Picture of the leg of someone with a typical circular Lyme disease rash after being bitten by a deer tick
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Researchers have created a genome-wide metabolic model of the bacterium responsible for Lyme disease, which could help create more bespoke treatments and avoid the need for broad-spectrum antibiotics.

The computational “subway map” of Borrelia burgdorferi makes it possible to identify potential drug targets and existing treatments that act only upon the bacterium, leaving others that are beneficial unaffected.

The researchers say the same approach could be applied to other bacteria with relatively small genomes, such as those that cause syphilis or chlamydia.

The findings are published in mSystems, the journal of the American Society for Microbiology.

“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,” said senior author Peter Gwynne, PhD, from Tufts University School of Medicine.

“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.”

B. burgdorferi has a small genome and correspondingly limited metabolism, making it extremely dependent on its tick and vertebrate hosts.

The small genome means there are few redundant pathways or enzymes, and a higher proportion of essential genes that could be targeted through narrow-spectrum antibiotics.

Noting that currently recommended antibiotics for Lyme borreliosis can increase the risk of hospital-acquired infections such as Clostridiodes difficile due to disruption of the native microbiome and drive resistance in off-target bacteria, the researchers examined routes to more selective treatments.

They specifically investigated the value of genome-scale metabolic modeling, which enables the creation and analysis of in silico simulations of metabolism that are predicted by comparing an organism’s protein coding sequences with those of known metabolic enzymes.

The in-silico model of B. burgdorferi metabolism iBB151 they produced represented the most complete synthesis of predictive and experimental data to date.

It was sufficient to generate experimentally validated predictions and annotated 208 reactions to 151 genes. Many of the 77 genes that were predicted as essential represented candidate targets for the development of novel antibiotics against the bacterium.

Repurposing four existing inhibitors of related enzymes showed activity against B. burgdorferi in culture, with three exhibiting a degree of specificity.

While D-cycloserine is used as part of combination therapies against M. tuberculosis, the other compounds tested were not suitable for clinical use.

Bromopyruvate has been tested extensively against cultured cancer cells but has not been studied in clinical trials and its simple structure and broad range of cellular targets raise concerns of off-target toxicity.

Theophylline, meanwhile, is rarely used for its original indication against asthma due to a narrow therapeutic index and reported toxicity, while pemetrexed has a narrow therapeutic window like other anti-cancer drugs and significant associated toxicity.

The authors note that, while the inhibitory concentrations for theophylline and pemetrexed were high, their efficacy is likely to improve through modification to bind their B. burgdorferi enzyme targets more efficiently and specifically.

“These modifications, and others to improve bioavailability and activity, will be the focus of future work,” the authors noted.

They added: “Development of these targets into narrow-spec­trum antimicrobials could reduce the incidence of Lyme disease by reservoir-targeted elimination of B. burgdorferi or as prophylaxis in high-risk groups.

The team believes the in silico approach to drug target identification could be particularly useful for other fastidious organisms that are difficult to grow in culture, and for which experimental approaches are therefore difficult.

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