Researchers at Tufts University have employed a novel “subway map” model to pinpoint potential treatment targets for Lyme disease, promising more precise therapeutic approaches in the future.
This groundbreaking model, developed by Tufts scientists, has unveiled two existing drugs that exhibit promise for more selective treatment options.
Tufts University School of Medicine’s scientists have crafted a genome-scale metabolic model, akin to a “subway map,” detailing critical metabolic processes of the bacterium responsible for Lyme disease. By harnessing this map, they have successfully identified two compounds that specifically target the pathways employed exclusively by Lyme disease to infect a host. Their findings were published on October 19 in the journal mSystems.
However, it is essential to note that while these medications exhibit potential, they are not currently viable treatments for Lyme disease due to their numerous side effects. Nevertheless, the achievement of employing the computational “subway map” to forecast drug targets and potential existing treatments underscores the possibility of developing micro-substances that exclusively combat Lyme disease while preserving beneficial bacteria untouched.
Understanding Genome-Scale Metabolic Models
Genome-scale metabolic models (GEMs) compile comprehensive metabolic information about a biological system, encompassing genes, enzymes, metabolites, and other pertinent data. These models leverage big data and machine learning to aid scientists in comprehending molecular mechanisms, making predictions, and unveiling novel processes that may have been previously undisclosed and even counterintuitive to established biological processes.
Ongoing Challenges in Lyme Disease Treatment
Presently, Lyme disease is treated with broad-spectrum antibiotics that eliminate the Lyme bacterium Borrelia burgdorferi. However, this approach also eradicates a wide spectrum of other bacteria inhabiting a host’s microbiome, which serve various beneficial functions. Some individuals experiencing chronic Lyme symptoms or recurring Lyme disease resort to prolonged antibiotic use, despite contravening medical guidelines and lacking conclusive evidence of its efficacy.
Peter Gwynne, the lead author of the study and a research assistant professor of molecular biology and microbiology at Tufts University School of Medicine and the Tufts Lyme Disease Initiative, emphasizes the need for exploring micro-substances that target specific pathways in individual bacteria rather than relying on broad-spectrum antibiotics, considering the rising issue of antibiotic resistance.
Insights from the Computational Model
The two compounds identified through the “subway map” computational model include an anticancer drug with significant side effects that render it impractical for Lyme treatment and an asthma medication withdrawn from the market due to its side effects. Laboratory tests confirmed that both drugs effectively eliminate Lyme bacteria, exclusively targeting Lyme without affecting other organisms in culture.
Linden Hu, the senior author of the study and the Paul and Elaine Chervinsky Professor of Immunology, underscores the vulnerability of the Lyme bacterium, which is highly dependent on its environment and limited in its capabilities compared to other bacteria.
Accelerating Treatment Discovery
The utilization of the computational model, conceived during the COVID pandemic when onsite laboratory work was restricted, holds the potential to streamline testing and the development of more targeted treatments by bypassing certain arduous basic science steps.
Gwynne expresses optimism about utilizing this model to screen for compounds with similar efficacy to the anticancer and asthma drugs but without the same toxicity. Such compounds could potentially halt the Lyme disease process or other aspects thereof.
Additionally, Gwynne and Hu are engaged in further research to determine whether individuals with chronic Lyme symptoms are still infected or are grappling with immune dysfunction causing prolonged symptoms. Gwynne envisions a future where targeted Lyme treatments, taken for a brief period, replace broad-spectrum antibiotics. Subsequent testing could confirm the absence of infection, and medications could be administered to modulate the immune response if chronic symptoms persist.
Future Applications of Computational “Subway Maps”
Gwynne suggests that similar computational “subway maps” could 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 causative agent of Rocky Mountain Spotted Fever. His team is actively exploring the development of such maps for these bacteria.
Reference: “Metabolic modeling predicts unique drug targets in Borrelia burgdorferi” by Peter J. Gwynne, Kee-Lee K. Stocks, Elysse S. Karozichian, Aarya Pandit, and Linden T. Hu, 19 October 2023, mSystems.
DOI: 10.1128/msystems.00835-23
This research received support from the Bay Area Lyme Foundation and the National Institute of Allergy and Infectious Diseases at the National Institutes of Health under award R01AI122286. Co-authors include Kee-Lee Stocks, a Ph.D. student at Tufts Graduate School of Biomedical Sciences (GSBS); research assistant Aarya Pandit, E25, at GSBS; and former research assistant Elysse S. Karozichian, E23, at GSBS. Comprehensive author information, funding sources, methodology, and potential conflicts of interest can be found in the published paper.
The content presented here is the sole responsibility of the authors and may not necessarily represent the official views of the funding organizations.
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Frequently Asked Questions (FAQs) about Lyme Disease Treatment
What is the “subway map” model used in this research?
The “subway map” model is a genome-scale metabolic model that maps out key metabolic processes of the bacterium responsible for Lyme disease. It helps identify specific pathways used by Lyme disease to infect a host.
Why is Lyme disease treatment challenging?
Current Lyme disease treatment involves broad-spectrum antibiotics, which not only target the Lyme bacterium but also beneficial bacteria in a host’s microbiome, leading to various side effects and antibiotic resistance issues.
What were the two compounds identified through the “subway map” model?
The study identified two compounds: an anticancer drug with significant side effects and an asthma medication, both of which successfully eliminated Lyme bacteria in lab tests.
How does the “subway map” model accelerate treatment discovery?
This computational model streamlines the identification of potential drug candidates, bypassing certain time-consuming basic science steps and expediting the development of more targeted Lyme disease treatments.
What are the future applications of computational “subway maps” in this research?
The research team envisions creating similar maps for other bacteria with relatively small genomes, including those responsible for diseases like Syphilis, Chlamydia, and Rocky Mountain Spotted Fever, to explore more precise treatment options.
More about Lyme Disease Treatment
- Tufts University School of Medicine
- mSystems Journal
- Lyme Disease Information
- Antibiotic Resistance
- Bay Area Lyme Foundation
- National Institute of Allergy and Infectious Diseases
- Tufts Graduate School of Biomedical Sciences
