Improving the Molecular Diagnosis of Malaria: Droplet Digital PCR-Based Method Using Saliva as a DNA Source - Costa, Gabriel Luíz; Alvarenga, Denise Anete Madureira; Aguiar, Anna Caroline Campos; Louzada, Jaime; Pereira, Dhélio Batista; de Oliveira, Tatiana Flávia; Fonseca Júnior, Antônio Augusto; Carvalho, Luzia Helena; Ferreira Alves de Brito, Cristiana; Nóbrega de Sousa, Taís (Frontiers in Microbiology, 2022)
Malaria is an acute febrile disease caused by a protozoan of the genus Plasmodium. Light microscopy (LM) is the gold standard for the diagnosis of malaria. Despite this method being rapid and inexpensive, it has a low limit of detection, which hampers the identification of low parasitemia infections. By using multicopy targets and highly sensitive molecular techniques, it is possible to change this scenario. In this study, we evaluated the performance of droplet digital PCR (ddPCR) to detect Plasmodium DNA obtained from saliva samples (whole saliva and buccal swab) of 157 individuals exposed to malaria transmission from the Brazilian Amazon region. We used the highly sensitive ddPCR method with non-ribosomal multicopy targets for Plasmodium vivax (Pvr47) and Plasmodium falciparum (Pfr364). There was good concordance between the quantitative real-time PCR (qPCR) results from the saliva and blood, except for mixed-species infections. The sensitivity of qPCR was 93% for blood, 77% for saliva, and 47% for swabs. Parasite DNA was not detected in saliva samples in low-density infections compared with the detection in blood samples. ddPCR showed increased sensitivity for detecting Plasmodium in the blood and swabs (99% in blood, 73% in saliva, and 59% in swabs). Notably, ddPCR detected more mixed infections in the blood (15%), saliva (9%), and swabs (18%) than qPCR. Our data showed that the differences between ddPCR and qPCR were the result of a higher number of P. falciparum infections detected by ddPCR. Overall, there was a moderate correlation between parasite densities estimated by the different methods in the blood. Our findings highlight the possibility of using non-invasive sample collection methods for malaria diagnosis by targeting multicopy sequences combined with highly sensitive molecular methods.