Research Article: DNA aptamers for the recognition of HMGB1 from Plasmodium falciparum

Date Published: April 9, 2019

Publisher: Public Library of Science

Author(s): Diego F. Joseph, Jose A. Nakamoto, Oscar Andree Garcia Ruiz, Katherin Peñaranda, Ana Elena Sanchez-Castro, Pablo Soriano Castillo, Pohl Milón, Georges Snounou.


Rapid Diagnostic Tests (RDTs) for malaria are restricted to a few biomarkers and antibody-mediated detection. However, the expression of commonly used biomarkers varies geographically and the sensibility of immunodetection can be affected by batch-to-batch differences or limited thermal stability. In this study we aimed to overcome these limitations by identifying a potential biomarker and by developing molecular sensors based on aptamer technology. Using gene expression databases, ribosome profiling analysis, and structural modeling, we find that the High Mobility Group Box 1 protein (HMGB1) of Plasmodium falciparum is highly expressed, structurally stable, and present along all blood-stages of P. falciparum infection. To develop biosensors, we used in vitro evolution techniques to produce DNA aptamers for the recombinantly expressed HMG-box, the conserved domain of HMGB1. An evolutionary approach for evaluating the dynamics of aptamer populations suggested three predominant aptamer motifs. Representatives of the aptamer families were tested for binding parameters to the HMG-box domain using microscale thermophoresis and rapid kinetics. Dissociation constants of the aptamers varied over two orders of magnitude between nano- and micromolar ranges while the aptamer-HMG-box interaction occurred in a few seconds. The specificity of aptamer binding to the HMG-box of P. falciparum compared to its human homolog depended on pH conditions. Altogether, our study proposes HMGB1 as a candidate biomarker and a set of sensing aptamers that can be further developed into rapid diagnostic tests for P. falciparum detection.

Partial Text

Malaria is an infectious disease that affects animals and humans, caused by protozoans of the genus Plasmodium. Malaria remains the cause of 435,000 deaths worldwide, with 219 million cases reported during 2017 [1]. Accurate treatment requires the identification of the parasite of the genus Plasmodium, in addition to the species causing the disease. Currently, there are several methods to diagnose malaria, such as PCR-based, Giemsa microscopy, and Rapid Diagnostic Tests (RDTs). Among these options, the last two appear to be suitable for low-income and mostly affected countries; nonetheless, they also present certain limitations [2].

The identification of potential biomarkers is a complex process where many factors must be considered. In the case of Plasmodium species, an ideal biomarker should establish the presence or absence of infection, determine the species involved, be detectable at low concentrations, and be proportional to parasite density [46]. To account for these characteristics, the successful entrepreneurship requires several experimental stages, from basic research towards final clinical validations. Initial steps for biomarker selection can be pursued using bioinformatic strategies, with large databases of the -omics providing valid alternatives [47,48]. Genome-wide translation dynamics studies as revealed by RP, and in contrast to transcriptomic approaches, offer a precise approximation for the P. falciparum proteome [9]. Secondary analysis of RP datasets allowed selecting HGMB1 as a highly expressed protein during all the blood stages of P. falciparum (Fig 1, S1 Table). Structural stability, conservation among Plasmodium spp., and diversity to human homologs provided additional properties to cope for an ideal biomarker (Fig 1). Conserved regions of proteins from Plasmodium could be exploited to generate biosensors for the genus. Conversely, non-conserved regions allow generating species-specific biosensors [49]. Alternative approaches for the selection of biomarkers for malaria have been described [50], leading to different candidates for biomarkers, however, considering biomarker abundance shall increase the detection likelihood of the corresponding biosensors. As a comparison, during merozoite blood-stage, HGMB1 showed 88.45 folds more expression than the commonly used PfLDH biomarker, while HRP-2 expression is null.




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