Researchers have developed a genome-scale metabolic model or “subway map” of key metabolic activities of the bacterium that causes Lyme disease.
Using this map, they have successfully identified two compounds that selectively target routes only used by Lyme disease to infect a host.
While neither medication is a viable treatment for Lyme because they have numerous side effects, the successful use of the computational “subway map” to predict drug targets and possible existing treatments demonstrates that it may be possible to develop micro-substances that only block Lyme disease while leaving other helpful bacteria untouched.
Genome-scale metabolic models (GEMs) collect all known metabolic information on a biological system, including the genes, enzymes, metabolites, and other information. These models use big data and machine learning to help scientists understand molecular mechanisms, make predictions, and identify new processes that might be previously unknown and even counterintuitive to known biological processes.
Killing Lyme bacteria, and only Lyme
Currently, Lyme disease is treated with broad-spectrum antibiotics that kill the Lyme bacterium Borrelia burgdorferi, but simultaneously also kill a wide range of the other bacteria that inhabit a host’s microbiome and perform many helpful functions. Some people with chronic Lyme symptoms or recurring Lyme disease take antibiotics for years, although it is against medical guidelines and there is no proof that it works.
“Most of the antibiotics we still use are based on discoveries that are decades old, and antibiotic resistance is an increasing problem across many bacterial diseases,” says Peter Gwynne, research assistant professor of molecular biology and microbiology at Tufts University School of Medicine and the Tufts Lyme Disease Initiative and first author of the paper in mSystems.
“There is a growing movement to find micro-substances that target a specific pathway in a single bacterium, rather than treating patients with broad spectrum antibiotics that wipe out the microbiome and cause antibiotic resistance.”
The two compounds identified using the “subway map” computational model are an anticancer drug with significant side effects that make it impractical to use in treating Lyme, and an asthma medication taken off the market because of its side effects. The researchers tested both drugs identified by the model in the lab and found they successfully kill Lyme bacteria—and only Lyme—in culture.
“The Lyme bacterium is a great test case for narrow spectrum drugs because it is so limited in what it can do and so highly dependent upon its environment. This leaves it vulnerable in ways other bacteria are not,” says senior author Linden Hu, a professor of immunology and of molecular biology and microbiology.
Maps for more bacteria
Use of the computational model—which Gwynne and collaborators developed during COVID when they couldn’t work onsite in the lab—has the potential to enable scientists to skip some painstaking basic science steps and lead to swifter testing and development of more targeted treatments.
“We can now use this model to screen for similar compounds that don’t have the same toxicity of the anticancer and asthma medications but could potentially stop the same or another part of the Lyme disease process,” Gwynne says.
The researchers are conducting other research to determine whether people with chronic Lyme symptoms are still infected or are suffering from an immune malfunction that creates chronic symptoms.
“I can imagine a day when people take a targeted Lyme treatment for two weeks rather than a broad-spectrum antibiotic, are tested and determined to be clear of the infection, and then take drugs to tame their immune response if chronic symptoms persist,” Gwynne says.
Similar computational “subway maps” can be developed for other bacteria with relatively small genomes, such as those that cause the sexually transmitted diseases syphilis and chlamydia, and rickettsia, which causes Rocky Mountain Spotted Fever, Gwynne says. His team is looking at developing maps for some of these bacteria.
The Bay Area Lyme Foundation and the National Institute of Allergy and Infectious Diseases at the National Institutes of Health funded the work.
The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders.
Source: Tufts University
