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Virulence gene expression in vivo
Virulence gene expression in vivo Samuel A Shelburne1 and James M Musser2 The ability to identify and isolate bacterial RNA from animals or humans with infections has markedly advanced the capacity to examine microbial gene expression in vivo. This advance has been coupled with the development of quantitative real-time reverse transcription polymerase chain reaction and expression microarrays to allow investigators to accurately measure how organisms are manipulating their genetic expression during actual infections. Though the full ramifications of these technologies have yet to be realized, they promise to open new avenues of therapeutics for a broad range of infectious diseases by allowing researchers to focus on in vivo expressed genes. These developments provide a framework for efficient utilization of the vast amount of information being generated by the accelerating pace of genomic sequencing of microbes. Addresses 1 Department of Medicine, Section of Infectious Diseases, Baylor College of Medicine, 1 Baylor Plaza, Houston, Texas 77300, USA 2 Department of Pathology, Baylor College of Medicine, 1 Baylor Plaza, Houston, Texas 77030, USA e-mail: [email protected]
Current Opinion in Microbiology 2004, 7:283–289 This review comes from a themed issue on Techniques Edited by Simon Foster and Andrew Camilli Available online 10th May 2004 1369-5274/$ – see front matter ß 2004 Elsevier Ltd. All rights reserved. DOI 10.1016/j.mib.2004.04.013 Abbreviations DECAL differential expression analysis using a custom-amplified library GAS group A Streptococcus gfp green fluorescent protein IVET in vitro expression technology OspA outer surface protein A QRT-PCR quantitative RT-PCR RT-PCR reverse transcription polymerase chain reaction STM signature-tagged mutagenesis
Introduction Two hundred bacterial genomes have been sequenced in the past decade with many more close to completion [1]. The resulting genetic information has provided tremendous new opportunities for research into microbial pathogenesis, epidemiology and therapeutics [2,3,4]. However, with this remarkable accumulation of data, difficulties have arisen concerning its efficient use. For example, given that the average bacterial genome contains www.sciencedirect.com
many thousands of open reading frames, how can investigators decide which genes to focus on? As we move into the post-genomic era, there has been renewed interest in the crucial role that differential gene transcription plays in host–pathogen interactions. Accurate measurement of microbial RNA transcript levels in vivo provides excellent insight into how organisms selectively employ their genome during contact with the host and other environments encountered in their life cycle. Focused attention on genes that are differentially expressed in vivo has expanded our understanding of microbial pathogenesis and contributed to the enhanced use of the massive amount of genetic information that has been generated recently. The importance of analyzing gene transcription has been known for decades, and many methods have been used, including northern blot hybridization and reverse transcription polymerase chain reaction (RT-PCR). Because of the technical difficulties in isolating and detecting microbial RNA in tissue samples, the vast majority of research has focused on bacteria grown in laboratory media and subjected to environmental stimuli designed to mimic conditions found in the host. However, the limitations of this strategy are well known, and as a consequence there has been much effort directed toward detecting microbial gene transcription during infections. It is now known that many microbial genes are differentially transcribed during infection. Thus, concentration of experimental efforts on these genes is likely to be a productive line of inquiry [5]. New methods have been introduced over the past fifteen years to elucidate bacterial genes that are actively transcribed in the host. Moreover, in the past several years the ability to quantitate in vivo gene expression has become possible. In this review, we briefly discuss methods for qualitative analysis of in vivo bacterial gene transcription and then focus on the more recent methods for quantitation of transcripts. The sheer number of experiments precludes a detailed discussion of each, but rather research that has produced singular results or reflects the immense potential of particular approaches will be highlighted. The progress made in these areas has opened new avenues of research for understanding how microbes cause infection and methods for preventing and treating these diseases.
Non-quantitative methods Molecular techniques developed during the 1980s and 1990s resulted in an initial understanding of how organisms adjust gene transcription in response to the host Current Opinion in Microbiology 2004, 7:283–289
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environment. By placing reporter genes that produce assayable proteins under the control of promoters of genes of interest, analysis of genes transcribed in vivo can be accomplished [6]. Examples of reporter systems include b-galactosidase, luciferases and green fluorescent protein (gfp). Combining these reporters with such powerful techniques as confocal microscopy and flow cytometry allows for visualization of gene transcription for individual bacteria [7]. Reporters also have been used in selection procedures to isolate genes that are preferentially expressed under in vivo compared with in vitro conditions. In vivo expression technology (IVET) couples the insertion of random chromosomal segments upstream of an identifiable gene needed for survival in an animal model [8]. IVET has been used to identify hundreds of in vivo expressed genes but suffers from only being able to identify genes that are highly expressed in vivo and also are not expressed in vitro. A similar method, differential fluorescence induction, uses enhanced gfp to evaluate promoter activity by incorporating a library of DNA fragments upstream of a promoterless gfp gene. Fluorescence activated cell sorting is then used to isolate gfp-expressing bacteria proliferating under a variety of conditions, including animal models of infections [9]. Finally, signature-tagged mutagenesis (STM) is a high-throughput system based on mutagenesis due to random insertions that relies on negative selection in animal models of infection [10]. STM and its derivatives have been used to identify genes essential for colonization and infection for a wide variety of microorganisms [11,12].
Quantitative methods for measurement of in-vivo gene transcription Although very useful, IVET and STM lack the capacity to quantitate RNA levels. The two methods that can be used to accomplish this goal are quantitative RTPCR (QRT-PCR) and DNA expression microarrays. Both techniques share the common preconditions of requiring adequate quantities of high quality messenger RNA (mRNA) and entailing complex statistical analysis [13]. These issues will be addressed before discussing the particular advantages and limitations of each approach. The preparation of microbial mRNA from tissue is hindered by two main problems. First, the half-life of bacterial mRNA is usually less than two minutes, which means that mRNA needs to be quickly stabilized during extraction. Second, bacteria can rapidly change gene expression in response to environmental stimuli resulting in experimental artifact if RNA preparation is conducted without great attention to detail [14]. The introduction of commercial products that protect RNA from degradation and eliminate contaminating DNA have mitigated some of these issues. Once the RNA is obtained, both Current Opinion in Microbiology 2004, 7:283–289
QRT-PCR and expression microarrays require the conversion of mRNA to complementary DNA (cDNA) by reverse transcriptase. There are many options for the types of primers used for reverse transcription, and excellent technical reviews of this area have been published recently [15,16]. The interpretation of results generated with both expression microarrays and QRT-PCR carries much uncertainty. The numerical results for both methods are generally expressed as a ratio; determining the cut-off for calling a gene ‘differentially expressed’ or ‘highly expressed’ is somewhat arbitrary. Most researchers assign between 1.5- to 2.0-fold changes in transcript level as significant, but the biological relevance of these numbers is not fully understood. A final issue that both of these methods share is that what they actually measure is the level of RNA present in the tissue. This transcript level reflects both gene transcription and the rate of mRNA decay. Whether differential half-lives among various mRNA transcripts cause artifactual results has not been fully addressed at present.
QRT-PCR Although RT-PCR has been used widely for many years, QRT-PCR did not become available until the mid-1990s. The competitive form of QRT-PCR involves the addition of different known amounts of a competitor RNA target that has similar amplification properties but can be distinguished from the target mRNA following RT-PCR. When equivalent products of competitor and target are produced, the amount of the mRNA in question can be quantified by comparing with the known quantity of the competitor [17]. This approach is relatively labor intensive because of the difficulty in designing and preparing effective competitors but has been successfully used for in vivo gene transcript quantitation in Staphylococcus aureus, Borrelia burgdorferi, and Helicobacter pylori (Table 1) [13,18,19]. The difficulties associated with competitive QRT-PCR in part led to the more widespread employment of realtime QRT-PCR in which product accumulation is measured during each PCR cycle by the use of fluorescent probes. Direct comparison of competitive and real-time QRT-PCR for measurement of S. aureus gene expression in human sputum showed that the real-time method was easier to perform and interpret, as least as sensitive, but more easily subject to artifact [20]. The vast majority of in vivo gene expression studies have employed real-time systems with either TaqMan probes or SYBR green I intercalating dyes. These systems rely on the probes emitting fluorescent signals upon binding to amplified PCR products and, with the use of accompanying software, can generate direct quantitation of mRNA. The results are usually reported as a ratio to a gene transcript that is known to be present at a continuous level www.sciencedirect.com
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Table 1 Quantitative studies of microbial in vivo gene expression. Species
Sample source
Method
Reference
Borrelia burgdorferi Borrelia burgdorferi Borrelia burgdorferi Borrelia burgdorferi Borrelia burgdorferi Borrelia burgdorferi Borrelia burgdorferi Borrelia burgdorferi Borrelia burgdorferi Helicobacter pylori Helicobacter pylori Mycobacterium tuberculosis Mycobacterium tuberculosis Pasturella multocida Porphyromonas gingivalis Plasmodium berghei Staphylococcus aureus Staphylococcus aureus Staphylococcus aureus Staphylococcus aureus Staphylococcus aureus Staphylococcus epidermidis Staphylococcus epidermidis Streptococcus pneumoniae Streptococcus pyogenes Streptococcus pyogenes Streptococcus pyogenes Vibrio cholerae Vibrio cholerae Vibrio cholerae
Various mouse tissue Various mouse tissue Mouse dialysis membrane chamber Various mouse tissues Rat dialysis membrane chamber Rat dialysis membrane chamber Ticks Medulla and heart of rhesus monkeys Ticks Mouse and human stomach Mouse stomachs Mouse and human lung Mouse lung Chicken blood Human dental plaque Mosquitoes Human sputum Human sputum Guinea pig tissue cages Guinea pig tissue cages Rabbit implanted golf balls Rat-implanted catheters Rat-implanted catheters Mouse blood Mouse soft tissue Human pharynx Human and macaque pharynx Human stool Human stool Rabbit ileal loop model
Competitive QRT-PCR Real-time QRT-PCR Real-time QRT-PCR Real-time QRT-PCR Expression microarray/real-time Expression microarray/real-time DECAL expression microarrays DECAL expression microarrays Real-time QRT-PCR Competitive QRT-PCR Real-time QRT-PCR Real-time QRT-PCR Expression microarray/real-time Expression microarray/real-time Real-time QRT-PCR Real-time QRT-PCR Competitive QRT-PCR Real-time QRT-PCR Real-time QRT-PCR Real-time QRT-PCR Expression microarray/real-time Real-time QRT-PCR Real-time QRT-PCR Real-time QRT-PCR Real-time QRT-PCR Real-time QRT-PCR Real-time QRT-PCR Expression microarray Expression microarray Expression microarray
[19] [25] [27] [28] [32] [35] [45] [46] [49] [13] [50] [31] [36] [34] [24] [26] [18] [20] [21] [22] [33] [51] [52] [53] [23] [29] [30] [40] [41] [42]
throughout the growth cycle of the particular organism or to the total RNA present in the specimen [14]. On multiple occasions, real-time QRT-PCR has shown the importance of using in vivo models when analyzing gene expression. In vitro work with S. aureus suggested the agr locus, via its effector molecule RNA III, played a key role in controlling virulence gene expression. However, analysis of human sputum containing S. aureus showed significant expression of multiple virulence genes despite the absence of RNA III [21]. Similarly, in contrast to the in vitro situation, analysis of mRNA encoding for clumping factor A (clfA) of S. aureus present in an animal model of catheter-related infection revealed marked increases in clfA mRNA levels beginning about one week after the initiation of infection [22]. Group A Streptococcus (GAS) is the leading cause of bacterial pharyngitis in humans, and infection with GAS can result in rheumatic fever with serious cardiac complications. In a GAS mouse subcutaneous infection model, mutation of a two-component regulatory system led to differential transcription of multiple genes when compared with wild-type strains [23]. Interestingly, the effects of this mutation in vivo were different than in vitro, illustrating the complex interplay between the host and GAS transcription control. www.sciencedirect.com
QRT-PCR QRT-PCR
QRT-PCR QRT-PCR
QRT-PCR
Importantly, real-time QRT-PCR also has been used to associate bacterial transcript levels with clinical presentations as well as microbial tissue specificity. Porphyromonas gingivalis is a major source of dental disease in humans. The expression of specific genes in P. gingivalis isolates from different subgingival plaque samples has been correlated with the severity of periodontitis at those sites [24]. B. burgdorferi, the causative agent of Lyme disease, needs to disseminate from the site of tick inoculation to cause systemic disease. By comparing gene expression to bacterial quantitation at various sites in a mouse model of infection, genes that facilitate the migration of B. burgdorferi were identified [25]. Similar methods have also been used to delineate the gene expression patterns that underlie the infectious nature of plasmodium sporozoites found in the salivary gland of mosquitoes as compared to the avirulent forms found in the mid-gut [26]. Real-time QRT-PCR can be used along with manipulation of the animal model to study how changes in the host lead to differences in bacterial gene expression and more closely examine the role of the host immune system in influencing microbial gene expression. Using the implanted dialysis membrane chamber in mice to study Current Opinion in Microbiology 2004, 7:283–289
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B. burgdorferi, Zymosan A was administered to the mice to induce peritoneal inflammation [27]. The expression of B. burgdorferi outer surface protein A (OspA) was markedly increased in the animals that received Zymosan A. To further understand the effects of the immune system on B. burgdorferi gene transcription, wild-type and scid mice were infected, and QRT-PCR was used to examine various sites at differing time points of infection [28]. OspA expression was consistently present in the scid-mice but not in the wild-type, implying that the host immune response has an influence on OspA transcription. Because many rodent animal models do not accurately reproduce human infections, real-time QRT-PCR has been applied in non-human primate and even human infections. The irr gene product is critical to the ability of GAS to evade neutrophil phagocytosis. High levels of irr mRNA were shown to be present in the pharynx of persons with acute GAS pharyngitis [29]. This work was extended to the analysis of 17 genes expressed by GAS during pharyngitis in humans and experimentally infected cynomolgus macaques [30]. The use of the macaques allowed for the monitoring of clinical symptoms, antibody levels, and temporal changes in gene expression over a two-week period, thereby giving unique insights into the interplay between the GAS and the oropharynx. For many years, the difficulty of working with Mycobacterium tuberculosis, an organism that infects fully one-third of the world’s population, has limited the understanding of how the organism causes a wide range of diseases. The use of real-time QRT-PCR on M. tuberculosis strains obtained from human lung surgical specimens has markedly increased knowledge of how this organism overcomes the challenges posed by the host environment [31]. Besides being used in a stand-alone fashion, results generated with real-time QRT-PCR have been compared with DNA expression microarrays to validate the broader microarray data [23,32–34,35,36]. While there is some discussion in the literature about whether the values should be log-transformed before comparison, the concordance ratios for the two techniques have generally been around 0.80 suggesting similar and supporting results. Although a major advancement, the use of real-time QRT-PCR for measurement for gene expression in vivo does have limitations [14]. As previously noted, isolation of sufficient, high-quality bacterial mRNA is a significant limiting factor. Unlike DNA expression microarrays, QRT-PCR requires knowledge of the gene in question a priori such that primers can be constructed, and thus this technique cannot be used as a scanning tool to generate testable hypotheses. Finally, the identification of a valid reference allowing for normalization of data generated during the RT-PCR reactions remains problematic. Current Opinion in Microbiology 2004, 7:283–289
DNA expression microarrays One of the most important consequences of sequencing bacterial genomes was the subsequent development of DNA microarrays [37–39]. DNA-DNA microarrays generally are used to determine the difference in gene content among bacterial strains. By contrast, expression microarrays provide the ability to perform functional genomics by examining RNA levels in bacteria subjected to different conditions [3]. For analysis of in vivo gene expression, expression microarray experiments are usually designed to compare mRNA levels present in vitro with levels in animal models or from infected patients. A separate dye for each of the two states being compared is incorporated into the reverse transcription step. After allowing for hybridization of the cDNA to the microarray, the differential fluorescence of the two cDNA pools is then measured and a ratio is determined for each gene on the microarray that reflects the difference in mRNA levels between the two situations. Vibrio cholerae, which causes pandemics of diarrheal disease, has been one of the organisms most closely examined with expression microarrays [40]. It was recently observed that V. cholerae collected from human stool were hyperinfectious for mice compared with samples undergoing passage on laboratory media. This led to the use of expression microarrays to correlate this observation with changes in gene expression [41]. The microarray data showed that many genes that were repressed in bacteria recovered from human stool were involved in chemotaxis; further elucidation of the relationship between downregulation of these genes and the hyperinfectious phenotype may give insight into epidemics of this potent pathogen. When the same organism was studied in a rabbit model of intestinal infection, only three of the genes found to be differentially expressed in human stool had similar profiles in rabbits [42]. In rabbits, up-regulation of groups of genes involved in iron-transport, anaerobic energy utilization, and nutrient synthesis were all observed. The discrepancies observed between the two studies are likely to have resulted from using different in vivo models and from variations in growth phases of the organisms. Expression microarrays also have been used to measure S. aureus gene expression in vivo. As noted previously, in S. aureus, the agr locus was thought to be critical to the elaboration of multiple toxins through RNA III [43]. Via the use of subgenomic expression microarrays in a subcutaneous rabbit infection model, it was found that RNA III was actually down-regulated in vivo despite extensive up-regulation of multiple virulence genes [33]. This data, along with that presented earlier, suggests that the proposed use of therapeutic agents to treat S. aureus infections by interrupting agr signaling is unlikely to be effective. This example illustrates the direct impact that expression microarrays can have on drug development. www.sciencedirect.com
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In situations where animals represent the natural host for an infectious agent, expression microarrays have been employed. Pasturella multocida, the causative agent of fowl cholera, was isolated from the blood of chickens following experimental infection [34]. As seen in the V. cholerae experiments, up-regulation of genes involved in amino acid metabolism and energy production was prominent. Not unexpectedly, bacterial gene expression varied markedly between the three chickens despite having highly reproducible results with replicate hybridizations from the same infection. Whether this represented harvesting of the bacteria at various growth phases or disparities in the effects of the immune system of the individual chicken on the bacteria could not be clearly identified. The technically demanding nature of using in vivo expression microarrays is illustrated by examining different studies of the same organism. PCR-generated glassspotted microarrays were used to evaluate RNA obtained from B. burgdorferi grown in dialysis membrane chambers implanted in rats [32]. A similar study employed nylonspotted membrane microarrays [35]. Despite using the same strain of B. burgdorferi and the same infection model, less than 10% concordance was obtained between the two studies regarding genes differentially expressed in vivo. This inconsistency is likely to be attributable to the marked discrepancies that occur when different in vitro conditions are chosen for comparative analysis.
tivity. However, in a manner similar to that observed for genome sequencing, expression microarrays result in the production of tremendous amounts of information. The wise use of this technology requires substantive knowledge of how to interpret this data and to proceed with more focused genetic experiments to prevent the generation of mere lists of genes [48].
DNA expression microarrays versus QRT-PCR Over the past few years, it has become clear that expression microarrays and real-time QRT-PCR are the preferred methods for studying microbial gene expression in animal models or even infected humans. While expression microarrays have the advantage of being able to generate a snapshot of the entire genome, this comes with the attendant problems of analysis of such vast amounts of information [48]. Unlike real-time QRTPCR, microarray data generated in one experimental situation or location needs to be measured against a reference, which makes comparative analysis between experiments more difficult and prevents absolute quantification. However, real-time QRT-PCR has its own set of analytical issues and can only be used to confirm, rather than generate, hypotheses. In summary, the two methods seem best suited to be used in a complementary fashion so as to maximize the strengths of each.
Conclusions One of the major technical problems limiting the use of microarrays for analysis of in vivo gene expression is the amount of bacterial mRNA needed. Given that this requirement mandates a larger number of bacteria than are often present in most human or animal models of infection, efforts are under way to develop methods requiring less mRNA [44]. For example, differential expression analysis using a custom-amplified library (DECAL) along with expression microarrays requires as little as 10 ng of mRNA. Rather than strict quantitation, this technique gives a score from 0–3 for mRNA in different environments. This technique has been applied to examine the difference in B. burgdorferi gene expression in fed versus unfed ticks and in the central nervous system compared with the heart of infected non-human primates [45,46]. Although DECAL suffers from having limited range for detection of mRNA and cannot give direct differential quantitation, it has the advantage of being able to investigate infections where bacterial burden is quite low. Overall, the use of expression microarrays for measuring in vivo gene expression represents a major advance, although its full effects have yet to be realized. Expression microarrays provide an overview of transcription for the entire genome allowing for multiple avenues of new investigation and insight into how groups of genes are coordinated [47]. It can suggest that genes whose functions are completely unknown may be crucial for infecwww.sciencedirect.com
The past few years have witnessed tremendous technological advances in the ability to understand the genetic mechanisms employed by microorganisms to cause disease. By measuring gene expression in vivo, researchers have opened entire new avenues of approach towards the diagnosis, treatment and prevention of infectious diseases. Although results generated with these new techniques have yet to result in direct patient applications, there is every reason to believe that the advancements being made in this area will be at the cornerstone of new vaccines, pharmaceuticals and infection control measures. The progress made in unraveling the complex interactions between the pathogen and the host needs to be tempered by an understanding of the limitations of the methods that have generated these data. A continuing commitment to improving the techniques of acquiring information regarding in vivo gene expression and improving the ability to compare experiments done at various laboratories will be essential factors in making effective use of these methodologies.
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48. Forster T, Roy D, Ghazal P: Experiments using microarray technology: limitations and standard operating procedures. J Endocrinol 2003, 178:195-204.
40. Bina J, Zhu J, Dziejman M, Faruque S, Calderwood S, Mekalanos J: ToxR regulon of Vibrio cholerae and its expression in vibrios shed by cholera patients. Proc Natl Acad Sci USA 2003, 100:2801-2806.
49. Gilmore RD Jr, Mbow ML, Stevenson B: Analysis of Borrelia burgdorferi gene expression during life cycle phases of the tick vector Ixodes scapularis. Microbes Infect 2001, 3:799-808.
41. Merrell DS, Butler SM, Qadri F, Dolganov NA, Alam A, Cohen MB, Calderwood SB, Schoolnik GK, Camilli A: Host-induced epidemic spread of the cholera bacterium. Nature 2002, 417:642-645. This paper is among the few examining in vivo gene expression that correlates a clinical phenotype with a gene transcription pattern. The authors also identified highly expressed genes that were previously not recognized as open reading frames in the V. cholerae genome. 42. Xu Q, Dziejman M, Mekalanos JJ: Determination of the transcriptome of Vibrio cholerae during intraintestinal growth and midexponential phase in vitro. Proc Natl Acad Sci USA 2003, 100:1286-1291. Using a rabbit ileal loop model, the transcriptome of V. cholerae was examined. There is an excellent discussion of the difficulties of accounting for the impact of growth phase on gene expression. 43. Novick R: Pathogenicity factors and their regulation. In Gram-positive pathogens. Edited by Fischetti V, Novick R, Ferretti J, Portnoy D, Rood J: ASM Press; 2000:392-407.
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50. Vandecasteele SJ, Peetermans WE, Merckx R, Van Ranst M, Van Eldere J: Use of gDNA as internal standard for gene expression in staphylococci in vitro and in vivo. Biochem Biophys Res Commun 2002, 291:528-534. 51. Rokbi B, Seguin D, Guy B, Mazarin V, Vidor E, Mion F, Cadoz M, Quentin-Millet MJ: Assessment of Helicobacter pylori gene expression within mouse and human gastric mucosae by real-time reverse transcriptase PCR. Infect Immun 2001, 69:4759-4766. 52. Ogunniyi AD, Giammarinaro P, Paton JC: The genes encoding virulence-associated proteins and the capsule of Streptococcus pneumoniae are upregulated and differentially expressed in vivo. Microbiology 2002, 148:2045-2053. 53. Vandecasteele SJ, Peetermans WE, Merckx R, Van Eldere J: Expression of biofilm-associated genes in Staphylococcus epidermidis during in vitro and in vivo foreign body infections. J Infect Dis 2003, 188:730-737.
Current Opinion in Microbiology 2004, 7:283–289
Current Opinion in Microbiology 2004, 7:283–289 This review comes from a themed issue on Techniques Edited by Simon Foster and Andrew Camilli Available online 10th May 2004 1369-5274/$ – see front matter ß 2004 Elsevier Ltd. All rights reserved. DOI 10.1016/j.mib.2004.04.013 Abbreviations DECAL differential expression analysis using a custom-amplified library GAS group A Streptococcus gfp green fluorescent protein IVET in vitro expression technology OspA outer surface protein A QRT-PCR quantitative RT-PCR RT-PCR reverse transcription polymerase chain reaction STM signature-tagged mutagenesis
Introduction Two hundred bacterial genomes have been sequenced in the past decade with many more close to completion [1]. The resulting genetic information has provided tremendous new opportunities for research into microbial pathogenesis, epidemiology and therapeutics [2,3,4]. However, with this remarkable accumulation of data, difficulties have arisen concerning its efficient use. For example, given that the average bacterial genome contains www.sciencedirect.com
many thousands of open reading frames, how can investigators decide which genes to focus on? As we move into the post-genomic era, there has been renewed interest in the crucial role that differential gene transcription plays in host–pathogen interactions. Accurate measurement of microbial RNA transcript levels in vivo provides excellent insight into how organisms selectively employ their genome during contact with the host and other environments encountered in their life cycle. Focused attention on genes that are differentially expressed in vivo has expanded our understanding of microbial pathogenesis and contributed to the enhanced use of the massive amount of genetic information that has been generated recently. The importance of analyzing gene transcription has been known for decades, and many methods have been used, including northern blot hybridization and reverse transcription polymerase chain reaction (RT-PCR). Because of the technical difficulties in isolating and detecting microbial RNA in tissue samples, the vast majority of research has focused on bacteria grown in laboratory media and subjected to environmental stimuli designed to mimic conditions found in the host. However, the limitations of this strategy are well known, and as a consequence there has been much effort directed toward detecting microbial gene transcription during infections. It is now known that many microbial genes are differentially transcribed during infection. Thus, concentration of experimental efforts on these genes is likely to be a productive line of inquiry [5]. New methods have been introduced over the past fifteen years to elucidate bacterial genes that are actively transcribed in the host. Moreover, in the past several years the ability to quantitate in vivo gene expression has become possible. In this review, we briefly discuss methods for qualitative analysis of in vivo bacterial gene transcription and then focus on the more recent methods for quantitation of transcripts. The sheer number of experiments precludes a detailed discussion of each, but rather research that has produced singular results or reflects the immense potential of particular approaches will be highlighted. The progress made in these areas has opened new avenues of research for understanding how microbes cause infection and methods for preventing and treating these diseases.
Non-quantitative methods Molecular techniques developed during the 1980s and 1990s resulted in an initial understanding of how organisms adjust gene transcription in response to the host Current Opinion in Microbiology 2004, 7:283–289
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environment. By placing reporter genes that produce assayable proteins under the control of promoters of genes of interest, analysis of genes transcribed in vivo can be accomplished [6]. Examples of reporter systems include b-galactosidase, luciferases and green fluorescent protein (gfp). Combining these reporters with such powerful techniques as confocal microscopy and flow cytometry allows for visualization of gene transcription for individual bacteria [7]. Reporters also have been used in selection procedures to isolate genes that are preferentially expressed under in vivo compared with in vitro conditions. In vivo expression technology (IVET) couples the insertion of random chromosomal segments upstream of an identifiable gene needed for survival in an animal model [8]. IVET has been used to identify hundreds of in vivo expressed genes but suffers from only being able to identify genes that are highly expressed in vivo and also are not expressed in vitro. A similar method, differential fluorescence induction, uses enhanced gfp to evaluate promoter activity by incorporating a library of DNA fragments upstream of a promoterless gfp gene. Fluorescence activated cell sorting is then used to isolate gfp-expressing bacteria proliferating under a variety of conditions, including animal models of infections [9]. Finally, signature-tagged mutagenesis (STM) is a high-throughput system based on mutagenesis due to random insertions that relies on negative selection in animal models of infection [10]. STM and its derivatives have been used to identify genes essential for colonization and infection for a wide variety of microorganisms [11,12].
Quantitative methods for measurement of in-vivo gene transcription Although very useful, IVET and STM lack the capacity to quantitate RNA levels. The two methods that can be used to accomplish this goal are quantitative RTPCR (QRT-PCR) and DNA expression microarrays. Both techniques share the common preconditions of requiring adequate quantities of high quality messenger RNA (mRNA) and entailing complex statistical analysis [13]. These issues will be addressed before discussing the particular advantages and limitations of each approach. The preparation of microbial mRNA from tissue is hindered by two main problems. First, the half-life of bacterial mRNA is usually less than two minutes, which means that mRNA needs to be quickly stabilized during extraction. Second, bacteria can rapidly change gene expression in response to environmental stimuli resulting in experimental artifact if RNA preparation is conducted without great attention to detail [14]. The introduction of commercial products that protect RNA from degradation and eliminate contaminating DNA have mitigated some of these issues. Once the RNA is obtained, both Current Opinion in Microbiology 2004, 7:283–289
QRT-PCR and expression microarrays require the conversion of mRNA to complementary DNA (cDNA) by reverse transcriptase. There are many options for the types of primers used for reverse transcription, and excellent technical reviews of this area have been published recently [15,16]. The interpretation of results generated with both expression microarrays and QRT-PCR carries much uncertainty. The numerical results for both methods are generally expressed as a ratio; determining the cut-off for calling a gene ‘differentially expressed’ or ‘highly expressed’ is somewhat arbitrary. Most researchers assign between 1.5- to 2.0-fold changes in transcript level as significant, but the biological relevance of these numbers is not fully understood. A final issue that both of these methods share is that what they actually measure is the level of RNA present in the tissue. This transcript level reflects both gene transcription and the rate of mRNA decay. Whether differential half-lives among various mRNA transcripts cause artifactual results has not been fully addressed at present.
QRT-PCR Although RT-PCR has been used widely for many years, QRT-PCR did not become available until the mid-1990s. The competitive form of QRT-PCR involves the addition of different known amounts of a competitor RNA target that has similar amplification properties but can be distinguished from the target mRNA following RT-PCR. When equivalent products of competitor and target are produced, the amount of the mRNA in question can be quantified by comparing with the known quantity of the competitor [17]. This approach is relatively labor intensive because of the difficulty in designing and preparing effective competitors but has been successfully used for in vivo gene transcript quantitation in Staphylococcus aureus, Borrelia burgdorferi, and Helicobacter pylori (Table 1) [13,18,19]. The difficulties associated with competitive QRT-PCR in part led to the more widespread employment of realtime QRT-PCR in which product accumulation is measured during each PCR cycle by the use of fluorescent probes. Direct comparison of competitive and real-time QRT-PCR for measurement of S. aureus gene expression in human sputum showed that the real-time method was easier to perform and interpret, as least as sensitive, but more easily subject to artifact [20]. The vast majority of in vivo gene expression studies have employed real-time systems with either TaqMan probes or SYBR green I intercalating dyes. These systems rely on the probes emitting fluorescent signals upon binding to amplified PCR products and, with the use of accompanying software, can generate direct quantitation of mRNA. The results are usually reported as a ratio to a gene transcript that is known to be present at a continuous level www.sciencedirect.com
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Table 1 Quantitative studies of microbial in vivo gene expression. Species
Sample source
Method
Reference
Borrelia burgdorferi Borrelia burgdorferi Borrelia burgdorferi Borrelia burgdorferi Borrelia burgdorferi Borrelia burgdorferi Borrelia burgdorferi Borrelia burgdorferi Borrelia burgdorferi Helicobacter pylori Helicobacter pylori Mycobacterium tuberculosis Mycobacterium tuberculosis Pasturella multocida Porphyromonas gingivalis Plasmodium berghei Staphylococcus aureus Staphylococcus aureus Staphylococcus aureus Staphylococcus aureus Staphylococcus aureus Staphylococcus epidermidis Staphylococcus epidermidis Streptococcus pneumoniae Streptococcus pyogenes Streptococcus pyogenes Streptococcus pyogenes Vibrio cholerae Vibrio cholerae Vibrio cholerae
Various mouse tissue Various mouse tissue Mouse dialysis membrane chamber Various mouse tissues Rat dialysis membrane chamber Rat dialysis membrane chamber Ticks Medulla and heart of rhesus monkeys Ticks Mouse and human stomach Mouse stomachs Mouse and human lung Mouse lung Chicken blood Human dental plaque Mosquitoes Human sputum Human sputum Guinea pig tissue cages Guinea pig tissue cages Rabbit implanted golf balls Rat-implanted catheters Rat-implanted catheters Mouse blood Mouse soft tissue Human pharynx Human and macaque pharynx Human stool Human stool Rabbit ileal loop model
Competitive QRT-PCR Real-time QRT-PCR Real-time QRT-PCR Real-time QRT-PCR Expression microarray/real-time Expression microarray/real-time DECAL expression microarrays DECAL expression microarrays Real-time QRT-PCR Competitive QRT-PCR Real-time QRT-PCR Real-time QRT-PCR Expression microarray/real-time Expression microarray/real-time Real-time QRT-PCR Real-time QRT-PCR Competitive QRT-PCR Real-time QRT-PCR Real-time QRT-PCR Real-time QRT-PCR Expression microarray/real-time Real-time QRT-PCR Real-time QRT-PCR Real-time QRT-PCR Real-time QRT-PCR Real-time QRT-PCR Real-time QRT-PCR Expression microarray Expression microarray Expression microarray
[19] [25] [27] [28] [32] [35] [45] [46] [49] [13] [50] [31] [36] [34] [24] [26] [18] [20] [21] [22] [33] [51] [52] [53] [23] [29] [30] [40] [41] [42]
throughout the growth cycle of the particular organism or to the total RNA present in the specimen [14]. On multiple occasions, real-time QRT-PCR has shown the importance of using in vivo models when analyzing gene expression. In vitro work with S. aureus suggested the agr locus, via its effector molecule RNA III, played a key role in controlling virulence gene expression. However, analysis of human sputum containing S. aureus showed significant expression of multiple virulence genes despite the absence of RNA III [21]. Similarly, in contrast to the in vitro situation, analysis of mRNA encoding for clumping factor A (clfA) of S. aureus present in an animal model of catheter-related infection revealed marked increases in clfA mRNA levels beginning about one week after the initiation of infection [22]. Group A Streptococcus (GAS) is the leading cause of bacterial pharyngitis in humans, and infection with GAS can result in rheumatic fever with serious cardiac complications. In a GAS mouse subcutaneous infection model, mutation of a two-component regulatory system led to differential transcription of multiple genes when compared with wild-type strains [23]. Interestingly, the effects of this mutation in vivo were different than in vitro, illustrating the complex interplay between the host and GAS transcription control. www.sciencedirect.com
QRT-PCR QRT-PCR
QRT-PCR QRT-PCR
QRT-PCR
Importantly, real-time QRT-PCR also has been used to associate bacterial transcript levels with clinical presentations as well as microbial tissue specificity. Porphyromonas gingivalis is a major source of dental disease in humans. The expression of specific genes in P. gingivalis isolates from different subgingival plaque samples has been correlated with the severity of periodontitis at those sites [24]. B. burgdorferi, the causative agent of Lyme disease, needs to disseminate from the site of tick inoculation to cause systemic disease. By comparing gene expression to bacterial quantitation at various sites in a mouse model of infection, genes that facilitate the migration of B. burgdorferi were identified [25]. Similar methods have also been used to delineate the gene expression patterns that underlie the infectious nature of plasmodium sporozoites found in the salivary gland of mosquitoes as compared to the avirulent forms found in the mid-gut [26]. Real-time QRT-PCR can be used along with manipulation of the animal model to study how changes in the host lead to differences in bacterial gene expression and more closely examine the role of the host immune system in influencing microbial gene expression. Using the implanted dialysis membrane chamber in mice to study Current Opinion in Microbiology 2004, 7:283–289
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B. burgdorferi, Zymosan A was administered to the mice to induce peritoneal inflammation [27]. The expression of B. burgdorferi outer surface protein A (OspA) was markedly increased in the animals that received Zymosan A. To further understand the effects of the immune system on B. burgdorferi gene transcription, wild-type and scid mice were infected, and QRT-PCR was used to examine various sites at differing time points of infection [28]. OspA expression was consistently present in the scid-mice but not in the wild-type, implying that the host immune response has an influence on OspA transcription. Because many rodent animal models do not accurately reproduce human infections, real-time QRT-PCR has been applied in non-human primate and even human infections. The irr gene product is critical to the ability of GAS to evade neutrophil phagocytosis. High levels of irr mRNA were shown to be present in the pharynx of persons with acute GAS pharyngitis [29]. This work was extended to the analysis of 17 genes expressed by GAS during pharyngitis in humans and experimentally infected cynomolgus macaques [30]. The use of the macaques allowed for the monitoring of clinical symptoms, antibody levels, and temporal changes in gene expression over a two-week period, thereby giving unique insights into the interplay between the GAS and the oropharynx. For many years, the difficulty of working with Mycobacterium tuberculosis, an organism that infects fully one-third of the world’s population, has limited the understanding of how the organism causes a wide range of diseases. The use of real-time QRT-PCR on M. tuberculosis strains obtained from human lung surgical specimens has markedly increased knowledge of how this organism overcomes the challenges posed by the host environment [31]. Besides being used in a stand-alone fashion, results generated with real-time QRT-PCR have been compared with DNA expression microarrays to validate the broader microarray data [23,32–34,35,36]. While there is some discussion in the literature about whether the values should be log-transformed before comparison, the concordance ratios for the two techniques have generally been around 0.80 suggesting similar and supporting results. Although a major advancement, the use of real-time QRT-PCR for measurement for gene expression in vivo does have limitations [14]. As previously noted, isolation of sufficient, high-quality bacterial mRNA is a significant limiting factor. Unlike DNA expression microarrays, QRT-PCR requires knowledge of the gene in question a priori such that primers can be constructed, and thus this technique cannot be used as a scanning tool to generate testable hypotheses. Finally, the identification of a valid reference allowing for normalization of data generated during the RT-PCR reactions remains problematic. Current Opinion in Microbiology 2004, 7:283–289
DNA expression microarrays One of the most important consequences of sequencing bacterial genomes was the subsequent development of DNA microarrays [37–39]. DNA-DNA microarrays generally are used to determine the difference in gene content among bacterial strains. By contrast, expression microarrays provide the ability to perform functional genomics by examining RNA levels in bacteria subjected to different conditions [3]. For analysis of in vivo gene expression, expression microarray experiments are usually designed to compare mRNA levels present in vitro with levels in animal models or from infected patients. A separate dye for each of the two states being compared is incorporated into the reverse transcription step. After allowing for hybridization of the cDNA to the microarray, the differential fluorescence of the two cDNA pools is then measured and a ratio is determined for each gene on the microarray that reflects the difference in mRNA levels between the two situations. Vibrio cholerae, which causes pandemics of diarrheal disease, has been one of the organisms most closely examined with expression microarrays [40]. It was recently observed that V. cholerae collected from human stool were hyperinfectious for mice compared with samples undergoing passage on laboratory media. This led to the use of expression microarrays to correlate this observation with changes in gene expression [41]. The microarray data showed that many genes that were repressed in bacteria recovered from human stool were involved in chemotaxis; further elucidation of the relationship between downregulation of these genes and the hyperinfectious phenotype may give insight into epidemics of this potent pathogen. When the same organism was studied in a rabbit model of intestinal infection, only three of the genes found to be differentially expressed in human stool had similar profiles in rabbits [42]. In rabbits, up-regulation of groups of genes involved in iron-transport, anaerobic energy utilization, and nutrient synthesis were all observed. The discrepancies observed between the two studies are likely to have resulted from using different in vivo models and from variations in growth phases of the organisms. Expression microarrays also have been used to measure S. aureus gene expression in vivo. As noted previously, in S. aureus, the agr locus was thought to be critical to the elaboration of multiple toxins through RNA III [43]. Via the use of subgenomic expression microarrays in a subcutaneous rabbit infection model, it was found that RNA III was actually down-regulated in vivo despite extensive up-regulation of multiple virulence genes [33]. This data, along with that presented earlier, suggests that the proposed use of therapeutic agents to treat S. aureus infections by interrupting agr signaling is unlikely to be effective. This example illustrates the direct impact that expression microarrays can have on drug development. www.sciencedirect.com
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In situations where animals represent the natural host for an infectious agent, expression microarrays have been employed. Pasturella multocida, the causative agent of fowl cholera, was isolated from the blood of chickens following experimental infection [34]. As seen in the V. cholerae experiments, up-regulation of genes involved in amino acid metabolism and energy production was prominent. Not unexpectedly, bacterial gene expression varied markedly between the three chickens despite having highly reproducible results with replicate hybridizations from the same infection. Whether this represented harvesting of the bacteria at various growth phases or disparities in the effects of the immune system of the individual chicken on the bacteria could not be clearly identified. The technically demanding nature of using in vivo expression microarrays is illustrated by examining different studies of the same organism. PCR-generated glassspotted microarrays were used to evaluate RNA obtained from B. burgdorferi grown in dialysis membrane chambers implanted in rats [32]. A similar study employed nylonspotted membrane microarrays [35]. Despite using the same strain of B. burgdorferi and the same infection model, less than 10% concordance was obtained between the two studies regarding genes differentially expressed in vivo. This inconsistency is likely to be attributable to the marked discrepancies that occur when different in vitro conditions are chosen for comparative analysis.
tivity. However, in a manner similar to that observed for genome sequencing, expression microarrays result in the production of tremendous amounts of information. The wise use of this technology requires substantive knowledge of how to interpret this data and to proceed with more focused genetic experiments to prevent the generation of mere lists of genes [48].
DNA expression microarrays versus QRT-PCR Over the past few years, it has become clear that expression microarrays and real-time QRT-PCR are the preferred methods for studying microbial gene expression in animal models or even infected humans. While expression microarrays have the advantage of being able to generate a snapshot of the entire genome, this comes with the attendant problems of analysis of such vast amounts of information [48]. Unlike real-time QRTPCR, microarray data generated in one experimental situation or location needs to be measured against a reference, which makes comparative analysis between experiments more difficult and prevents absolute quantification. However, real-time QRT-PCR has its own set of analytical issues and can only be used to confirm, rather than generate, hypotheses. In summary, the two methods seem best suited to be used in a complementary fashion so as to maximize the strengths of each.
Conclusions One of the major technical problems limiting the use of microarrays for analysis of in vivo gene expression is the amount of bacterial mRNA needed. Given that this requirement mandates a larger number of bacteria than are often present in most human or animal models of infection, efforts are under way to develop methods requiring less mRNA [44]. For example, differential expression analysis using a custom-amplified library (DECAL) along with expression microarrays requires as little as 10 ng of mRNA. Rather than strict quantitation, this technique gives a score from 0–3 for mRNA in different environments. This technique has been applied to examine the difference in B. burgdorferi gene expression in fed versus unfed ticks and in the central nervous system compared with the heart of infected non-human primates [45,46]. Although DECAL suffers from having limited range for detection of mRNA and cannot give direct differential quantitation, it has the advantage of being able to investigate infections where bacterial burden is quite low. Overall, the use of expression microarrays for measuring in vivo gene expression represents a major advance, although its full effects have yet to be realized. Expression microarrays provide an overview of transcription for the entire genome allowing for multiple avenues of new investigation and insight into how groups of genes are coordinated [47]. It can suggest that genes whose functions are completely unknown may be crucial for infecwww.sciencedirect.com
The past few years have witnessed tremendous technological advances in the ability to understand the genetic mechanisms employed by microorganisms to cause disease. By measuring gene expression in vivo, researchers have opened entire new avenues of approach towards the diagnosis, treatment and prevention of infectious diseases. Although results generated with these new techniques have yet to result in direct patient applications, there is every reason to believe that the advancements being made in this area will be at the cornerstone of new vaccines, pharmaceuticals and infection control measures. The progress made in unraveling the complex interactions between the pathogen and the host needs to be tempered by an understanding of the limitations of the methods that have generated these data. A continuing commitment to improving the techniques of acquiring information regarding in vivo gene expression and improving the ability to compare experiments done at various laboratories will be essential factors in making effective use of these methodologies.
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