Environmental application of array technology - Department of ...

311

Environmental application of array technology: promise, problems and practicalities Kimberly L Cook
and Gary S Saylery Array technology has been applied in environmental research using innovative approaches in gene expression, comparative genomics and mixed community analysis. Greater fundamental understanding of sources of experimental and analytical error in array experiments should facilitate the future application of array technology to environmental analysis.

must be addressed if this technology is to realize its full potential in environmental studies. In this review, applications, issues and the potential for the application of array technology when addressing environmental and ecological questions are discussed.

The array platform Addresses Department of Microbiology, Center for Environmental Biotechnology, 676 Dabney Hall, University of Tennessee, Knoxville, TN 37996, USA
e-mail: [email protected] y e-mail: [email protected]

Current Opinion in Biotechnology 2003, 14:311–318 This review comes from a themed section on Environmental biotechnology Edited by Ian M Head and Mark J Bailey 0958-1669/03/$ – see front matter ß 2003 Elsevier Science Ltd. All rights reserved. DOI 10.1016/S0958-1669(03)00057-0

Abbreviations ISR intergenic spacer SRP sulfate-reducing prokaryote

Introduction Array technology has been widely used and reviewed [1–5] in the biomedical, pharmaceutical, and clinical research fields, but has only recently been applied to environmental analysis. Whereas opportunities to use this technology to address important questions in microbial ecology and environmental microbiology are abundant, practical limitations may slow its implementation. The term array technology describes any high-throughput methodology permitting analysis of hundreds or thousands of genetic sequences in parallel. It is most commonly associated with genome-wide or system-wide analysis of gene expression. More recently, arrays have been used to characterize environmentally important biochemical functions in organisms [6–9]. The use of array technology in environmental studies should facilitate a greater fundamental understanding of the ecology, physiology, structure and function of complex environmental systems. However, application of this technology to environmental samples presents unique challenges. Issues relating to the extent of the genetic diversity in the environment and to the potentially low concentrations of biomass and high concentrations of contaminants www.current-opinion.com

Arrays have been fabricated on a variety of different materials (Table 1), but the most common are oligonucleotide- or DNA-based arrays fabricated on glass slides or nylon membranes. In array technology, the probe or known sequence is the arrayed material, whereas the unknown or target sequence is labeled and hybridized to the array. Oligonucleotide arrays can be synthesized in situ using photolithographic techniques [10,11] or synthesized ex situ and attached to derivatized substrate [12,13]. Typically, oligonucleotide arrays contain perfectmatch probes and mismatch probes arranged into probe sets [14,15]. Probe sets are used for quantification, subtraction of non-specific signals and determination of transcript abundance. Affymetrix now carries GeneChips1 with probe sets representing as many as 20 000 transcripts [5,16]. DNA arrays, on the other hand, consist of larger DNA fragments spotted onto glass slides or nylon membranes. The spotted probe material generally consists of PCR product. DNA arrays of 10 000 to 15 000 genetic elements are common [17,18]. A comparison of general parameters for synthesized oligonucleotide arrays and spotted DNA arrays is given in Table 2. In an array experiment, target sequences from environmental samples are labeled and hybridized to the arrayed probes (Figure 1). The intensity of hybridized sequences is quantified and the resulting data are subjected to detailed statistical or quantitative analysis.

Application of array technology to environmental samples Gene expression analysis

Gene expression analysis has been used to evaluate transcriptional profiles for sporulation [6], biosynthesis [14] and anaerobic growth [9] in Bacillus subtilis. Such studies provided insight into unique aspects of an organism’s physiology and demonstrated the feasibility of applying array technology to microbial systems in order to obtain a broader, more complete view of the cellular response to the environment and to other organisms [19]. Consequently, gene expression analysis has been applied in novel ways to address questions in environmental studies. DeLisa et al. [20] used an Escherichia coli genome-based array to evaluate transcriptional changes occurring in response to the alternative autoinducer Current Opinion in Biotechnology 2003, 14:311–318

312 Environmental biotechnology

Table 1 The diversity of array formats available for environmental analysis. Array format

Array surface

Probe type

Special features

References

Oligonucleotide array Spotted DNA array

Coated slides Coated slides, plastic, membrane Polyacrylamide gel matrix Polystyrene, colloidal suspension Glass, silicon

O D/O

[12,16,57] [41,50,51]

O

Specificity to 1 bp mismatch Longer probe provides sensitivity; no prior sequence information required Highly specific; multiple reuse

D/O/P

Addressable polymer beads

[58–60]

Porous microchannels with bound probe provides a three-dimensional surface for hybridization Magnetic capture hybridization

[61]

Coated slides spotted with nanoliter volumes of protein are used to capture targeted binding molecules Arrayed lipids with attached probe molecules used to detect specific compounds (e.g. toxins) Fiber-optic imaging of signature-dyed single cells in a high-density array format Arrayed bioreporter organisms permit analysis of whole-cell response

[56,63]

Polyacrylamide gel pad array Microsphere array Flow-through chip Bacterial magnetic particles Protein array

Magnetic particles

O

Coated slides

P

Membrane arrays

Coated slides

L

Single-cell arrays

Optical imaging fiber

LC

Lux array

Membranes, broth cultures

GF

[32,34]

[62]

[64] [65] [54]

D, DNA; GF, reporter-gene fusions; LC, live cells; L, lipids; O, oligonucleotide; P, protein.

(AI-2), a furanosyl borate diester quorum-sensing compound. Results indicated that 242 genes, including 10 sensors and transcriptional regulators and 10 signaltransduction genes, responded to the signal molecule. Whiteley et al. [21] investigated gene expression in Pseudomonas aeruginoa biofilm formation by comparing the expression profiles of free-living versus attached cells. They found that 1% of genes were differentially expressed in the two different growth forms. These results led to the identification of genes important in biofilm formation and of other genes that may be responsible for the antibiotic resistance of biofilms.

An alternative approach to gene expression analysis involves the use of partial arrays of a specified subset of genes. For example, Schut et al. [8] evaluated sulfurmediated gene regulation using an array of 271 ORFs from a hyperthermophilic archaeon, Pyrococcus furiosus. The array consisted of genes encoding proteins proposed to be involved in metabolic pathways, energy conservation and metal metabolism. Cytoplasmic hydrogenases, which were previously thought to function in sulfur reduction, were actually repressed by sulfur. Two uncharacterized ORFs, however, were upregulated more than 25-fold in the presence of sulfur and were proposed to be

Table 2 Comparison of typical parameters for synthesized oligonucleotide arrays versus spotted DNA arrays. Parameter

Synthesized oligonucleotide arrays

Spotted DNA arrays 1–10 ng/spot 75–80% >106 copies 104/cm2 Not applicable 60 000 (typically 5000–15 000) Medium >200 base pairs

Sequence information Probe material

50–75 ng/spot 90–95% >109 copies 106/cm2 22 000 20 000 (typically 2000–5000) High Short oligos — 15–25 nucleotides Long oligos — 50–80 nucleotides Required Oligonucleotides synthesized in situ

Printing method

Photolithography¥




Deposited material Specificityy Sensitivityz Probe density§ Probe sets# Represented genesô Qualtity control Probe size

Not required DNA from clonal libraries cDNAs, SAGE, PCR product, etc Robotic pin printing, inkjet printing




Amount of probe material deposited, although the maximum loading capacity for glass slides may be as low as 10–20 pg/spot (see [41]). Discrimination of related genes. zReported values for detection limits in environmental samples (see [29,41]). §Maximum attainable density of arrayed probes. #Each probe set contains 10–20 probes in perfect match/mismatch pairs that represent a single transcript. ôGenes represented on the array using either oligonucleotide probe sets or arrayed DNA. ¥Light-directed combinatorial synthesis (see [10]).

y

Current Opinion in Biotechnology 2003, 14:311–318

www.current-opinion.com

Environmental applications of array technology Cook and Sayler 313

Figure 1

(b) Array fabrication

Target preparation and array hybridization

DNA probe sequences are cloned and extracted or PCR amplified

DNA or RNA from pure culture or mixed community extracted

Purified DNA is placed into 386-well plates

(c)

Target sequences are labeled with fluorophores Test sample

Image acquisition and data analysis Reference sample

(a)

Test sample

Reference sample

Data analysis

DNA is spotted onto slides or membranes

Labeled target sequences are mixed and hybridized to the array

Hybridization

Image analysis

Image acquisition Current Opinion in Biotechnology

Schematic showing the steps involved in (a) fabrication, (b) hybridization, and (c) analysis of a spotted DNA microarray with target sequences from a test sample and a reference sample that have been fluorescently labeled with different fluorophores, mixed and hybridized to the same array. The probe sequence is DNA from gene sequences, expressed sequence tags (ESTs), genome fragments, oligonucleotides or similar. The reference sample is from an untreated control, a time-course reference point, a known standard or similar.

a novel sulfur-reducing enzyme complex. These results illustrate the breadth of information obtained through the use of directed methodology. Evaluation of gene expression using a defined group of genes may provide as much information as genome-wide analysis, but in a more practical and manageable format [22]. Comparative genomics

Comparative genomic analysis is used to assess genetic similarities and differences between species or strains of closely related organisms. Genomic sequences from one bacterial species are arrayed and genomic DNA from closely related species or strains is labeled and hybridized to the array. Dziejman et al. [23] used an array of 93% of the predicted genes from the etiologic agent of the seventh pandemic outbreak of Vibrio cholera (El Tor strain) to differentiate nine other V. cholera isolates from different geographical locations. Despite 99% similarity between the arrayed and the test strains, the seventhpandemic isolates of V. cholera contained two unique gene clusters hypothesized to impart adaptive properties enhancing the organisms’ fitness. A similar approach was used to identify genetic differences between a pathogenic and a nonpathogenic species of Listeria [24]. Comparative hybridization has also been used for phylogenetic analysis among related species and has provided www.current-opinion.com

insight into the importance of horizontal gene transfer as a mechanism of bacterial speciation [25,26,27]. A partial array containing 192 ORFs from Shewanella oneidenis was used for comparison to nine other Shewanella species and E. coli [25]. Hybridization profiles for some genes, including gyrB (encoding DNA topoisomerase subunit B) and arcA (encoding a key gene in the two-component system responsible for repressing transcription of aerobic genes under low oxygen conditions), were highly conserved between Shewanella spp., suggesting that these universally conserved genes may provide new phylogenetic markers to complement 16S rRNA sequence analysis [25]. In fact, sequence divergence patterns of closely related species were better predicted by gyrB hybridization profiles than by the hybridization profiles of 16S rRNA gene sequences, suggesting an evolutionary lineage based more on functional than on structural characteristics [25]. In another variation of the comparative genomics approach, Cho and Tiedje [28] arrayed 338 total-genome fragments from four fluorescent Pseudomonas strains. Cluster analysis of hybridization profiles for 12 test strains resulted in phylogenetic dendograms providing species-level to strain-level resolution. The results of these studies suggest that a comparative genomics approach can be used to determine important discriminating characteristics of organisms without the need for whole-genome sequence data. Current Opinion in Biotechnology 2003, 14:311–318

314 Environmental biotechnology

Mixed community analysis

The ultimate goal of community-based array analysis is the characterization of community structure and function. In some instances identification of target organisms is desired, whereas in others the characterization of specific functional aspects of the community is the goal. Oligonucleotide-based arrays have been used to target specific diagnostic sequences of pathogens identified as candidates for biological terrorism [11], to detect 16S rRNA from Geobacter and Desulfovibrio sequences in unpurified soil extracts [29] and to evaluate cyanobacterial abundance in mesotrophic and eutrophic lakes [30]. Most recently, Loy et al. [13] developed an oligonucleotidebased array targeting the 16S rRNA of sulfate-reducing prokaryotes (SRPs). A PCR-based technique was used to label lake samples and periodontal samples in order to obtain a fingerprint of SRP diversity. Modifications of the conventional oligonucleotide arrays have also been used to characterize target sequences in environmental samples. For example, Valinsky et al. [31] arrayed 16S rRNA sequences from soil clonal libraries and hybridized these using 27 10-mer-discriminating oligonucleotides. Cluster analysis from the fingerprint profiles of 1536 rDNA clones from two agricultural soils revealed compositional differences that may allow the correlation of specific bacterial populations to particular diseasesuppressive qualities of soil. Guschin et al. [32] adapted a polyacrylamide-gel-pad technique for use in environmental studies. This method combines the specificity of an oligonucleotide-based technique with the additional advantage of greater dynamic range via enhanced probe-binding capacity. In separate studies, this methodology was used for the characterization of nitrifying bacteria [32] and of toluene- and ethylbenzene-degrading consortia [33]. Results thus far suggest that detection sensitivity in environmental samples is limited. However, optimized probe design and improved hybridization conditions may ultimately alleviate some difficulties related to the sensitivity and specificity of this array format [34]. DNA-based arrays have also been used in community analysis. We have developed an array of plasmid-containing 16S–23S rRNA intergenic spacer (ISR) probesequence inserts (KL Cook, AC Layton, HM Dionisi, JT Fleming, GS Sayler, unpublished data). Conserved sequences in the 16S rRNA and 23S rRNA were used as primer targets to amplify ISR sequences from an activated-sludge community. Phylogenetic information was obtained by the analysis of 550 bp of 16S rRNA sequence. The PCR products were cloned into plasmid vectors and extracted plasmid was arrayed on to glass slides. ISR sequences are preferable to 16S rRNA gene sequences as probe material because the ISR’s higher sequence variability provides greater specificity. By arraying ISRcontaining plasmid, analysis of mixed community clonal Current Opinion in Biotechnology 2003, 14:311–318

libraries is possible without the need for sequence data or for costly and time-consuming PCR amplification steps. Wu et al. [35] developed and evaluated the specificity, sensitivity and quantitative characteristics of a DNAbased functional gene array containing genes important in biogeochemical cycles. These authors were able to qualitatively detect hybridization to functional genes encoding key denitrification, nitrification and methaneoxidation enzymes. However, to date, quantitative analysis of specific genetic sequences using array technology, especially in mixed community environmental samples, remains an elusive goal.

Array issues: specificity and sensitivity Researchers have begun to rigorously evaluate the experimental and analytical aspects of array experiments. In the past, the high cost and overwhelming quantity of data accrued from one array experiment limited replication and validation of results. This practice has resulted in an unprecedented lack of scientific stringency when reporting data. However, the trend is abating as scientific groups begin to establish guidelines for publication [36]. To avoid misinterpretation of array results, potential sources of experimental and analytical error must be pinpointed and proper standards and controls established. This is particularly important for environmental array applications where there is less control over the target population than there is in traditional reductionistic monoculture studies. Another important issue when applying array technology to environmental samples concerns the differentiation of changes in hybridization signal resulting from population differences from those resulting from sequence divergence [37]. The breadth of genetic diversity in the environment influences both array fabrication and data interpretation. Structural gene sequence diversity in functionally related populations (e.g. nitrifying bacteria, SRPs or nitrogen fixers) is large and makes upfront bioinformatics work cumbersome, but essential, especially when short oligonucleotide probes are used. Specificity is critical, as parallel hybridization of hundreds or thousands of arrayed probes increases the potential for cross-hybridization between unrelated genetic sequences. The interpretation of data is equally vexing. Changes in hybridization patterns between samples are quite likely to represent genuine differences between samples; however, a lack of differences between hybridization patterns does not necessarily mean that the samples are similar. It may instead reflect the possibility that dissimilarity exists in sequences that are not represented in the arrayed probes [37]. Some researchers have made progress toward the determination of confidence limits for the evaluation of array data. For example, Wu et al. [35] reported that their functional gene arrays could accurately discriminate probe sequences of 80–100% identity. Oligonucleotidebased arrays are sensitive to base-pair mismatches and www.current-opinion.com

Environmental applications of array technology Cook and Sayler 315

therefore exhibit greater hybridization specificity [34]. However, recent research has shown that cross-hybridization with oligonucleotide-based arrays is also an issue that needs to be taken into account [13]. More detailed studies of array specificity are needed to permit proper analysis of array data. Sensitivity is also an important parameter in environmental array-based studies. Low biomass or target-sequence concentrations and high concentrations of contaminants reduce detection limits [35,38]. Therefore, many researchers have resorted to enrichment or PCR amplification in order to obtain sufficient labeled target for analysis [13,39]. This obviously alters in situ environmental conditions and may have a profound effect on gene expression and community composition. The method of target labeling may also influence sensitivity, depending on the type and incorporation efficiency of the fluorophore [38]. The length of the arrayed probe and the location, type and number of base-pair mismatches between probe and target affect the signal intensity of oligonucleotide-based arrays [34]. The efficiency of extracting and labeling target material from low-copy probe sequences must be improved to eliminate the need for enrichment or PCR amplification. Until these problems are addressed, quantitative analysis of array data from environmental samples will be primarily hindered by lack of sensitivity. Specificity and sensitivity are both dependent on experimental factors relating to array fabrication and hybridization. Slide-surface chemistry, spot morphology, probe binding capacity and hybridization and wash conditions all affect specificity and sensitivity in array experiments [40]. These factors are all related to the material used for the adherence of arrayed probe sequences. Glass slides and nylon membrane are the most common array surfaces. Glass slides are smaller, give lower background readings and require reduced hybridization volumes. However, glass slides cannot be re-used whereas membrane arrays can be stripped and re-used several times. Fluorescent signals on hybridized glass slides do not exhibit ‘blooming’ effects as readily as radioactive signals on nylon membranes, thereby permitting denser array spacing. An additional benefit is that hybridization signals from multiple probes, differentially labeled and hybridized to the same slide, can be detected separately [40]. However, the sensitivity of detection is significantly less using glassslide-based techniques because of the lower dynamic range; there is up to 105-fold difference in the amount of DNA target that can be immobilized on a glass slide versus a nylon membrane, resulting in an equivalent difference in detection sensitivity [41].

Array issues: analysis Analytical issues related to the extraction and interpretation of relevant data from array experiments are the focus www.current-opinion.com

of intensive investigation. The interpretation of data is made difficult by the inherent variability in array experiments due, in part, to the experimental factors discussed above. Additionally, the lack of replication and the large quantities of simultaneously derived data points make the extraction of relevant information cumbersome. With a few notable exceptions [28,34,41], even the most cursory attention to analytical standards has been ignored in environmental studies. This is due, in large part, to the fact that most environmental array technology is currently at the developmental stage. As the technology becomes better established, methods for the normalization and standardization of data should be incorporated into the experimental design and a movement is underway to achieve this [42–44]. Until these techniques have been established for environmental studies, however, array data should be held to high standards and rigorously validated. As many experimental factors are difficult to calibrate (e.g. efficiency of incorporation of fluorophores, concentration of spotted target material, or the amount of target available for hybridzation), proper controls and methods for normalizing for slide-to-slide variability are needed [13,38]. The pre-processing of array data via background subtraction and data normalization alleviates some experimental error. Background subtraction removes local and nonspecific background from spot intensity values and controls for nonspecific hybridization, differential blocking and some mechanical spotting errors. Normalizing against the total image intensity or the average intensity of all spots on the array is the most common way to do this [38,45]. Recently, methods have been developed incorporating arrayed control spots [46], internal standards [28] or calibrated standards [47] in order to correct for some experimental variability and to make slide-to-slide comparison more feasible. Following pre-processing, the actual data analysis presents an enormous obstacle. The challenge has been to arrange data in a meaningful context without losing important information or overstating its statistical significance. In this, no consensus has been established for monoculture studies, let alone environmental applications. Most commonly, array data are analyzed by comparing ratios of gene expression from control and treatment samples. Confounding experimental factors are eliminated when control and treatment probes are hybridized simultaneously because the confound affects both probes equally [40]. However, these values cannot be related to absolute expression levels and samples without similar controls cannot be compared. The approach is reductionistic and does not consider the breadth of information available from an array experiment. To glean more information from array data, Eisen et al. [48] introduced a hierarchical clustering technique, which has been used extensively and modified by others [42,44,49]. Hierarchical clustering organizes large sets of Current Opinion in Biotechnology 2003, 14:311–318

316 Environmental biotechnology

data on the basis of similarities in patterns of gene expression. The resulting groups of co-expressed genes have been shown to have similar properties with respect to gene function, genetic regulation or physical location [50,51]. Importantly, genes of unknown function often cluster with genes with defined function, making these genes obvious candidates for further analysis [17].

protein targets of small molecules [56]. Regardless of the form, array technology promises to revolutionize environmental studies. Further technical improvements in the field will permit the exploration of ecology, physiology, systematics, structure and function in a more complete and probing manner than ever before.

References and recommended reading Conclusions The integration of disparate technologies and disciplinary fields is required to incorporate array technology into a holistic approach to the analysis of complex environmental systems. For example, Ideker et al. [52] used a combination of deletion mutants, microarray analysis and proteomics to evaluate the yeast galactose-utilization pathway. Similarly, Fouts et al. [53] used an iterative approach to identify genes involved in Pseudomonas syringae pathogenicity. Using a combination of mutagenesis, gene expression and computational analysis, these researchers identified promoters that control virulence factors responsible for the activation of the hypersensitive response and pathogenicity (Hrp) system. These studies illustrate how directed research using array technology in conjunction with other proven methodologies can produce new fundamental knowledge about important concepts in environmental science. To date, array technology has been successfully applied to the study of environmental samples using innovative approaches to gene expression, comparative genomics and mixed community analysis. However, issues relating to sensitivity and specificity as well as to data analysis and interpretation make it important to proceed with caution when applying array technology to studies into the environment. Recognizing the fact that mRNA expression does not necessarily reflect the cellular response (which is more closely approximated by protein expression), researchers are developing new techniques to evaluate the whole-cell response (see Table 1). For example, the Lux array developed by DuPont provides a cellular array of gene reporter fusions in E. coli. This array is composed of E. coli cells with defined reporter gene fusions in a 96-well array format. The Lux array is used in the same fashion as a traditional array, but enables analysis of the whole cell response and can be used to collect kinetic data (by measuring bioluminescence for a single array over time) [54]. Other researchers are arraying plasmid DNAs on to glass slides and spreading cells in a monolayer over the slide surface. Cells coming into contact with the expression vector divide and form clusters of transfected cells, forming living cell arrays [55]. Proteomics represents the newest challenge in array research. To evaluate protein function, robotic technologies are being used to spot proteins at high density. Covalently attached proteins have been shown to interact with other proteins or molecules, revealing new information about protein–protein interactions, the substrates of protein kinases and the Current Opinion in Biotechnology 2003, 14:311–318

Papers of particular interest, published within the annual period of review, have been highlighted as:  of special interest  of outstanding interest 1.

Krutz GW, Schueller JK: Advanced engineering: future directions for the agricultural and biological engineering profession. J Agric Eng Res 2000, 76:251-265.

2.

Lio P: Investigating the relationship between genome structure, composition, and ecology in prokaryotes. Mol Biol Evol 2002, 19:789-800.

3.

Pridmore RD, Crouzillat D, Walker C, Foley S, Zink R, Zwahlen MC, Brussow H, Petiard V, Mollet B: Genomics, molecular genetics and the food industry. J Biotechnol 2000, 78:251-258.

4.

Gray CP, Keck W: Bacterial targets and antibiotics: genomebased drug discovery. Cell Mol Life Sci 1999, 56:779-787.

5.

Shoemaker DD, Linsley PS: Recent developments in DNA microarrays. Curr Opin Microbiol 2002, 5:334-337.

6.

Fawcett P, Eichenberger P, Losick R, Youngman P: The transcriptional profile of early to middle sporulation in Bacillus subtilis. Proc Natl Acad Sci USA 2000, 97:8063-8068.

7.

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, 181:6425-6440.

8.

Schut GJ, Zhou JZ, Adams MWW: DNA microarray analysis of the hyperthermophilic archaeon Pyrococcus furiosus: evidence for a new type of sulfur-reducing enzyme complex. J Bacteriol 2001, 183:7027-7036.

9.

Ye RW, Tao W, Bedzyk L, Young T, Chen M, Li L: Global gene experssion profiles of Bacillus subtilis grown under anaerobic conditions. J Bacteriol 2000, 182:4458-4465.

10. Cronin MT, Holmes CP, Fodor SP: Light-generated oligonucleotide arrays for rapid DNA sequence analysis. Proc Natl Acad Sci USA 1994, 91:5022-5026. 11. Wilson WJ, Strout CL, DeSantis TZ, Stilwell JL, Carrano AV, Andersen GL: Sequence-specific identification of 18 pathogenic microorganisms using microarray technology. Mol Cell Probes 2002, 16:119-127. 12. Call DR, Chandler DP, Brockman F: Fabrication of DNA microarrays using unmodified oligonucleotide probes. Biotechniques 2001, 30:368-376. 13. Loy A, Lehner A, Lee N, Adamczyk J, Meier H, Ernst J, Schleifer  K-H, Wagner M: Oligonucleotide microarray for 16S rRNA genebased detection of all recognized lineages of sulfate-reducing prokaryotes in the environment. Appl Environ Microbiol 2002, 68:5064-5081. The authors demonstrate the use of array technology for the detection and phylogenetic analysis of sulfate-reducing organisms in environmental samples. They present a comprehensive comparative analysis of oligonucleotide 16S rRNA gene array detection versus traditional PCR, cloning and sequence analysis. 14. Lee J-ML, Zhang S, Saha S, Santa Anna S, Jian C, Perkins J: RNA expression analysis using an antisense Bacillus subtilis genome array. J Bacteriol 2001, 183:7371-7380. 15. Warrington JA, Dee S, Trulson M: Large-scale genomic analysis using Affymetrix GeneChip probe arrays. In Microarray Biochip Technology. Edited by Natick SM. BioTechniques Books; 2000:119-148. www.current-opinion.com

Environmental applications of array technology Cook and Sayler 317

16. Array Design for the GeneChip Human Genome U133 Set. URL: http://www.affymetrix.com/support/technical/technotes/ hgu133_performance_technote.pdf 17. Kao CM: Functional genomic technologies: creating new paradigms for fundamental and applied biology. Biotechnol Prog 1999, 15:304-311. 18. Blohm D, Guiseppi-Elie A: New developments in microarray technology. Curr Opin Biotechnol 2001, 12:41-47. 19. Lockhart D, Winzeler EA: Genomics, gene expression and DNA arrays. Nature 2000, 405:827-836. 20. DeLisa MP, Wu C-F, Wang L, Valdes JJ, Bentley WE: DNA microarray-based identification of genes controlled by autoinducer 2-stimulated quorum sensing in Escherichia coli. J Bacteriol 2001, 183:5239-5247. 21. Whiteley M, Bangera MG, Bumgarner RE, Parsek MR, Teitzel GM, Lory S, Greenberg EP: Gene expression in Pseudomonas aeruginosa biofilms. Nature 2001, 413:860-864. 22. Hoheisel JD, Vingron M: Transcriptional profiling: is it worth the money? Res Microbiol 2000, 151:113-119. 23. Dziejman M, Balon E, Boyd D, Fraser CM, Heidelberg JF, Mekalanos JJ: Comparative genomic analysis of Vibrio cholerae: genes that correlate with cholera endemic and pandemic disease. Proc Natl Acad Sci USA 2002, 99:1556-1561. 24. Glaser P, Frangeul L, Buchrieser C, Rusniok C, Amend A, Baquero F, Berche P, Bloecker H, Brandt P, Chakraborty T et al.: Comparative genomics of Listeria species. Science 2001, 294:849-852. 25. Murray AE, Lies D, Li G, Nealson K, Zhou JZ, Tiedje JM: DNA/DNA  hybridisation to microarrays reveals gene-specific differences between closely related microbial genomes. Proc Natl Acad Sci USA 2001, 98:9853-9858. An excellent example of how a comparative genomics approach can be used to provide new fundamental information about a species without the need for full-length sequence data. 26. Ochman H, Moran NA: Genes lost and genes found: evolution of bacterial pathogenesis and symbiosis. Science 2001, 292:1096-1098. 27. Malloff CA, Fernandez RC, Lam WL: Bacterial comparative genomic hybridization: a method for directly identifying lateral gene transfer. J Mol Biol 2001, 312:1-5. 28. Cho J-C, Tiedje JM: Bacterial species determination from DNA–DNA hybridization by using genome fragments and DNA microarrays. Appl Environ Microbiol 2001, 67:3677-3682. 29. Small J, Call DR, Brockman FJ, Straub TM, Chandler DP: Direct detection of 16S rRNA in soil extracts by using oligonucleotide microarrays. Appl Environ Microbiol 2001, 67:4708-4716. 30. Rudi K, Skulberg OM, Skulberg R, Jakobsen KS: Application of sequence-specific labeled 16S rRNA gene oligonucleotide probes for genetic profiling of cyanobacterial abundance and diversity by array hybridization. Appl Environ Microbiol 2000, 66:4004-4011. 31. Valinsky L, Vedova GD, Scupham AJ, Alvey S, Figueroa A, Yin B, Hartin RJ, Chrobak M, Crowley DE, Jiang T et al.: Analysis of bacterial community composition by oligonucleotide fingerprinting of rRNA genes. Appl Environ Microbiol 2002, 68:3243-3250. 32. Guschin DY, Mobarry BK, Proudnikov D, Stahl DA, Rittmann BE, Mirzabekov AD: Oligonucleotide microchips as genosensors for determinative and enviornmental studies in microbiology. Appl Environ Microbiol 1997, 63:2397-2402. 33. Koizumi Y, Kelly JJ, Nakagawa T, Urakawa H, El-Fantroussi S, Al-Muzaini S, Fukui M, Urushigawa Y, Stahl DA: Parallel characterization of anaerobic toluene- and ethylbenzenedegrading microbial consortia by PCR-denaturing gradient gel electrophoresis, RNA-DNA membrane hybridization, and DNA microarray technology. Appl Environ Microbiol 2002, 68:3215-3225. 34. Urakawa H, Noble PA, Fantroussi SE, Kelly JJ, Stahl DA: Single-base-pair discrimination of terminal mismatches www.current-opinion.com

by using oligonucleotide microarrays and neural network analyses. Appl Environ Microbiol 2002, 68:235-244. 35. Wu L, Thompson DK, Li G, Hurt RA, Tiedje JM, Zhou J: Development and evaluation of functional gene arrays for detection of selected genes in the environment. Appl Environ Microbiol 2001, 67:5780-5790. 36. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman PT, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC et al.: Minimum information about a microarray experiment (MIAME) — toward standards for microarray data. Nat Genet 2001, 29:365-371. 37. Zhou JZ, Thompson DK: Challenges in applying microarrays to environmental studies. Curr Opin Biotechnol 2002, 13:204-207. 38. Liao JC, Sabatti C: Microanalysis of DNA microarrays. ASM News 2002, 68:432-437. 39. Revel AT, Talaat AM, Norgard MV: DNA microarray analysis of differential gene expression in Borrelia burgdorferi, the Lyme disease spirochete. Proc Natl Acad Sci USA 2002, 99:1562-1567. 40. Shalon D, Smith SJ, Brown PO: A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization. Genome Res 1996, 6:639-645. 41. Cho J-C, Tiedje JM: Quantitative detection of microbial genes  by using DNA microarrays. Appl Environ Microbiol 2002, 68:1425-1430. The authors of this paper demonstrate the use of reference DNA to normalize for slide-to-slide variability. This is the first comprehensive study of quantitative detection sensitivity in environmental analysis. The methodology is used to detect genes important in biogeochemical processes and antigen biosynthesis. 42. Mendez MA, Hodar C, Vulpe C, Gonzalez M, Cambiazo V: Discriminant analysis to evaluate clustering of gene expression data. FEBS Lett 2002, 522:24-28. 43. de la Fuente A, Brazhnik P, Mendes P: Linking the genes: inferring quantitative gene networks from microarray data. Trends Genet 2002, 18:395-398. 44. Olshen AB, Jain AN: Deriving quantitative conclusions from microarray expression data. Bioinformatics 2002, 18:961-970. 45. Nadon R, Woody E, Shi P, Rghei N, Hubschle H, Susko E, Ramm P: Statistical inference in array genomics. In Microarrays for the Neurosciences: An Essential Guide. Edited by Geschwind DH, Gregg JP. Cambridge: The MIT Press; 2002:109-140. 46. Weil MR, Macatee T, Garner HR: Toward a universal standard: comparing two methods for standardizing spotted microarray data. Biotechniques 2002, 32:1310-1324. 47. Dudley AM, Aach J, Steffen MA, Church GM: Measuring absolute expression with microarrays with a calibrated reference sample and an extended signal intensity range. Proc Natl Acad Sci USA 2002, 99:7554-7559. 48. Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 1998, 95:14863-14868. 49. Kerr MK, Churchill GA: Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments. Proc Natl Acad Sci USA 2001, 98:8961-8965. 50. Brown PO, Botstein D: Exploring the new world of the genome with DNA microarrays. Nat Genet Supplement 1999, 21:33-37. 51. Eisen MB, Brown PO: DNA arrays for analysis of gene expression. Methods Enzymol 1999, 303:179-205. 52. Ideker T, Thorsson V, Ranish JA, Christmas R, Buhler J, Eng JK, Bumgarner R, Goodlett DR, Aebersold R, Hood L: Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 2001, 292:929-934. 53. Fouts DE, Abramovitch RB, Alfano JR, Baldo AM, Buell CR,  Cartinhour S, Chatterjee AK, D’Ascenzo M, Gwinn ML, Lazarowitz SG et al.: Genomewide identification of Pseudomonas syringae pv. tomato DC3000 promoters controlled by HrpL alternative sigma factor. Proc Natl Acad Sci USA 2002, 99:2275-2280. Current Opinion in Biotechnology 2003, 14:311–318

318 Environmental biotechnology

This paper illustrates the power of an integrated approach incorporating proven methodologies with array technology. Genes involved in pathogenesis of the tomato pathogen, Pseudomonas syringae pv. tomato DC3000, were identified using a combination of miniTn5 gus mutagenesis, hidden markov models and microarray and RNA-blot analysis. 54. Van Dyk TK, DeRose EJ, Gonye GE: LuxArray, a high-density, genomewide transcription analysis of Escherichia coli using bioluminescent reporter strains. J Bacteriol 2001, 183:5496-5505. 55. Ziauddin J, Sabatini D: Microarrays of cells expressing defined cDNAs. Nature 2001, 411:107-110. 56. MacBeath G, Schreiber SL: Printing proteins as microarrays for high-throughput function determination. Science 2000, 289:1760-1763.

sequences using flow cytometry. Appl Environ Microbiol 2000, 66:4258-4265. 60. Steemers FJ, Ferguson JA, Walt DR: Screening unlabeled DNA targets with randomly ordered fiber-optic gene arrays. Nat Biotechnol 2000, 18:91-94. 61. Steel A, Torres M, Hartwell J, Yu Y-Y, Ting N, Hoke G, Yang H: The Flow-thru Chip: a three-dimensional biochip platform. In Microarray Biochip Technology. Edited by Schena M. Natick: BioTechniques Books; 2000:87-117. 62. Matsunaga T, Nakayama H, Okochi M, Takeyama H: Fluorescent detection of cyanobacterial DNA using bacterial magnetic particles on a MAG-microarray. Biotechnol Bioeng 2001, 73:400-405.

57. Lipshutz RJ, Fodor SP, Gingeras TR, Lockhart DJ: High density synthetic oligonucleotide arrays. Nat Genet 1999, 21:20-24.

63. Delehanty JB, Ligler FS: A microarray immunoassay for simultaneous detection of proteins and bacteria. Anal Chem 2002, 74:5681-5687.

58. Battersby BJ, Lawrie GA, Johnston APR, Trau M: Optical barcoding of colloidal suspensions: applications in genomics, proteomics and drug discovery. Chem Commun 2002, 14:1435-1441.

64. Fang Y, Frutos AG, Lahiri J: Ganglioside microarrays for toxin detection. Langmuir 2003, 19:1500-1505.

59. Spiro A, Lowe M, Brown D: A bead-based method for multiplexed identification and quantification of DNA

Current Opinion in Biotechnology 2003, 14:311–318

65. Biran I, Walt DR: Optical imaging fiber-based single live cell arrays: a high-density cell assay platform. Anal Chem 2002, 74:3046-3054.

www.current-opinion.com