Metabolic map of Lyme disease bacterium offers hope for cure

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Researchers have created a metabolic model of the genome of the bacterium responsible for Lyme disease, which could help create more tailored treatments and avoid the need for broad-spectrum antibiotics.

Computational metro map Borrelia burgdorferi enables the identification of potential drug targets and existing treatments that only work on bacteria, leaving others that are beneficial.

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 mSystems:Journal of the American Society for Microbiology.

Most of the antibiotics we still use are based on decades-old discoveries, and antibiotic resistance is a growing problem for many bacterial diseases, said senior author Peter Gwin, PhD, of Tufts University School of Medicine.

There is a growing movement to find micronutrients that target specific pathways in individual bacteria, rather than treating patients with broad-spectrum antibiotics that wipe out the microbiome and cause antibiotic resistance.

B. burgdorferi has a small genome and a correspondingly limited metabolism, making it highly dependent on ticks and vertebrates.

A small genome means there are fewer redundant pathways or enzymes, and a greater proportion of essential genes that can be targeted by narrow-spectrum antibiotics.

Noting that currently recommended antibiotics for Lyme borreliosis may increase the risk of hospital-acquired infections such as: Clostridium difficile due to disruption of the native microbiome and driving resistance in target bacteria, researchers have explored more selective treatment pathways.

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

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

It was sufficient to generate experimentally confirmed predictions and annotate 208 reactions of 151 genes. Many of the 77 genes predicted to be essential represented candidate targets for the development of new antibiotics against the bacteria.

Repurposing of four existing inhibitors of related enzymes showed activity against B. burgdorferi in culture, three of which show some uniqueness.

While D-cycloserine is used as part of combination therapy against M. tuberculosisother compounds tested were not suitable for clinical use.

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

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

The authors note that although the inhibitory concentrations of theophylline and pemetrexed were high, their efficacy is likely to be improved by altering their binding. B. burgdorferi enzyme targets are more efficient and specific.

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

They added: Development of these targets for narrow-spectrum antimicrobials may reduce the incidence of Lyme disease through targeted elimination of reservoirs. B. burgdorferi or as prophylaxis in high-risk groups.

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

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