New Approach to Target Lyme Disease Identified through Subway Map of Pathways


Researchers at Tufts University School of Medicine have created a genome-scale metabolic model, comparable to a subway map, that outlines the key metabolic activities of the bacterium responsible for Lyme disease. By utilizing this model, they have successfully identified two compounds that selectively target specific routes utilized by Lyme disease to infect a host. This groundbreaking research was published on October 19 in the journal mSystems.

Though neither of the identified compounds is a viable treatment for Lyme due to their multiple side effects, the study’s successful use of the computational subway map to predict drug targets and potential existing treatments demonstrates the possibility of developing micro-substances that specifically block Lyme disease while leaving other beneficial bacteria unharmed.

Genome-scale metabolic models, or GEMs, encompass all known metabolic information about a biological system, including genes, enzymes, metabolites, and other relevant data. These models employ big data and machine learning techniques to aid scientists in understanding molecular mechanisms, making predictions, and identifying new processes that were previously unknown and even counter-intuitive to established biological processes.

Micro-substances as a Target for the Bacterium
Currently, Lyme disease is treated with broad-spectrum antibiotics that kill the Borrelia burgdorferi bacterium responsible for the disease. However, these antibiotics also eliminate a wide range of other bacteria that inhabit the host’s microbiome and perform crucial functions. Some individuals with chronic Lyme symptoms or recurring Lyme disease continue taking antibiotics for years, despite contravening medical guidelines and a lack of evidence supporting its efficacy.

Peter Gwynne, the first author of the paper and research assistant professor of molecular biology and microbiology at Tufts University School of Medicine and the Tufts Lyme Disease Initiative, notes that most of the antibiotics in current use are based on discoveries from several decades ago, and antibiotic resistance poses an increasing problem across various bacterial diseases. As a result, researchers are championing the search for micro-substances that target specific pathways in individual bacteria, instead of employing broad-spectrum antibiotics that eradicate the microbiome and contribute to antibiotic resistance.

The two compounds discovered using the subway map computational model are an anticancer drug known for its significant side effects, rendering it impractical for Lyme treatment, and an asthma medication that was withdrawn from the market due to its side effects. Both drugs were tested in the laboratory and found to effectively kill Lyme bacteria while leaving other bacteria untouched.

Linden Hu, the Paul and Elaine Chervinsky Professor of Immunology, a professor of molecular biology and microbiology, and senior author of the study, explains that the Lyme bacterium serves as an excellent test case for narrow-spectrum drugs due to its limited capabilities and high dependence on its environment, making it vulnerable in ways that other bacteria are not.

Accelerating the Discovery of Treatments
The utilization of the computational model, developed by Gwynne and collaborators during the COVID-19 pandemic when lab work was impractical, has the potential to enable scientists to bypass certain laborious basic science steps, leading to more rapid testing and development of targeted treatments.

Gwynne envisions a future in which individuals take a targeted Lyme treatment for two weeks instead of receiving broad-spectrum antibiotics. They would then undergo testing to confirm the absence of the infection, followed by the administration of drugs to manage their immune response if chronic symptoms persist. Gwynne and Hu are conducting further research to determine whether individuals with chronic Lyme symptoms are still infected or are suffering from an immune malfunction that causes ongoing symptoms.

Gwynne also suggests that similar computational subway maps can be developed for other bacteria with relatively small genomes, such as those responsible for sexually transmitted diseases like Syphilis and Chlamydia, as well as Rickettsia, the bacterium behind Rocky Mountain spotted fever. The team is currently exploring the development of maps for these bacteria.


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