Breakthrough fluorescence signaling analysis speeds detection of drug resistance in malarial parasites
Non-traditional mathematical analysis has proved more accurate than traditional methods for analyzing fluorescence signals for the study of drug resistance in parasites—an advance that promises to save lives in the fight against malaria.
Researchers at Case Western Reserve University developed techniques to quickly identify evolution of drug resistance in strains of malaria. Their goal is to enable the medical community to react quickly to inevitable resistance and thereby save lives while increasing the lifespan of drugs used against the disease. The new methods can also provide more information in just days.
The researchers have tailored genetic assays and mathematical analysis to uncover and track drug immunity of the deadliest form of the disease, caused by the parasite Plasmodium falciparum. But the technology could be used for other forms of malaria and other diseases.
Earlier detection of resistance enables healthcare workers to adjust treatments sooner, ideally before resistance becomes fully established in a population and eliminates a drug from use, explained Peter Zimmerman, a professor of international health at Case Western Reserve School of Medicine.
Zimmerman led the development of assays that take a few drops of blood from a patient and tags molecular markers associated with infection with fluorescent beads. One fluorescent tag locks onto the drug-sensitive form of P. falciparum; another tag marks the drug-resistant form.
The assays are sensitive enough to reveal change in a single nucleotide—one location among millions on the parasite genome—that has mutated and made the parasite drug-resistant.
But because there is such a tiny difference between the strains, and therefore fluorescent signals, traditional analysis failed to provide an accurate picture of who, among 264 volunteers from Papua, New Guinea, was infected and by what.
In a traditional analysis, infections are plotted by strength of fluorescent signal as Cartesian points on a graph. The drug-sensitive infections cluster along the x-axis, the drug-resistant along the y-axis. Those with both infections would be somewhere in between and those with no infection would be clustered where the axes meet—at zero.
But even when a standard deviation was introduced to account for crossover among the fluorescent signals, Zimmerman wasn't getting a clean delineation of who was infected and with what.
Former math major Drew P. Kouri, who has since graduated, was working in Zimmerman's lab and took the problem to Peter J. Thomas, assistant professor of mathematics and biology. They found they could produce an accurate picture by plotting the data points using polar coordinates.
In this method, the x-axis equals the fluorescence for drug-sensitive infection and the y-axis equals the fluorescence for drug-resistant. The 264 blood samples are plotted as points in between the axes, according to strength of signal.
The distance and the angle of each point from zero are calculated, and the information is graphed with the x-axis equal to distance and the y-axis angle. The results: 4 distinct groups that reflect the four possible diagnoses.
Using the polar coordinates analysis, 86 of the 264 samples were reclassified.
Zimmerman said further studies are needed to verify that resistance to a specific drug is associated with specific genetic variants.
Thomas said they could seek funding to produce a computer program that automatically processes data using polar coordinates: "If we set up a Web-based data analysis tool, it could be useful for field researchers without specialized mathematical expertise."
The investigators report their work in the online journal BioMed Central Genetics.