Comparison of PfHRP-2/pLDH ELISA, qPCR and microscopy for the detection of plasmodium events and prediction of sick visits during a malaria vaccine study

Abstract

Background: Compared to expert malaria microscopy, malaria biomarkers such as Plasmodium falciparum histidine rich protein-2 (PfHRP-2), and PCR provide superior analytical sensitivity and specificity for quantifying malaria parasites infections. This study reports on parasite prevalence, sick visits parasite density and species composition by different diagnostic methods during a phase-I malaria vaccine trial.

Methods: Blood samples for microscopy, PfHRP-2 and Plasmodium lactate dehydrogenase (pLDH) ELISAs and real time quantitative PCR (qPCR) were collected during scheduled (n = 298) or sick visits (n = 38) from 30 adults participating in a 112-day vaccine trial. The four methods were used to assess parasite prevalence, as well as parasite density over a 42-day period for patients with clinical episodes.

Results: During scheduled visits, qPCR (39.9%, N = 119) and PfHRP-2 ELISA (36.9%, N = 110) detected higher parasite prevalence than pLDH ELISA (16.8%, N = 50) and all methods were more sensitive than microscopy (13.4%, N = 40). All microscopically detected infections contained P. falciparum, as mono-infections (95%) or with P. malariae (5%). By qPCR, 102/119 infections were speciated. P. falciparum predominated either as monoinfections (71.6%), with P. malariae (8.8%), P. ovale (4.9%) or both (3.9%). P. malariae (6.9%) and P. ovale (1.0%) also occurred as co-infections (2.9%). As expected, higher prevalences were detected during sick visits, with prevalences of 65.8% (qPCR), 60.5% (PfHRP-2 ELISA), 21.1% (pLDH ELISA) and 31.6% (microscopy). PfHRP-2 showed biomass build-up that climaxed (1813±3410 ng/mL SD) at clinical episodes.

Conclusion: PfHRP-2 ELISA and qPCR may be needed for accurately quantifying the malaria parasite burden. In addition, qPCR improves parasite speciation, whilst PfHRP-2 ELISA is a potential predictor for clinical disease caused by P. falciparum.

Trial registration: ClinicalTrials.gov NCT00666380.

Conflict of interest statement

Competing Interests: The authors wish to declare that GlaxoSmithKline Biologicals (GSK) provided their proprietary adjuvant “AS01B”. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.

Figures

Figure 1. Trends in malaria prevalence by diagnostic method among the study participants that did not develop clinical malaria during the 112-day study.

At every visit, malaria prevalences are highest when detected by PfHRP-2 ELISA and qRT-PCR methods and lowest when measured with microscopy and pLDH ELISA.

Figure 2. Comparison of routine microscopy, pLDH/PfHRP-2 ELISA and qPCR for a group of study participants who had acute blood smears prepared at sick visits.

Each column (1–38) represents one blood sample with the corresponding microscopy, pLDH/PfHRP-2 ELISA and qRT-PCR results, ordered by parasite density as determined by microscopy (top graph) and antigen levels (pLDH/PfHRP-2) or Ct values (qPCR). As the levels of parasitemia decreases, the concordance between the different methods also decreases. PfHRP-2 and qPCR detect parasites densities way beyond the detection limit of microscopy.

Figure 3. Utility of microscopy, qPCR, PfHRP-2 and pLDH ELISAs in predicting clinical episodes.

Parasite dynamics before clinical malaria attack (day 0) as measured by (A) PfHRP-2, (B) pLDH (C) Microscopy and (D) qPCR, for the 12 participants with microscopically confirmed clinical malaria. Parasite dynamics after clinical attack are also presented for PfHRP-2 (A). Error bars represent standard error of mean of the parasitemia values at each time point. The arrows indicate the day of treatment. Microscopy, pLDH and qPCR did not detect malaria parasites after the treatment.