CAMBRIDGE, Mass—Most antibiotics work by interfering with critical functions such as DNA replication or construction of the bacterial cell wall, but as it turns out, these mechanisms represent only part of the picture of how antibiotics act. Massachusetts Institute of Technology (MIT) researchers developed a new machine-learning approach to discover an additional mechanism that helps some antibiotics kill bacteria, noting in a new paper about their study of antibiotic action that this secondary mechanism involves activating the bacterial metabolism of nucleotides that the cells need to replicate their DNA.
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A better picture of how antibiotics kill
Machine learning reveals metabolic pathways disrupted by the drugsBrought to you byDDNews Staff
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CAMBRIDGE, Mass—Most antibiotics work by interfering with critical functions such as DNA replication or construction of the bacterial cell wall, but as it turns out, these mechanisms represent only part of the picture of how antibiotics act. Massachusetts Institute of Technology (MIT) researchers developed a new machine-learning approach to discover an additional mechanism that helps some antibiotics kill bacteria, noting in a new paper about their study of antibiotic action that this secondary mechanism involves activating the bacterial metabolism of nucleotides that the cells need to replicate their DNA.
“There are dramatic energy demands placed on the cell as a result of the drug stress. These energy demands require a metabolic response, and some of the metabolic byproducts are toxic and help contribute to killing the cells,” said Dr. James Collins, the Termeer Professor of Medical Engineering and Science in MIT’s Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering, and the senior author of the study.
Exploiting this mechanism could help researchers to discover new drugs that could be used along with antibiotics to enhance their killing ability, the researchers say.
Collins and Dr. Graham Walker, an MIT professor of biology, have studied the mechanisms of antibiotic action for many years, and their work has shown that antibiotic treatment tends to create a great deal of cellular stress that makes huge energy demands on bacterial cells. In the new study, Collins and Dr. Jason Yang—an IMES research scientist and the lead author of the paper, which appeared in the May 9 issue of Cell—decided to take a machine-learning approach to investigate how this happens and what the consequences are.
Before they began their computer modeling, the researchers performed hundreds of experiments in E. coli. They treated the bacteria with one of three antibiotics—ampicillin, ciprofloxacin or gentamicin—and in each experiment, they also added one of about 200 different metabolites, including an array of amino acids, carbohydrates and nucleotides. For each combination of antibiotics and metabolites, they measured the effects on cell survival.
“We used a diverse set of metabolic perturbations so that we could see the effects of perturbing nucleotide metabolism, amino acid metabolism and other kinds of metabolic subnetworks,” Yang explained. “We wanted to fundamentally understand which previously undescribed metabolic pathways might be important for us to understand how antibiotics kill.”
Their model yielded the novel discovery that nucleotide metabolism, especially metabolism of purines such as adenine, plays a key role in antibiotics’ ability to kill bacterial cells. Antibiotic treatment leads to cellular stress, which causes cells to run low on purine nucleotides. The cells’ efforts to ramp up production of these nucleotides, which are necessary for copying DNA, boost the cells’ overall metabolism and leads to a buildup of harmful metabolic byproducts that can kill the cells.
The findings suggest that it may be possible to enhance the effects of some antibiotics by delivering them along with other drugs that stimulate metabolic activity. “If we can move the cells to a more energetically stressful state, and induce the cell to turn on more metabolic activity, this might be a way to potentiate antibiotics,” Yang said.
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Adapted from an article written by Anne Trafton for the MIT News Office
