Many different types of bacteria and viruses can cause pneumonia, but there is no easy way to determine which microbe is causing a particular patient’s illness. This uncertainty makes it more difficult for doctors to choose effective treatments because the antibiotics that are commonly used to treat bacterial pneumonia will not help patients with viral pneumonia. Additionally, limiting the use of antibiotics is an important step in curbing antibiotic resistance.
MIT researchers have now designed a sensor that can distinguish between viral and bacterial pneumonia infections, which they hope will help doctors choose the right treatment.
“The challenge is that there are many different pathogens that can cause different types of pneumonia, and even with the most extensive and advanced testing, the specific pathogen causing someone’s illness cannot be identified in about half of patients. And if you treat a viral pneumonia with antibiotics, then you could be contributing to antibiotic resistance, which is a big problem, and the patient is not going to get better,” says Sangeeta Bhatia, professor of health sciences and technology at the John and Dorothy Wilson. and Electrical Engineering and Computer Science at MIT and a member of the Koch Institute for Integrative Cancer Research and the MIT Institute of Engineering and Medical Sciences.
In a mouse study, the researchers showed that their sensors could accurately distinguish bacterial and viral pneumonia within two hours, using a simple urine test to read the results.
Bhatia is the lead author of the study, which appears this week in the Proceedings of the National Academy of Sciences. Melodi Anahtar ’16, PhD ’22 is the lead author of the article.
infection signatures
One of the reasons it has been difficult to distinguish between viral and bacterial pneumonia is that there are so many microbes that can cause pneumonia, including the bacteria Streptococcus pneumoniae and Haemophilus influenzae, and viruses such as influenza and respiratory syncytial virus (RSV).
In designing their sensor, the research team decided to focus on measuring the host’s response to infection, rather than trying to detect the pathogen itself. Viral and bacterial infections cause different types of immune responses, including the activation of enzymes called proteases, which break down proteins. The MIT team discovered that the pattern of activity of those enzymes can serve as a signature of bacterial or viral infection.
The human genome encodes more than 500 proteases, many of which are used by cells that respond to infection, including T cells, neutrophils, and natural killer (NK) cells. A team led by Purvesh Khatri, an associate professor of medicine and biomedical data science at Stanford University and one of the paper’s authors, compiled 33 datasets of publicly available genes that are expressed during respiratory infections. Analyzing that data, Khatri was able to identify 39 proteases that appear to respond differently to different types of infection.
Bhatia and her students then used that data to create 20 different sensors that can interact with those proteases. The sensors consist of nanoparticles coated with peptides that can be cleaved by particular proteases. Each peptide is tagged with a reporter molecule that is released when the peptides are cleaved by proteases that increase in infection. Those reporters are eventually excreted in the urine. The urine can then be analyzed with mass spectrometry to determine which proteases are most active in the lungs.
The researchers tested their sensors on five different mouse models of pneumonia, caused by Streptococcus pneumoniae, Klebsiella pneumoniae, Haemophilus influenzae, influenza virus, and pneumonia virus infections in mice.
After reading the urine test results, the researchers used machine learning to analyze the data. Using this approach, they were able to train algorithms that could differentiate between pneumonia and healthy controls, and also distinguish whether an infection was viral or bacterial, based on those 20 sensors.
The researchers also found that their sensors could distinguish between the five pathogens they tested for, but with less accuracy than the test for distinguishing between viruses and bacteria. One possibility that researchers can pursue is to develop algorithms that can not only distinguish bacterial from viral infections, but also identify the class of microbes that cause a bacterial infection, which could help doctors choose the best antibiotic to combat that infection. type of bacteria.
The urine-based reading can also be detected in the future with a strip of paper, similar to a pregnancy test, which would allow for a point-of-care diagnosis. To this end, the researchers identified a subset of five sensors that could put home testing more within reach. However, more work is needed to determine whether the reduced panel would work as well in humans, who have more genetic and clinical variability than mice.
response patterns
In their study, the researchers also identified some patterns of host response to different types of infection. In mice with bacterial infections, proteases secreted by neutrophils were seen more prominently, which was expected because neutrophils tend to respond more to bacterial than viral infections.
Viral infections, on the other hand, elicited protease activity from T cells and NK cells, which are generally more responsive to viral infections. One of the sensors that generated the strongest signal was linked to a protease called granzyme B, which triggers programmed cell death. The researchers found that this sensor was highly activated in the lungs of mice with viral infections, and that both NK and T cells were involved in the response.
To deliver the sensors to mice, the researchers injected them directly into the trachea, but they are now developing versions for human use that could be delivered with a nebulizer or an inhaler similar to an asthma inhaler. They are also working on a way to detect the results using a breathalyzer instead of a urine test, which could give results even faster.
The research was supported, in part, by the Bill and Melinda Gates Foundation, Janssen Research and Development, the National Cancer Institute Koch Institute Support (Core) Grant, and the National Institute of Environmental Health Sciences.
Source: news.mit.edu