutants of certain known genes, the unknown ORF would likely belong to the same functional pathway as those known genes. Matching of expression profiles was carried out through agglomerative hierarchical clustering with an error-weighted correlation coefficient as the similarity measure. The validity of this approach was first demonstrated by the grouping of known genes that was apparently consistent with their known affiliation with specific functional pathways. Several uncharacterized ORFs or drug treatments were then assigned or linked to certain functional pathways according to their clustering with known genes or treatments. For example, YER044c, an unknown ORF, was assigned to the yeast ergosterol biosynthesis pathways because its mutations resulted in expression profiles that clustered with a group of genes involved in this pathway. This was verified by showing that YER044c mutant yeasts accumulated more sterols and less ergosterol compared to the wild-type. The authors noted that identifying the role of YER044c would have been difficult using traditional approaches since the mutations of this ORF only had a modest effect on ergosterol biosynthesis. Thus, through the analysis of microarray-generated gene expression profiles in combination with gene targeting or pharmacological intervention, the understanding of the functional network of the yeast was being refined and improved at a pace exceeding the capacity of conventional methods. As acknowledged by the authors, this approach was based on the assumption that perturbations of most, if not all, functional pathways would be associated with unique expression profiles. Moreover, disruption of any steps of a pathway had to be sufficient to elicit responses characteristic of that pathway. The validity of these assumptions would be questionable especially in more complex mammalian systems where the boundaries between many pathways are ambiguous as a gene may be involved in several distinct pathways and different pathways may have overlapping functions.
Several other studies attempted to assemble global transcriptional regulatory networks in the yeast based on microarray data. In a study by Lee et al. (2002), chromatin immunoprecipitation and a promoter microarray were first used to identify approximately 4000 interactions between 106 transcriptional regulators and promoter regions based on an arbitrary P-value threshold that was believed to be stringent. The regulatory motifs revealed by this location analysis were then refined for common expression by reiterative matching of expression profiles obtained from a large number of microarray experiments. In this case, gene expression profiles were used to partially overcome the limitation of the location analysis. As an example, the approach was used to assemble a cell cycle transcriptional regulatory network. The regulatory network assembled was found to be largely consistent with previous findings and to suggest roles for several less well-characterized transcriptional regulators. Tavazoie et al. (1999) used a k-means algorithm with Euclidean distance as the similarity measure to cluster expression profiles obtained from a cell cycle microarray study (Cho et al. 1998). Some, but not all, gene clusters appeared to be enriched for genes with similar functions or contain common upstream cis-regulatory motifs. This work was extended to emphasize the combinatorial nature of transcriptional regulation through analysis of Euclidean distance-based expression coherence score utilizing several microarray datasets (Pilpel et al. 2001).
These studies demonstrated the possibility and the power of integrating genome, transcriptome, and physiome (see Fig. 2). It is also possible to integrate proteomic data into this paradigm. Ideker et al. (2001) carried out a proof-of-principle study for integrating genomic and proteomic analyses to refine a model of a metabolic pathway. Specifically, the galactose utilization pathway in yeast was systematically perturbed by a series of genetic deletions and environmental stresses. The resulting global changes of mRNA expression were measured by microarrays. Changes in protein expression were measured by tandem mass spectrometry using isotope-coded affinity tags to distinguish control and treated samples. The results of microarray and proteomic analysis were used to refine the initial model of this pathway. The integration could be extended to include other levels of regulation. For example, by comparing microarray-based mRNA expression profiles and protein–protein interaction profiles generated by yeast two-hybrid analyses, Ge et al. (2001) found that genes with similar mRNA expression profiles were more likely to encode interacting proteins.
The utilization of microarray data in the integration of regulatory networks has also been demonstrated in other species such as Drosophila (Furlong et al. 2001; Stathopoulos et al. 2002; Halfon & Michelson, 2002) and C. elegans (Kim et al. 2001).
Studies in simpler organisms often provide useful models that can be applied to the study of more complex mammalian organisms such as rats and mice that physiologists commonly use. Although the complexity of these mammalian organisms remains a major obstacle, high throughput expression profiling techniques in combination with other approaches are beginning to generate evidence supporting the feasibility of the multidimensional integration of mammalian physiology depicted in Fig. 2.
Utilizing a chromosomal substitution approach, Jacob and colleagues (Cowley et al. 2001; PhysGen website) have been generating a panel of consomic rats in which chromosomes of one strain are being replaced one at a time by those from another strain. Parental and consomic rats are functionally characterized by measuring several hundred phenotypes related to cardiovascular, renal and other functions. The analysis of the F2 population derived from two parental strains, Dahl salt-sensitive (SS) and Brown Norway (BN) rats, generated an extensive map that linked hundreds of phenotypes with specific regions of the genome (Stoll et al. 2001). To extend this link between the genome and the physiome, initial DNA microarray analyses have been carried out in SS and consomic SS-13BN/Mcw rats (Liang et al. 2002, 2003b). In SS-13BN/Mcw, chromosome 13 of SS has been replaced by that of BN. As a result, the blood pressure salt-sensitivity and salt-induced renal injury were substantially reduced compared to SS (Cowley et al. 2001). Using a custom-made microarray representing the majority of known rat genes, 50 genes were identified to exhibit distinct temporal expression profiles in the renal medulla between SS and SS-13BN/Mcw following dietary high-salt exposure for overnight, three days, or two weeks (Liang et al. 2003b). A Euclidean distance-based index was used as the similarity measure for expression profiles. Interestingly, 30 of these 50 genes could be assembled into two networks related to the regulation of blood pressure and/or renal injury. One of the networks is shown in Fig. 3. Importantly, the expression patterns of the majority of these genes were consistent with reduced blood pressure salt-sensitivity and reduced renal injury in SS-13BN/Mcw. For example, glucagon receptor in the renal medulla was down-regulated after an overnight exposure to a high-salt diet in both SS and SS-13BN/Mcw. However, it returned to the control level after 3 days in SS-13BN/Mcw while remaining suppressed in SS through 2 weeks. On the basis of the known effect of glucagon to increase glomerular filtration rate and sodium excretion, this expression pattern would be consistent with glucagon receptor contributing to the lower blood pressure in SS-13BN/Mcw rats and suggest a direct renal effect of glucagon in addition to indirect effects through the liver. Furthermore, inspecting the entire network revealed several general characteristics. For example, multiple pathways appeared to contribute to the development of salt-induced hypertension in a temporally specific manner. Several genes related to blood pressure regulation were differentially expressed on early time points, others on later time points. Without using high-throughput profiling techniques, it would have been difficult to identify network-wide characteristics of gene expression or the differential expression of many genes shown in Fig. 3 that have less well-known links to the regulation of blood pressure and renal injury. These regulatory networks are obviously not comprehensive and require additional data to complete our understanding. Interactions between various components of these networks, which most certainly exist, are not apparent. New roles of genes suggested by these networks need to be experimentally validated. Nonetheless, these studies supported the feasibility of using high-throughput expression profiling techniques in combination with genetic and functional approaches to integrate mammalian physiology.
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Technical considerations
Despite the demonstrated power of DNA microarray techniques, some investigators still hesitate to accept them. One of the sharpest criticisms that microarray-based studies receive from physiologists is its lack of hypotheses, and thus the term ‘fishing expedition’. In response to or in anticipation of such criticism, some investigators try to attach some sort of hypotheses to their microarray-based studies. Such hypotheses often turn out to be either too vague to be relevant or so specific that they undermine the rationale for using microarrays.
Philosophically, the lack of a hypothesis hardly qualifies as a reason to dismiss a study. Although hypothesis-driven research has been very effective and widely accepted, it is in essence an approach to achieve the objective of finding the answer to a scientific question. Mandating a hypothesis for every study is confusing approaches with objectives. If one knows that a question has a thousand potential answers and can practically examine all of them, it becomes redundant to hypothesize that answer X is the right one. In cases like this, it makes more sense to bypass hypothesis and carry out experiments that directly address the question (Fig. 4). In fact, the minimal need for a hypothesis can sometimes be an advantage of microarray-based studies. Because of the high-throughput ability provided by microarray techniques, one could carry out question-driven studies without prior knowledge of the involvement or even the function of many genes and generate un-biased, global views of biological process, which may in turn become a rich source of testable hypotheses and discoveries.
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Once one has adapted a set of techniques, the immediate concern is how reliable the results are. Assessing reliability is not an easy task, especially when a ‘gold standard’ is not available as in the case of high-throughput gene expression profiling (see Liang et al. 2003a; for further discussion). It is also self-evident that the reliability of microarray results, as with any other complex techniques, would be substantially affected by the technical details of each step of the experiment. However, several pieces of evidence suggest that the massive amount of data generated by microarray techniques can be reliable. As shown in Fig. 5A, microarray images generated by repeating the same hybridization on two arrays can be visually reproducible. In the study by Liang et al. (2002), the reproducibility of array results was further demonstrated by the significant correlation and outlier concordance between experimental results of thousands of genes and results predicted on the basis of a four-way comparison design. As shown in Fig. 5B, ln (ratio)s of dozens of randomly selected genes obtained from microarrays also significantly correlated with those obtained from Northern blots (Liang et al. 2002, 2003b; Yuan et al. 2003).
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Other methods for high-throughput gene expression profiling
In addition to DNA microarray, a number of other experimental techniques have been developed and utilized to screen or determine mRNA expression in a high-throughput manner. They include subtractive hybridization (Sargent & Dawid, 1983), representational difference analysis (Lisitsyn et al. 1993; Hubank & Schatz, 1994), suppression subtractive hybridization (Diatchenko et al. 1996), mRNA differential display (Liang & Pardee, 1992), and serial analysis of gene expression (SAGE, Velculescu et al. 1995). Subtractive hybridization basically involves hybridizing cDNAs generated from two mRNA populations and identifying mRNA species that are differentially expressed in the two populations by separating single-stranded and double-stranded cDNAs. Representational difference analysis and suppression subtractive hybridization are variants of subtractive hybridization that incorporate PCR-based steps to improve the efficiency of the method. These methods are mainly used for screening purposes, especially to identify mRNAs that are present in one sample, but absent in the other. mRNA differential display is a PCR-based method that distinguishes mRNA species by the size of PCR products and allows the comparison of the expression levels of genes that are covered by the primer sets. SAGE makes use of a pair of restriction endonucleases (the anchoring and the tagging enzymes) to create a short sequence tag from each mRNA molecule. The sequence of the tag is likely unique for each gene. These tags are then amplified, concatenated, and sequenced. The copy number of each unique tag sequence represents the abundance of the corresponding mRNA. SAGE is an attractive profiling technique because of its potential ability to provide truly global view of expression even for genes whose sequences are unknown.
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Summary |
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References |
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TopAbstractIntroductionSummaryReferences |
|---|
Chien KR (2000). Genomic circuits and the integrative biology of cardiac diseases. Nature 407, 227–232.[CrossRef][Medline]
Chipping Forecast (2002). Nat Genet 32 (Suppl.).
Chipping Forecast (1999). Nat Genet 21 (Suppl.).
Cho RJ, Campbell MJ, Winzeler EA, Steinmetz L, Conway A, Wodicka L, Wolfsberg TG, Gabrielian AE, Landsman D, Lockhart DJ & Davis RW (1998). A genome-wide transcriptional analysis of the mitotic cell cycle. Mol Cell 2, 65–73.[CrossRef][Medline]
Cowley Jr. AW (1997). The Banbury Conference. Genomics to physiology and beyond: how do we get there?Physiologist 40, 205–211.[Medline]
Cowley
Jr.
AW (2003). Genomics and homeostasis. Am J Physiol Regul Integr Comp Physiol
284, R611–627.
Cowley
Jr.
AW, Roman
RJ, Kaldunski
ML, Dumas
P, Dickhout
JG, Greene
AS
&
Jacob
HJ (2001). Brown Norway chromosome 13 confers protection from high salt to consomic Dahl S rat. Hypertension
37, 456–461.
Diatchenko
L, Lau
YF, Campbell
AP, Chenchik
A, Moqadam
F, Huang
B, Lukyanov
S, Lukyanov
K, Gurskaya
N, Sverdlov
ED
&
Siebert
PD (1996). Suppression subtractive hybridization. a method for generating differentially regulated or tissue-specific cDNA probes and libraries. Proc Natl Acad Sci USA
93, 6025–6030.
Furlong
EE, Andersen
EC, Null
B, White
KP
&
Scott
MP (2001). Patterns of gene expression during Drosophila mesoderm development. Science
293, 1629–1633.
Ge H, Liu Z, Church GM & Vidal M (2001). Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae. Nat Genet 29, 482–486.[CrossRef][Medline]
Guyton AC, Coleman TG & Granger HJ (1972). Circulation: overall regulation. Annu Rev Physiol 34, 13–46.[CrossRef][Medline]
Hacia JG (1999). Resequencing and mutational analysis using oligonucleotide microarrays. Nat Genet 21 (Suppl.), 42–47.[CrossRef][Medline]
Halfon
MS
&
Michelson
AM (2002). Exploring genetic regulatory networks in metazoan development. methods and models. Physiol Genomics
10, 131–143.
Hubank
M
&
Schatz
DG (1994). Identifying differences in mRNA expression by representational difference analysis of cDNA. Nucl Acids Res
22, 5640–5648.
Hughes TR, Mao M, Jones AR, Burchard J, Marton MJ, Shannon KW, Lefkowitz SM, Ziman M, Schelter JM, Meyer MR, Kobayashi S, Davis C, Dai H, He YD, Stephaniants SB, Cavet G, Walker WL, West A, Coffey E, Shoemaker DD, Stoughton R, Blanchard AP, Friend SH & Linsley PS (2001). Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer. Nat Biotechnol 19, 342–347.[CrossRef][Medline]
Hughes TR, Marton MJ, Jones AR, Roberts CJ, Stoughton R, Armour CD, Bennett HA, Coffey E, Dai H, He YD, Kidd MJ, King AM, Meyer MR, Slade D, Lum PY, Stepaniants SB, Shoemaker DD, Gachotte D, Chakraburtty K, Simon J, Bard M & Friend SH (2000). Functional discovery via a compendium of expression profiles. Cell 102, 109–126.[CrossRef][Medline]
Ideker
T, Thorsson
V, Ranish
JA, Christmas
R, Buhler
J, Eng
JK, Bumgarner
R, Goodlett
DR, Aebersold
R
&
Hood
L (2001). Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science
292, 929–934.
Kim
SK, Lund
J, Kiraly
M, Duke
K, Jiang
M, Stuart
JM, Eizinger
A, Wylie
BN
&
Davidson
GS (2001). A gene expression map for Caenorhabditis elegans. Science
293, 2087–2092.
Kita Y, Shiozawa M, Jin W, Majewski RR, Besharse JC, Greene AS & Jacob HJ (2002). Implications of circadian gene expression in kidney, liver and the effects of fasting on pharmacogenomic studies. Pharmacogenetics 12, 55–65.[CrossRef][Medline]
Kitano
H (2002). Systems biology: a brief overview. Science
295, 1662–1664.
Kononen J, Bubendorf L, Kallioniemi A, Barlund M, Schraml P, Leighton S, Torhorst J, Mihatsch MJ, Sauter G & Kallioniemi OP (1998). Tissue microarrays for high-throughput molecular profiling of tumor specimens. Nat Med 4, 844–847.[CrossRef][Medline]
Kuruvilla FG, Shamji AF, Sternson SM, Hergenrother PJ & Schreiber SL (2002). Dissecting glucose signalling with diversity-oriented synthesis and small-molecule microarrays. Nature 416, 653–657.[CrossRef][Medline]
Lee
TI, Rinaldi
NJ, Robert
F, Odom
DT, Bar-Joseph
Z, Gerber
GK, Hannett
NM, Harbison
CT, Thompson
CM, Simon
I, Zeitlinger
J, Jennings
EG, Murray
HL, Gordon
DB, Ren
B, Wyrick
JJ, Tagne
JB, Volkert
TL, Fraenkel
E, Gifford
DK
&
Young
RA (2002). Transcriptional regulatory networks in Saccharomyces cerevisiae. Science
298, 799–804.
Liang
M, Briggs
AG, Rute
E, Greene
AS
&
Cowley
Jr.
AW (2003a). Quantitative assessment of the importance of dye switching and biological replication in cDNA microarray studies. Physiol Genomics
14, 199–207.
Liang
P
&
Pardee
AB (1992). Differential display of eukaryotic messenger RNA by means of the polymerase chain reaction. Science
257, 967–971.
Liang
M, Yuan
B, Rute
E, Greene
AS, Olivier
M
&
Cowley
Jr.
AW (2003b). Insights into Dahl salt-sensitive hypertension revealed by temporal patterns of renal medullary gene expression. Physiol Genomics
12, 229–237.
Liang
M, Yuan
B, Rute
E, Greene
AS, Zou
AP, Soares
PMC, Question
GD, Slocum
GR, Jacob
HJ
&
Cowley
Jr.
AW (2002). Renal medullary genes in salt-sensitive hypertension: a chromosomal substitution and cDNA microarray study. Physiol Genomics
8, 139–149.
Lisitsyn N, Lisitsyn N & Wigler M (1993). Cloning the differences between two complex genomes. Science 259, 946–951.[Abstract]
Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, Chee MS, Mittmann M, Wang C, Kobayashi M, Horton H & Brown EL (1996). Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol 14, 1675–1680.[CrossRef][Medline]
Luo L, Salunga RC, Guo H, Bittner A, Joy KC, Galindo JE, Xiao H, Rogers KE, Wan JS, Jackson MR & Erlander MG (1999). Gene expression profiles of laser-captured adjacent neuronal subtypes. Nat Med 5, 117–122.[CrossRef][Medline]
Nature Focus on Computational Biology. http://www.nature.com/nature/computationalbiology.
Okamoto T, Suzuki T & Yamamoto N (2000). Microarray fabrication with covalent attachment of DNA using bubble jet technology. Nat Biotechnol 18, 438–441.[CrossRef][Medline]
PhysGen. Programs for genomic applications. http://pga.mcw.edu.
Physiome Project. http://www.physiome.org.
Pilpel Y, Sudarsanam P & Church GM (2001). Identifying regulatory networks by combinatorial analysis of promoter elements. Nat Genet 29, 153–159.[CrossRef][Medline]
Quackenbush J (2001). Computational analysis of microarray data. Nat Rev Genet 2, 418–427.[CrossRef][Medline]
Randolph
JB
&
Waggoner
AS (1997). Stability, specificity and fluorescence brightness of multiply-labeled fluorescent DNA probes. Nucl Acids Res
25, 2923–2929.
Rat Genome Database. http://rgd.mcw.edu.
Rat Genome Project. http://www.hgsc.bcm.tmc.edu/projects/rat.
Sargent
TD
&
Dawid
IB (1983). Differential gene expression in the gastrula of Xenopus laevis. Science
222, 135–139.
Schena
M, Shalon
D, Davis
RW
&
Brown
PO (1995). Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science
270, 467–470.
Science Functional Genomics. http://www.sciencemag.org/feature/plus/sfg.
Stathopoulos A, Van Drenth M, Erives A, Markstein M & Levine M (2002). Whole-genome analysis of dorsal-ventral patterning in the Drosophila embryo. Cell 111, 687–701.[CrossRef][Medline]
Stears
RL, Getts
RC
&
Gullans
SR (2000). A novel, sensitive detection system for high-density microarrays using dendrimer technology. Physiol Genomics
3, 93–99.
Stoll
M, Cowley
Jr.
AW, Tonellato
PJ, Greene
AS, Kaldunski
ML, Roman
RJ, Dumas
P, Schork
NJ, Wang
Z
&
Jacob
HJ (2001). A genomic-systems biology map for cardiovascular function. Science
294, 1723–1726.
Tavazoie S, Hughes JD, Campbell MJ, Cho RJ & Church GM (1999). Systematic determination of genetic network architecture. Nat Genet 22, 281–285.[CrossRef][Medline]
Van Gelder
RN, Zastrow
ME, Yool
A, Dement
WC, Barchas
JD
&
Eberwine
JH (1990). Amplified RNA synthesized from limited quantities of heterogeneous cDNA. Proc Natl Acad Sci U S A
87, 1663–1667.
Velculescu
VE, Zhang
L, Vogelstein
B
&
Kinzler
KW (1995). Serial analysis of gene expression. Science
270, 484–487.
Vidal M (2001). A biological atlas of functional maps. Cell 104, 333–339.[CrossRef][Medline]
Yang YH & Speed T (2002). Design issues for cDNA microarray experiments. Nat Rev Genet 3, 579–588.[CrossRef][Medline]
Yuan
B, Liang
M, Yang
Z, Rute
E, Taylor
N, Olivier
M
&
Cowley
Jr.
AW (2003). Gene expression reveals vulnerability to oxidative stess and interstitial fibrosis of the renal outer medulla to non-hypertensive elevations of angiotensin II. Am J Physiol Regul Integr Comp Physiol
284, R1219–1230.
Zhu
H, Bilgin
M, Bangham
R, Hall
D, Casamayor
A, Bertone
P, Lan
N, Jansen
R, Bidlingmaier
S, Houfek
T, Mitchell
T, Miller
P, Dean
RA, Gerstein
M
&
Snyder
M (2001). Global analysis of protein activities using proteome chips. Science
293, 2101–2105.
Ziauddin J & Sabatini DM (2001). Microarrays of cells expressing defined cDNAs. Nature 411, 107–110.[CrossRef][Medline]
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