Tufts University scientists have used a "subway map" model to identify compounds that target Lyme disease, pointing the way to more precise treatments in the future. The model created by the researchers shows that two existing drugs have the potential for more selective treatment options. They developed a genome-scale metabolic model, or "metro map," that shows the key metabolic activities of the bacteria that cause Lyme disease.
Using this map, they successfully identified two compounds that selectively target the only way Lyme disease infects its host. Their research results were published in the journal mSystems on October 19.
While neither drug is a viable treatment for Lyme disease because they have many side effects, the successful use of computational "subway maps" to predict drug targets and possible existing treatments suggests that it may be possible to develop microscopic substances that only block Lyme disease without touching other beneficial bacteria.
Genome-scale metabolic models (GEM) collect all known metabolic information of biological systems, including genes, enzymes, metabolites and other information. These models leverage big data and machine learning to help scientists understand molecular mechanisms, make predictions, and identify novel processes that may be previously unknown or even contrary to known biological processes.
Currently, Lyme disease is treated with broad-spectrum antibiotics that kill the Lyme disease bacterium Acinetobacter baumannii along with a variety of other bacteria that inhabit the host microbiota and perform a variety of beneficial functions. Some people with chronic Lyme symptoms or recurring Lyme disease take antibiotics year-round, even though this goes against medical guidelines and there is no evidence that it is effective.
"Most of the antibiotics we still use are based on discoveries made decades ago, and antibiotic resistance is a growing problem in many bacterial diseases," said first author Peter Gwin, an assistant professor of molecular biology and microbiology at Tufts University School of Medicine. "There is an increasing preference to find trace amounts of substances that target specific pathways in individual bacteria, rather than treating patients with broad-spectrum antibiotics, which can disrupt the microbiome and lead to antibiotic resistance."
The two compounds identified using the "subway map" computational model were an anti-cancer drug with severe side effects and an asthma drug that was withdrawn from the market due to side effects. Both drugs identified by the model were tested in the laboratory and found to be successful in killing Lyme bacteria - and only in culture.
"Lyme disease is an excellent test case for narrow-spectrum drugs because its effects are very limited and it is highly environment-dependent. This makes it vulnerable in a way that other bacteria do not have," said Linden Hu, the Paul and Elaine Cherwinski Professor of Immunology and Professor of Molecular Biology and Microbiology and senior author of the study.
Gwen and her collaborators developed this computational model during the COVID-19 pandemic, when they were unable to work in-person in the lab. Using this model could potentially allow scientists to skip some of the painstaking basic science steps to test and develop more targeted treatments more quickly.
"We can now use this model to screen for similar compounds that don't have the toxicity of the cancer and asthma drugs but have the potential to block the same or another part of the Lyme disease process," said Gwin, who recently received the Bay Area Lyme Foundation's Emerging Leader Award.
Gwen and Hu are conducting other studies to determine whether people with chronic Lyme symptoms are still infected or if immune dysfunction is causing the chronic symptoms. "I can imagine a day when people receive two weeks of targeted Lyme treatment instead of broad-spectrum antibiotics, are tested to be clear of infection, and then take medication to tame their immune response if chronic symptoms persist."
Gwin said similar computational "subway maps" could be used for other bacteria with relatively small genomes, such as those that cause the sexually transmitted diseases syphilis and chlamydia, and the rickettsiae that causes Rocky Mountain spotted fever. Gwen's team is working on mapping some of these bacteria.