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ABSTRACT |
 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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While studying the process of antigenic variation in P. falciparum is of key interest, it is fraught with technical issues, not the least of which is the changing parasite transcriptome and small quantities of PfEMP1 expressed on the infected red blood cells (RBCs).7 For the last decade, the composition of mRNA has been primarily assessed by cloning the products of an reverse transcription-polymerase chain reaction (RT-PCR) reaction into a bacterial plasmid vector and transforming the ligated plasmid into bacteria to provide a small library representing the repertoire of transcripts present.8–11 This technique measures the transcript composition of the PCR reaction end products, as opposed to the composition of the starting material. The advent of real-time PCR has overcome this problem by allowing the transcript composition of samples to be assessed during amplification. Although this is a technically superior method for quantitating specific mRNA, there are several practical problems that may limit its widespread use in assessing var gene (the gene encoding PfEMP1) transcription.
First, knowledge of the var gene repertoire of the specific parasite genome is required to develop specific primers. The studies that have used real-time PCR to analyze various aspects of var gene transcription generally used the 3D7 parasite line (or its parent NF54), for which the complete genome is known.12–14 Other laboratory lines such as ItG have also been used after the sequences of the var genes were determined.15 Analysis of field samples using this method is not possible without prior knowledge of the var gene repertoire, and with each parasite isolate likely to have a different set of var genes,16 this approach will require a major sequencing effort before the analysis can start. Second, the amount of material required to conduct a real-time RT-PCR experiment using a full set of primers representing the whole var gene repertoire exceeds that required for conventional RT-PCR followed by cloning and sequencing. Previous studies have overcome this problem by culturing ex vivo samples in the laboratory for ~30 days to increase the amount of parasite material.14 However, this approach does not take into account the switching parasite population; it is well documented that ~1–2% of parasites change their gene expression in each replication cycle.7,17
Because antigenic variation is thought to play a role in immunity, pathology, and drug resistance, it is going to become increasing important to be able to study the process using field samples. Currently this is not feasible using real-time RT-PCR, although this method is theoretically technically superior to other PCR techniques. Hence it has become necessary to assess and compare the results obtained using different techniques for quantitating mRNA to add perspective to the results of future laboratory and field studies.
The primary aim of this study was to compare the results of RT-PCR combined with cloning and sequencing and real-time RT-PCR conducted on the same sample materials. The results indicate that the number of amplification cycles required for the number of fluorescence labeled amplicons to exceed background levels (CT value) in real-time RT-PCR experiments can be used as an indicator of the likelihood of observing specific amplicons in a sample and that RT-PCR tended to overestimate the quantity of minor types compared with real-time RT-PCR.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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RNA and DNA extraction. Total RNA was extracted from each of the selected clones using the NucleoSpin RNAII Kit (Macherey-Nagel, Duren, Germany) following the manufacturer’s instructions. A second elution step was included to maximize yield. All samples were treated with DNase and tested as previously described to ensure no DNA was present.9 DNA from cultured 3D7 was extracted using standard methods and used as a control template to assess primer bias in the real-time PCR assay.
RT-PCR, vector cloning, and sequencing of AFBR fragments.
The universal primers, 
AF [GCACG(A/C)AG-TTTGC] and BR [GCCCATTC(G/C)TCGAACCA]8 were used to amplify a segment of the highly conserved DBL1 region of the var gene using the SuperScript One Step RT-PCR System with Platinum Taq DNA Polymerase (Invitrogen, Carlsbad, CA) under the following conditions: 42°C for 30 minutes, 94°C for 2 minutes, followed by 40 cycles of 93°C for 30 seconds, 42°C for 50 seconds, and 72°C for 1 minute. The products amplified using the above-described primers were termed AFBR fragments and were inserted into PCR-Script (Stratagene, La Jolla, CA) according to the manufacturer’s instructions. Typically between 40 and 50 insert-containing bacterial colonies were selected, grown, amplified, purified, and sequenced from each parasite clone. AFBR fragment sequences were identified and named by alignment with previously described fragments of the DBL region of P. falciparum 3D7 var genes,8,9 and the frequency of each transcript within the clone sample was noted.
Real-time RT-PCR and PCR.
Purified RNA was reverse transcribed using SuperScript III Reverse Transcriptase (Invitrogen) and primed with random hexamer primers according to the manufacturer’s instructions. Serial dilutions covering a 100-fold range of 3D7 genomic DNA were prepared and used as references. In each experiment, all primers tested on the clone’s cDNA were also tested on the 3D7 DNA dilution series. For real-time analysis, previously published12 and newly designed var gene specific primers were used (Tables 1
and 2). The housekeeping gene seryl-tRNA synthetase (PF07–0073), shown to be transcribed stably throughout different erythrocytic stages,19 was used as an endogenous control (normalizer). A previously reported primer set (forward: AAGTAGCAGGTCATCGTGGTT; reverse: TTCGGCACATTCTTCCATAA) was used to amplify the seryl-tRNA synthetase (s-tRNA syn) gene.12 Real-time RT-PCR and PCR was performed using a MX4000 (Stratagene), and all samples and controls were run in triplicate. The amount of cDNA added to each real time reaction for clones harvested on Day 12 was calculated to be equivalent to that from ~40 parasites. The Absolute QPCR SYBR Green Mix (Abgene, Surrey, UK) was used according to manufacturer’s instructions under the following cycling conditions: 95°C, 15 minutes followed by 40 cycles of 94°C, 30 seconds; 53°C, 40 seconds; and 68°C, 50 seconds. For real-time RT-PCR, var gene-specific primers tested on each of the parasite clones were selected based on the AFBR transcripts observed in the RT-PCR results. A dissociation curve thermal profile (from 53°C with 1°C increments for 41 cycles) was included after the amplification cycles. All samples showing primer dimer formation or spurious, non-specific peaks, as indicated by the dissociation curve, were excluded from the analysis.
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Because the relative proportion of each variant in a clone is not independent of other variant proportions in the same clone, standard statistical methods to estimate confidence intervals (CIs) of the relative proportion could not be used. Instead, Markov chain Monte Carlo (MCMC) simulation was performed using the WinBUGS package (Version 1.4; Imperial College and MRC, London, UK). The ratio of each variant to the dominant variant in a sample was simulated assuming a multinomial distribution for the number of sequences of each variant, and non-informative prior distributions for the probability of selecting a bacterial colony corresponding to the variant. The model was fit to the RT-PCR sequence frequency data and the 2.5 and 97.5 percentiles for the ratio of variants was determined from 5,000 simulations after a run-in in period of 1,000 simulations. These 2.5 and 97.5 percentiles are referred to as the 95% CIs for the relative proportion of a variant.
Data analysis of real-time RT-PCR and PCR output.
The average CT value of each triplicate from the RNA experiments was calculated, and the number of positive wells per triplicate was noted. These data were sorted by CT value and grouped into one-cycle intervals. Results from the DNA control experiments were treated in an identical manner. The SD of CT values and the detection frequency was calculated for each CT group. Detection frequency was given by (
positive wells)/(3 x number of samples in the CT group).
To account for potential differences in the performance of individual primer pairs, the specific var primers and the primer set for the endogenous control gene, s-tRNA syn, were tested using serial dilutions of 3D7 DNA as a template. All var primer pairs except for two were specific for one var gene within the 3D7 genome. Hence, the "primer bias" for a specific primer set could be assessed by comparing the CT value for a specific var variant to that of s-tRNA syn:
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where pi is the primer bias for primer set i, cti is the CT value for primer i on a DNA template, and cts-tRNA syn is the CT value for the s-tRNA syn primer on the same DNA template. To overcome any inter-experimental variation, amplification of each specific primer pair on 3D7 DNA was repeated at least three times and always run in triplicate. The average "primer bias" was calculated from these replicates. Two primer sets were able to bind two var genes each (PFL1970w/PFL1955w and PFD0995c/PFD1000c), so their primer bias was adjusted to –(ln[2–pi/2])/ln(2).
The amplification efficiencies of the s-tRNA syn and specific var primer sets were compared by calculating pi for several DNA concentrations and plotting log(pi) against DNA concentration. A simple linear regression model was fitted. The amplification efficiencies for the specific primer set and the s-tRNA syn primer set were considered to be similar if the slope of the regression line was not significantly different from zero at P = 0.05.20
The amount of each variant in the sample was expressed relative to the amount of the endogenous reference gene, s-tRNA syn (equations 1 and 2). The total quantity of var transcript detected in the sample was estimated by summing fi,c across all transcripts tested for in a sample.
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where cT,i is the mean CT value for the triplicates of var variant i on an RNA template, cT,s-tRNAsyn is the mean CT value for the triplicates of the s-tRNA syn on an RNA template, pi is the primer bias for variant i, and fi,c is the amount of transcript i relative to the control gene.
The amount of each variant in the sample relative to the dominant variant was also calculated (equation 3), along with corresponding CIs (equations 4 and 5). CIs were calculated using standard statistical methods for the difference between means.
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where i is the variant of interest, j is the dominant variant, and fi,d is the amount of transcript i relative to the dominant transcript.
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where sei,d is the SE for the relative amount of variant i compared with the dominant variant j, sdc,i and sdc,j are the SDs of the triplicates for variant i and dominant variant j, respectively, sdp,i and sdp,j are the SDs for the primer bias of variant i and dominant variant j, nc,i and nc,j are the number of positive wells for variant i and j (nc,i, nc,j = 2 or 3), and np,i and np,j are the number of samples used to estimate the primer bias of transcripts i and j. Where only one well in a triplicate returned a positive result, the value [ki=1(sdc,i2/nc,i)/k] used as an estimate of (sdc,i2/nc,i) in equation 4.
Using RT-PCR results to predict real-time RT-PCR outcomes. The samples used in this study, although derived from clonal parasite cultures, continue to undergo antigenic variation. As a consequence, a sample is likely to contain sub-populations, the largest of which is the dominant var transcript, with numerous minor populations transcribing different variants. To estimate if the real-time PCR was likely to detect these minor populations, the expected CT value for each variant within a parasite sample was calculated based on the CT value of the dominant transcript, the primer bias of the specific primers, and the expected difference in transcript frequencies (from conventional RT-PCR results; equation 6).
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where yi is the expected CT value for transcript i in the real-time RT-PCR assay and ri is the relative frequency of transcript i compared with the dominant transcript in the RT-PCR results.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Primer bias and amplification efficiencies for real-time PCR. This study involved the use of 39 different primer sets: 1 for s-tRNA syn and 38 specific ones for individual members of the var multi-gene family. For the 38 specific primers, the mean primer bias compared with the s-tRNA syn was 0.08 cycles with interquartile range of –1.27 to –0.17 cycles. Twenty-three primers (61%) had an absolute primer bias of less than one cycle, whereas six (16%) had a bias of greater than two cycles. All primers with a bias of greater than two cycles had a positive primer bias (i.e., performed less well than s-tRNA syn).
The amplification efficiencies of 17 randomly selected specific var primer sets were compared with that of the s-tRNA syn primer. The remaining specific primers were not tested because limited results were available across the different DNA concentrations. Sixteen primers showed approximately equal amplification efficiencies with s-tRNA syn (P > 0.05; data not shown). One set of primers did not show equal efficiencies over the entire DNA range having reduced efficiency at the lowest DNA concentration, a concentration typically lower than that of the RNA samples.
Detection frequency and reproducibility of real-time RT-PCR and PCR.
During this study, 414 and 123 CT values were obtained from DNA and RNA samples, respectively. The observed CT values ranged from 26.3 to 40.0 cycles for DNA samples and 28.5 to 40.0 cycles for RNA samples (Figure 1). The patterns in detection frequency and reproducibility were similar for the DNA and RNA samples. The detection frequency (number of wells yielding a product/number of test wells) was consistently high for CT values < 32.5, but decreased rapidly for CT values > 37.5 cycles. Based on the average detection frequency, samples with CT values > 37.5 would be expected to return at least one negative result per triplicate.
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) was used to assess the reproducibility of the real-time RT-PCR. Only samples having at least two positive wells per triplicate were included in this analysis. A gradual increase in the SD of triplicates was observed with increasing CT value; however, the range of values dramatically increased for CT values > 33.5, indicating decreased reproducibility.
Predicting real-time RT-PCR outcomes.
By calculating the expected CT value for minor transcripts in the real-time RT-PCR assay, 17 variants in 12 samples (including multiple testing of some samples) were identified as having expected CT values within the region of decreased real-time RT-PCR detection sensitivity (Table 3). The majority (83%) of these samples were from parasites harvested at Day 12 of culturing. Of these 17 variants, 14 were not seen in the corresponding real-time experiment (Table 3).
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The total quantity of var transcripts detected in each sample, relative to s-tRNA syn transcription, ranged from 0.2 to 14.8, with a mean of 4.1 ± 3.4 (SD) (Figure 2). Two clones (10_12 and 12_12) appeared to have a small relative quantity of var transcript compared with the other clones used in this study (Figure 2) and other studies conducted in the laboratory (data not shown). In 11 of the 19 (58%) samples tested, the number of transcripts detected using the real-time assay was less than that obtained from the RT-PCR (Figure 2); a total of 18 transcripts were not detected in these 11 samples.
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.These samples are excluded from the following analysis. For those transcripts detected using both techniques, the relative amount of each variant within a sample compared with the dominant variant (as identified in the conventional RT-PCR experiment) was calculated for each sample, and the values obtained using the real-time RT-PCR and conventional RT-PCR were compared (supplemental data). Forty-six percent (17/37) of comparisons indicated that the real-time RT-PCR and conventional RT-PCR produced similar estimates. Of the comparisons where differences were identified, all except one (95%) had lower estimates of relative quantity from the real-time RT-PCR compared with the RT-PCR results.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The use of real-time RT-PCR to assess the relative quantities of different var transcripts within samples necessitated developing a way to compare transcripts amplified using different specific primers. To address any potential differences in primer performance, repeated real-time amplification of a 3D7 DNA dilution series was performed. The majority of var genes in the 3D7 genome are present as single copies, as is the housekeeping gene seryl-tRNA synthetase,22 meaning each gene will be present in equal quantities in the prepared DNA standards. Differences in the CT values obtained on amplification of the DNA were assumed to be related to primer performance (efficiency in annealing to templates and extension, etc.) and were termed "primer bias." The majority of primers showed little primer bias; however, several primers showed dramatically decreased CT values compared with the internal control. Ideally these primers should be redesigned.
The small quantity of RNA able to be extracted from the parasites and used in this study meant that only a sub-set of var transcripts could be tested for in the real-time assay. This presents a limitation of the study. Selection of the var primers used in the real-time assay was based on the assumption that the major transcripts would be correctly identified by the RT-PCR assay. This was not always the case, with a change in dominant transcript noted in four samples (21%). In each of these four samples, AFBR28 (MAL6P1.1) was the dominant transcript in the RT-PCR system, but either AFBR40 or AFBRNTA was dominant in the real-time assay. One possible explanation for the discrepancy is that the universal primers used in the RT-PCR assay have a reduced affinity for AFBR40 and AFBRNTA when AFBR28 is present above a certain level in the population. In a previous study of primer bias,8 the AFBRNTA transcript was not observed in 140 individual AF/BR inserts obtained from 3D7 P. falciparum genomic DNA, suggesting a possible bias against PCR amplification of this transcript.
The total quantity of var transcript measured in the real-time assay, relative to the endogenous control, was used as an indicator of whether the primers used in the assay represented the majority of transcripts in the sample. There was considerable variability in the estimated total var production between clones. This may be caused by differences in the overall production of var transcripts by the parasite or by our limited testing for different transcripts. Two clones (10_12 and 12_12) had noticeably lower estimates of total var than the other clones. Interestingly, these two clones were both identified as having AFBR28 dominant in the RT-PCR results. Of our 19 samples, 6 were identified as having AFBR28 dominant in the RT-PCR system. The results of the real-time analysis revealed that four of the six AFBR28 dominant samples had a change in dominant type, whereas the remaining two had the lowest estimate of total var transcription, indicating that one or more major transcripts may not have been detected in these samples. Notably, the two transcripts identified as dominant by the real-time assay (AFBR40 and AFBRNTA) in another clone were not detected by RT-PCR in clones 10_12 and 12_12 and were consequently not tested for in the real-time analysis.
Before this study, we hypothesized that quantifying transcript frequency using conventional RT-PCR amplification followed by cloning and sequencing may over-estimate the frequency of minor transcripts. This was primarily based on the assumption that dominant transcripts become abundant and would plateau earlier in the PCR process, whereas the minor transcripts, although not abundant early in the amplification process, could, after 40 cycles, also peak and plateau. Our comparison of transcript quantification by RT-PCR and real-time RT-PCR indicated that, while there was little difference in the estimates obtained in 46% of cases, where differences were apparent, there was a propensity for estimates obtained from the real-time system to be lower than those of the corresponding RT-PCR, supporting the hypothesis. The real-time RT-PCR assay was better able to rank the prevalence of transcripts compared with the RT-PCR assay caused by the CT values being continuous variables compared with the discrete count data on which the RT-PCR results are based. The failure of the real-time PCR assay to detect transcripts in some samples was most probably caused by the CT value being below the limits of detection for the real-time PCR assay caused by the relatively low abundance of the transcript.
The results of this study have shown that when there is sufficient abundance of RNA transcripts for the real-time RT-PCR assay to be operating within the region of good reproducibility, RT-PCR and real-time RT-PCR tended to produce similar results in relation to determining major transcripts. Discrepancies in identifying the major transcript are most probably caused by primer issues that can now, with hindsight, be resolved. Estimates of the prevalence of minor transcripts were either similar or over-predicted by the conventional RT-PCR system. This is an important consideration when research into antigenic variation spans both laboratory and field studies, particularly when real-time RT-PCR is not currently a practical option for the analysis of field samples.
Although this study focuses on the var gene family in P. falciparum, other organisms also use antigenic variation. In some organisms such as trypanosomes, the process is well characterized and understood; however, in others, such as Anaplasma phagoctophilum,23 Giardia lamblia,24 G. muris,25 and Campylobacter fetus,26 the process is still under study. As time passes, it is likely that more organisms will be identified as pathogens using antigenic variation as a survival strategy. This being the case, the results of this study will provide valuable baseline information on methodological issues associated with low abundance samples.
Received January 8, 2006. Accepted for publication April 1, 2006.
Acknowledgments: We thank the Australian Red Cross Blood Service for supply of human blood and serum for the in vitro cultivation of the parasites used in this study, and Dr. Dennis Kyle for support of the real-time PCR component of this project. The opinions expressed herein are those of the authors and do not necessarily reflect those of the Defence Health Service or any extant policy of Department of Defence, Australia.
Note: Supplemental data for this manuscript can be found online at www.ajtmh.org.
Financial support: This work was supported by NIH grants 2RO1 AI-47500-04, -05. Michelle Gatton was supported in part by a University of Queensland Postdoctoral Research Fellowship.
* Address correspondence to Michelle Gatton, Malaria Drug Resistance and Chemotherapy Laboratory, Queensland Institute of Medical Research, PO Royal Brisbane Hospital, Queensland 4029, Australia. E-mail: michelle.gatton{at}qimr.edu.au 
Authors’ addresses: Michelle L. Gatton, Jennifer M. Peters, Karryn Gresty, and Elizabeth V. Fowler, Malaria Drug Resistance and Chemotherapy Laboratory, Queensland Institute of Medical Research, PO Royal Brisbane Hospital, Herston, Queensland, Australia 4029, Emails: michelle.gatton{at}qimr.edu.au, jennifer.peters{at}defence.gov.au, and beth.fowler{at}qimr.edu.au. Nanhua Chen and Qin Cheng, Australian Army Malaria Institute, Weary Dunlop Drive, Gallipoli Barracks, Enoggera, Queensland, Australia, 4051, Emails: nanhua.chen{at}defence.gov.au and qin.cheng{at}defence.gov.au.
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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cT method. Methods 25: 402–408.[ISI][Medline]
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| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
) samples. Error bars represent the range of SDs obtained from DNA (heavy line) and RNA (light line) samples.