The discovery of antibiotics in 1928 changed human history forever. Infectious diseases such as pneumonia, tuberculosis, and sepsis were widespread and deadly until penicillin made them treatable.
Surgical procedures that once carried a high risk of infection have become safer and more routine. Antibiotics marked a moment of scientific triumph that transformed medical practice and saved countless lives.
But antibiotics come with their own caveats. Overuse can cause bacteria to develop resistance to these drugs. The World Health Organization estimates that these superbugs killed 1.27 million people worldwide in 2019 and will pose a growing threat to global public health in the coming years.
New discoveries are helping scientists address this challenge in innovative ways. Research shows that nearly a quarter of drugs not normally prescribed as antibiotics, such as those used to treat cancer, diabetes and depression, can kill bacteria at the doses typically prescribed to people. I know.
Understanding the mechanisms underlying how certain drugs become toxic to bacteria can have far-reaching implications for medicine. If non-antibiotics target bacteria differently than standard antibiotics, they could provide clues for the development of new antibiotics.
However, if non-antibiotics kill bacteria in a similar way to known antibiotics, long-term use of antibiotics, such as in the treatment of chronic diseases, can inadvertently promote antibiotic resistance.
In recently published research, my colleagues and I developed a new machine learning method that not only identifies how non-antibiotics kill bacteria, but also helps discover new bacterial targets for antibiotics.
A new way to kill germs
Many scientists and physicians around the world are working on the problem of drug resistance, including myself and my colleagues in the Mitchell lab at Massachusetts Chan School of Medicine. We use bacterial genetics to study which mutations make bacteria more resistant or sensitive to drugs.
When my team and I learned that non-antibiotics have a wide range of antibacterial effects, we became obsessed with the challenge it posed: figuring out how these drugs kill bacteria. I did.
To answer this question, I used genetic screening techniques recently developed by colleagues to study how cancer drugs target bacteria. This method identifies which specific genes and cellular processes change when bacteria mutate. By monitoring how these changes affect bacterial survival, researchers can deduce the mechanisms these drugs use to kill bacteria.
I have collected and analyzed nearly 2 million cases of toxicity between 200 drugs and thousands of mutant bacteria. Using a machine learning algorithm they developed to estimate similarities between different drugs, they grouped the drugs into networks based on how they affected the mutant bacteria.
My map clearly showed that known antibiotics are tightly grouped by classes of known lethal mechanisms. For example, all antibiotics that target the cell wall (the thick protective layer that surrounds bacterial cells) were grouped together and well separated from antibiotics that interfere with bacterial DNA replication.
Interestingly, when non-antibiotics were added to the analysis, they formed a separate hub from antibiotics. This shows that non-antibiotics and antibiotics kill bacterial cells differently. Although these groupings do not reveal how each drug specifically kills antibiotics, they do indicate that drugs clustered together are likely to act in similar ways. is showing.
The final piece of the puzzle, whether we can find new drug targets within bacteria to kill them, came from the work of my colleague Carmen Li.
She grew hundreds of generations of bacteria exposed to a variety of non-antibiotics commonly prescribed to treat anxiety, parasitic infections, and cancer.
By sequencing the genomes of bacteria that have evolved to adapt to the presence of these drugs, we have determined the specific bacterial proteins that triclabendazole (a drug used to treat parasitic infections) targets to kill bacteria. was able to identify. Importantly, current antibiotics typically do not target this protein.
Additionally, they discovered that triclabendazole and two other non-antibiotics that use a similar mechanism also target the same protein. This demonstrated the power of my drug similarity map to identify drugs with similar lethal mechanisms, even if that mechanism was not yet known.
Supporting antibiotic discovery
Our findings present many opportunities for researchers to study how non-antibiotic drugs work differently than standard antibiotics. Our method of mapping and testing drugs also has the potential to address critical bottlenecks in antibiotic development.
The search for new antibiotics typically involves screening thousands of bacteria-killing chemicals and devoting significant resources to figuring out how they work. Most of these chemicals have been found to work similarly to existing antibiotics and are being discarded.
Our research shows that combining genetic screening and machine learning can help researchers discover chemical needles in the haystack that can kill bacteria in a way never used before. Masu.
There are many different ways to kill bacteria that we have not yet taken advantage of, and there is still a path forward for us to take to combat the threat of bacterial infections and antibiotic resistance.
Dr. Mariana Noto Guillen, Candidate in Systems Biology, Massachusetts Chan College of Medicine
This article is republished from The Conversation under a Creative Commons license. Read the original article.
