(News from Nanowerk) Many different types of bacteria and viruses can cause pneumonia, but there is no easy way to determine which microbe is causing illness in a particular patient. This uncertainty makes it harder for doctors to choose effective treatments because antibiotics commonly used to treat bacterial pneumonia will not help patients with viral pneumonia. Furthermore, limiting the use of antibiotics is an important step towards reducing antibiotic resistance.
MIT researchers have now designed a sensor that can distinguish between viral and bacterial pneumonia, 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 thorough and advanced testing, the specific pathogen causing a person’s illness cannot be identified in approximately half of patients. And if you treat viral pneumonia with antibiotics, then you could be contributing to antibiotic resistance, which is a big problem, and the patient won’t get better,” says Sangeeta Bhatia, John and Dorothy Wilson Professor of Science and Technology of health. and Electrical Engineering and Computer Science at MIT and Fellow of MIT’s Koch Institute for Integrative Cancer Research and Institute of Engineering and Medical Sciences.
In a study on mice, the researchers showed that their sensors could accurately distinguish between 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 in the Proceedings of the National Academy of Sciences (“Host protease activity categorizes pneumonia etiology”). 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).
When 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 distinct types of immune responses, including the activation of enzymes called proteases, which break down proteins. The MIT team found that the activity pattern of these enzymes can serve as a signature of a bacterial or viral infection.
The human genome codes for more than 500 proteases, and many of these 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, collected 33 publicly available datasets of genes expressed during respiratory infections. By analyzing this data, Khatri was able to identify 39 proteases that appear to respond differently to different types of infection.
Bhatia and his students then used this data to create 20 different sensors that can interact with these proteases. The sensors consist of nanoparticles coated with peptides that can be cleaved by particular proteases. Each peptide is labeled with a reporter molecule which is released when the peptides are cleaved by proteases which are up-regulated upon infection. These reporters are ultimately excreted in the urine. The urine can then be analyzed by mass spectrometry to determine which proteases are most active in the lungs.
The researchers tested their sensors in five different mouse models of pneumonia, caused by infections with Streptococcus pneumoniae, Klebsiella pneumoniae, Haemophilus influenzae, influenza virus and mouse pneumonia virus.
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 pneumonia from healthy controls, and also distinguish whether an infection was viral or bacterial, based on these 20 sensors.
The researchers also found that their sensors could distinguish between the five pathogens they tested, but with lower accuracy than the test at distinguishing between viruses and bacteria. One possibility that researchers could pursue is to develop algorithms that can not only distinguish bacterial infections from viral infections, but also identify the class of microbes causing a bacterial infection, which could help doctors to choose the best antibiotic to fight this type of bacteria.
The urine-based reading also lends itself to future detection with a strip of paper, similar to a pregnancy test, which would allow for point-of-care diagnosis. To that end, the researchers identified a subset of five sensors that could bring home testing closer. However, more work is needed to determine if the reduced panel would work the same way in humans, which have more genetic and clinical variability than mice.
Response Templates
In their study, the researchers also identified certain patterns of host response to different types of infection. In mice with bacterial infections, proteases secreted by neutrophils were observed more prominently, which was expected because neutrophils tend to respond more to bacterial infections than to viral infections.
Viral infections, on the other hand, elicited protease activity from T cells and NK cells, which generally respond more 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 strongly activated in the lungs of mice with viral infections and that 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 currently developing versions for human use that could be administered using a nebulizer or an inhaler similar to an inhaler for the asthma. They’re also working on a way to detect results using a breathalyzer instead of a urine test, which could yield results even faster.
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