Health scan: Bacteria can evolve biological timer to survive antibiotics

Researchers at the Hebrew University of Jerusalem tried to predict a different evolutionary process and follow it in real time

resistant bacteria 311 (photo credit: Courtesy)
resistant bacteria 311
(photo credit: Courtesy)
The ability of microorganisms to overcome antibiotic treatments – known as resistance – is one of the leading concerns of modern medicine, as the effectiveness of many antibiotics has been reduced by bacteria’s ability to rapidly evolve. Bacteria achieve this via specific mechanisms that are tailored to the molecular structure or function of a particular antibiotic. For example, bacteria would typically develop drug resistance by evolving a mutation that breaks down the drug.
Researchers at the Hebrew University of Jerusalem tried to predict a different evolutionary process and follow it in real time. Using the quantitative approach of physicists, the team developed experimental tools to measure precisely the bacterial response to antibiotics and developed a mathematical model of the process. The model led them to hypothesize that a daily three-hour dose would make it possible for the bacteria to predict delivery of the drug and “go to sleep” for that period so they could survive.
The research was led by Prof. Nathalie Balaban at HU’s Racah Institute of Physics, working with colleagues at Racah, HU’s Sudarsky Center for Computational Biology and the Broad Institute of Harvard and MIT. The research paper appeared recently in the journal Nature.
To test their hypothesis, the researchers “fed” antibiotics to bacterial populations in the lab for exactly three hours every day. After only 10 days, they were able to observe the bacteria using a new survival tactic. When exposed to these repeated cycles of antibiotic treatments, the bacteria evolved an adaptation to the duration of the antibiotic stress by remaining dormant for the treatment period. The results showed that bacteria can evolve within days and can develop a biological timer to survive under antibiotic exposure.
To further test their hypothesis, the researchers delivered antibiotics for different periods, exposing three different bacteria populations to repeated daily antibiotic exposures lasting three, five or eight hours.
Remarkably, each of the populations adapted by prolonging their dormant stage to match the exposure duration.
The researchers said their work demonstrates that quantitative approaches from physics can be used to address fundamental as well as clinically relevant issues in biology.
With this new understanding of how bacterial populations evolve survival strategies against antibiotics, scientists could develop new approaches for slowing the evolution of antibiotic resistance.
Now that they have identified the mutation responsible for the biological timer, the researchers want to gather clinical data to see if a similar timed response to antibiotics is active in humans, allowing bacteria to weaken the antibiotics people take on a fixed schedule. If this is found to be true, it may explain the failure of antibiotic treatments observed in several diseases. In the future, it may help doctors to recommend different treatment schedules. It could also lead to the development and greater use of drugs that can maintain constant levels in the body.
The body mass index (BMI) has long been used to calculate from one’s weight and height whether adults are underweight, overweight, obese or are at a normal weight. But when applied to children, the results can be skewed. Doctors using BMI to diagnose children as obese may be missing 25 percent of kids who have excess body fat despite a normal BMI, according to a new Mayo Clinic study recently published in the journal Pediatric Obesity. The result could be a serious concern for long-term health.
The researchers found that BMI has high specificity in identifying pediatric obesity, meaning BMI accurately identifies children who are obese, but has a moderate sensitivity, meaning the BMI tool misses children who actually should be considered obese, according to the percent of fat in their bodies.
“If we are using BMI to find out which children are obese, it works if the BMI is high, but what about the children who have a normal BMI but do have excess fat? Those parents may get a false sense of reassurance that they do not need to focus on a better weight for their children,” said Dr. Francisco Lopez-Jimenez, the senior study author and director of preventive cardiology at the medical institution in Rochester, Minnesota.
It was the first systematic review and meta-analysis to assess the diagnostic performance of BMI to identify excess body fat as compared with techniques considered reference standard to measure obesity.The other techniques include skin-fold thickness measurement and dual-energy X-ray absorptiometry, which can be used to measure body composition and fat content.
In the meta-analysis, the researchers looked at 37 studies that evaluated 53,521 patients ages four through 18.
It is known that childhood obesity can lead to an increased risk of type II diabetes and cardiovascular disease, said Dr. Asma Javed, a pediatric endocrinology fellow at Mayo.
“Our research raises the concern that we very well may be missing a large group of children who potentially could be at risk for these diseases as they get older.
We hope our results shine a light on this issue for physicians, parents, public health officials and policymakers.”