Research at Rutgers Health has discovered why a relatively new antibiotic for tuberculosis (TB) works against multidrug-resistant strains, potentially inspiring improved treatments and drug development strategies.

The study from scientists from Rutgers New Jersey Medical School and other institutions have found that deficiencies in a critical enzyme make tuberculosis bacteria resistant to old antibiotics more vulnerable to the new antibiotic bedaquiline.

“Understanding how a drug works could help us design new molecules that work better and prevent bacteria from becoming resistant,” said Jason Yangassistant professor at the medical school and lead author of the study.

Tuberculosis is still among the world’s deadliest infectious diseases, killing more than 1.5 million people annually. Multidrug-resistant TB, defined as disease resistant to at least two first-line drugs, is a growing threat to global TB control efforts.

Approved in 2012 by the US Food and Drug Administration (FDA), bedaquiline was the first new TB drug in more than 40 years. It works well against multi-drug-resistant strains of TB, but the mechanisms behind its effectiveness have not been fully understood.

The findings could help boost TB treatment and drug development. Understanding these vulnerabilities could inspire strategies to make bedaquiline more effective, allowing for lower doses or shorter treatment times. It could also guide the development of new drugs or combinations of drugs.

“We can prevent resistance by developing other drugs that make bedaquiline work better,” Yang said. For example, combining bedaquiline with another antibiotic called isoniazid appears to prevent the development of resistance to either drug, he said.

While tuberculosis is primarily a problem in developing countries, it remains a global concern. However, outbreaks are still occurring in the United States. There were, for example, about 500 cases in New York City last year.

“TB itself is a ridiculously big problem right now, and so is antibiotic resistance,” Yang said. “There was only one new one report in Lancet designing if antibiotic resistance gets worse, then we can’t treat infections, and if we can’t treat infections, much of modern medicine dies. You couldn’t even do surgery because the surgical infections would kill the patients.”

Researchers in the study, published in nature communications, examined both clinical isolates and laboratory strains of Mycobacterium tuberculosis, the bacterium that causes TB. The researchers used a systems biology approach, combining genetic studies, RNA sequencing and metabolic modeling.

They found that deficiencies in an enzyme called catalase-peroxidase, encoded by a gene called katG, make drug-resistant tuberculosis more susceptible to bedaquiline. Mutations in katG are the most common cause of resistance to isoniazid, a first-line drug for tuberculosis.

This catalase deficiency leads to several changes that make the bacteria more vulnerable to the newer drug. It increases reactive oxygen species accumulation and susceptibility to DNA damage while altering transcriptional programs that regulate bacterial biology and repressing multiple biosynthetic pathways.

“We discovered some previously unreported mechanisms,” Yang said. “We show that these are the different types of vulnerabilities in TB biology or TB physiology that specifically occur in drug-resistant TB.”

The research also highlights potential avenues for repurposing existing drugs. The researchers found that trimethoprim and sulfamethoxazole, antibiotics used to treat other diseases, were also effective against catalase-deficient drug-resistant strains of TB.

The new work was one of a related pair that appeared in Communication of nature from the same team. The the second work used whole-genome CRISPRi screening to identify druggable vulnerabilities in a drug-resistant strain of the disease.

Looking ahead, Yang and his colleagues are pursuing several lines of research based on these findings.

“We are developing machine learning tools to understand other changes that occur in the biology or physiology of TB caused by other types of drug resistance,” Yang said. “We’re extending those machine learning models to see if we can extrapolate results from a laboratory setting directly to patients and clinical strains.”

This could lead to personalized medical approaches to TB, tailoring treatments based on the specific characteristics of the infecting strain.

The team is also developing synthetic biology tools to study how TB evolves drug resistance and how this process could be targeted to prevent resistance to bedaquiline and any new drugs that are developed.