Skip directly to site content Skip directly to page options Skip directly to A-Z link Skip directly to A-Z link Skip directly to A-Z link
Volume 6, Number 5—October 2000

Using DNA Microarrays to Study Host-Microbe Interactions

Craig A. Cummings*Comments to Author  and David A. Relman*†

Author affiliations: *Stanford University, Stanford, California, USA; VA Palo Alto; †Health Care System, Palo Alto, California, USA

Article in Chinese

Cite This Article


Complete genomic sequences of microbial pathogens and hosts offer sophisticated new strategies for studying host-pathogen interactions. DNA microarrays exploit primary sequence data to measure transcript levels and detect sequence polymorphisms, for every gene, simultaneously. The design and construction of a DNA microarray for any given microbial genome are straightforward. By monitoring microbial gene expression, one can predict the functions of uncharacterized genes, probe the physiologic adaptations made under various environmental conditions, identify virulence-associated genes, and test the effects of drugs. Similarly, by using host gene microarrays, one can explore host response at the level of gene expression and provide a molecular description of the events that follow infection. Host profiling might also identify gene expression signatures unique for each pathogen, thus providing a novel tool for diagnosis, prognosis, and clinical management of infectious disease.

The complex interaction between a microbial pathogen and a host is the underlying basis of infectious disease. By understanding the molecular details of this interaction, we can identify virulence-associated microbial genes and host-defense strategies and characterize the cues to which they respond and mechanisms by which they are regulated. This information will guide the design of a new generation of medical tools.

Genomic sequencing will provide the data needed to unravel the complexities of the host-pathogen interaction. As of August 10, 2000, draft sequence was available for 87% of the human genome (, and at least 39 prokaryotic genomes, including those of more than a dozen human pathogens, had been completely sequenced ( The pace of gene discovery rapidly accelerates, but its potential for explaining life at the molecular level remains largely unrealized because our understanding of gene function lags increasingly far behind. For example, even in the heavily studied Escherichia coli, no function has been assigned to more than one third of its genes (1). High-throughput methods for assessment of function are clearly required if this wealth of primary sequence information is to be used.

Global profiling of gene expression is one attractive approach to assessing function. Because a gene is usually transcribed only when and where its function is required, determining the locations and conditions under which a gene is expressed allows inferences about its function. Several independent high-throughput methods for differential gene expression (including SAGE and differential display) may enable function annotation of sequenced genomes (2). DNA microarray hybridization analysis stands out for its simplicity, comprehensiveness, data consistency, and high throughput.

Transcription control plays a key role in host-pathogen interaction (3,4); thus, genomewide transcription profiling seems particularly appropriate for the study of this process. This review focuses on microarray-based approaches for studying transcription response because they hold exceptional promise for the study of infectious disease. Microarray-based genotyping applications, although expected to make substantial contributions in this field, are covered only briefly here.

High-Density DNA Microarrays: Basic Tools

First described in 1995 (5), high-density DNA microarray methods have already made a marked impact on many fields, including cellular physiology (6-11), cancer biology (12-17), and pharmacology (18,19). The first results of gene expression profiling of the host-pathogen interaction have just begun to emerge. Before exploring these results, we briefly review the methods.


Figure 2

Thumbnail of DNA microarray--Lymphochip. (Center) Lymphochip version 8.0, printed on a coated glass microscope slide using a 32-tip printing head, contains 17,856 cDNA clones (overhead illumination) (14). (Left) U.S. penny, for scale. (Right) Scanned image demonstrating differential hybridization of Cy3- and Cy5-labeled cDNA to this microarray. (Illustration by A. Alizadeh, M. Eisen, and P. Brown, Stanford University; and L. Staudt, National Cancer Institute).

Figure 2. DNA microarray--Lymphochip. (Center) Lymphochip version 8.0, printed on a coated glass microscope slide using a 32-tip printing head, contains 17,856 cDNA clones (overhead illumination) (14). (Left) U.S. penny, for...

Figure 1

Thumbnail of Measuring relative gene expression by using DNA microarrays. Capillary printing is used to array DNA fragments onto a glass slide (upper right). RNA is prepared from the two samples to be compared, and labeled cDNA is prepared by reverse transcription, incorporating either Cy3 (green) or Cy5 (red)(upper left). The two labeled cDNA mixtures are mixed and hybridized to the microarray, and the slide is scanned. In the resulting pseudocolor image, the green Cy3 and red Cy5 signals are o

Figure 1. Measuring relative gene expression by using DNA microarrays. Capillary printing is used to array DNA fragments onto a glass slide (upper right). RNA is prepared from the two samples to be...

The key unifying principle of all microarray experiments is that labeled nucleic acid molecules in solution hybridize, with high sensitivity and specificity, to complementary sequences immobilized on a solid substrate, thus facilitating parallel quantitative measurement of many different sequences in a complex mixture (20,21). Although several methods for building microarrays have been developed (22,23), two have prevailed. In one, DNA microarrays are constructed by physically attaching DNA fragments such as library clones or polymerase chain reaction (PCR) products to a solid substrate (5) (Figure 1). By using a robotic arrayer and capillary printing tips, we can print at least 23,000 elements on a microscope slide (P. Brown, pers. comm.; Figure 2). In the other method, arrays are constructed by synthesizing single-stranded oligonucleotides in situ by use of photolithographic techniques (24,25). Advantages of the former method include relatively low cost and substantial flexibility (which explain its wide implementation in the academic setting); in addition, primary sequence information is not needed to print a DNA element. Advantages of the latter method include higher density (>280,000 features on a 1.28X1.28-cm array) and elimination of the need to collect and store cloned DNA or PCR products. Continued commercial interest in microarray technology promises increasing array element density, better detection sensitivity, and cheaper, faster methods. Technical descriptions of microarray construction methods and hybridization protocols are available (26-28; and

Messenger RNA from eukaryotic cells is usually specifically labeled by affinity purification of mRNA with an oligo-dT resin, followed by incorporation of dye-labeled nucleotides into cDNA molecules by reverse transcriptase (RT) with random or oligo-dT oligonucleotide primers (Figure 1). In prokaryotes, the absence of polyadenylation on transcripts makes labeling of mRNA more difficult. One method is labeling of total RNA either by covalent linkage (29) or by incorporating dye-labeled nucleotides into complementary DNA through RT and random oligonucleotide primers (30). In spite of the high copy number of labeled ribosomal and tRNA molecules in the hybridization reaction, specific hybridization of mRNA to the array can be achieved under appropriate stringency. An alternative method is to prime reverse transcription with a mixture of reverse-strand oligonucleotides specific for open reading frames (ORFs), either those used to construct the microarray (M. Laub and L. Shapiro, pers. comm.) or a minimally complex mixture of octamers sufficient to hybridize to the 3 end of every ORF (31). This method results in higher signal-to-noise ratios by preferentially synthesizing cDNA from coding regions.

For printed DNA microarrays, relative transcript abundance is measured by labeling two samples with different fluorescent dyes (e.g., Cy3 and Cy5), hybridizing them simultaneously, and determining the fluorescence ratio for each spot on the array (Figure 1). On oligonucleotide arrays, multiple probes from the same gene, each with a corresponding mismatch probe that serves as internal control, as well as labeled transcript of known amounts for standard genes makes quantitative measurement of transcript abundance possible after hybridizing a single labeled sample (25). For both techniques, use of fluorescent labeling enhances sensitivity and the dynamic range of measurement.

Gene expression array experiments can also be performed by hybridizing a single labeled mRNA sample to "macroarrays" of DNA elements on positively charged filters (10,11,32-34). Because this format does not require any special arraying or scanning equipment, specialty arrays can be made and analyzed relatively cheaply. Human, mouse, and microbial macroarrays are also commercially available (SigmaGenosys, The Woodlands, TX; Research Genetics, Huntsville, AL; Clontech Laboratories, Palo Alto, CA; Genome Systems, St. Louis, MO). The major disadvantages of this format are reduced sensitivity (32), limited elements, and the need for higher concentrations of labeled cDNA.

Microarray Data Analysis

Microarrays are likely to become a standard tool of the microbiology laboratory. However, because genomewide datasets are large and comprehensive, analysis of an experiment can become daunting. Careful experimental design can simplify analysis and interpretation of the dataset by minimizing the number of variables that affect gene expression. For example, strain differences can be minimized by using isogenic mutants, tissue complexity can be reduced by studying clonal cell lines, and complex regulatory pathways can be tamed by experimental modulation of transgene expression (6).

Because microarray experiments result in such large amounts of data, false-positive results are likely. Analyzing multiple independent experiments may eliminate spurious results (32). Also important is validation of differentially expressed genes by independent methods. When checked by a number of methods including quantitative RT-PCR (6,35), Northern blotting (33,34,36), and protein expression (33, 34), most differentially expressed genes have been confirmed. For example, 72 of 72 mRNAs found to be regulated in response to cytomegalovirus (CMV) infection were confirmed by either prior reports or Northern blotting (37). Future challenges for microarray researchers will include developing databases and algorithms to manage and analyze vast genomic-scale datasets.

Image Analysis Software

The first step after hybridization is capturing an image of the array and from it, extracting numerical data for each element (Figure 1). Several software applications, including those packaged with most commercial scanners, can perform this task. However, not all programs use the same algorithms to calculate signal intensity, and each of the programs exports a different constellation of signal quality measurements, complicating comparisons between data acquired with different applications (38). If gene expression datasets are to be compared, these measurements must be standardized. Furthermore, standard, robust statistical methods must be developed for assigning significance values to gene expression measurements.


Although many laboratories are now capable of collecting microarray data, few have access to a database that can effectively meet their data requirements. With considerable investment of resources, a few full-featured, relational gene expression databases have been developed, but these are not available for public deposition of data (e.g.,; Recently released, the freely available AMAD software package ( provides basic microarray data storage and retrieval capabilities to the average laboratory.

A grander goal for the community is establishing a consolidated resource for public distribution of microarray data (39-41). Again, the lack of a standard format for microarray data interferes with creating such a resource (38,39). The European Bioinformatics Institute, recognizing this obstacle, has proposed defining a standard based upon XML, a computer markup language that combines data and formatting in a single file for distribution over the World-Wide Web (40;


Inferring biologically meaningful information from microarray data requires sophisticated data exploration. Most global gene expression analyses have used some form of unsupervised clustering algorithm (16,42-44) to find genes coregulated across the dataset (Figure 1). A primary justification for this approach is that shared expression often implies shared function (38,43). In datasets containing many experiments, clustering can also group experiments on the basis of gene expression profiles, an approach that has been successful in classifying tumor-derived cell lines (19,45) and tumor subtypes (12-17).

When a coregulated class of genes is known, supervised clustering algorithms, which are trained to recognize known members of the class, can assign uncharacterized genes to that class. For example, a machine-learning method known as a support vector machine has been used to classify yeast genes by function on the basis of shared regulation (46). Robust determination of coregulated gene clusters may be achieved by using a tiered approach: unsupervised clustering to identify coregulated genes followed by testing and refinement with supervised algorithms (47).

Although clustering algorithms will continue to be a mainstay in the analysis of gene expression datasets, a wealth of other data-mining techniques have yet to be applied (38,48). Preliminary reports indicate that many algorithms and visualization methods are being developed, but their ability to extract biologic insight has yet to be established (49-51).

The study of microbial pathogens, and prokaryotes in general, will require the development of some specialized analysis tools. First, the compact and modular structure of prokaryotic genomes--and in particular, the presence of operons and pathogenicity islands--suggests that important insights may be gained by mapping gene expression information onto genomic structure. In addition, because gene expression will be measured in many different pathogens, often under the same environmental conditions, tools for cross-species comparison of gene expression data will permit the detection of conserved transcription responses.

Examining a Microorganism: Application of DNA Microarrays

Microarray technology promises to speed the study of uncharacterized or poorly characterized microbes by contributing to annotation of the microbial genome, enabling exploration of microbial physiology, and identifying candidate virulence factors.

Designing a Microbial Genome Microarray

Designing a whole-genome DNA microarray for a fully sequenced microbe is conceptually straightforward. Several sensitive microbial gene-finding programs can quickly and accurately predict most ORFs (52-57). DNA fragments representing each of the ORFs can be obtained by PCR amplification that uses ORF-specific oligonucleotides, the design of which can be automated with primer design software such as Primer3 (58). Homology-searching algorithms should be used to choose regions of genes that will not cross-hybridize with other regions of the genome. After a simple purification step, PCR fragments can be arrayed by a robotic arrayer (5). This basic approach has been used to construct a 4,290-ORF E. coli microarray (10,11) and a 3,834-ORF Mycobacterium tuberculosis microarray (30) as well as full-genome arrays for Helicobacter pylori (S. Falkow, pers. comm.) and Caulobacter crescentus (L. Shapiro, pers. comm.). Microarray fabrication based on photolithographic synthesis of oligonucleotides in situ is also a viable approach and has been successfully used for the production of an E. coli complete ORF chip (E. coli Genome Array, Affymetrix, Santa Clara, CA).

The utility of microarrays is not restricted to fully sequenced organisms. A powerful screening tool can be obtained by arraying DNA libraries, as has been done for the eukaryotic pathogen, Plasmodium falciparum (59). A DNA microarray of 3,648 random genomic clones was used to identify >50 genes for which expression differed significantly between the trophozoite and gametocyte stages. The major limitation of this approach is that the identity of any element of interest must be determined after the experiment.

Annotating the Function of a Microbial Genome

For many pathogens, the number of genes for which function information is available is usually low. Moreover, the relative insufficiency of genetic tools can make obtaining such information difficult. However, because >70% of bacterial proteins have orthologs in other organisms (60,61), one can leverage extensive knowledge of function from the model organisms to infer function for a pathogen's genome. Similarity searches alone will predict functions of many genes.

We expect the study of genomewide expression patterns to contribute even further to annotation of function. The rationale for this belief follows from the observation that shared expression often implies shared function (38). As suggested by Brown and Botstein (21), the inclusion of a gene with a characterized ortholog in a coregulated gene cluster can predict the function of the remaining genes in that cluster, thus boot-strapping the function annotation of the pathogen's genome. This assertion is borne out in a study of global gene expression in Saccharomyces cerevisiae. Clustering of 2,467 gene expression profiles across a series of 78 experiments representing eight cellular processes demonstrated coregulation of genes that participated in shared cellular function (43). Therefore, the acquisition of a pathogen's gene expression data from even a modest number of experimental conditions may lead to testable hypotheses about function for a substantial number of genes, even those lacking sequence similarity to genes whose function has been characterized.

Probing a Microbe's Physiologic State

The assumption that genes are preferentially expressed when their function is required allows inference of gene function directly from physiologic gene response. For example, genes preferentially transcribed during the diauxic shift in yeast are predicted to contribute in the metabolic transition to respiration (9). Thus, gene expression studies will contribute to function annotation by identifying the specific environmental and physiologic conditions in which each gene is expressed. Furthermore, as annotation improves, the direction of this inference may be reversed, i.e., if information on function is known for many genes, genomic expression profiling may reveal the physiologic state of the organism.

Two studies have used whole-genome DNA arrays to explore gene expression response to environmental stimuli in E. coli. First, treatment with isopropyl--D-thiogalactopyranoside (IPTG) was shown to induce only the lac operon, and to a lesser extent, the melibiose operon (11). In a second study, comparison of strains grown in minimal versus rich media revealed 344 genes that were differentially expressed between the two conditions: preferential expression of the translation apparatus in rich media and the amino acid biosynthetic pathways in minimal media were entirely consistent with prior data (10). Finally, examination of gene expression during heat shock revealed 119 genes with altered expression levels, all but 35 of which were previously recognized as heat shock genes (11). These studies confirm that the physiologic state of bacteria can be inferred from gene expression data.

In the first report of global gene expression monitoring in a bacterial pathogen, oligonucleotide microarrays were used to measure the relative transcript levels of 100 Streptococcus pneumoniae genes during the development of natural competence and during stationary phase (29). The results confirmed induction of the cin operon and identified 11 genes differentially regulated in stationary versus exponential phase. Of course, gene expression monitoring is not restricted to the study of bacterial pathogens. Transcription of the CMV genome was measured during infection by using an array of 75-mer oligonucleotides representing each of the 226 predicted CMV ORFs (62). By blocking translation or DNA replication, the researchers revealed a detailed classification of CMV genes into four kinetic classes, in agreement with previous reports, and assigned many ORFs, for which expression data were not previously available, into these groups.

Identifying Candidate Virulence Factors

Because expression of virulence-associated genes is tightly regulated (4), measuring a pathogen's gene expression in microenvironments specific to the pathogen and germane to the disease process is critical. Exploration of pathogen gene expression in the host environment may be technically challenging because of the relatively small number of pathogens present in an infected animal (29). Until more sensitive detection protocols are developed, examining global gene expression will be more practical in environmental conditions that mimic aspects of the host environment, such as elevated temperature, iron limitation, and changes in pH (4,63) and in cell culture models. In fact, a microarray has been used to monitor gene expression in M. tuberculosis while it infects cultured monocytes (64). Even after measurement of bacterial gene expression from infected hosts becomes feasible, the ex vivo datasets will facilitate deconstruction of the in vivo gene expression response into component responses, leading to detailed understanding of the pathways of virulence factor regulation.

Identifying candidate virulence factors through a global gene expression method relies on two assumptions. First, because virulence-associated genes are often coordinately regulated (4), new virulence factors are likely to be coregulated with known ones. By clustering gene expression profiles across a large number of conditions, we can precisely monitor coregulation, thus revealing subtleties of regulation and leading to the identification of bona fide regulons. Second, because virulence-associated genes are tightly regulated (4), genes that are specifically expressed during infection or under conditions mimicking infection are candidate virulence factors. This assumption has been justified by numerous studies using in vivo expression technology (IVET) and differential fluorescence induction (DFI), in which genes induced during infection are often required for virulence (4,65). When RNA from in vivo microbial samples can be efficiently isolated and labeled, microarrays will provide substantial advantages over IVET and DFI technologies for identifying putative virulence factors, including immediate identification of differentially expressed genes and detection of temporal profiles of transcription induction and repression. As is demanded for candidate genes identified by any expression screening approach, a role in pathogenesis must be confirmed by mutation and subsequent assays of virulence.

By identifying factors expressed in the host, microarray methods may also identify potential vaccine targets. Furthermore, one could identify candidate epitopes for vaccine development for intracellular pathogens by predicting whether genes that are preferentially expressed inside host leukocytes will encode promiscuous human leukocyte antigen class II ligands (66).

Gene expression studies may also reveal key regulatory differences that lead to differing virulence between closely related pathogen strains. For example, variations in virulence of Listeria monocytogenes serotypes have been correlated with differential transcription of PrfA-regulated virulence genes (67,68). However, because microarrays cannot measure expression of genes that are absent from the reference strain, genotypic differences such as horizontal transfer of virulence factors will not be detectable by this method.


Yet another application for microarrays is the study of drug effects on microbial cellular physiology, as revealed by global gene expression patterns (69). This approach has been used to identify drug-specific gene expression signatures in yeast and human cells (18,19,70). Correlation of gene expression with drug activity may suggest molecular details of drug action, and correlation of transcription profiles in untreated cells with drug response may reveal mechanisms for sensitivity and resistance (19).

This approach has recently been used to characterize gene expression response in M. tuberculosis exposed to known inhibitors of the mycolic acid biosynthesis pathway, isoniazid and ethionamide (30). Both of these compounds elicited a similar gene expression response profile, characterized by pronounced transcription induction of five adjacent genes encoding fatty acid biosynthesis enzymes. Because a proven isoniazid target, KasA, was among these genes, the authors proposed that the adjacent, coregulated loci might be targets for new anti-tuberculosis drugs. Finally, these results suggested that the mode of action of a novel compound may be inferred from gene expression response to that compound.

Using microarrays to detect microbial polymorphisms linked to known drug-resistance phenotypes will also influence diagnosis and subsequent drug treatment. For example, an oligonucleotide array was used to detect mutant alleles of the M. tuberculosis rpoB gene, which are known to confer resistance to rifampicin (71).

Microbial Genotyping

One microarray application that interrogates DNA rather than RNA is the identification of genomic deletions in mutant strains and environmental isolates by measuring the number of DNA copies at each locus, a technique termed array-based comparative genome hybridization (72). This technique was used to identify several large deletions in a number of BCG vaccine strains and reconstruct their phylogeny (73).

Oligonucleotide arrays have also been used for fine-scale genotyping of polymorphisms in related pathogens. Accurate identification of Mycobacterium species using a GeneChip containing a set of 82 polymorphic oligonucleotides from the 16S ribosomal RNA gene demonstrated the potential power of this approach for molecular diagnostics (71). As additional microbial genome ORF microarrays become available, molecular surveys of the genomic structure of multiple strains will become far more precise and feasible. Two caveats should be mentioned: the ability to characterize genome insertions relative to the reference sequence is lacking, and the degree to which sequence variability can be characterized on the basis of microarray hybridization is unknown.

Examining a Host: Application of DNA Microarrays

Designing Microarrays for Host Organisms

The currently described human DNA microarrays are largely composed of expressed sequence tags (ESTs). Culling ESTs from many different tissue sources and limiting representation of any single Unigene cluster (see, have resulted in better than 50% representation of the predicted 80,000-100,000 human coding regions (28). A variety of human DNA and oligonucleotide microarrays are available commercially (e.g., Incyte, Palo Alto, CA; Affymetrix; NEN Life Science Products, Boston, MA).

For in vivo studies of host response, infection of animal models will often be necessary. If the animal is a primate, human DNA microarrays might be used to monitor host gene expression because of the high level of primary sequence similarity between species. Sequence similarity is too low to permit reliable cross-hybridization with nonprimate vertebrates, but microarrays composed of mouse and rat sequences have been described (74) and are available (e.g., Incyte, Affymetrix).

Understanding Pathogenesis

Microarrays promise to accelerate our understanding of the host side of the host-pathogen interaction. A large fraction of the genome can be simultaneously interrogated, and clustering of the data may identify groups of genes that implicate activation or repression of key regulatory pathways. Microarrays also allow the temporal sequence of transcription induction and repression to be followed, a prerequisite for determining the order of events following an encounter. Finally, ascertainment of the host cell's physiologic state, particularly apoptosis and necrosis, by genomewide profiling will facilitate separation of primary and secondary effects.

One important caveat of studying transcription in any system is that post-transcription regulatory events cannot be detected. This is particularly important in the case of host response because many important host cell events, such as cytoskeletal rearrangements, occur after transcription (75). Therefore, some key aspects of the molecular program may not be easily characterized by gene expression profiling. Eventually, it may be possible to monitor simultaneously the levels, activities, and interactions of all proteins in the cell (76).

Although analyzing gene expression of infected tissues is feasible, cellular heterogeneity may make analysis of host response complicated. Examining the response in infected cultured cells by using cell types most likely to encounter the pathogen may reduce the complexity of the system being examined. Results obtained in cell culture systems will be instrumental in interpreting gene expression profiles of specific cell types from whole tissue datasets.

The first application of global gene expression methods to pathogenesis used oligonucleotide arrays to monitor gene expression in primary human fibroblasts infected by human CMV (37). The transcript abundance of 258 out of 6,600 human genes changed by more than fourfold compared to uninfected cells at either 8 or 24 hours after infection. Some of these changes, such as induction of cytokines, stress-inducible proteins, and many interferon-inducible genes, were consistent with induction of cellular immune responses.

A similar experimental design has been used to examine the global effects of HIV-1 infection on cultured CD4-positive T cells. One study concluded that HIV-1 infection resulted in differential expression of 20 of the 1,506 human genes monitored and that most of these changes occurred only after 3 days in culture (36). In contrast, the preliminary results of an independent study using a similar design indicated that substantial HIV-induced transcription changes began very early after inoculation (77). The latter study confirmed activation of nuclear factor-κB (NF-κB), p68 kinase, and RNase L.

DNA expression arrays have recently been used to examine the response of host cells to infection by bacterial pathogens. Transcription profiling of macrophages and epithelial cells infected by Salmonella confirmed increased expression of many proinflammatory cytokines and chemokines, signaling molecules, and transcription activators and identified several genes previously unrecognized to be regulated by infection (33,34). The macrophage study demonstrated that exposure to purified Salmonella lipopolysaccharide resulted in a very similar response profile to whole cells and that activation of macrophages with gamma interferon before infection modified the response (34). In epithelial cells, overexpression of IκB (an inhibitor of NF-κB) blocked induction of gene expression for a number of regulated genes, underscoring the importance of NF-κB in the proinflammatory response (33).

Similarly, the transcription response of human promyelocytic cells to L. monocytogenes infection has been determined by both oligonucleotide arrays and filter-based arrays (32). Comparison of these data with the Salmonella infection data suggests that the proinflammatory response is grossly conserved: in both cases many key components including interleukin-1, intercellular adhesion molecule-1, and macrophage inflammatory protein 1-β are induced. Although differences were observed between the two experiments, including induction of apoptosis-promoting genes by Salmonella versus induction of anti-apoptotic genes by L. monocytogenes, the disparities between cell lines, methods, and genes assayed in these reports make direct comparison difficult. However, we speculate that differences in pathogen virulence strategies may account for some of these differences in host response at the molecular level.

The initial reports demonstrate the potential power of using microarrays to characterize host response but also suggest that interpretation of host gene expression profiles will be challenging. For example, modulation of mRNAs encoding components of the prostaglandin E2 biosynthetic pathway suggested that CMV induced synthesis of this proinflammatory second messenger (37). The authors of this study proposed three potential explanations for this observation: this pathway could be induced by a cellular response intended to limit spread of the infection by promoting the killing of infected cells; viral regulators could induce prostaglandin E2 production to lure monocytes, which could subsequently be infected, leading to viral dissemination within the host; and these genes could be induced secondarily through induction of interleukin-1β since a similar pattern of regulation was observed in cells treated with that cytokine. Microarrays can identify interesting cellular events, but because expression patterns cannot distinguish between these mechanisms, the need for further investigation is obvious.

The experiments described above are strictly exploratory and attempt to catalog the transcription events that occur after an infection. However, expression profiling also lends itself to a more hypothesis-driven experimental design. For example, comparison of host responses to related strains of the same pathogen could explain differences in pathogenesis. In fact, comparison of gene expression in human monocytes infected by two distinct strains of Ebola virus, one infectious for humans and one not, revealed divergent transcription responses (78). Similarly, by examining responses to isogenic mutant pathogen strains lacking single virulence genes, or virulence factor-associated biologic activities, one might attribute components of the response to specific virulence attributes, which in turn might yield mechanistic insight into those virulence factors. Finally, comparing transcription responses to families of structurally related virulence factors, e.g., bacterial pore-forming toxins, may explain how pathogens expressing similar virulence factors can cause different pathologic responses.

Diagnostic Gene Expression Profiles

Most microarray-based gene expression studies in humans have searched for genes that are differentially expressed in various pathologic states. For example, clustering gene expression profiles can classify tumors into separate molecular subtypes (12-17). In the case of diffuse large B-cell lymphoma, two distinct molecular classes exhibit substantially different survival rates, suggesting that future clinical intervention, at least in the case of cancer, could be guided by diagnostic gene expression profiling (14). Microarrays have also been used to measure the response of cultured cells to distinct external stimuli, including drugs (19) and environmental toxins (79).

How can this paradigm be applied to the diagnosis of infectious disease? In collaboration with Pat Brown (Stanford) and Lou Staudt (National Cancer Institute), we hypothesize that the unique constellation of virulence factors expressed by a specific pathogen will elicit a unique transcription response in the host (80). By extension, the cascade of events leading to inflammation and acquired immunity, including secretion of mediators and subsequent cell-cell interactions, might leave a unique trail of transcription signatures in the leukocytes participating in that response. Despite conserved overall virulence strategies, microbial pathogens exhibit specialization and unique attributes for any given strategy at the molecular level (81). Thus, by measuring the aggregate gene expression pattern in peripheral blood mononuclear leukocytes, for example, we may find signatures diagnostic of infection by specific pathogens or categories of pathogens.

The potential advantages of using host gene expression signatures as diagnostic markers of infection are profound. First, this technique might permit early detection of exposure to pathogens, even uncultivatable or uncharacterized pathogens. Second, variations in host signatures could be used to infer time since exposure. Third, because host response may continue in the absence of the pathogen, this method might detect exposure to pathogens that only transiently colonize the host, are sequestered in poorly accessed anatomic sites, or do not colonize the host at all (e.g., Clostridium botulinum and C. perfringens, in some cases). Finally, a single, easily collected sample could be used for diagnosing exposure to a wide array of agents.

Before the proposed method becomes an accepted diagnostic tool, one must determine whether exposure to a pathogen leads to a robust, persistent, and specific gene expression signature in peripheral blood mononuclear leukocytes and whether this signature is universal in patients of different genetic backgrounds. Experiments are under way in our laboratory to assess the feasibility of this approach. Thus far, identification of gene expression profiles common to many different pathogens is leading to a more detailed understanding of early events in the development of immune response, and inflammation in particular, but the goal of these experiments (to define unique signatures for each pathogen) has not yet been realized.

Conclusion: The Two-Way Conversation

The few published studies reviewed here represent what is certain to be the beginning of a deluge of genome-scale pathogen data. At Stanford University alone, microarray-based studies of Bordetella pertussis, Salmonella, H. pylori, Campylobacter jejuni, V. cholerae, M. tuberculosis, and E. coli, as well as the nonpathogenic microbes Streptomyces coelicolor and C. crescentus, are under way (S. Falkow, G. Schoolnik, S. Cohen, and L. Shapiro, pers. comm.).

The longer term goals of functional genomics and microarray technology in infectious diseases include describing the host-pathogen interaction in molecular detail and identifying critical target molecules and pathways for diagnosis and intervention. Realizing these goals will require additional technology, extensive data collection, sophisticated computational tools, and efforts to discern cause and effect. We are on the verge of being able to listen to the two-way conversation between pathogen and host through devices of immense power.

Dr. Cummings is a postdoctoral fellow in the Department of Microbiology & Immunology at Stanford University, Stanford, California. His interests include Bordetella molecular pathogenesis and gene expression bioinformatics.

Dr. Relman is assistant professor of microbiology, immunology and medicine at Stanford University. His interests are in the development and use of molecular methods for pathogen discovery, human microbial ecology, genomewide host and microbial responses to infection, and Bordetella pathogenesis.



We gratefully acknowledge P. Brown, S. Falkow, L. Shapiro, S. Cohen, G. Schoolnik, and members of the Brown, Falkow, and Relman laboratories for helpful discussions. J. Boldrick and A. Alizadeh kindly provided illustrations.

This work was supported by grants from the Defense Advanced Research Projects Agency (#N65236-99-1-5428) and the Department of Veterans Affairs REAP Program.



  1. Blattner  FR, Plunkett  G III, Bloch  CA, Perna  NT, Burland  V, Riley  M, The complete genome sequence of Escherichia coli K-12. Science. 1997;277:145374. DOIPubMedGoogle Scholar
  2. Carulli  JP, Artinger  M, Swain  PM, Root  CD, Chee  L, Tulig  C, High throughput analysis of differential gene expression. J Cell Biochem Suppl. 1998;30-31:28696. DOIPubMedGoogle Scholar
  3. Svanborg  C, Godaly  G, Hedlund  M. Cytokine responses during mucosal infections: role in disease pathogenesis and host defence. Curr Opin Microbiol. 1999;2:99105. DOIPubMedGoogle Scholar
  4. Cotter  PA, Miller  JF. In vivo and ex vivo regulation of bacterial virulence gene expression. Curr Opin Microbiol. 1998;1:1726. DOIPubMedGoogle Scholar
  5. Schena  M, Shalon  D, Davis  RW, Brown  PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 1995;270:46770. DOIPubMedGoogle Scholar
  6. Spellman  PT, Sherlock  G, Zhang  MQ, Iyer  VR, Anders  K, Eisen  MB, Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell. 1998;9:327397.PubMedGoogle Scholar
  7. Cho  RJ, Campbell  MJ, Winzeler  EA, Steinmetz  L, Conway  A, Wodicka  L, A genome-wide transcriptional analysis of the mitotic cell cycle. Mol Cell. 1998;2:6573. DOIPubMedGoogle Scholar
  8. Chu  S, DeRisi  J, Eisen  M, Mulholland  J, Botstein  D, Brown  PO, The transcriptional program of sporulation in budding yeast. Science. 1998;282:699705. DOIPubMedGoogle Scholar
  9. DeRisi  JL, Iyer  VR, Brown  PO. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science. 1997;278:6806. DOIPubMedGoogle Scholar
  10. Tao  H, Bausch  C, Richmond  C, Blattner  FR, Conway  T. Functional genomics: expression analysis of Escherichia coli growing on minimal and rich media. J Bacteriol. 1999;181:642540.PubMedGoogle Scholar
  11. Richmond  CS, Glasner  JD, Mau  R, Jin  H, Blattner  FR. Genome-wide expression profiling in Escherichia coli K-12. Nucleic Acids Res. 1999;27:382135. DOIPubMedGoogle Scholar
  12. Khan  J, Simon  R, Bittner  M, Chen  Y, Leighton  SB, Pohida  T, Gene expression profiling of alveolar rhabdomyosarcoma with cDNA microarrays. Cancer Res. 1998;58:500913.PubMedGoogle Scholar
  13. Perou  CM, Jeffrey  SS, van de Rijn  M, Rees  CA, Eisen  MB, Ross  DT, Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc Natl Acad Sci U S A. 1999;96:92127. DOIPubMedGoogle Scholar
  14. Alizadeh  AA, Eisen  MB, Davis  RE, Ma  C, Lossos  IS, Rosenwald  A, Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000;403:50311. DOIPubMedGoogle Scholar
  15. Golub  TR, Slonim  DK, Tamayo  P, Huard  C, Gaasenbeek  M, Mesirov  JP, Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999;286:5317. DOIPubMedGoogle Scholar
  16. Alon  U, Barkai  N, Notterman  DA, Gish  K, Ybarra  S, Mack  D, Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci U S A. 1999;96:674550. DOIPubMedGoogle Scholar
  17. Wang  K, Gan  L, Jeffery  E, Gayle  M, Gown  AM, Skelly  M, Monitoring gene expression profile changes in ovarian carcinomas using cDNA microarray. Gene. 1999;229:1018. DOIPubMedGoogle Scholar
  18. Marton  MJ, DeRisi  JL, Bennett  HA, Iyer  VR, Meyer  MR, Roberts  CJ, Drug target validation and identification of secondary drug target effects using DNA microarrays. Nat Med. 1998;4:1293301. DOIPubMedGoogle Scholar
  19. Scherf  U, Ross  DT, Waltham  M, Smith  LH, Lee  JK, Tanabe  L, A gene expression database for the molecular pharmacology of cancer. Nat Genet. 2000;24:23644. DOIPubMedGoogle Scholar
  20. Southern  E, Mir  K, Shchepinov  M. Molecular interactions on microarrays. Nat Genet. 1999;21:59. DOIPubMedGoogle Scholar
  21. Brown  PO, Botstein  D. Exploring the new world of the genome with DNA microarrays. Nat Genet. 1999;21:337. DOIPubMedGoogle Scholar
  22. Ramsay  G. DNA chips: state-of-the art. Nat Biotechnol. 1998;16:404. DOIPubMedGoogle Scholar
  23. Watson  A, Mazumder  A, Stewart  M, Balasubramanian  S. Technology for microarray analysis of gene expression. Curr Opin Biotechnol. 1998;9:60914. DOIPubMedGoogle Scholar
  24. Fodor  SP, Rava  RP, Huang  XC, Pease  AC, Holmes  CP, Adams  CL. Multiplexed biochemical assays with biological chips. Nature. 1993;364:5556. DOIPubMedGoogle Scholar
  25. Lockhart  DJ, Dong  H, Byrne  MC, Follettie  MT, Gallo  MV, Chee  MS, Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol. 1996;14:167580. DOIPubMedGoogle Scholar
  26. Cheung  VG, Morley  M, Aguilar  F, Massimi  A, Kucherlapati  R, Childs  G. Making and reading microarrays. Nat Genet. 1999;21:159. DOIPubMedGoogle Scholar
  27. Duggan  DJ, Bittner  M, Chen  Y, Meltzer  P, Trent  JM. Expression profiling using cDNA microarrays. Nat Genet. 1999;21:104. DOIPubMedGoogle Scholar
  28. Lipshutz  RJ, Fodor  SP, Gingeras  TR, Lockhart  DJ. High density synthetic oligonucleotide arrays. Nat Genet. 1999;21:204. DOIPubMedGoogle Scholar
  29. de Saizieu  A, Certa  U, Warrington  J, Gray  C, Keck  W, Mous  J. Bacterial transcript imaging by hybridization of total RNA to oligonucleotide arrays. Nat Biotechnol. 1998;16:458. DOIPubMedGoogle Scholar
  30. Wilson  M, DeRisi  J, Kristensen  HH, Imboden  P, Rane  S, Brown  PO, Exploring drug-induced alterations in gene expression in Mycobacterium tuberculosis by microarray hybridization. Proc Natl Acad Sci U S A. 1999;96:128338. DOIPubMedGoogle Scholar
  31. Talaat  AM, Hunter  P, Johnston  SA. Genome-directed primers for selective labeling of bacterial transcripts for DNA microarray analysis. Nat Biotechnol. 2000;18:67982. DOIPubMedGoogle Scholar
  32. Cohen  P, Bouaboula  M, Bellis  M, Baron  V, Jbilo  O, Poinot-Chazel  C, Monitoring cellular responses to Listeria monocytogenes with oligonucleotide arrays. J Biol Chem. 2000;275:1118190. DOIPubMedGoogle Scholar
  33. Eckmann  L, Smith  JR, Housley  MP, Dwinell  MB, Kagnoff  MF. Analysis by high density cDNA arrays of altered gene expression in human intestinal epithelial cells in response to infection with the invasive enteric bacteria salmonella. J Biol Chem. 2000;275:1408494. DOIPubMedGoogle Scholar
  34. Rosenberger  CM, Scott  MG, Gold  MR, Hancock  RE, Finlay  BB. Salmonella Typhimurium infection and lipopolysaccharide stimulation induce similar changes in macrophage gene expression. J Immunol. 2000;164:5894904.PubMedGoogle Scholar
  35. Iyer  VR, Eisen  MB, Ross  DT, Schuler  G, Moore  T, Lee  JCF, The transcriptional program in the response of human fibroblasts to serum. Science. 1999;283:837. DOIPubMedGoogle Scholar
  36. Geiss  GK, Bumgarner  RE, An  MC, Agy  MB, van 't Wout  AB, Hammersmark  E, Large-scale monitoring of host cell gene expression during HIV-1 infection using cDNA microarrays. Virology. 2000;266:816. DOIPubMedGoogle Scholar
  37. Zhu  H, Cong  JP, Mamtora  G, Gingeras  T, Shenk  T. Cellular gene expression altered by human cytomegalovirus: global monitoring with oligonucleotide arrays. Proc Natl Acad Sci U S A. 1998;95:144705. DOIPubMedGoogle Scholar
  38. Bassett  DE Jr, Eisen  MB, Boguski  MS. Gene expression informatics--it's all in your mine. Nat Genet. 1999;21:515. DOIPubMedGoogle Scholar
  39. Khan  J, Bittner  ML, Chen  Y, Meltzer  PS, Trent  JM. DNA microarray technology: the anticipated impact on the study of human disease. Biochim Biophys Acta. 1999;1423:M1728.PubMedGoogle Scholar
  40. Brazma  A, Robinson  A, Cameron  G, Ashburner  M. One-stop shop for microarray data. Nature. 2000;403:699700. DOIPubMedGoogle Scholar
  41. Nat Genet. 1999;22:2112. DOIPubMedGoogle Scholar
  42. Tamayo  P, Slonim  D, Mesirov  J, Zhu  Q, Kitareewan  S, Dmitrovsky  E, Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci U S A. 1999;96:290712. DOIPubMedGoogle Scholar
  43. Eisen  MB, Spellman  PT, Brown  PO, Botstein  D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A. 1998;95:148638. DOIPubMedGoogle Scholar
  44. Ben-Dor  A, Shamir  R, Yakhini  Z. Clustering gene expression patterns. J Comput Biol. 1999;6:28197. DOIPubMedGoogle Scholar
  45. Ross  DT, Scherf  U, Eisen  MB, Perou  CM, Rees  C, Spellman  P, Systematic variation in gene expression patterns in human cancer cell lines. Nat Genet. 2000;24:22735. DOIPubMedGoogle Scholar
  46. Brown  MP, Grundy  WN, Lin  D, Cristianini  N, Sugnet  CW, Furey  TS, Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci U S A. 2000;97:2627. DOIPubMedGoogle Scholar
  47. Gaasterland  T, Bekiranov  S. Making the most of microarray data. Nat Genet. 2000;24:2046. DOIPubMedGoogle Scholar
  48. Berry  MJA, Linoff  G. Data mining techniques for marketing, sales and customer support. New York: John Wiley and Sons;1997.
  49. Weaver  DC, Workman  CT, Stormo  GD. Modeling regulatory networks with weight matrices. In: Altman RB, Lauderdale K, Dunker AK, Hunter L, Klein TE, editors. Biocomputing '99: Proceedings of the Pacific Symposium. River Edge (NJ): World Scientific Press;1999:112-23.
  50. Chen  T, He  HL, Church  GM. Modeling gene expression with differential equations. In: Altman RB, Lauderdale K, Dunker AK, Hunter L, Klein TE, editors. Biocomputing '99: Proceedings of the Pacific Symposium. River Edge (NJ): World Scientific Press; 1999:29-40.
  51. Davidson  GS, Hendrickson  B, Johnson  DK, Meyers  CE, Wylie  BN. Knowledge mining with VxInsight: discovery through interaction. Journal of Intelligent Information Systems. Integrating Artificial Intelligence and Database Technologies. 1998;11:25985.
  52. Delcher  AL, Harmon  D, Kasif  S, White  O, Salzberg  SL. Improved microbial gene identification with GLIMMER. Nucleic Acids Res. 1999;27:463641. DOIPubMedGoogle Scholar
  53. Ramakrishna  R, Srinivasan  R. Gene identification in bacterial and organellar genomes using GeneScan. Comput Chem. 1999;23:16574. DOIPubMedGoogle Scholar
  54. Hayes  WS, Borodovsky  M. How to interpret an anonymous bacterial genome: machine learning approach to gene identification. Genome Res. 1998;8:115471.PubMedGoogle Scholar
  55. Audic  S, Claverie  JM. Self-identification of protein-coding regions in microbial genomes. Proc Natl Acad Sci U S A. 1998;95:1002631. DOIPubMedGoogle Scholar
  56. Lukashin  AV, Borodovsky  M. GeneMark.hmm: new solutions for gene finding. Nucleic Acids Res. 1998;26:110715. DOIPubMedGoogle Scholar
  57. Borodovsky  M, McIninch  J. Recognition of genes in DNA sequence with ambiguities. Biosystems. 1993;30:16171. DOIPubMedGoogle Scholar
  58. Rozen  S, Skaletsky  HJ. Primer3; 1996, 1997, 1998. Cambridge (MA): Whitehead Institute. Code available at
  59. Hayward  RE, Derisi  JL, Alfadhli  S, Kaslow  DC, Brown  PO, Rathod  PK. Shotgun DNA microarrays and stage-specific gene expression in Plasmodium falciparum malaria. Mol Microbiol. 2000;35:614. DOIPubMedGoogle Scholar
  60. Koonin  EV, Mushegian  AR, Galperin  MY, Walker  DR. Comparison of archaeal and bacterial genomes: computer analysis of protein sequences predicts novel functions and suggests a chimeric origin for the archaea. Mol Microbiol. 1997;25:61937. DOIPubMedGoogle Scholar
  61. Tatusov  RL, Galperin  MY, Natale  DA, Koonin  EV. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 2000;28:336. DOIPubMedGoogle Scholar
  62. Chambers  J, Angulo  A, Amaratunga  D, Guo  H, Jiang  Y, Wan  JS, DNA microarrays of the complex human cytomegalovirus genome: profiling kinetic class with drug sensitivity of viral gene expression. J Virol. 1999;73:575766.PubMedGoogle Scholar
  63. Mekalanos  JJ. Environmental signals controlling expression of virulence determinants in bacteria. J Bacteriol. 1992;174:17.PubMedGoogle Scholar
  64. Mangan  JA, Monahan  IM, Wilson  MA, Schnappinger  D, Schoolnik  GK, Butcher  PD. The expression profile of Mycobacterium tuberculosis infecting the human monocytic cell line THP-1 using whole genome microarray analysis. Nat Genet. 1999;23:61. DOIGoogle Scholar
  65. Chiang  SL, Mekalanos  JJ, Holden  DW. In vivo genetic analysis of bacterial virulence. Annu Rev Microbiol. 1999;53:12954. DOIPubMedGoogle Scholar
  66. Sturniolo  T, Bono  E, Ding  J, Raddrizzani  L, Tuereci  O, Sahin  U, Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat Biotechnol. 1999;17:55561. DOIPubMedGoogle Scholar
  67. Sokolovic  Z, Schuller  S, Bohne  J, Baur  A, Rdest  U, Dickneite  C, Differences in virulence and in expression of PrfA and PrfA-regulated virulence genes of Listeria monocytogenes strains belonging to serogroup 4. Infect Immun. 1996;64:400819.PubMedGoogle Scholar
  68. Bohne  J, Kestler  H, Uebele  C, Sokolovic  Z, Goebel  W. Differential regulation of the virulence genes of Listeria monocytogenes by the transcriptional activator PrfA. Mol Microbiol. 1996;20:118998. DOIPubMedGoogle Scholar
  69. Debouck  C, Goodfellow  PN. DNA microarrays in drug discovery and development. Nat Genet. 1999;21:4850. DOIPubMedGoogle Scholar
  70. Gray  NS, Wodicka  L, Thunnissen  AM, Norman  TC, Kwon  S, Espinoza  FH, Exploiting chemical libraries, structure, and genomics in the search for kinase inhibitors. Science. 1998;281:5338. DOIPubMedGoogle Scholar
  71. Troesch  A, Nguyen  H, Miyada  CG, Desvarenne  S, Gingeras  TR, Kaplan  PM, Mycobacterium species identification and rifampin resistance testing with high-density DNA probe arrays. J Clin Microbiol. 1999;37:4955.PubMedGoogle Scholar
  72. Pollack  JR, Perou  CM, Alizadeh  AA, Eisen  MB, Pergamenschikov  A, Williams  CF, Genome-wide analysis of DNA copy-number changes using cDNA microarrays. Nat Genet. 1999;23:416. DOIPubMedGoogle Scholar
  73. Behr  MA, Wilson  MA, Gill  WP, Salamon  H, Schoolnik  GK, Rane  S, Comparative genomics of BCG vaccines by whole-genome DNA microarray. Science. 1999;284:15203. DOIPubMedGoogle Scholar
  74. Khan  J, Bittner  ML, Saal  LH, Teichmann  U, Azorsa  DO, Gooden  GC, cDNA microarrays detect activation of a myogenic transcription program by the PAX3-FKHR fusion oncogene. Proc Natl Acad Sci U S A. 1999;96:132649. DOIPubMedGoogle Scholar
  75. Finlay  BB, Falkow  S. Common themes in microbial pathogenicity revisited. Microbiol Mol Biol Rev. 1997;61:13669.PubMedGoogle Scholar
  76. Uetz  P, Giot  L, Cagney  G, Mansfield  TA, Judson  RS, Knight  JR, A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature. 2000;403:6237. DOIPubMedGoogle Scholar
  77. Corbeil  J, Sheeter  D, Rought  S, Du  P, Ferguson  M, Masys  DR, Magnitude and specificity of temporal gene expression during HIV-1 infection of a CD4+ T cell. Nat Genet. 1999;23:3940. DOIGoogle Scholar
  78. Xiang  C, Young  H, Alterson  H, Reynolds  D, Bittner  M, Chen  Y, Comparison of cellular gene expression in Ebola-Zaire and Ebola-Reston virus-infected primary human monocytes. Nat Genet. 1999;23:82. DOIGoogle Scholar
  79. Nuwaysir  EF, Bittner  M, Trent  J, Barrett  JC, Afshari  CA. Microarrays and toxicology: the advent of toxicogenomics. Mol Carcinog. 1999;24:1539. DOIPubMedGoogle Scholar
  80. Roebuck  KA, Carpenter  LR, Lakshminarayanan  V, Page  SM, Moy  JN, Thomas  LL. Stimulus-specific regulation of chemokine expression involves differential activation of the redox-responsive transcription factors AP-1 and NF-kappaB. J Leukoc Biol. 1999;65:2918.PubMedGoogle Scholar
  81. Lo  D, Feng  L, Li  L, Carson  MJ, Crowley  M, Pauza  M, Integrating innate and adaptive immunity in the whole animal. Immunol Rev. 1999;169:22539. DOIPubMedGoogle Scholar




Cite This Article

DOI: 10.3201/eid0605.000511

Table of Contents – Volume 6, Number 5—October 2000

EID Search Options
presentation_01 Advanced Article Search – Search articles by author and/or keyword.
presentation_01 Articles by Country Search – Search articles by the topic country.
presentation_01 Article Type Search – Search articles by article type and issue.



Please use the form below to submit correspondence to the authors or contact them at the following address:

Craig Cummings, VAPAHCS 154T, Building 100, Room D4-123, 3801 Miranda Ave., Palo Alto, CA 94304, USA; fax: 650-852-3291

Send To

10000 character(s) remaining.


Page created: December 17, 2010
Page updated: December 17, 2010
Page reviewed: December 17, 2010
The conclusions, findings, and opinions expressed by authors contributing to this journal do not necessarily reflect the official position of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.