Detection and Quantification of Plasmodium falciparumin Blood Samples Using Quantitative Nucleic Acid Sequence-Based Amplification

Sample collection and storage.Blood samples were collected from malaria patients visiting two health centers, Oyugis Bay and Kendu Bay in Kenya, located in an area where malaria is holoendemic. Fifty microliters of EDTA-anticoagulated blood was mixed with 950 μl of guanidinium isothiocyanate (GuSCN) L6 lysis buffer, and the mixture was stored in liquid nitrogen until it was processed for RNA isolation. At the same time, thick blood films were prepared from these blood samples for counting of the parasites microscopically.

Lysis buffer was made by dissolving 120 g of GuSCN in 100 ml of 0.1 M Tris-HCl (pH 6.4), after which 22 ml of 0.2 M EDTA (pH 8.0) and 2.6 g of Triton X-100 were added (2).

Microscopy.The microscopic examination of thick blood smears was used as the “gold standard” for comparison with the quantification of parasites by QT-NASBA. Thick blood films were stained with Giemsa and were examined microscopically (8). Parasites and white blood cells were counted to a total of 200 white blood cells. The number of parasites per microliter of blood was calculated by assuming that there are 8,000 white blood cells per μl of blood.

Cloning and production of in vitro RNA.Primers Plas-1F (5′-TCAGATACCGTCGTAATCTTA-3′) and Plas-2R (5′-AACTTTCTCGCTTGCGCGAA-3′) were used to amplify by PCR a 170-bp region of the P. falciparum asexual-phase 18S rRNA gene. The amplified fragment was cloned into plasmid pCR2.1-TOPO (Invitrogen, Carlsbad, Calif.), and large quantities of in vitro RNA were produced with the transcription kit SP6/T7 (Boehringer, Mannheim, Germany).

Site-directed mutagenesis.In order to quantify the number of parasites in the blood samples by QT-NASBA, an internal standard RNA was coamplified as a competitor by using the same amplification primers used for the wild-type (WT) RNA.

For this construct, 20 bases in the center of the WT target sequence were randomly rearranged by using a custom-made computer program designed by Organon Teknika, Boxtel, The Netherlands. The resulting sequence was checked by computer analysis for the presence of secondary structures and possible homology with the original 170-bp sequence. Next, the modified sequence was constructed by site-directed mutagenesis (7) and was cloned into plasmid pCR2.1-TOPO. In vitro RNA was transcribed as described above. This modified in vitro RNA is referred to as “quantification RNA” (Q-RNA).

For the quantification of parasites in blood, 10⁷ molecules of in vitro Q-RNA were added to each sample.

Q-RNA.We determined the detection limits of in vitro WT and Q-RNAs with their homologous, ruthenium-labeled detection probes. After amplification by NASBA followed by electrochemiluminescence (ECL) detection, amounts as low as 200 copies of in vitro RNA could be detected for both RNAs. No cross hybridization between the two probes and target RNAs could be observed. This made the in vitro Q-RNA ideally suited for use as competitor RNA since Q-RNA and WT RNA were amplified with equal efficiencies.

Nucleic acid isolation.After thawing of the blood sample-L6 lysis buffer mixture, 10⁷ molecules of in vitro Q-RNA were added as competitor RNA. Next, the nucleic acids were isolated from the blood samples by the GuSCN-silica procedure, as described by Boom et al. (2). Briefly, the blood-lysis buffer mixture was mixed with activated silica. The nucleic acids bound to the silica were washed twice with wash buffer (10 M GuSCN, 100 mM Tris-HCl [pH 6.4]), twice with 70% ethanol, and once with acetone. The nucleic acids were eluted from the silica with 100 μl of water.

For each experiment a calibration series of blood samples spiked with 10⁸, 10⁶, 10⁴, 10³, and 0 P. falciparum parasites per ml of blood were used as control samples and for the standardization of parasite quantification.

QT-NASBA.Primers and probes were selected on the basis of the published sequences of the 18S rRNA genes of P. falciparum and were described earlier by Smits et al. (13). For the NASBA, primers Plas-1F (5′-TCAGATACCGTCGTAATCTTA-3′) and Plas-2R T7 (5′-AATTCTAATACGACTCACTATAGGGAGAGAACTTTCTCGCTTGCGCGAA-3′) were used. On the basis of the nucleotide sequence data, RNAs from P. falciparum, P. malariae, P. vivax, and P. ovale should all be amplified by the NASBA isolate with these primers.

Two microliters of the nucleic acid isolated was used in the QT-NASBA. The NASBA reaction was performed as described by Smits et al. (13), but instead of 41 mM KCl, a final concentration of 70 mM KCl was used in the NASBA mixture. The NASBA products were stored at −20°C until further analysis.

Detection.The NASBA amplification products were diluted 25 times in water and were subsequently hybridized to a capture probe (5′-ACCATAAACTATGCCGACTAGG-3′) which was bound to streptavidin-coated magnetic beads. The samples were then separately hybridized to ruthenium-labeled WT (5′-CCTTATGAGAAATCAAAGTC 3′) and Q (5′-AATAACTGCACCAGTGTATA-3′) detection probes, followed by ECL detection in a NucliSens ECL reader (Organon Teknika). The light emitted is detected by a photoelectric cell, thus offering a precise measurement for quantification (15).

Calculations and statistical analysis.In order to quantify the number of parasites in the blood samples by QT-NASBA, the WT RNA was coamplified with the internal standard RNA (Q-RNA) as a competitor by using the same amplification primers in a single-tube NASBA reaction.

After hybridization of the NASBA product to the WT and Q detection probes, the ECL provided for each sample signals for WT RNA and Q-RNA. Due to the competition for the amplification primers, a low parasite concentration in the sample generates a low signal for WT RNA and a high signal for Q-RNA, whereas a high parasite concentration shows a high signal for WT RNA and a low signal for Q-RNA. The signals for WT RNA and Q-RNA are directly correlated to the number of parasites present in the samples.

In each experiment, a standard curve was made by using blood samples containing known numbers of parasites: 10⁸, 10⁶, 10⁴, 10³, and 0 parasites per ml of blood mixed with 10⁷ molecules of in vitro Q-RNA. Final ECL results were calculated as log [(WT RNA signal/Q-RNA signal) × 1,000] and were plotted on a standard curve in which the x axis represents the log parasite concentration and they axis represents the final ECL result. Best-fit regression analysis was performed with the Excel software package. For an example, see Fig. 1.

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Fig. 1.

Correlation of the final ECL results and the number of parasites in the sample. The final ECL results were calculated as indicated in the Materials and Methods section. The best-fit regression line (dotted line) was generated as indicated in the Materials and Methods section.

The parasite concentration in samples with unknown numbers of parasites was then calculated by using the mathematical equation y = ax + b, in which y is the calculated log number of parasites, a is the slope as calculated from the best-fit regression line through the standard curve, x is the final ECL result, and b is the intercept with the yaxis of the best-fit regression line through the standard curve.

Detection limit.One-milliliter blood samples were spiked with various numbers of P. falciparum parasites ranging from 0 to 440,000 parasites and were mixed with 10⁷ molecules Q-RNA. These samples were tested blindly by the QT-NASBA.

Reproducibility.The reproducibility of the QT-NASBA was evaluated by using the guidelines (EP10-A) of the National Committee for Clinical Laboratory Standards (11).

Nine samples were analyzed on 5 successive days by using in each run three samples each with low, middle, and high levels of parasites in a specific order, as indicated in the guidelines. For these samples, “low” comprises samples with 200 P. falciparum parasites per ml of blood, “middle” comprises samples with 10⁵parasites per ml of blood and “high” comprises samples with 10⁷ parasites per ml blood. All samples were tested by the procedure described above.

The imprecision of the test method was determined by calculating the mean and standard deviation (SD) log number of parasites for all samples with low, medium, and high parasite loads. The imprecision was calculated by using the following formula: (SD/mean) · 100. The within-run variability was determined by calculating the mean ± SD log number of parasites for the samples with low, medium, and high parasite loads per day by the same formula described above. The between-run variability was determined by calculating the mean ± SD log number of parasites for the samples with low, medium, and high parasite loads over 5 days, again by the formula mentioned above.