Published On: Mon, Jul 22nd, 2019

Algorithm developed in Israel can predict early diagnosis for infectious diseases

Researchers at the Weizmann Institute claim they have developed an algorithm to predict the onset of infectious diseases, including tuberculosis.

Researchers at Weizmann Institute of Science have developed an algorithm to predict the onset of infectious diseases as tuberculosis.

The findings recently published in Nature Communications claim that the cells of our immune system, form when they first meet a new bacterium, take some 24-48 hours after infection to determine the future of this meeting. When immune cell and bacterium meet, there can be several outcomes.

“Whether the immune system kills the bacteria,” explains Dr. Roi Avraham, from the Institute’s Biological Regulation Department, “the bacteria overcome the immune defenses or, in the case of diseases like tuberculosis, the bacterium can lie dormant for years, sometimes causing disease at a later stage and sometimes remaining in hibernation for good.”

that the junction in which one of those paths is chosen takes place early on – some 24-48 hours after infection.”

Unlike in standard laboratory tests, the researchers used a method developed at the Weizmann Institute to sequence gene activity in real meetings between thousands of immune cells and Salmonella bacterium.

They could see what each cell looked like as it responded to the Salmonella bacteria and they could map out the activation profiles of each. This indeed revealed patterns not seen in lab tests before, and it confirms their hypothesis – there were differences that enabled them to trace responses from the first meetings to the later outcomes.

Building on their single-cell sequencing for Salmonella infection, which is still limited to specialized labs, the researchers developed an algorithm based on a method known as deconvolution – that would then enable them to extract similar information from standard data sets.

This algorithm uses the information available from the standard blood tests.

“The algorithm we developed can not only define the ensemble of immune cells that take part in the response, but it can also reveal their activity levels and thus the potential strength of the immune response.”,” says Dr. Noa Bossel Ben Moshe, who co-led the research together with Dr. Shelly Hen-Avivi in Avraham’s group.

The first test of the algorithm was in blood samples taken from healthy people in the Netherlands. These samples were infected, with Salmonella bacteria, in a lab dish, and the immune response recorded.

Comparisons with existing genomic analysis methods showed that the standard methods did not uncover differences between groups, while the algorithm that had developed in Weizmann Institute revealed important ones that were tied to later variations in bacteria-killing abilities.

The group then asked whether the same algorithm could be used to diagnose the onset of tuberculosis, which is caused by a bacterium that often chooses the third way – dormancy — and thus can hide out in the body for years.

Up to a third of the world’s population carries the tuberculosis bacterium, though only a small percentage of these actually become ill. Still, some two million die of the disease each year, mostly in underdeveloped areas of China, Russia, and Africa.

The researchers turned to another database – a British one that followed patients and carriers for a period of two years — so the group could apply the algorithm to blood test results from both groups, as well as from the subset who went from carrier to disease onset during that period.

The researchers found that the activity levels of immune cells called monocytes could be used to predict the onset or course of the disease. “The algorithm is based on the ‘first impressions’ of immune cells and Salmonella, which cause a very different type of illness than mycobacterium tuberculosis,” says Hen-Avivi. “Still, we were able to predict, early on, which of the carriers would develop the active form of the disease.”

Once tuberculosis symptoms appear, patients have to take three different antibiotics over the course of nine months, and antibiotic resistance has become rampant in these bacteria.

“If those who are at risk of active disease could be identified when the bacterial load is smaller, their chances of recovery will be better,” says Avraham. “And the state medical systems in countries where tuberculosis is endemic might have a better way to keep the suffering and incidence of sickness down while reducing the cost of treatment.”

The researchers intend to expand their own database on other pathogens and work on developing the tools that may, in the future, be used to predict who will develop full-blown disease.

The refined algorithm may lead to methods of predicting the of a number of infectious diseases.

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