Researchers use proteomics & machine learning to identify group of proteins to gauge severity of malaria
Malaria is one of the deadliest diseases to affect humankind, killing more than 4 lakh people worldwide in 2019. Transmitted by the female Anopheles mosquitoes, it is caused by various species of microscopic parasites called Plasmodium. Among them, malaria due to two species, Plasmodium vivax and, especially, Plasmodium falciparum, is most abundant. The worsening of falciparum malaria from mild to severe in patients can be attributed to several factors such as host immunity, parasite level in the blood, and parasite invasion of specific organs such as the brain for cerebral malaria. Many people have good immunity towards the parasite, even while being severely infected, and do not show any significant visible signs like fever, headache, and chills. An empirical method in use today to estimate the severity of the infection in India is based on mere observations and experiences of the patients.
In a recent study, researchers from the Indian Institute of Technology Bombay (IIT Bombay) along with their collaborating hospitals have created a panel of proteins that can help in differentiating malaria parasite species (Plasmodium vivax or Plasmodium falciparum) and gauge the severity of infection. The study, published in the journal Communications Biology - Nature, was funded by the Department of Biotechnology, Government of India.
The standard laboratory approach to detect malaria using a microscope involves examining blood samples of suspected patients to spot the parasites. However, it may not help in prognosis of the infection. There are other ways of diagnostics such as rapid diagnostics test (RDT) and nucleic acid amplification (NAA) of one of the RNA. On one hand, RDTs are quick but have low sensitivity and specificity towards diagnosis of malaria parasites. However, at some geological locations, deletion of the genes (due to mutation) used for diagnosis in the parasites leads to an incorrect diagnosis. On the other hand, NAA is highly specific but requires proper lab setting and continuous electricity supply which is an unlikely scenario in rural, malaria-endemic regions. Thus, there is a need for better tests to diagnose and differentiate malaria parasites. This will assist in not only predicting the disease and also in charting out a treatment plan.
“Our findings will help in providing a better quality of life to the malaria-vulnerable population. Along with this, it will offer an effective and efficient treatment plan for individuals in nations with minimal resources, owing to better prognosis of the patients,” says Ms Shalini Aggarwal from IIT Bombay. She is a research fellow and one of the researchers involved in the study.
Proteins are complex molecules that perform a wide variety of critical functions in our body. Chemically, these are made up of amino acids, which in turn are made up of peptides. When a parasite enters and stays in the human body, there is a fluctuation in the host proteins, which reflects the cellular and molecular effect caused by the parasite in the body.
The researchers collected blood samples from patients with severe and mild cases of falciparum malaria, vivax malaria, and dengue, as well as from a healthy group of people. They obtained all the proteins from the plasma — the light-yellow liquid part of blood that transports proteins to different parts of the body. Using a combination of techniques like liquid chromatography and mass spectrometry, they quantified and identified the proteins. Each type of protein present in the plasma samples is displayed in the form of peaks, with the area under the peak signifying its amount. By comparing the spectra of peaks to the protein sequences available on web databases, they identified the proteins and compared their amount in mild and severe cases of falciparum malaria, vivax malaria, and dengue.
The researchers then fed this data as examples for each disease and its severity to a machine learning model. This is a type of statistical algorithm that can learn to make meaningful connections between the input and the corresponding known result. The model uses these learnt connections to distinguish mild and severe cases of malaria, and also between malaria and dengue. The trained model can be used to classify new cases and also their severity, thus making the proteins a potential diagnostic panel of biomarkers for malaria.
The researchers further narrowed down this panel to include only the proteins ultimately present in dysregulated levels in the plasma samples. They found unusually high numbers of 25 proteins in severe falciparum malaria patients than in non-severe cases. These proteins controlled the activation of platelets against the parasite and clumping together of red blood cells (RBCs) which blocks the small blood vessels leading to severe organ damage. In the case of vivax malaria, they found 45 proteins abundantly present in severe cases, which were involved in immunity and growth and proliferation of the parasite inside human RBCs.
The researchers also consistently noticed six parasite proteins belonging to P. falciparum in malaria patients. These proteins represent enzymes that accelerate the disease-causing ability of the parasite. Moreover, they identified proteins associated with critical complications due to falciparum malaria, that is, brain damage and severe anaemia.
The identification of these proteins paves the way for the creation of diagnostic kits to detect severe cases of malaria and to differentiate between malaria and dengue. In clinical uses, one would be able to detect these ailments by comparing the protein levels in the patient sample with a set standard, just like in diabetes or cholesterol testing. A timely diagnosis would assist the timely treatment of the patients giving a positive outcome.
The current study illuminates a new approach to controlling malaria in India, where 85% of the population lives in malaria-affected zones. “In future, we aim to translate this information in the form of a dip-chip assay or a user-friendly kit, where protein panels, which are capable of distinguishing and prognosis, can be infused on a substrate for efficient diagnosis followed by treatment,” says Shalini, about their plans.
Article written by
Photo by Syed Ali
Gubbi Page link