SEARCH

SEARCH BY CITATION

Keywords:

  • Q-PCR;
  • RT-Q-PCR;
  • 16S rRNA gene;
  • mRNA

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Advantages of Q-PCR over traditional end-point PCR
  5. Fluorescence detection chemistries used to detect template amplification during Q-PCR
  6. Target quantification using the cycle threshold (Ct) method
  7. Biological and methodological factors affecting quantification of genes and transcripts from environmental samples
  8. Quantifying gene expression by RT-Q-PCR
  9. Practicalities of (RT)-Q-PCR protocols
  10. Application of Q-PCR for investigating the microbial genetic potential within the environment
  11. Quantifying gene expression in environmental samples using RT-Q-PCR: a step closer to determining the functioning of target genes in the environment
  12. Combining (RT)-Q-PCR with other approaches to provide greater insight into community function and dynamics
  13. Conclusions
  14. Acknowledgements
  15. References

Quantitative PCR (Q-PCR or real-time PCR) approaches are now widely applied in microbial ecology to quantify the abundance and expression of taxonomic and functional gene markers within the environment. Q-PCR-based analyses combine ‘traditional’ end-point detection PCR with fluorescent detection technologies to record the accumulation of amplicons in ‘real time’ during each cycle of the PCR amplification. By detection of amplicons during the early exponential phase of the PCR, this enables the quantification of gene (or transcript) numbers when these are proportional to the starting template concentration. When Q-PCR is coupled with a preceding reverse transcription reaction, it can be used to quantify gene expression (RT-Q-PCR). This review firstly addresses the theoretical and practical implementation of Q-PCR and RT-Q-PCR protocols in microbial ecology, highlighting key experimental considerations. Secondly, we review the applications of (RT)-Q-PCR analyses in environmental microbiology and evaluate the contribution and advances gained from such approaches. Finally, we conclude by offering future perspectives on the application of (RT)-Q-PCR in furthering understanding in microbial ecology, in particular, when coupled with other molecular approaches and more traditional investigations of environmental systems.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Advantages of Q-PCR over traditional end-point PCR
  5. Fluorescence detection chemistries used to detect template amplification during Q-PCR
  6. Target quantification using the cycle threshold (Ct) method
  7. Biological and methodological factors affecting quantification of genes and transcripts from environmental samples
  8. Quantifying gene expression by RT-Q-PCR
  9. Practicalities of (RT)-Q-PCR protocols
  10. Application of Q-PCR for investigating the microbial genetic potential within the environment
  11. Quantifying gene expression in environmental samples using RT-Q-PCR: a step closer to determining the functioning of target genes in the environment
  12. Combining (RT)-Q-PCR with other approaches to provide greater insight into community function and dynamics
  13. Conclusions
  14. Acknowledgements
  15. References

The application of PCR in combination with the extraction of nucleic acids (DNA and RNA) from environmental matrices has been central to the development of culture-independent approaches in microbial ecology. These methods, which have been applied since the early 1990s (e.g. Giovannoni et al., 1990), enabling the analysis of the total microbial communities present within environmental systems, have revolutionized our understanding of microbial community structure and diversity within the environment. Coupling environmental nucleic acid isolation to subsequent PCR amplification of both taxonomic (i.e. rRNA) and functional gene markers and in combination with DNA fingerprinting- and sequencing-based analyses has enabled description of the hitherto uncharacterized majority of environmental microorganisms (Head et al., 1998) driving the discovery of new microbial lineages and enabling the description of genetic diversity in a wealth of functional gene markers (Larkin et al., 2005). Although recently developed ultra-high-throughput sequencing technologies such as pyrosequencing (Margulies et al., 2005; Edwards et al., 2006) now dwarf PCR-based sequence studies in terms of sequence coverage, the ability of the PCR to specifically target particular taxonomic or functional markers from domain – down to strain – or phylotype levels means that PCR will remain an invaluable method in the molecular microbial ecologist's toolbox. Nevertheless, PCR has inherent limitations (von Wintzingerode et al., 1997), particularly those that result in biases in the template to product ratios of target sequences amplified during PCR from environmental DNA (Suzuki & Giovannoni, 1996; Polz & Cavanaugh, 1998), with such amplification biases found to increase with increasing numbers of PCR cycles. These limitations presented a significant challenge to microbial ecologists who were interested in determining the abundance of individual genes present in environmental samples. To circumvent such challenges, an adaptation of the PCR method developed by Holland et al. (1991) utilizing the so-called ‘5′ nuclease assay’ was applied to quantify target 16S rRNA genes amplified from environmental DNA by PCR (Becker et al., 2000; Suzuki et al., 2000; Takai & Horikoshi, 2000). This development had been facilitated by the earlier combination of the 5′ nuclease assay developed by Holland et al. (1991) with fluorescence detection following cleavage of an internal (TaqMan) DNA probe (Livak et al., 1995), enabling the accumulation of amplicons to be monitored after each cycle (in real-time) and hence facilitating quantitative determination of the initial template gene (or transcript) numbers.

Quantitative-PCR or Q-PCR (often referred to as real-time PCR) is now widely used in microbial ecology to determine gene and/or transcript numbers present within environmental samples. The target specificity of any Q-PCR assay is determined by the design of the primers (and in some cases an internal probe), allowing quantification of taxonomic or functional gene markers present within a mixed community from the domain level down to the quantification of individual species or phylotypes. Q-PCR has been shown to be a robust, highly reproducible and sensitive method to quantitatively track phylogenetic and functional gene changes across temporal and spatial scales under varying environmental or experimental conditions. Moreover, the quantitative data generated can be used to relate variation in gene abundances and/or levels of gene expression (in terms of transcript numbers) in comparison with variation in abiotic or biotic factors and/or biological activities and process rates. The provision of Q-PCR data sets that describe the abundance of specific bacteria or genes to complement other quantitative environmental data sets is of increasing importance in microbial ecology as it furthers understanding of the roles and contributions of particular microbial and functional groups within ecosystem functioning. Furthermore, reverse transcription (RT) analyses are now increasingly combined with Q-PCR methods in RT-Q-PCR assays, offering a powerful tool for quantifying gene expression (in terms of numbers of rRNA and mRNA transcripts) and relating biological activity to ecological function.

In this review, we firstly discuss the mechanistic aspects of Q-PCR and RT-Q-PCR methodologies, hereafter defined collectively as (RT)-Q-PCR, and highlight the key experimental considerations in the design and implementation of (RT)-Q-PCR protocols and the analysis of resultant data sets. Secondly, we explore the application of (RT)-Q-PCR approaches in microbial ecology, and finally we discuss how these methods can be applied together with other molecular and also conventional approaches to provide an increased understanding of microorganisms within environmental systems.

Advantages of Q-PCR over traditional end-point PCR

  1. Top of page
  2. Abstract
  3. Introduction
  4. Advantages of Q-PCR over traditional end-point PCR
  5. Fluorescence detection chemistries used to detect template amplification during Q-PCR
  6. Target quantification using the cycle threshold (Ct) method
  7. Biological and methodological factors affecting quantification of genes and transcripts from environmental samples
  8. Quantifying gene expression by RT-Q-PCR
  9. Practicalities of (RT)-Q-PCR protocols
  10. Application of Q-PCR for investigating the microbial genetic potential within the environment
  11. Quantifying gene expression in environmental samples using RT-Q-PCR: a step closer to determining the functioning of target genes in the environment
  12. Combining (RT)-Q-PCR with other approaches to provide greater insight into community function and dynamics
  13. Conclusions
  14. Acknowledgements
  15. References

Q-PCR approaches combine the detection of target template with quantification by recording the amplification of a PCR product via a corresponding increase in the fluorescent signal associated with product formation during each cycle in the PCR. Quantification of gene (or transcript) numbers is determined during the exponential phase of the PCR amplification when the numbers of amplicons detected are directly proportional to the initial numbers of target sequences present within the environment (discussed in more detail in Target quantification). Quantification of the target gene during exponential amplification avoids problems that are associated with so-called ‘end-point’ PCR (in which amplicons are only analysed after completion of the final PCR cycle). In end-point PCR, the proportions of numerically dominant amplicons do not necessarily reflect the actual abundances of sequences present within the environment due to the inherent biases of PCR that are associated with amplification of targets from mixed template community DNA (Reysenbach et al., 1992; Suzuki & Giovannoni, 1996; Polz & Cavanaugh, 1998). Moreover, Q-PCR that uses fluorescence-based detection offers greater sensitivity and enables discrimination of gene numbers across a wider dynamic range than is found with end-point PCR; for example twofold changes in target concentration can be discriminated using Q-PCR. Before the development of fluorescence-based Q-PCR-based methods, two alternative PCR-based methods for gene number quantification had been developed, namely competitive PCR (Diviacco et al., 1992) and limiting dilutions or most probable number (MPN)-PCR (Skyes et al., 1992). However, these methods are time- and resource-consuming, requiring post-PCR analysis, and have now largely been replaced by fluorescence-based Q-PCR methods.

Fluorescence detection chemistries used to detect template amplification during Q-PCR

  1. Top of page
  2. Abstract
  3. Introduction
  4. Advantages of Q-PCR over traditional end-point PCR
  5. Fluorescence detection chemistries used to detect template amplification during Q-PCR
  6. Target quantification using the cycle threshold (Ct) method
  7. Biological and methodological factors affecting quantification of genes and transcripts from environmental samples
  8. Quantifying gene expression by RT-Q-PCR
  9. Practicalities of (RT)-Q-PCR protocols
  10. Application of Q-PCR for investigating the microbial genetic potential within the environment
  11. Quantifying gene expression in environmental samples using RT-Q-PCR: a step closer to determining the functioning of target genes in the environment
  12. Combining (RT)-Q-PCR with other approaches to provide greater insight into community function and dynamics
  13. Conclusions
  14. Acknowledgements
  15. References

Quantitative real-time PCR works in essentially the same manner as end-point PCR, i.e. multiple amplification cycles in which template DNA is initially denatured, followed by annealing of oligonucleotide primers targeting specific sequences, followed by subsequent extension of a complementary strand from each annealed primer by a thermostable DNA polymerase, resulting in an exponential increase in amplicon numbers during the PCR. However, in contrast to end-point PCR, the increase in amplicon numbers is recorded in ‘real-time’ during the PCR via detection of a fluorescent reporter that indicates amplicon accumulation during every cycle. Two reporter systems are commonly used, namely, the intercalating SYBR green assay (Wittwer et al., 1997) and the TaqMan probe system (Holland et al., 1991; Livak et al., 1995).

SYBR green binds to all double-stranded DNA via intercalation between adjacent base pairs. When bound to DNA, a fluorescent signal is emitted following light excitation (Fig. 1a). As amplicon numbers accumulate after each PCR cycle, there is a corresponding increase in fluorescence. Because SYBR green binds to all double-stranded DNA, it is essential to use primer pairs that are highly specific to their target sequence to avoid generation of nonspecific products that would contribute to the fluorescent signal, resulting in an overestimation of the target. Extensive optimization of primer concentrations used in SYBR green Q-PCR assays may be required to ensure that only the targeted product is formed. Primer pairs that exhibit self-complementarity should also be avoided to prevent primer–dimer formation. A post-PCR dissociation (melting) curve analysis should be carried out to confirm that the fluorescence signal is generated only from target templates and not from the formation of nonspecific PCR products. During a dissociation curve, the double-stranded template is heated over a temperature gradient and fluorescence levels are measured at each discrete temperature point. As the double-stranded template is heated, it denatures, resulting in a corresponding decline in fluorescence due to SYBR green dissociation from the double-stranded product (Giglio et al., 2003; Gonzalez-Escalona et al., 2006). The temperature at which 50% of the double-stranded template is denatured can be used to confirm that the template being targeted is present, along with the presence of other nonspecific template and primer dimers in much the same way as agarose gel electrophoresis of an end-point PCR product is used.

image

Figure 1.  Real-time PCR chemistries: (a) SYBR green detection. SYBR green binds to all double-stranded DNA and emits a fluorescent signal. In its unbound state, SYBR green does not fluoresce. Template amplification is therefore measured in each cycle by the corresponding increase in fluorescence. (b) TaqMan (5′ nuclease) assay using TaqMan® probes. During annealing, the TaqMan probe and primers bind to the template. When the TaqMan probe is intact, energy is transferred between the quencher and the reporter; as a result, no fluorescent signal is detected. As the new strand is synthesized by Taq polymerase, the 5′ exonuclease activity of the enzyme cleaves the labelled 5′ nucleotide of the probe, releasing the reporter from the probe. Once it is no longer in close proximity, the fluorescent signal from the probe is detected and template amplification is recorded by the corresponding increase in fluorescence.

Download figure to PowerPoint

The TaqMan probe method utilizes a fluorescently labelled probe that hybridizes to an additional conserved region that lies within the target amplicon sequence. The TaqMan probe is fluorescently labelled at the 5′ end and contains a quencher molecule at the 3′ end (Livak et al., 1995). The close proximity on the probe of the quencher molecule to the fluorophore prevents it from fluorescing due to fluorescent resonance energy transfer. During the annealing step of each cycle of the PCR, primers and the intact probe bind to their target sequences. During subsequent template extension, the 5′ exonuclease activity of the Taq polymerase enzyme cleaves the fluorophore from the TaqMan probe and a fluorescent signal is detected as the fluorophore is no longer in close proximity to the quencher (Fig. 1b). Amplification of the template is thus measured by the release and accumulation of the fluorophore during the extension stage of each PCR cycle. The additional specificity afforded by the presence of the TaqMan probe ensures that the fluorescent signal generated during Q-PCR is derived only from amplification of the target sequence. Multiple TaqMan probes and primer sets can be used in different Q-PCR assays to differentiate between closely related sequences (Smith et al., 2007), or alternatively, probes can be labelled with different fluorophores, facilitating the development of multiplex Q-PCR protocols whereby different targets can be coamplified and quantified within a single reaction (Neretin et al., 2003; Baldwin et al., 2003, 2008). For example, Baldwin et al. (2003) developed a multiplex Q-PCR assay targeting a number of different aromatic oxygenase genes using bacterial strains and then subsequently applied the assay to simultaneously quantify aromatic oxygenase genes in contaminated groundwater (Baldwin et al., 2008). TaqMan probes are, however, a more expensive option than using SYBR green chemistry and the former requires the presence of an additional conserved site within the short amplicon sequence to be present. Identification of three conserved regions within a short region (typically c.<100 bp) may not always be possible, especially when primer/probe combinations are being designed to target divergent gene sequences. More recent advances in TaqMan probe technology have involved the introduction of the minor groove binder (MGB) probe (Kutyavin et al., 2000). The MGB molecule is attached to the 3′ end of the probe and essentially folds back onto the probe. This not only increases the stability of the probe, but allows the design of shorter probes (13–20 bp) than are required for traditional TaqMan probes (20–40 bp), while at the same time, maintaining the required hybridization annealing temperature.

Target quantification using the cycle threshold (Ct) method

  1. Top of page
  2. Abstract
  3. Introduction
  4. Advantages of Q-PCR over traditional end-point PCR
  5. Fluorescence detection chemistries used to detect template amplification during Q-PCR
  6. Target quantification using the cycle threshold (Ct) method
  7. Biological and methodological factors affecting quantification of genes and transcripts from environmental samples
  8. Quantifying gene expression by RT-Q-PCR
  9. Practicalities of (RT)-Q-PCR protocols
  10. Application of Q-PCR for investigating the microbial genetic potential within the environment
  11. Quantifying gene expression in environmental samples using RT-Q-PCR: a step closer to determining the functioning of target genes in the environment
  12. Combining (RT)-Q-PCR with other approaches to provide greater insight into community function and dynamics
  13. Conclusions
  14. Acknowledgements
  15. References

Irrespective of the fluorescence chemistry used, quantification of the target template DNA is carried out in essentially the same manner. There are a number of different commercially available instruments to carry out Q-PCR, each with its own associated software. Currently, there is considerable debate as to which algorithms are the best used to analyse Q-PCR data (reviewed in Rebrikov & Trofimov, 2006). All the Q-PCR platforms collect fluorescent data from every amplification cycle and the increase in fluorescence is plotted against the cycle number, resulting in the typical amplification curve shown in Fig. 2. The Q-PCR amplification curve can be subdivided into four stages, namely background noise, where the background fluorescence still exceeds that derived from initial exponential template accumulation, exponential amplification, linear amplification and a plateau stage. During the exponential phase of the amplification, the amount of target amplified is proportional to the starting template and it is during these cycles that gene numbers are quantified using the Ct method. The Ct is reached when the accumulation of fluorescence (template) is significantly greater than the background level (Heid et al., 1996). During the initial cycles, the fluorescence signal due to background noise is greater than that derived from the amplification of the target template. Once the Ct value is exceeded, the exponential accumulation of product can be measured. When the initial concentration of the target template is higher, the Ct will be reached at an earlier amplification cycle.

image

Figure 2.  Q-PCR amplification from known concentrations of template DNA to construct standard curves for quantification of unknown environmental samples. (a) Log plot of the increase in fluorescence vs. cycle number of DNA standards ranging from 1 × 104 to 1 × 108 16S rRNA gene amplicons μL−1. (b) Linear plot indicating the three phases of a PCR amplification, the corresponding Ct values for each of the amplified standards and for the NTC. (c) Simple linear regression of the Ct values (from b) vs. log of the initial rRNA gene number. Q-PCR descriptors are shown (boxed).

Download figure to PowerPoint

Quantification of the initial target sequences of an unknown concentration is determined from the Ct values and can be described either in relative or in absolute terms. In relative quantification, changes in the unknown target are expressed relative to a coamplified steady state (typically a housekeeping) gene. Any variation in the presence (or expression) of the housekeeping gene can potentially mask real changes or indicate artificial changes in the abundance of the gene of interest. While this approach is commonly applied for studying eukaryotic gene expression (reviewed in Bustin, 2002), it is more difficult to apply this method for studying prokaryotic genes where the identification of a valid steady-state reference gene is problematic. Burgmann et al. (2007) nevertheless successfully utilized such an approach when confirming microarray-based determination of the transcriptional responses of Silicibacter pomeroyi to dimethylsulphoniopropionate additions. From microarray experiments, they identified a gene whose expression was not altered by experimental conditions and used the expression of this gene to normalize levels of expression of the target genes of interest in RT-Q-PCR assays. In a number of other studies, gene and transcript numbers of the target gene of interest have been normalized to the numbers of 16S rRNA gene or transcripts (Neretin et al., 2003; Treusch et al., 2005; Kandeler et al., 2006). For example, Treusch et al. (2005) normalized the number of amoA transcripts to numbers of 16S rRNA gene transcripts in RNA extracted from ammonia-amended or unamended soils. They reported a statistically significant increase in amoA transcript numbers in the ammonia-amended soils. However, although 16S rRNA genes and transcripts are now commonly used in this manner, the application of such an approach is controversial, especially when studying genes/transcripts amplified from nucleic acids extracted from complex environmental samples. This is, in particular, because 16S rRNA gene copy and transcript numbers are highly variable, with the number of 16S rRNA genes per operon varying dramatically between species (1–15 copies) while 16S rRNA gene transcription rates are regulated primarily by resource availability (Klappenbach et al., 2000). The 16S rRNA genes and transcripts cannot therefore be considered as a steady-state (housekeeping) gene, especially when studying genes/transcripts in environmental samples.

In absolute quantification protocols, the numbers of a target gene or transcript are determined from a standard curve generated from amplification of the target gene present at a range of initial template concentrations, and then the Ct values for each template concentration are determined. Subsequently, a simple linear regression of these Ct values is plotted against the log of the initial copy number (Fig. 2). It should be ensured that the Ct value of the most diluted template DNA used to construct the standard curve is at least a log fold lower (3.3 cycles) than the Ct value of the no template control (NTC). Quantification of the unknown target template is determined by comparison of the Ct values of the target template against the standard curve. However, in reality, this ‘absolute’ quantification of the target gene represents quantification of the target in comparison against a constructed standard curve, rather than as an absolute measurement of the number of target genes present within an environmental sample. Any number of factors involved in the construction of the standard curve including the initial quantification of the standard curve template, serial dilution of the template and the algorithmic determination of the Ct value (Love et al., 2006) contribute to the final quantification of the environmental sample. As a consequence, it is recommended that the following descriptors of the standard curve are reported for each Q-PCR amplification: amplification efficiency (E), the linear regression coefficient (r2) and especially the y-intercept value, which uniquely describes the standard curve and indicates the sensitivity of the reaction (Smith et al., 2006; Fig. 2). Furthermore, the Ct value of the NTC and its equivalent value in terms of gene numbers should be reported. Moreover, we have previously demonstrated that even highly reproducible standard curves may result in statistically significant differences in gene numbers for the same template (with equivalent Ct values) when gene numbers are quantified within different Q-PCR assays (Smith et al., 2006) due to the log nature of the curve, whereby minor differences in Ct values and standard curves result in large differences in gene copy numbers.

Biological and methodological factors affecting quantification of genes and transcripts from environmental samples

  1. Top of page
  2. Abstract
  3. Introduction
  4. Advantages of Q-PCR over traditional end-point PCR
  5. Fluorescence detection chemistries used to detect template amplification during Q-PCR
  6. Target quantification using the cycle threshold (Ct) method
  7. Biological and methodological factors affecting quantification of genes and transcripts from environmental samples
  8. Quantifying gene expression by RT-Q-PCR
  9. Practicalities of (RT)-Q-PCR protocols
  10. Application of Q-PCR for investigating the microbial genetic potential within the environment
  11. Quantifying gene expression in environmental samples using RT-Q-PCR: a step closer to determining the functioning of target genes in the environment
  12. Combining (RT)-Q-PCR with other approaches to provide greater insight into community function and dynamics
  13. Conclusions
  14. Acknowledgements
  15. References

Q-PCR-based quantification of gene/transcript numbers amplified from nucleic acids isolated from environmental samples is further influenced by a number of other compounding factors. Firstly, the choice of method used for nucleic acid extraction will be a major determinant on the final quantification. Nucleic acid extraction efficiencies vary considerably between different methods and the final nucleic acid yield is dependent on both the method used and the type of environmental sample being studied (Martin-Laurent et al., 2001). Moreover, many different extraction protocols are used for different environmental samples and within different laboratories, making direct comparison of absolute gene numbers between studies extremely problematic. Hence, in order to compare gene/transcript numbers from different environmental samples, it must first be ensured that the same extraction procedure is used for each sample. Furthermore, while the presence of PCR inhibitors in nucleic acids extracted from environmental samples and their subsequent effect on Q-PCR is well established (Stults et al., 2001), the concentration at which inhibitors no longer affect the Q-PCR for any sample is not known a priori and must be determined empirically (Stults et al., 2001) to ensure that the environmental template and the standard curve target gene have equivalent amplification efficiencies.

The sensitivity of Q-PCR allows quantification of very low numbers of target genes, with detection limits as low as two copies of a gene in a Q-PCR (Fey et al., 2004) reported in the literature. However, statements pertaining to the sensitivity and lower detection limits of Q-PCR should be qualified by providing information on the amplification signal detected, if any, within the NTC. This is because quantification of low numbers of the target gene may be artificially increased by the presence of an amplification signal within the reaction that is equivalent to that quantified within the NTC. While details of amplification signals in the NTC are sometimes reported in the literature (Suzuki et al., 2000; Gruntzig et al., 2001; Baldwin et al., 2003; Smith et al., 2006; McKew et al., 2007), many studies do not provide such details (Panicker et al., 2004; Kandeler et al., 2006; Coolen et al., 2008). It is, however, recommended that details of the Ct values of the NTC and their equivalent gene numbers should be reported for all Q-PCR assays in order to determine the lower limits of detection for the reaction. To ensure that the NTC (if detected) does not contribute to the fluorescence signal of either the standard curve or the target sequence in the environmental DNA sample, it is recommended that the Ct value of both the most dilute DNA standard and of the unknown target gene should have Ct values of 3.3 cycles (a log value) fewer than that of the NTC Ct value (Smith et al., 2006).

The determination of gene and transcript numbers amplified from environmental samples generated by different research groups will entail any number of the aforementioned variables in the Q-PCR protocol and may be affected by the initial extraction of nucleic acids, the preparation, quantification and amplification of the standard curve template (Love et al., 2006), variations in the efficiencies of the subsequent Q-PCR, differences in the Q-PCR platform, associated software and reagents that are used, as well as variations due to different researchers and laboratories. Therefore, the generation of ‘absolute’ gene numbers can only be considered as being meaningful for the individual study in question and even then, such a direct comparison should be used only for gene numbers determined within a single Q-PCR assay and using the same standard curve (Smith et al., 2006).

Quantifying gene expression by RT-Q-PCR

  1. Top of page
  2. Abstract
  3. Introduction
  4. Advantages of Q-PCR over traditional end-point PCR
  5. Fluorescence detection chemistries used to detect template amplification during Q-PCR
  6. Target quantification using the cycle threshold (Ct) method
  7. Biological and methodological factors affecting quantification of genes and transcripts from environmental samples
  8. Quantifying gene expression by RT-Q-PCR
  9. Practicalities of (RT)-Q-PCR protocols
  10. Application of Q-PCR for investigating the microbial genetic potential within the environment
  11. Quantifying gene expression in environmental samples using RT-Q-PCR: a step closer to determining the functioning of target genes in the environment
  12. Combining (RT)-Q-PCR with other approaches to provide greater insight into community function and dynamics
  13. Conclusions
  14. Acknowledgements
  15. References

Combining Q-PCR with an initial RT reaction facilitates the quantification of RNA transcripts (rRNA or mRNA), enabling quantitative estimates of the activity of specific taxa or functional guilds within a microbial community. Isolation of total RNA directly from complex environmental samples is typically problematic, as RNA is a labile molecule with a potentially short half-life (Grunberg-Manago, 1999). As with DNA quantification, the first step towards accurate RNA quantification lies in the preparation of a high-quality template, free from inhibitors (Stults et al., 2001). However, simple dilution to reduce the concentration of inhibitors present in the RNA template may be problematic as the kinetics of the RT reaction can be affected adversely by low RNA template concentrations (Chandler et al., 1998). This effect has been demonstrated in environmental samples using a dilution series of environmental RNA within the RT reaction, which resulted in at least log fold differences in the number of transcripts (transcripts per gram sediment) quantified from different dilutions of RNA (Smith et al., 2006). Moreover, due to the sensitivity of (fluorescence-based) Q-PCR methods, it is particularly important that the RNA template is free from contaminating DNA that could contribute to the final amplification signal. Absolute numbers of RNA transcripts should be determined from standard curves constructed from cDNA (i.e. reverse transcribed RNA) and not from a double-stranded DNA template. Furthermore, the efficiency of the initial RT step is critical for sensitive and accurate quantification as the amount of cDNA produced must accurately reflect the starting RNA concentration.

RT-Q-PCR amplifications can be conducted using either a one-step or a two-step reaction. In a one-step RT-Q-PCR, both the RT reaction and the Q-PCR are carried out consecutively in a single tube. RNA is first reverse transcribed, with all resultant cDNA serving as templates in the subsequent Q-PCR amplification. In addition to the reduced risk of contamination and the convenience of setting up only a single reaction, a further advantage of this method is that all the resulting cDNA produced is used to quantify the target RNA sequence. However, for the study of eukaryotes, one-step RT-Q-PCR reactions have been reported to have reduced sensitivity (Bustin, 2002) as reaction conditions are compromised to accommodate the two different enzymes required within a single reaction. In a two-step RT-Q-PCR protocol, the RT reaction and the subsequent Q-PCR are carried out separately. Firstly, cDNA is generated in an independent RT reaction and subsequently an aliquot of this cDNA is used as a template for the Q-PCR. An advantage of this method is that the RT reaction can be optimized to increase cDNA yield as can the subsequent Q-PCR amplification. Furthermore, cDNA generated in the RT reaction can be used as a template for a number of different Q-PCR reactions. If random primers are used in the initial RT reaction, then any number of subsequent gene-specific Q-PCR amplifications using the randomly primed cDNA can be conducted, making a two-step reaction a more economically viable option for RT-Q-PCR than a one-step reaction. While random primers can maximize the number of different cDNA templates generated, gene-specific reverse primers can increase the sensitivity and specificity of the cDNA created and at the same time reduce the amount of unspecific background cDNA. However, this may be dependent on the gene-specific primer. For example, Nícolaisen et al. (2008), in a study of tfdA gene expression in soil, showed that use of random primers in the RT reaction as opposed to using gene-specific primers was optimal for RT-Q-PCR of tfdA transcripts. Clearly, the choice of the protocol, enzyme, primer and reaction conditions will influence the quantification of the RNA template from the environment.

Practicalities of (RT)-Q-PCR protocols

  1. Top of page
  2. Abstract
  3. Introduction
  4. Advantages of Q-PCR over traditional end-point PCR
  5. Fluorescence detection chemistries used to detect template amplification during Q-PCR
  6. Target quantification using the cycle threshold (Ct) method
  7. Biological and methodological factors affecting quantification of genes and transcripts from environmental samples
  8. Quantifying gene expression by RT-Q-PCR
  9. Practicalities of (RT)-Q-PCR protocols
  10. Application of Q-PCR for investigating the microbial genetic potential within the environment
  11. Quantifying gene expression in environmental samples using RT-Q-PCR: a step closer to determining the functioning of target genes in the environment
  12. Combining (RT)-Q-PCR with other approaches to provide greater insight into community function and dynamics
  13. Conclusions
  14. Acknowledgements
  15. References

Although the physical set-up of (RT)-Q-PCR amplifications to quantify gene or transcript numbers from environmental nucleic acids is straightforward, the quantitative data generated from these reactions can be affected by many compounding factors. Consequently, such factors as discussed earlier in this review need to be carefully considered when designing, developing and implementing (RT)-Q-PCR protocols. Details of some additional key considerations and recommendations for the use of (RT)-Q-PCR are given below.

(RT)-Q-PCR amplicons should be short, ideally between 50 and 150 bp in length. While, the GC content of the primers can range between 20% and 80% (although paired primers should have similar melting temperatures; Tm), a high GC content will increase the specificity of the reaction, which is of particular importance for SYBR green assays. When designing a TaqMan probe, the probe should be situated as close as possible to the forward primer without overlapping. The probe should not have a guanine nucleotide at the 5′ end or have more guanines than cytosines as guanine residues are natural quenchers. The Tm of the probe should be 8–10 °C above the Tm of the primers. When designing primers and probes, it may be difficult to meet all the above criteria. However, satisfying as many of these as possible within the constraints of the assay design will maximize the likelihood of successful quantification. As outlined earlier (see Fluorescence detection chemistry section), SYBR green primer sets may require extensive optimization to ensure a single specific amplicon and that primer dimers are not produced; this must also be confirmed by dissociation curve analysis. Highly reproducible DNA and RNA standard curves can be created by dilution of known concentrations of standards (see Smith et al., 2006 for details). Care should be taken to avoid repeated freeze–thawing of templates used to construct standard curves. As quantification of genes or transcripts from an environmental samples is calculated from the standard curve, a full description of the standard curve (r2, slope, efficiency and y-intercept value) should be given when reporting gene and/or transcript numbers. Biological (not just technical) replication (at least n=3) is essential for (RT)-Q-PCR to enable statistical investigation of differences in gene or transcript numbers between samples or treatments. Finally, as there are numerous compounding factors that can affect quantification, we recommend that comparisons between absolute gene or transcript numbers generated from different Q-PCR assays (or indeed studies) should not be made.

Application of Q-PCR for investigating the microbial genetic potential within the environment

  1. Top of page
  2. Abstract
  3. Introduction
  4. Advantages of Q-PCR over traditional end-point PCR
  5. Fluorescence detection chemistries used to detect template amplification during Q-PCR
  6. Target quantification using the cycle threshold (Ct) method
  7. Biological and methodological factors affecting quantification of genes and transcripts from environmental samples
  8. Quantifying gene expression by RT-Q-PCR
  9. Practicalities of (RT)-Q-PCR protocols
  10. Application of Q-PCR for investigating the microbial genetic potential within the environment
  11. Quantifying gene expression in environmental samples using RT-Q-PCR: a step closer to determining the functioning of target genes in the environment
  12. Combining (RT)-Q-PCR with other approaches to provide greater insight into community function and dynamics
  13. Conclusions
  14. Acknowledgements
  15. References

The first applications of Q-PCR in microbial ecology were reported in three papers published in November 2000, which used TaqMan-based assays to target 16S rRNA genes (Becker et al., 2000; Suzuki et al., 2000; Takai & Horikoshi, 2000). Becker et al. (2000) demonstrated the ability of TaqMan probes to determine the abundance of a specific ecotype of Synechococcus sp. BO 8807 against a mixed background of phylogenetically related bacteria using artificial mixed communities. Suzuki et al. (2000) exploited the specificity and the sensitivity of TaqMan Q-PCR assays to determine spatial and temporal quantitative differences in the distributions of Synechococcus, Prochlorococcus and archaea in marine waters, while Takai & Horikoshi (2000) quantified archaeal 16S rRNA gene numbers within samples from a deep sea hydrothermal vent effluent, hot spring water and from hot spring and freshwater sediments. By targeting highly conserved regions of the 16S rRNA gene, Q-PCR assays have been designed to quantify ‘total’ bacterial (and/or archaeal) numbers while targeting of taxa-specific sequences within hypervariable regions within the gene enables quantification of sequences from phylum to species levels, provided that there are sequence data available that enable the design of primers and probes. A caveat of this approach must be stressed; 16S rRNA gene numbers from environmental samples cannot be converted to cell numbers as the exact number of copies of the 16S rRNA gene in any given bacterial species varies (Klappenbach et al., 2000). Table 1 details commonly used rRNA Q-PCR primer and probe sets.

Table 1.   Quantitative PCR primer and probe sets targeting small subunit ribosomal RNA genes of bacteria, archaea and fungi
TargetDetection chemistryPrimer/probeSequence (5′–3′)Amplicon length (bp)Temp. (°C)References
  1. TM, TaqMan probe; SG, SYBR green; Temp., annealing temperature.

ProkaryoteTMUni 340FCCT ACG GGR BGC ASC AG46657Takai & Horikoshi (2000)
16S rRNA gene Uni 806RGGA CTA CNN GGG TAT CTA AT   
 TM 516FTGY CAG CMG CCG CGG TAA HAC VNR S   
BacterialTMBACT1369FCGG TGA ATA CGT TCY CGG12356Suzuki et al. (2000)
16S rRNA gene PROK1492RGGW TAC CTT GTT ACG ACT T   
 Probe TM 1389FCTT GTA CAC ACC GCC CG   
BacterialTM331FTTC TAC GGG AGG CAG CAG46660Nadkarni et al. (2002)
16S rRNA gene 797RGGA CTA CCA GGG TAT CTA ATC CTG TT   
 Probe BacTaqCGT ATT ACC GCG GCT GCT GGC AC   
ArchaealTMArch 349FGYG CAS CAG KCG MGA AW45759Takai & Horikoshi (2000)
16S rRNA gene Arch 806RGGA CTA CVS GGG TAT CTA AT   
 TM Arch 516FTGY CAG CCG CCG CGG TAA HAC CVG C   
ArchaealSGAr109fACK GCT CAG TAA CAC GT80652Lueders & Friedrich (2003)
16S rRNA gene Ar915rGTG CTC CCC CGC CAA TTC CT   
FungalSGEUK 345FAAG GAA GGC AGC AGG CG14960Zhu et al. (2005)
18S rRNA gene EUK 499RCAC CAG ACT TGC CCT CYA AT   
FungalSGFung5fGTAAAAGTCCTGGTTCCCC55048Smit et al. (1999)
18S rRNA gene FF390rCGA TAA CGA ACG AGA CCT  Vainio & Hantula (2000)
     Lueders et al. (2004)

Quantification of eukaryotes within environmental samples by Q-PCR can be carried out by targeting the 18S rRNA gene (Lueders et al., 2004; Zhu et al., 2005) or the internal transcribed spacer (ITS) region (Landeweert et al., 2003; Kennedy et al., 2007). The ITS region is often targeted for the design of taxon-specific Q-PCR assays as it provides a greater degree of sequence differentiation between species and lower within-species variability (Kennedy et al., 2007) than is seen for the 18S rRNA gene. As with quantification of 16S rRNA gene numbers, Q-PCR-derived ITS region and 18S rRNA gene numbers cannot be directly equated to cell numbers. However, numbers of fungal rRNA gene or ITS numbers per volume of sample can be used to compare the relative numbers of fungi between different environmental samples (Guidot et al., 2002).

In addition to quantitative data on taxonomic markers, Q-PCR has also been applied to quantify functional genes within the environment. By targeting functional genes that encode enzymes in key metabolic or catabolic pathways, the (genetic) potential for a particular microbial function within a particular environment can be assessed. To understand microbial functioning in the environment at a molecular level, it is essential not only to know what genes are present and the diversity of these genes but also to determine their abundance and distribution within the environment. To this end, Q-PCR assays have been designed to target microbially mediated biogeochemical processes in the environment. Quantification of functional genes involved in ammonia oxidation (Hermansson & Lindgren, 2001; Okano et al., 2004; Treusch et al., 2005; Leininger et al., 2006; Mincer et al., 2007), nitrate reduction and denitrification (Lopez-Gutiérrez et al., 2004; Henry et al., 2006; Smith et al., 2007), sulphate reduction (Leloup et al., 2007), methanogenesis (Denman et al., 2007) and methane oxidation (Kolb et al., 2003) have been investigated (see Table 2 for details of nitrogen cycle Q-PCR analyses). In a particularly striking example of the value of such functional gene Q-PCR assays, the relative contributions of ammonia-oxidizing archaea and bacteria to the first step of nitrification (ammonia oxidation) have been investigated both in soils (Leininger et al., 2006; He et al., 2007b) and in seawater (Mincer et al., 2007) by determination of the abundance of archaeal- and bacterial-related amoA genes. These studies have suggested that archaea and not bacteria are the numerically dominant ammonia oxidizers in both environments. The results of such studies are therefore encouraging a re-evaluation of our basic understanding of nitrogen cycling and the relative importance of bacteria and archaea (or specific taxa or functional guilds within the domains) within key environmental processes. While these studies have greatly enhanced our understanding of gene numbers in the environment, the next step to further our understanding is to link variation in genetic potential (i.e. gene numbers) within a system in relation to variation in rates and activity of the biologically driven environmental processes in question, and hence enabling improved understanding of the underpinning factors that influence microbial functioning within the environment. As Q-PCR is a sensitive and specific method to track changes in the abundance (and expression) of specific target functional genes, it lends itself to experiments that further investigate the environmental controls/effects on the numbers of the target gene (and hence the organisms carrying these genes) and subsequently on the environmental process that these genes (and organisms) encode. A recent study by Dandie et al. (2007) has adopted such an approach by quantifying the response of denitrifying populations within soil microcosms amended with varying concentrations of glucose (as an electron donor) designed to induce different rates of denitrification. Denitrifier population numbers were assessed using the nitric oxide reductase (cnorB) gene as a proxy for denitrifier numbers targeting two populations (cnorBP: Pseudomonas and cnorBB: Bosea, Bradyrhizobium, Ensifer). These mesocosm experiments indicated that denitrification rates and microbial respiration increased significantly with increasing addition of glucose and that this was accompanied by increases in cnorBP, but not cnorBB populations, revealing population-specific responses to carbon amendment.

Table 2.   Q-PCR primers and probes targeting genes encoding enzymes involved in nitrogen cycling
Functional groupTarget geneComment/environmentReferences
Nitrogen fixationnifHSuite of TaqMan probes and primers designed to quantify nifH transcripts from seawaterChurch et al. (2005)
 SYBR green primers targeting the nifH gene of Synechococcus sp. OS-B' isolate and used to quantify nifH transcripts from a hot spring microbial matSteunou et al. (2006)
Ammonia oxidationamoATaqman probe and primers targeting known bacterial ammonia oxidizers. Used to quantify genes from soilOkano et al. (2004)
 TaqMan probe and primers designed from the alignment of environmental mRNA and DNA clones from soil samples. Used to quantify transcripts from a soil microcosmTreusch et al. (2005)
Nitrate reductionnarGSYBR green primers designed from environmental soil clone libraries. Used to quantify narG from a range of soil typesLopez-Gutiérrez et al. (2004)
 Suite of TaqMan primers and probes designed from environmental clone library. Used to target narG genes and transcripts from estuarine sedimentsSmith et al. (2007)
 SYBR green primer set designed from all available narG sequences in the public database and used to quantify genes from river sediment, range of soils, water and biofilmsBru et al. (2007)
napASuite of TaqMan primers and probes used to target napA genes and transcripts from estuarine sedimentsSmith et al. (2007)
 SYBR green primer set used to quantify genes from river sediment, soils, water and biofilmsBru et al. (2007)
Nitrite reductionnirSTaqMan probes and primers targeting Pseudomonas stutzeri-related nirS genes. Used to quantify nirS genes from soil and contaminated groundwaterGruntzig et al. (2001)
 Suite of TaqMan primers and probes designed from nirS mRNA clone library. Used to quantify genes and transcripts from estuarine sedimentsSmith et al. (2007)
nirKSYBR green primer set designed from all nirK sequences available at the time. Used to quantify genes from a range of soil typesHenry et al. (2006)
Nitric oxide reductionnorBTwo SYBR green primer sets targeting the cytochrome c electron donor (cNOR) form of the enzyme; designed from cultured soil isolates. Used to quantify cnorB from soil microcosmsDandie et al. (2007)
Nitrous oxide reductionnosZTwo SYBR green primer sets designed from diverse nosZ sequences. Used to quantify nosZ genes from a range of soil typesHenry et al. (2006)
Nitrate ammonificationnrfATaqMan primers and probe targeting nrfA in estuarine sedimentsSmith et al. (2007)

Functional genes encoding key reactions in biodegradation pathways of environmental pollutants have also been targeted by Q-PCR analysis (Baldwin et al., 2003, 2008; Devers et al., 2004; Gonod et al., 2006; McKew et al., 2007; see Table 3 for details of Q-PCR primer and/or probe sets). The accurate quantification of key genes such as those encoding mono-oxygenase and diooxygenase enzymes involved in the catabolic conversions of environmental pollutants in situ will greatly enhance our understanding and importantly improve our knowledge of the biotic potential within an environment for successful bioremediation, and further how indigenous or augmented microorganisms respond to biostimulation protocols. For example, the effects of biostimulation and bioaugmentation remediation strategies on the activity of hydrocarbon degrading bacteria in seawater microcosms containing crude oil were investigated over a 30-day period by targeting alkane hydroxylase and aromatic ring hydroxylating dioxygenase genes by Q-PCR while simultaneously measuring the degradation of the crude oil (McKew et al., 2007). This study revealed that specific taxa within these hydrocarbon-degrading bacterial communities were directly influenced by application of different biostimulation approaches involving addition of nutrients and/or bioemulsifiers. Q-PCR is a valuable tool for investigating the potential within the environment for biodegradation of other pollutants, such as herbicides. For example, Q-PCR has been used to study the potential for biodegradation of the herbicide 4-chloro-2-methylphenoxyacetic acid (MCPA) in different soil types (Bælum et al., 2006) by targeting and quantifying the tfdA gene involved in the initial degradation step of the compound. This study showed a five- and threefold log increase in tfdA gene numbers over time in soil microcosms amended with either a high (20 mg kg−1) or a low (2.3 mg kg−1) dose of MCPA, respectively, with increases in tfdA genes inversely proportional to MCPA degradation. Moreover, this study also demonstrated the diagnostic potential of using SYBR green dissociation curve analysis of Q-PCR products to identify shifts in the dominant tfdA populations over time and during degradation. Subsequent clone library analysis showed that class III tfdA genes were responsible for MCPA degradation and not class I tfdA genes, which were dominant before the degradation process was initiated.

Table 3.   Q-PCR primers and probes targeting genes involved in biodegradation
Target chemicalFunctional groupTarget geneComment/environmentReferences
Herbicide atrazineAtrazine degrading bacteriaatzA, B, C, E and FSYBR green primers targeting atz catabolic gene expression in two atrazine-degrading bacteriaDevers et al. (2004)
Herbicide MCPAMCPA (4-chloro-2- methylphenoxy-acetic acid) and 2,4-dichlorophenoxyacetic degrading bacteriatfdASYBR green primers targeting tfdA gene in soil. Primers described originally by Vallaeys and colleagues, and later adapted by Gonod and colleagues, for Q-PCRVallaeys et al. (1996) (primers) Gonod et al. (2006) (Q-PCR assay conditions)
  SYBR green Q-PCR primer set designed from the alignment of 23 known tfdA genes and used to track quantitative changes in tfdA gene numbers in soil during degradation of MCPABælum et al. (2006)
Trichloroethene and cis-dichloroethene (cis-DCE)Bacteria involved in reductive dechlorination of TCE 2 and oxidation of cis-DCE16S rRNA geneSuite of group specific primers for SYBR green Q-PCR targeting of CFB, Alphaproteobacteria and Burkholderiales in hydrocarbon, trichloroethene and cis-DCE contaminated groundwaterMiller et al. (2007)
Chlorinated ethenesAnaerobic reductive dehalogenases (RDase)tceA, vcrA and bvcATaqman primer and probe sets targeting the tceA, vcrA and bvcA genes of Dehalococcoides spp. in groundwaterLee et al. (2008)
Halogenated compoundsReductive dehalogenating bacteria16S rRNA and rdh genesSYBR green primer sets targeting the 16S rRNA gene of two known dehalogenating bacteria and a SYBR green primer set targeting a rdh gene designed from sequences retrieved from marine sediments amended with 1,2,3,4-tetrachlorodibenzo-p-dioxin (TeCDD)Ahn et al. (2007)
Methyl tert-butyl ether (MTBE)MTBE-degrading bacterial strain PM116S rRNA geneTaqman primer and probe set targeting the MTBE degrading bacterial strain PM1 in groundwater and sedimentsHristova et al. (2001)
Hydrocarbons (aliphatic and aromatic)Hydrocarbonoclastic bacteriaalkB2, alkB, phnASYBR green primers targeting the alkane hydroxylase in Alcanivorax borkumensis and Thakassolituus oleivorans and the aromatic ring-hydroxylating dioxygenase gene from Cycloclasticus spp.McKew et al. (2007)
Hydrocarbons (aromatic)Toluene- and xylene-degrading bacteriabssATaqman probe and primer set targeting bssA gene in a variety of toluene-degrading denitrifying bacteriaBeller et al. (2002)
Hydrocarbons (aromatic)Aromatic compound degrading bacteriaEntire subfamilies of related oxygenase genes rather than species-specific genesPaper outlines the development of a suite of SYBR green primer sets targeting biphenyl dioxygenase, naphthalene dioxygenase, toluene dioxygenase, toluene/xylene monooxygenases, phenol monooxygenase and ring-hydroxylating toluene monooxygenase genesBaldwin et al. (2003)

Quantifying gene expression in environmental samples using RT-Q-PCR: a step closer to determining the functioning of target genes in the environment

  1. Top of page
  2. Abstract
  3. Introduction
  4. Advantages of Q-PCR over traditional end-point PCR
  5. Fluorescence detection chemistries used to detect template amplification during Q-PCR
  6. Target quantification using the cycle threshold (Ct) method
  7. Biological and methodological factors affecting quantification of genes and transcripts from environmental samples
  8. Quantifying gene expression by RT-Q-PCR
  9. Practicalities of (RT)-Q-PCR protocols
  10. Application of Q-PCR for investigating the microbial genetic potential within the environment
  11. Quantifying gene expression in environmental samples using RT-Q-PCR: a step closer to determining the functioning of target genes in the environment
  12. Combining (RT)-Q-PCR with other approaches to provide greater insight into community function and dynamics
  13. Conclusions
  14. Acknowledgements
  15. References

RT-Q-PCR can be used to detect and quantify mRNA transcripts of interest in complex environmental samples both in a sensitive and a specific manner. However, RT-Q-PCR to investigate gene expression (rRNA or mRNA) within environmental samples has been far less widely applied than Q-PCR-based assessment of gene numbers (i.e. from DNA) in the environment. This is primarily due to the difficulties of extracting intact RNA, and particularly intact mRNA, from environmental samples. While the quantification of both rRNA genes and/or functional genes from the environment can be used as an indicator of the genetic potential within an environment and is suggestive of potential functional activity within a community, molecular investigation of biological activity should preferably determine changes in gene expression and ideally of mRNA transcripts encoded by specific functional genes. A limited number of studies have indeed shown the successful quantification by RT-Q-PCR of a number of functional gene transcripts from a range of environments including aquatic ecosystems (Holtzendorff et al., 2002; Wawrik et al., 2002; Fey et al., 2004; Church et al., 2005; Gonzalez-Escalona et al., 2006; Lee et al., 2008) but also in estuarine sediments (Smith et al., 2007), soil (Treusch et al., 2005; Nícolaisen et al., 2008), hot spring microbial mats (Steunou et al., 2006) and blood and faecal samples (Matsuda et al., 2007). In a particularly elegant application of RT-Q-PCR, Steunou et al. (2006) investigated changes in expression of Synechococcus spp. nif (nifH, nifD, nifK) genes in a hot spring microbial mat over a 12-h period. Transcripts in the mat were only detected and quantified at the end of the day, when the mat became anoxic. They further quantified expression of key genes involved in photosynthesis, respiration and fermentation processes within the hot spring microbial mat to build an overview of the energy-generating processes that may drive N2 fixation. Lee et al. (2008) used RT-Q-PCR to quantify expression of reductive dehalogenase (vcrA, bvcA and tceA) genes as biomarkers of Dehalococcoides spp. activity and to distinguish the roles of different strains of Dehalococcoides during bioremediation and bioaugmentation of groundwater contaminated with trichloroethene. RT-Q-PCR indicated that vcrA and bvcA gene transcripts were highly expressed in all samples, whereas the tceA transcripts were inconsistently quantified and were at lower levels, indicating that Dehalococcoides spp. carrying vcrA and bvcA genes played a more important role in trichloroethene in situ bioremediation. These two examples highlight how the application of RT-Q-PCR in the environment will undoubtedly further our understanding of the many important processes that are mediated by microorganisms.

Combining (RT)-Q-PCR with other approaches to provide greater insight into community function and dynamics

  1. Top of page
  2. Abstract
  3. Introduction
  4. Advantages of Q-PCR over traditional end-point PCR
  5. Fluorescence detection chemistries used to detect template amplification during Q-PCR
  6. Target quantification using the cycle threshold (Ct) method
  7. Biological and methodological factors affecting quantification of genes and transcripts from environmental samples
  8. Quantifying gene expression by RT-Q-PCR
  9. Practicalities of (RT)-Q-PCR protocols
  10. Application of Q-PCR for investigating the microbial genetic potential within the environment
  11. Quantifying gene expression in environmental samples using RT-Q-PCR: a step closer to determining the functioning of target genes in the environment
  12. Combining (RT)-Q-PCR with other approaches to provide greater insight into community function and dynamics
  13. Conclusions
  14. Acknowledgements
  15. References

Linking the structure and composition of microbial communities with the biological function that individual species or functional guilds convey is a key objective within microbial ecology. Stable isotope probing (SIP) (Radajewski et al., 2000; Manefield et al., 2002) can be used to directly link distinct taxa within a mixed microbial community to specific metabolic processes, particularly carbon utilization/degradation, using labelled substrates such as 13C. During microbial growth, these substrates are incorporated into the nucleic acids (DNA or RNA) from members of the community that are directly (or indirectly) utilizing the labelled substrate, and the ‘heavy’ labelled nucleic acid can be separated using density gradient ultracentrifugation from the (unlabelled) nucleic acids representative of other members of the community that do not utilize the substrate. Lueders et al. (2004) combined SIP with (RT)-Q-PCR for quantitative measurements of a domain-specific template distribution through the differentially labelled fractions of DNA and RNA extracted from soil microcosms following SIP incubation. This enabled the community dynamics of methanotrophs in rice field soils to be tracked over time. Bacterial, archaeal and eukaryote rRNA genes were quantified in the heavy labelled fractions, indicating not only the presence of a dynamic methanotroph community that was enriched over time but also direct or indirect incorporation of the labelled 13C into eukaryotes (fungi and protozoa).

Microarrays are now being increasingly used to simultaneously screen microbial communities within diverse environments for the presence (and, in principle, the abundance) of specific ribo- or phylotypes (taxa) and/or functional genes. Microarray platforms such as the PhyloChip (Brodie et al., 2007) and GeoChip (He et al., 2007a) are now affording a previously unparalleled opportunity to undertake targeted phylogenetic marker-based and functional gene surveys of environments. Nevertheless, the potential for providing quantitative assessments of gene abundance from microarrays as applied to nucleic acids extracted from environmental samples is often compromised by the requirement for an initial amplification step from the environmental DNA (or RNA), often via PCR amplification (Brodie et al., 2007), but alternatively via rolling linear amplification (He et al., 2007a), before microarray hybridization. Consequently, and especially in those microarray studies requiring an intermediate PCR amplification, any quantitative interpretation of such data sets should be treated with caution, as these results will be susceptible to the same biases that are associated with any end-point PCR protocol (Reysenbach et al., 1992). Nevertheless, microarray experiments can be used to identify potentially interesting quantitative changes in taxon- or gene-specific abundance between environmental samples that can then be validated by Q-PCR-based approaches, and hence Q-PCR can be recommended as a fast, target-specific method for validation of the (semi-) quantitative results generated from the increasing number of environmental microarrays (Rhee et al., 2004; Brodie et al., 2006, 2007; Burgmann et al., 2007; He et al., 2007a).

One major disadvantage of Q-PCR-based approaches is the requirement for prior sequence data of the specific target gene of interest. Consequently, Q-PCR can only be used for targeting of known genes. Historically, and until recently, sequence information has primarily been derived from genome or gene fragment sequences from cultured organisms and/or from clone libraries generated by PCR using primers that are themselves based on current sequence knowledge. Hence, accessing the ‘unknown’ using Q-PCR or indeed any PCR-based methods is inevitably limited to the analysis of sequences related to those that have already been characterized. Because molecular analysis of environmental microorganisms has repeatedly shown that the majority of microorganisms (and their genes) in the environment are highly divergent from those of most cultured organisms, this represents a Catch 22 situation for the development of new PCR-based assays. In recent years, this problem has, however, been circumvented by the introduction of metagenomic approaches that provide a PCR-independent assessment of microbial diversity. Two main strategies have been utilized, namely clone library-based metagenomes (Vergin et al., 1998; Beja et al., 2000; Venter et al., 2004) and more recently ultra-high-throughput sequencing approaches such as pyrosequencing (Edwards et al., 2006; Dinsdale et al., 2008). The latter, particularly, offers considerable benefits both in terms of providing much larger data sets than can be generated via library-based approaches, and as importantly, by avoiding potential sequence-specific cloning biases. Moreover, where pyrosequencing is used to target-specific genes (e.g. rRNA genes; Sogin et al., 2006), such data sets provide only semi-quantitative assessments of the diversity and/or the abundance of particular phylotypes that can again be validated by (RT)-Q-PCR-based approaches.

Conclusions

  1. Top of page
  2. Abstract
  3. Introduction
  4. Advantages of Q-PCR over traditional end-point PCR
  5. Fluorescence detection chemistries used to detect template amplification during Q-PCR
  6. Target quantification using the cycle threshold (Ct) method
  7. Biological and methodological factors affecting quantification of genes and transcripts from environmental samples
  8. Quantifying gene expression by RT-Q-PCR
  9. Practicalities of (RT)-Q-PCR protocols
  10. Application of Q-PCR for investigating the microbial genetic potential within the environment
  11. Quantifying gene expression in environmental samples using RT-Q-PCR: a step closer to determining the functioning of target genes in the environment
  12. Combining (RT)-Q-PCR with other approaches to provide greater insight into community function and dynamics
  13. Conclusions
  14. Acknowledgements
  15. References

In conclusion, (RT)-Q-PCR-based approaches represent fast, effective methods enabling the quantification of gene and/or transcript numbers within environmental samples, providing unparalleled specificity and sensitivity to target sequences present within a mixed community background. As with all methodologies, the validity of the resulting data sets should be considered against the specificity and experimental variability associated with the method. In particular, for Q-PCR-based assays, the value of such data sets should be considered in relation to the specificity of the primer (and probes) used in the amplification and with respect to instrument, user and most importantly experimental variability associated with the method. Moreover, in order to maximize the value of (RT)-Q-PCR-based approaches for furthering biological understanding in microbial ecology, their value is greatest when used in combination with other (and often process-based) assessments of ecosystem function.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Advantages of Q-PCR over traditional end-point PCR
  5. Fluorescence detection chemistries used to detect template amplification during Q-PCR
  6. Target quantification using the cycle threshold (Ct) method
  7. Biological and methodological factors affecting quantification of genes and transcripts from environmental samples
  8. Quantifying gene expression by RT-Q-PCR
  9. Practicalities of (RT)-Q-PCR protocols
  10. Application of Q-PCR for investigating the microbial genetic potential within the environment
  11. Quantifying gene expression in environmental samples using RT-Q-PCR: a step closer to determining the functioning of target genes in the environment
  12. Combining (RT)-Q-PCR with other approaches to provide greater insight into community function and dynamics
  13. Conclusions
  14. Acknowledgements
  15. References
  • Ahn YB, Haggblom MM & Kerkhof LJ (2007) Comparison of anaerobic microbial communities from estuarine sediments amended with halogenated compounds to enhance dechlorination of 1,2,3,4-tetrachlorodibenzo-p-dioxin. FEMS Microbiol Ecol 61: 362371.
  • Bælum J, Henriksen T, Hansen HCB & Jacobsen CS (2006) Degradation of 4-chloro-2-methylphenoxyacetic acid in top- and sub-soil is quantitatively linked to the class III tfdA gene. Appl Environ Microb 72: 14761486.
  • Baldwin BR, Nakatsu CH & Nies L (2003) Detection and enumeration of aromatic oxygenase genes by multiplex and real-time PCR. Appl Environ Microb 69: 33503358.
  • Baldwin BR, Nakatsu CH & Nies L (2008) Enumeration of aromatic oxygenase genes to evaluate monitored natural attenuation at gasoline-contaminated sites. Water Res 42: 723731.
  • Becker S, Boger P, Oehlmann R & Ernst A (2000) PCR bias in ecological analysis: a case study for quantitative Taq nuclease assays in analyses of microbial communities. Appl Environ Microb 66: 49454953.
  • Beja O, Aravind L, Koonin EV et al. (2000) Bacterial rhodopsin: evidence for a new type of phototrophy in the sea. Science 289: 19021906.
  • Beller HR, Kane SR, Legler TC & Alvarez PJJ (2002) A real-time polymerase chain reaction method for monitoring anaerobic, hydrocarbon-degrading bacteria based on a catabolic gene. Environ Sci Technol 36: 39773984.
  • Brodie EL, DeSantis TZ, Joyner DC et al. (2006) Application of a high-density oligonucleotide microarray approach to study bacterial population dynamics during uranium reduction and reoxidation. Appl Environ Microb 72: 62886298.
  • Brodie EL, DeSantis TZ, Parker JPM, Zubietta IX, Piceno YM & Andersen GL (2007) Urban aerosols harbor diverse and dynamic bacterial populations. P Natl Acad Sci USA 104: 299304.
  • Bru D, Sarr A & Philippot L (2007) Relative abundances of proteobacterial membrane-bound and periplasmic nitrate reductases in selected environments. Appl Environ Microb 73: 59715974.
  • Burgmann H, Howard EC, Ye W, Sun F, Sun S, Napierala S & Moran MA (2007) Transcriptional response of Silicibacter pomeroyi DSS-3 to dimethylsulfoniopropionate (DMSP). Environ Microbiol 9: 27422755.
  • Bustin SA (2002) Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. J Mol Endocrinol 29: 2339.
  • Chandler DP, Wagnon CA & Bolton H Jr (1998) Reverse Transcriptase (RT) inhibition of PCR at low concentrations of template and its implications for quantitative RT-PCR. Appl Environ Microb 64: 669677.
  • Church MJ, Short CM, Jenkins BD, Karl DM & Zehr JP (2005) Temporal patterns of nitrogenase gene (nifH) expression in the oligotrophic North Pacific Ocean. Appl Environ Microb 71: 53625370.
  • Coolen MJL, Talbot HM, Abbas BA, Ward C, Schouten S, Volkman JK & Damste JSS (2008) Sources for sedimentary bacteriohopanepolyols as revealed by 16S rDNA stratigraphy. Environ Microbiol 10: 17831803.
  • Dandie CE, Miller MN, Burton DL, Zebarth BJ, Trevors JT & Goyer C (2007) Nitric oxide reductase-targeted real-time PCR quantification of denitrifier populations in soil. Appl Environ Microb 73: 42504258.
  • Denman SE, Tomkins NW & McSweeney CS (2007) Quantitation and diversity analysis of ruminal methanogenic populations in response to the antimethanogenic compound bromochloromethane. FEMS Microbiol Ecol 62: 313322.
  • Devers M, Soulas G & Martin-Laurent F (2004) Real-time reverse transcription PCR analysis of expression of atrazine catabolism genes in two bacterial strains isolated from soil. J Microbiol Meth 56: 315.
  • Dinsdale EA, Edwards RA, Hall D et al. (2008) Functional metagenomic profiling of nine biomes. Nature 452: 629632.
  • Diviacco S, Norio P, Zentilin L, Menzo S, Clementi M, Biamonti G, Riva S, Falaschi A & Giacca M (1992) A novel procedure for quantitative polymerase chain reaction by co-amplification of competitive templates. Gene 122: 313320.
  • Edwards R, Rodriguez-Brito B, Wegley L, Haynes M, Breitbart M, Peterson D, Saar M, Alexander S, Alexander EC & Rohwer F (2006) Using pyrosequencing to shed light on deep mine microbial ecology. BMC Genomics 7: 5770.
  • Fey A, Eichler S, Flavier S, Christen R, Höfle MG & Guzman CA (2004) Establishment of a real-time PCR-based approach for accurate quantification of bacterial RNA targets in water, using Salmonella as a model organism. Appl Environ Microb 70: 36183623.
  • Giglio S, Monis PT & Saint CP (2003) Demonstration of preferential binding of SYBR green I to specific DNA fragments in real-time multiplex PCR. Nucliec Acids Res 31: e136.
  • Giovannoni SJ, Britschgi TB, Moyer CL & Field KG (1990) Genetic diversity in Sargasso Sea bacterioplankton. Nature 345: 6062.
  • Gonod LV, Martin-Laurent F & Chenu C (2006) 2,4-D impact on bacterial communities, and the activity and genetic potential of 2,4-D degrading communities in soil. FEMS Microbiol Ecol 58: 529537.
  • Gonzalez-Escalona N, Fey A, Höfle MG, Espejo RT & Guzman A (2006) Quantitative reverse transcription polymerase chain reaction analysis of Vibrio cholerae cells entering the viable but non-culturable state and starvation in response to cold shock. Environ Microbiol 8: 658666.
  • Grunberg-Manago M (1999) Messenger RNA stability and its role in control of gene expression in bacteria and phages. Ann Rev Genet 33: 193227.
  • Gruntzig V, Nold SC, Zhou J & Tiedje JM (2001) Pseudomonas stutzeri nitrite reductase gene abundance in environmental samples measured by real-time PCR. Appl Environ Microb 67: 760768.
  • Guidot A, Debaud JC & Marmeisse R (2002) Spatial distribution of the below-ground mycelia of an ectomycorrhizal fungus inferred from specific quantification of its DNA in soil samples. FEMS Microbiol Ecol 42: 477486.
  • He JZ, Gentry TJ, Schadt CW et al. (2007a) GeoChip: a comprehensive microarray for investigating biogeochemical, ecological and environmental processes. ISME J 1: 6777.
  • He JZ, Shen JP, Zhang LM, Zhu YG, Zheng YM, Xu MG & Di H (2007b) Quantitative analyses of the abundance and composition of ammonia-oxidizing bacteria and ammonia-oxidizing archaea of a Chinese upland red soil under long-term fertilization practices. Environ Microbiol 9: 23642374.
  • Head IM, Saunders JR & Pickup RW (1998) Microbial evolution, diversity and ecology: a decade of ribosomal RNA analysis of uncultured microorganisms. Microb Ecol 35: 128.
  • Heid CA, Stevens J, Livak KJ & Williams P (1996) Real time quantitative PCR. Genome Res 6: 986994.
  • Henry S, Bru D, Stres B, Hallet S & Philippot L (2006) Quantitative detection of the nosZ gene, encoding nitrous oxide reductase, and comparison of the abundances of 16S rRNA, narG, nirK, and nosZ genes in soils. Appl Environ Microb 72: 51815189.
  • Hermansson A & Lindgren PE (2001) Quantification of ammonia-oxidizing bacteria in arable soil by real-time PCR. Appl Environ Microb 67: 972976.
  • Holland PM, Abramson RD, Watson R & Gelfand DH (1991) Detection of specific polymerase chain reaction product by utilizing the 5′–3′ exonuclease activity of Thermus aquaticus DNA polymerase. P Natl Acad Sci USA 88: 72767280.
  • Holtzendorff J, Marie D, Post AF, Partensky F, Rivlin A & Hess WR (2002) Synchronized expression of ftsZ in natural Prochlorococcus populations of the Red Sea. Environ Microbiol 4: 644653.
  • Hristova KR, Lutenegger CM & Scow KM (2001) Detection and quantification of methyl tert-butyl ether-degrading strain PM1 by real-time Taqman PCR. Appl Environ Microb 67: 51545160.
  • Kandeler E, Deiglmayr K, Tscherko D, Bru D & Philippot L (2006) Abundance of narG, nirS, nirK, and nosZ genes of denitrifying bacteria during primary successions of a glacier foreland. Appl Environ Microb 72: 59575962.
  • Kennedy PG, Bergemann SE, Hortal S & Bruns TD (2007) Determining the outcome of field-based competition between two Rhizopogon species using real-time PCR. Mol Ecol 16: 881890.
  • Klappenbach JA, Dunbar JM & Schmidt TM (2000) rRNA operon copy number reflects ecological strategies of bacteria. Appl Environ Microb 66: 13281333.
  • Kolb S, Knief C, Stubner S & Conrad R (2003) Quantitative detection of methanotrophs in soil by novel pmoA-targeted real-time PCR assays. Appl Environ Microb 69: 24232429.
  • Kutyavin IV, Afonina IA, Mills A et al. (2000) 3′-Minor groove binder-DNA probes increase sequence specificity at PCR extension temperatures. Nucl Acids Res 28: 655661.
  • Landeweert R, Veenman C, Kuyper TW, Fritze H, Wernars K & Smit E (2003) Quantification of ectomycorrhizal mycelium in soil by real-time PCR compared to conventional quantification techniques. FEMS Microbiol Ecol 45: 283292.
  • Larkin MJ, Osborn AM & Fairley D (2005) A molecular toolbox for bacterial ecologists: PCR primers for functional gene analysis. Molecular Microbial Ecology (OsbornAM & SmithCJ, eds), pp. 281301. Taylor & Francis, Abingdon, UK.
  • Lee PKH, Macbeth TW, Sorenson KS Jr, Deeb RA & Varez-Cohen L (2008) Quantifying genes and transcripts to assess the in situ physiology of “Dehalococcoides” spp. in a trichloroethene-contaminated groundwater site. Appl Environ Microb 74: 27282739.
  • Leininger S, Urich T, Schloter M, Schwark L, Qi J, Nicol GW, Prosser JI, Schuster SC & Schleper C (2006) Archaea predominate among ammonia-oxidizing prokaryotes in soils. Nature 442: 806809.
  • Leloup J, Loy A, Knab NJ, Borowski C, Wagner M & Jorgensen BB (2007) Diversity and abundance of sulfate-reducing microorganisms in the sulfate and methane zones of a marine sediment, Black Sea. Environ Microbiol 9: 131142.
  • Livak KJ, Flood SJ, Marmaro J, Giusti W & Deetz K (1995) Oligonucleotides with fluorescent dyes at opposite ends provide a quenched probe system useful for detecting PCR product and nucleic acid hybridization. PCR Methods Appl 4: 357362.
  • Lopez-Gutiérrez JC, Henry S, Hallet S, Martin-Laurent F, Catroux G & Philippot L (2004) Quantification of a novel group of nitrate-reducing bacteria in the environment by real time PCR. J Microbiol Meth 57: 399407.
  • Love JL, Scholes P, Gilpin B, Saville M, Lin S & Samuel L (2006) Evaluation of uncertainty in quantitative real-time PCR. J Microbiol Meth 67: 349356.
  • Lueders T & Friedrich MW (2003) Evaluation of PCR amplification bias by terminal restriction fragment length polymorphism analysis of small-subunit rRNA and mcrA genes by using defined template mixtures of methanogenic pure cultures and soil DNA extracts. Appl Environ Microb 69: 320326.
  • Lueders T, Wagner B, Claus P & Friedrich MW (2004) Stable isotope probing of rRNA and DNA reveals a dynamic methylotroph community and trophic interactions with fungi and protozoa in oxic rice field soil. Environ Microbiol 6: 6072.
  • Manefield M, Whiteley AS, Griffiths RI & Bailey MJ (2002) RNA stable isotope probing, a novel means of linking microbial community function to phylogeny. Appl Environ Microb 68: 53675373.
  • Margulies M, Egholm M, Altman WE et al. (2005) Genome sequencing in microfabricated high-density picolitre reactions. Nature 437: 376380.
  • Martin-Laurent F, Philippot L, Hallet S, Chaussod R, Germon JC, Soulas G & Catroux G (2001) DNA extraction from soils: old bias for new microbial diversity analysis methods. Appl Environ Microb 67: 23542359.
  • Matsuda K, Tsuji H, Asahara T, Kado Y & Nomoto K (2007) Sensitive quantitative detection of commensal bacteria by rRNA-targeted reverse transcription-PCR. Appl Environ Microb 73: 3239.
  • McKew BA, Coulon F, Yakimov MM, Denaro R, Genovese M, Smith CJ, Osborn AM, Timmis KN & McGenity TJ (2007) Efficacy of intervention strategies for bioremediation of crude oil in marine systems and effects on indigenous hydrocarbonoclastic bacteria. Environ Microbiol 9: 15621571.
  • Miller TR, Franklin MP & Halden RU (2007) Bacterial community analysis of shallow groundwater undergoing sequential anaerobic and aerobic chloroethene biotransformation. FEMS Microbiol Ecol 60: 299311.
  • Mincer TJ, Church MJ, Taylor LT, Preston C, Karl DM & DeLong EF (2007) Quantitative distribution of presumptive archaeal and bacterial nitrifiers in Monterey Bay and the North Pacific Subtropical Gyre. Environ Microbiol 9: 11621175.
  • Nadkarni MA, Martin FE, Jacques NA & Hunter N (2002) Determination of bacterial load by real-time PCR using a broad-range (universal) probe and primers set. Microbiology 148: 257266.
  • Neretin LN, Schippers A, Pernthaler A, Hamann K, Amann R & Jorgensen BB (2003) Quantification of dissimilatory (bi)sulphite reductase gene expression in Desulfobacterium autotrophicum using real time PCR. Environ Microbiol 5: 660671.
  • Nícolaisen MH, Bælum J, Jacobsen CS & Sørensen J (2008) Transcription dynamics of the functional tfdA gene during MCPA herbicide degradation by Cupriavidus necator AEO106 (pRO101) in agricultural soil. Environ Microbiol 10: 571579.
  • Okano Y, Hristova KR, Leutenegger CM, Jackson LE, Denison RF, Gebreyesus B, Lebauer D & Scow KM (2004) Application of real-time PCR to study effects of ammonium on population size of ammonia-oxidizing bacteria in soil. Appl Environ Microb 70: 10081016.
  • Panicker G, Myers ML & Bej AK (2004) Rapid detection of Vibrio vulnificus in shellfish and Gulf of Mexico water by real-time PCR. Appl Environ Microb 70: 498507.
  • Polz MF & Cavanaugh CM (1998) Bias in template-to-product ratios in multi-template PCR. Appl Environ Microb 64: 37243730.
  • Radajewski S, Ineson P, Parekh NR & Murrell JC (2000) Stable-isotope probing as a tool in microbial ecology. Nature 403: 646649.
  • Rebrikov DV & Trofimov DY (2006) Real-time PCR: a review of approaches to data analysis. Appl Biochem Micro 42: 455463.
  • Reysenbach AL, Giver LJ, Wickman GS & Pace NR (1992) Differential amplification of rRNA genes by polymerase chain reaction. Appl Environ Microb 58: 34173418.
  • Rhee SK, Liu X, Wu L, Chong SC, Wan X & Zhou J (2004) Detection of genes involved in biodegradation and biotransformation in microbial communities by using 50-mer oligonucleotide microarrays. Appl Environ Microb 70: 43034317.
  • Skyes PJ, Neoh SH, Brisco MJ, Hughes E, Condon J & Morley AA (1992) Quantitation of targets for PCR by use of limiting dilutions. BioTechniques 13: 444449.
  • Smit E, Leeflang P, Glandorf B, Van Elsas JD & Wernars K (1999) Analysis of fungal diversity in the wheat rhizosphere by sequencing of cloned PCR-amplified genes encoding 18S rRNA and temperature gradient gel electrophoresis. Appl Environ Microb 65: 26142621.
  • Smith CJ, Nedwell DB, Dong LF & Osborn AM (2006) Evaluation of quantitative polymerase chain reaction-based approaches for determining gene copy and gene transcript numbers in environmental samples. Environ Microbiol 8: 804815.
  • Smith CJ, Nedwell DB, Dong LF & Osborn AM (2007) Diversity and abundance of nitrate reductase genes (narG and napA), nitrite reductase genes (nirS and nrfA), and their transcripts in estuarine sediments. Appl Environ Microb 73: 36123622.
  • Sogin MK, Morrison HG, Huber JA, Welch DM, Huse SM, Neal PR, Arrieta JM & Herndl GJ (2006) Microbial diversity in the deep sea and the underexplored “rare phylosphere”. P Natl Acad Sci USA 103: 1211512120.
  • Steunou AS, Bhaya D, Bateson MM, Melendrez MC, Ward DM, Brecht E, Peters JW, Kühl M & Grossman AR (2006) In situ analysis of nitrogen fixation and metabolic switching in unicellular thermophilic cyanobacteria inhabiting hot spring microbial mats. P Natl Acad Sci USA 103: 23982403.
  • Stults JR, Snoeyenbos-West O, Methe B, Lovley DR & Chandler DP (2001) Application of the 5′ fluorogenic exonuclease assay (TaqMan) for quantitative ribosomal DNA and rRNA analysis in sediments. Appl Environ Microb 67: 27812789.
  • Suzuki MT & Giovannoni SJ (1996) Bias caused by template annealing in the amplification of mixtures of 16S rRNA genes by PCR. Appl Environ Microb 62: 625630.
  • Suzuki MT, Taylor LT & DeLong EF (2000) Quantitative analysis of small-subunit rRNA genes in mixed microbial populations via 5′-nuclease assays. Appl Environ Microb 66: 46054614.
  • Takai K & Horikoshi K (2000) Rapid detection and quantification of members of the archaeal community by quantitative PCR using fluorogenic probes. Appl Environ Microb 66: 50665072.
  • Treusch AH, Leininger S, Kletzin A, Schuster SC, Klenk HP & Schleper C (2005) Novel genes for nitrite reductase and Amo-related proteins indicate a role of uncultivated mesophilic crenarchaeota in nitrogen cycling. Environ Microbiol 7: 19851995.
  • Vainio EJ & Hantula J (2000) Direct analysis of wood-inhabiting fungi using denaturing gradient gel electrophoresis of amplified ribosomal DNA. Mycol Res 104: 927936.
  • Vallaeys T, Fulthorpe RR, Wright AM & Soulas G (1996) The metabolic pathway of 2,4-dichlorophenoxyacetic acid degradation involves different families of tfdA and tfdB genes according to PCR-RFLP analysis. FEMS Microbiol Ecol 20: 163172.
  • Venter JC, Remington K, Heidelberg JF et al. (2004) Environmental genome shotgun sequencing of the Sargasso sea. Science 304: 6674.
  • Vergin KL, Urbach E, Stein JL, DeLong EF, Lanoil BD & Giovannoni SJ (1998) Screening of a fosmid library of marine environmental genomic DNA fragments reveals four clones related to members of the order Planctomycetales. Appl Environ Microb 64: 30753078.
  • Von Wintzingerode F, Göbel UB & Stackebrandt E (1997) Determination of microbial diversity in environmental samples: pitfalls of PCR-based rRNA analysis. FEMS Microbiol Rev 21: 213229.
  • Wawrik B, Paul JH & Tabita FR (2002) Real-time PCR quantification of rbcL (ribulose-1,5-bisphosphate carboxylase/oxygenase) mRNA in diatoms and pelagophytes. Appl Environ Microb 68: 37713779.
  • Wittwer CT, Herrmann MG, Moss AA & Rasmussen RP (1997) Continuous fluorescence monitoring of rapid cycle DNA amplification. BioTechniques 22: 130138.
  • Zhu F, Massana R, Not F, Marie D & Vaulot D (2005) Mapping of picoeucaryotes in marine ecosystems with quantitative PCR of the 18S rRNA gene. FEMS Microbiol Ecol 52: 7992.