So what I can do is to detect bacterial conversations that lead to different collective behaviors like the fight you just saw. And what I did was to spy on bacterial communities inside the human body in patients at a hospital. I followed 62 patients in an experiment, where I tested the patient samples for one particular infection, without knowing the results of the traditional diagnostic test.
When I finished the study and I compared the tool results to the traditional diagnostic test and the validation test, I was shocked. It was far more astonishing than I had ever anticipated.
But before I tell you what the tool revealed, I would like to tell you about a specific patient I followed, a young girl. She had cystic fibrosis, a genetic disease that made her lungs susceptible to bacterial infections. This girl wasn’t a part of the clinical trial. I followed her because I knew from her medical record that she had never had an infection before.
Once a month, this girl went to the hospital to cough up a sputum sample that she spit in a cup. This sample was transferred for bacterial analysis at the central laboratory so the doctors could act quickly if they discovered an infection. And it allowed me to test my device on her samples as well.
Now, in bacterial diagnostics, a sample is smeared out on a plate, and if the bacteria grow within five days, the patient is diagnosed as infected. The first two months I measured on her samples, there was nothing.
But the third month, I discovered some bacterial chatter in her sample. The bacteria were coordinating to damage her lung tissue. But the traditional diagnostics showed no bacteria at all.
I measured again the next month, and I could see that the bacterial conversations became even more aggressive. Still, the traditional diagnostics showed nothing.
My study ended, but a half a year later, I followed up on her status to see if the bacteria only I knew about had disappeared without medical intervention. They hadn’t. But the girl was now diagnosed with a severe infection of deadly bacteria. It was the very same bacteria my tool discovered earlier. And despite aggressive antibiotic treatment, it was impossible to eradicate the infection. Doctors deemed that she would not survive her 20s.
When I measured on this girl’s samples, my tool was still in the initial stage. I didn’t even know if my method worked at all, therefore I had an agreement with the doctors not to tell them what my tool revealed in order not to compromise their treatment.
So when I saw these results that weren’t even validated, I didn’t dare to tell because treating a patient without an actual infection also has negative consequences for the patient. But now we know better, and there are many young boys and girls that still can be saved because, unfortunately, this scenario happens very often. Patients get infected, the bacteria somehow don’t show on the traditional diagnostic test, and suddenly, the infection breaks out in the patient with severe symptoms. And at that point, it’s already too late.
The surprising result of the 62 patients I followed was that my device caught bacterial conversations in more than half of the patient samples that were diagnosed as negative by traditional methods. In other words, more than half of these patients went home thinking they were free from infection, although they actually carried dangerous bacteria.
Inside these wrongly diagnosed patients, bacteria were coordinating a synchronized attack. They were whispering to each other. What I call “whispering bacteria” are bacteria that traditional methods cannot diagnose. So far, it’s only the translation tool that can catch those whispers.
I believe that the time frame in which bacteria are still whispering is a window of opportunity for targeted treatment. If the girl had been treated during this window of opportunity, it might have been possible to kill the bacteria in their initial stage, before the infection got out of hand.
What I experienced with this young girl made me decide to do everything I can to push this technology into the hospital. Together with doctors, I’m already working on implementing this tool in clinics to diagnose early infections.
Although it’s still not known how doctors should treat patients during the whispering phase, this tool can help doctors keep a closer eye on patients in risk. It could help them confirm if a treatment had worked or not, and it could help answer simple questions: Is the patient infected? And what are the bacteria up to?
Currently I am building a kind of enigma machine that can decrypt bacterial messages. The idea is that you place a sample on a sensor and hook the sensor to your smartphone. And that will automatically measure the communication in the sample and translate whatever the bacteria are saying to English on your screen. And it’s not far from being available for home use.
Patients will no longer need to go to the hospital each time they need a sample analyzed. They can simply do it on the couch at home and avoid being infected by other patients.
The translation tool is not only useful to predict upcoming infections by spying on bad bacteria but it can also help us gain insight into the secret communication the healthy bacteria have inside our body. Instead of spying on bacteria, I think we should strive to become allies with them and listen to them. If we listen to the healthy bacteria within us, they might have something important to say about how we should treat ourselves.
The natural microbiota in the human body constitute an effective protective barrier against infections. But this microbiota is susceptible to our lifestyle. If you have this translation tool at home it can offer you a way to understand the bacteria you’re coexisting with.