Human history changed forever with the discovery of antibiotics in 1928. Infectious diseases such as pneumonia, tuberculosis and sepsis were widespread and lethal until antibiotics made them treatable.
Surgical procedures that previously carried a high risk of infection have become safer and more routine. Antibiotics marked a triumphant moment in science that transformed medical practice and saved countless lives.
But antibiotics have an inherent caveat: When used in excess, bacteria can develop resistance to these drugs. The World Health Organization estimated that these superbugs caused 1.27 million deaths worldwide in 2019 and are likely to become a growing threat to global public health in the coming years.
New discoveries are helping scientists address this challenge in innovative ways.
Studies have found that nearly a quarter of medications that are not typically prescribed as antibiotics, such as medications used to treat cancer, diabetes and depression, can kill bacteria at doses typically prescribed to people.
Understanding the mechanisms underlying the toxicity of certain drugs to bacteria could have far-reaching implications for medicine. If non-antibiotic drugs target bacteria in different ways than standard antibiotics, they could serve as leads for developing new antibiotics.
However, if non-antibiotics kill bacteria in a similar way to known antibiotics, their long-term use, such as in the treatment of chronic diseases, may inadvertently promote antibiotic resistance.
In our recently published research, my colleagues and I developed a new machine learning method that not only identified how non-antibiotics kill bacteria, but could also help find new bacterial targets for antibiotics.
New ways to kill bacteria
Many scientists and doctors around the world are tackling the problem of drug resistance, including myself and my colleagues in the Mitchell Lab at UMass Chan Medical School. We use the genetics of bacteria to study which mutations make them more resistant or more sensitive to drugs.
When my team and I learned about the broad antibacterial activity of non-antibiotics, we were consumed by the challenge it presented: figuring out how these drugs kill bacteria.
To answer this question, I used a genetic screening technique that my colleagues recently developed to study how anticancer drugs target bacteria.
This method identifies which specific genes and cellular processes change when bacteria mutate. Monitoring how these changes influence the survival of bacteria allows researchers to infer the mechanisms these drugs use to kill bacteria.
I collected and analyzed almost 2 million instances of toxicity across 200 drugs and thousands of mutant bacteria. Using a machine learning algorithm I developed to deduce similarities between different drugs, I grouped the drugs into a network based on how they affected the mutant bacteria.
My maps clearly showed that the known antibiotics were tightly grouped by their known classes of killing 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 bacteria’s DNA replication.
Interestingly, when I added non-antibiotic medications to my analysis, they formed separate centers from antibiotics. This indicates that non-antibiotic and antibiotic medications have different ways of killing bacterial cells.
While these groupings don’t reveal how each drug specifically kills antibiotics, they do show that those grouped together likely work in similar ways.
The final piece of the puzzle – whether we could find new drug targets in bacteria to kill them – came from the research of my colleague Carmen Li. She grew hundreds of generations of bacteria that were exposed to different non-antibiotic drugs typically prescribed to treat anxiety, parasite infections and cancer.
Sequencing the genomes of bacteria that have evolved and adapted to the presence of these drugs allowed us to identify the specific bacterial protein that triclabendazole – a drug used to treat parasite infections – targets to kill the bacteria.
Importantly, current antibiotics typically do not target this protein.
Furthermore, we found that two other non-antibiotics that used a similar mechanism to triclabendazole also target the same protein.
This demonstrated the power of my drug similarity maps to identify drugs with similar elimination mechanisms, even when that mechanism was still unknown.
Helping in the discovery of antibiotics
Our findings open several opportunities for researchers to study how non-antibiotic medicines work differently than standard antibiotics.
Our drug mapping and testing method also has the potential to resolve a critical bottleneck in antibiotic development.
In general, the search for new antibiotics involves applying considerable resources to screening thousands of bacteria-killing chemicals and discovering how they work. Most of these chemicals are discovered to work similarly to existing antibiotics and are discarded.
Our work shows that combining genetic screening with machine learning can help uncover the chemical needle in the haystack that can kill bacteria in ways researchers haven’t used before.
There are different ways to kill bacteria that have not yet been explored, and there are still paths we can take to combat the threat of bacterial infections and antibiotic resistance.
*Mariana Noto Guillen, PhD student in systems biology, UMass Chan School of Medicine
This article is republished from The Conversation under a Creative Commons license. Read the original article.
Tags: Medicines antibiotics kill bacteria
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