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DNA Microarrays as Salivary Diagnostic Tools for Characterizing the Oral Cavity’s Microbial Community

L.M. Smoot¹, J.C. Smoot¹, H. Smidt², P.A. Noble¹, M. Könneke¹, Z.A. McMurry¹, and D.A. Stahl^1,*

¹ Civil and Environmental Engineering, 302 More Hall, Box 352700, University of Washington, Seattle, WA 98195-2700, USA; and ² Wageningen University, Wageningen, Netherlands;

Correspondence: ^* corresponding author, dastahl{at}u.washington.edu

KEY WORDS: Saliva • diagnostics • microarrays • microbiology

The interest in using saliva as a diagnostic medium has increased during the last decade, and recent technological developments are responsible for the advancement of its use as a diagnostic fluid (Streckfus and Bigler, 2002). There are several advantages to using saliva as a diagnostic fluid. Saliva is easy to collect, store, and ship, and, compared with the collection of blood, saliva collection is inexpensive and non-invasive, which is much safer for health-care workers (Slavkin, 1998). In the near future, salivary diagnostic devices based on highly parallel data collection methods (e.g., DNA microarrays) will be very useful tools for health-care professionals. DNA microarrays are now used as tools for developing a comprehensive characterization of oral diseases. For example, Li et al.(2004) used high-density oligonucleotide microarrays to profile transcripts found in saliva from head and neck cancer patients, and found that thousands of human mRNAs are present in cell-free saliva. In conjunction with collaborators, our laboratory is using DNA microarrays to detect micro-organisms from the human oral cavity and, ultimately, to develop a microarray-based device for clinical applications.

	The Oral Cavity’s Microbiota and Human Health

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The oral microbiota play critical roles in human health and are directly linked to diseases such as dental caries and periodontitis. Although it is clear that micro-organisms are intimately involved in disease, studies are revealing that the composition of the complex microbial assemblages resident in the human oral cavity is strongly associated with pathology, resistance, and predisposition to dental caries and periodontal diseases, which remain the most common chronic illnesses in humans. For example, Actinobacillus actinomycetemcomitans is strongly associated with juvenile periodontitis, and Streptococcus mutans is the primary etiologic agent of dental caries. In addition, the structure and activity of oral microbial populations may serve as sentinels of human systemic diseases. Evidence is accumulating that periodontal microbiota are involved in the development of various systemic diseases (Scannapieco, 1998; Greenstein and Lamster, 2000; Kinane and Marshall, 2001; Teng et al., 2002), including cardiovascular disease (Beck et al., 1998; Kinane and Lowe, 2000; Glurich et al., 2002), pneumonia (Scannapieco et al., 1998; Terpenning et al., 2001), arthritis (Mercado et al., 2001), diabetes (Miller et al., 1992; Grossi and Genco, 1998), and pre-term low-weight birth (Offenbacher et al., 1998, 2001; Madianos et al., 2001).

More than 600 microbial species are known to inhabit the human oral cavity (Moore and Moore, 1994; Kolenbrander, 2000; Paster et al., 2001). The oral microbiota are broadly distributed among many taxonomically distinct groups, and all domains of life have representatives in the oral cavity (Fig. 1). Only about half of the oral micro-organisms have been successfully cultured (Paster et al., 2001), and the identification of uncultured and novel microbial phylotypes from oral biofilms with small subunit ribosomal DNA (rDNA) clone libraries highlights the need for culture-independent methods for the accurate description of oral microbial communities (Kroes et al., 1999; Relman, 1999; Sakamoto et al., 2000; Paster et al., 2001).

View larger version (31K): [in this window] [in a new window]   Fig. 1 – Phylogenetic tree generated from 165 small subunit rRNA sequences of oral micro-organisms. Sequences not yet present in the latest available ARB database [http://www.arb-home.de/] were added from public databases. Alignment and phylogenetic analysis were performed with the ARB software (Strunk and Ludwig, 1995), and the tree was constructed with the use of the ‘neighbor joining’ method (E. coli position 40-1406, Olsen correction) (Saitou and Nei, 1987). The reference bar indicates 10 nucleotide exchanges per 100 nucleotides. Eukaryal (Candida sp.) and archaeal (Methanobrevibacter sp.) branches were removed.

Whole saliva provides a convenient and reliable means to sample the oral cavity microbiota. However, salivary flow rates vary based on an individual’s circadian rhythms and factors such as stress and exercise (Lawrence, 2002). Many bacteria survive and grow in saliva (de Jong et al., 1984, 1986; Rudney, 2000; Palmer et al., 2001), despite the important antimicrobial functions of saliva (Tenovuo, 1998; Rudney, 2000). Micro-organisms attached to the surfaces of the mouth are continuously shed into the salivary fluid, and bacteria residing in the periodontal pockets are constantly washed into saliva by the gingival crevicular fluids (Umeda et al., 1998). The presence of these micro-organisms can be indicative of health status [e.g., salivary levels of periodontal pathogens reflect the periodontal status of the patient (von Troil-Lindén et al., 1995; Umeda et al., 1998; Sakamoto et al., 2000)]. Thus, the development of salivary diagnostic tools to monitor and detect microbes in the human oral cavity will provide significant benefits to the field of clinical dentistry.

Highly sensitive instruments and highly parallel methods of analysis are needed to identify the microbial sentinels of disease and to listen to their messages. Conventional microbiological approaches that rely on cultivation for the detection of micro-organisms in the oral cavity are not sufficient for such comprehensive and intensive monitoring. These techniques are time-consuming, require many specialized and complex growth media, capture only a minor fraction of the oral microbiota, and do not provide in vivo data of gene expression during infection and subsequent disease. Molecular techniques such as clone libraries, quantitative PCR, and fluorescent in situ hybridization analyses, although informative, are labor-intensive and impractical for routine patient monitoring. Thus, the field needs to develop tools that provide high-fidelity data in a high-throughput format to characterize the complex microbial communities of the human oral cavity.

	Application of DNA Microarray Technology to Dentistry and Oral Diagnostics

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The genetic information of all living organisms is present in the nucleic acid polymers DNA and RNA. This information identifies an organism, its genotype, and its potential phenotype. DNA microarrays, ordered displays of genetic material deposited on a surface or matrix, provide a highly parallel means for the analysis of genetic information. For instance, from hundreds to hundreds of thousands of ordered DNA oligonucleotide probes may be present on a single microarray. Several types of DNA microarrays have been developed, and many reviews of the technology and its application exist (e.g., there are 34 citations for reviews of microarray technology in the PubMed database for the first half of 2004). DNA oligonucleotide microarrays are assemblages of short (from 8 to 70 bases long) nucleic acid sequences (probes) linked to a matrix. Sources of target nucleic acids (RNA or DNA molecules with sequences complementary to the probe) include reference or model organisms in pure culture, clinical specimens, and environmental samples. Target nucleic acids are isolated from a sample, labeled, and hybridized to the microarray. The nucleic acid target may be an amplified product of a gene of interest that is either labeled directly or indirectly during the PCR, or it may be directly isolated from a sample and labeled. Hybridization occurs when target nucleic acids bind to their complementary oligonucleotide probes.

DNA microarray analysis is an emerging technology that is being used in a diverse set of molecular applications (Cummings and Relman, 2000; Stears et al., 2003; Zhou, 2003). DNA microarrays were first used for the simultaneous measurement of differential gene expression of 45 Arabidopsis genes (Schena et al., 1995). Since the seminal work of Schena et al., multitudes of studies incorporating microarray analysis have been done (e.g., when the PubMed database was queried on ‘microarray and expression’, there were 1055 citations for the first half of 2004). These types of experiments provide information about what genes are ‘ON’ (up-regulated) and ‘OFF’ (down-regulated) under certain environmental conditions, under regulator control, or in specific tissue samples. Additional applications of microarrays include the examination of pathogen genetic diversity (Fitzgerald et al., 2001; Smoot et al., 2002; Cummings et al., 2004) and the detection of single nucleotide polymorphisms (SNPs). Microarray SNP analysis provides a simultaneous analysis of thousands of genetic loci and provides insight on chromosomal regions associated with particular diseases (Kuo et al., 2003). For example, high-density DNA microarrays can be used as molecular screens for certain cancers and tumor subtypes. Specific examples include the evaluation of genes involved in head and neck squamous cell carcinoma of the oral cavity (reviewed in Kuo et al., 2002). In addition to the study of oral cancers, microarrays can be used to study infectious diseases of the oral cavity. In fact, the National Institute of Dental and Craniofacial Research recently funded The Institute of Genomic Research to produce oligonucleotide microarrays (70mers) for S. mutans and Porphyromonas gingivalis. Studies with these arrays will undoubtedly lead to the discovery of novel disease-causing attributes, identification of targets for novel therapeutics, and characterization of the genetic network that allows for biofilm formation on the hard and soft surfaces in the oral cavity.

Another example of DNA microarrays is those comprised of DNA oligonucleotide probes complementary to different regions of the rRNA molecules. Typically, these types of microarrays contain oligonucleotide probes designed to regions that vary in conservation, providing a phylogenetic hierarchy to probe specificity (e.g., species, genus, division, domain). Two strategies have been used to detect specific rRNA gene sequences with DNA oligonucleotide microarrays. In both cases, target rRNA hybridizes to multiple hierarchically nested probes, thereby providing a high level of information redundancy, which is an essential design feature required for confident data interpretation (Amann et al., 1995; Stahl, 1995). Using a rational probe design approach, Guschin et al.(1997) used a microarray comprised of oligonucleotide probes complementary to a region of the rRNA molecule spanning bases 156–1390. In contrast, Wilson et al.(2002) used a high-density hierarchical microarray comprised of over 60,000 oligonucleotide probes complementary to bases 1409–1491 of the rRNA molecule. A significant feature of these types of microarrays is that they provide the phylogenetic signature of an organism. Hence, they can be used in applications that simultaneously detect specific pathogens and characterize entire microbial populations, such as flora resident in the human oral cavity.

Our laboratory is currently using rRNA phylogenetic microarrays in the MAGIChip^TM (MicroArray of Gel-Immobilized Compounds) format (Fig. 2B). In this format, oligonucleotide probes are covalently immobilized in three-dimensional polyacrylamide gel pads (Yershov et al., 1996). This format provides specific advantages over conventional glass microarrays with respect to microbial detection: (i) higher probe density, and thus larger dynamic range of target sequence capture; (ii) a local environment suitable for performing real-time measurements of probe-target duplex stability; and (iii) a re-usable format that may reduce experimental variation and cost (Guschin et al., 1997). In the current format, target RNA is fragmented with a hydroxyl radical-based reaction and is simultaneously end-labeled with a fluorescent dye (Fig. 2A; Bavykin et al., 2001). Following hybridization and washing at room temperature, the fluorescent signal from each gel element is quantified with the use of a custom-designed epifluorescence microscope equipped with a charge-coupled device camera. Non-equilibrium dissociation curves are determined with the use of image analysis software to capture intensity readings for each array element during controlled heating on a temperature-controlled stage (Fig. 2C; Yershov et al., 1996; Fotin et al., 1998).

View larger version (26K): [in this window] [in a new window]   Fig. 2 – Description of the MAGIChipTM DNA oligonucleotide microarray technology used in the Stahl laboratory. (A) Simplified schematic overview of the protocol used to fragment and end-label total RNA directly from saliva. The RNA pool is complex, and targets can range in size from 100 bases to several kilobases. Fragmentation is a chemical nuclease reaction, where the average desired size of molecules ranges from 80 to 150 bases. (B) Image from a MAGIChipTM. DNA oligonucleotide microarray following hybridization with total RNA isolated from a human saliva sample. Total RNA was isolated from 300 µL of a human saliva sample. RNA was fragmented, labeled, and hybridized to the microarray. A portion of the microarray image is shown. Individual gel pads are 100 x 100 x 20 µm. Gel pads are arrayed in a 26 x 26 matrix and have 300 µm center-to-center spacing. (C) Example of non-equilibrium thermal dissociation curves measured from 3 gel pads (replicate perfectly matched probes designed to detect all life, and a probe with a SNP which is used as a reference mismatch hybridization).

Despite the many advantages and capabilities of microarrays, the dental community has several challenges to face before microarray-based assays are used routinely in the dental office. Once the device is developed, individuals will need training on the new technique and instruction on data interpretation and analysis. Other challenges include the high costs associated with start-up of the technology and the management and analysis of data generated by the assays. Technical promise does exist for overcoming these obstacles. For example, costs for performing microarray analyses have dropped significantly since their inception, and the competition among, and continuous establishment of, new manufacturers of microarray equipment and reagents keep costs on the decline. Scientists are also now developing and using integrated databases to store the massive amounts of raw microarray data from multiple laboratories. These databases, which are typically Internet-accessible, are invaluable resources, since they allow for the storage, retrieval, and cross-comparison of data from experiments that were conducted in different laboratories.

	Technology Integration Required for Point-of-Care Instruments

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Recent reviews of microfluidic technology used in point-of-care and molecular diagnostic devices highlight the advances made in microelectro mechanical systems (MEMS) (Cunningham, 2001; Huang et al., 2002; Gardeniers and van den Berg, 2004). To produce microarray-based in vitro diagnostic devices for clinical use, scientists must couple DNA microarray technology with microfluidic technology. Microfluidic technology enables small sample sizes to be used, avoids reagent and waste costs, and allows for new types of assays that are impossible at macroscopic scales. Practical use of the technology in the clinical setting will require cost-effective integration of several microfluidic processes. This integrated circuitry of fluidic processes will overcome several limitations in microarray technology and move microarrays from basic science tools to devices suitable for application in the clinic. In fact, several different strategies have been used to improve hybridization kinetics and microarray fabrication with microfluidic technology (e.g., Cheek et al., 2001; Santacroce et al., 2002; Dill et al., 2004). However, few have attempted to manufacture fully integrated systems (Anderson et al., 2000; Baum et al., 2003; Liu et al., 2004). The merger of these technologies produces new challenges. Use of microarrays and their corresponding microfluidic support systems as diagnostic devices requires robust design and validation strategies, traditional quality assurance process control, and quality control testing. Development of point-of-care devices that interrogate oral microbiota nucleic acid targets will incorporate the economy of scale provided by microfluidics, a high-fidelity DNA microarray readout, and a highly integrated system consisting of components to miniaturize and automate cell lysis, target isolation, and target detection.

The initial steps in sample preparation are critical to the success of microfluidic microarray devices, and the variability in saliva viscosity, particle load, and microbial biomass makes these processes quite challenging. Preparation of saliva samples for DNA microarray analysis requires concentration of cells and disruption of microbe cell envelopes to make the RNA or DNA target molecules available for processing. An overview of different lysis and purification strategies is presented in a recent molecular diagnostics MEMS review (Huang et al., 2002). However, to date, no single universal cell lysis protocol exists for the quantitative extraction and purification of cellular nucleic acids from micro-organisms. Integration of efficient lysis and extraction processes within a microfluidic component is essential for the development of a point-of-care microarray-based device for use in dental applications.

The method of detection and desired assay specifications dictate what processes are performed within the microfluidic component of the device. For example, if the goal is to detect a specific gene, which is present in low copy in the chromosome or is expressed at low levels, amplification strategies such as PCR will likely need to be incorporated. The current reported limit of detection with microarray technology is approximately 10⁷ micro-organisms, with direct detection of rRNA (Small et al., 2001; El Fantroussi et al., 2003) and with PCR-amplification of functional genes (Taroncher-Oldenburg et al., 2003). Based on recent quantitative PCR amplification studies with subgingival samples (Lepp et al., 2004), this level of detection may not detect subtle changes in the microbial community in response to changes in health status. Currently, in our laboratory, we can detect on the order of 10⁶ micro-organisms with direct detection of rRNA using improved labeling techniques (unpublished observations). Further advancements in detection are enabled by microfluidic technology via increased target movement and hybridization buffer mixing (Adey et al., 2002; Asbury et al., 2002; Liu et al., 2003).

Microarrays provide great promise for advancements in oral cavity biology for dentists in the 21st century. They should be especially useful for the diagnosis of microbes in the oral cavity, because they have a high probe density that allows for the simultaneous detection of multiple microbes. As the technology matures, pivotal issues in the sample collection and processing component of point-of-care microfluidic devices include: (i) cell lysis efficiency, (ii) target-labeling reactions in MEMS, (iii) material compatibility with solvents and reagents, and (iv) integration of multiple microfluidic processes. Key areas of future development for microarrays and detection instrumentation include: (i) improved methods of discrimination between perfectly matched hybridizations and cross-hybridization events, (ii) heightened sensitivity and dynamic range of microarrays and detectors, and (iii) miniaturization of detectors. The use of microarray-based devices in the dental field will allow dentists and clinicians to detect microbial sentinels in the oral cavity and provide improvements in diagnoses, prevention, and monitoring methods, which will lead to better management of patients’ dental care.

	Acknowledgments

 
DAS and PAN research is supported by NIH grant U01 DE 14955-02. We thank our collaborators, J. Jackman, D. Relman, R. Lamont, and P. Milgrom. We are particularly indebted to M. Donlon, DARPA, and the Biosensor group at ANL, led by D. Chandler, for supporting our MAGIChip^TM studies.

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