Hacking into the Secret Communication of Bacteria: Fatima AlZahra’a Alatraktchi (Transcript)

Here is the full text of Fatima AlZahra’a Alatraktchi’s talk titled “Hacking into the Secret Communication of Bacteria” at TEDxAarhus conference.

Fatima AlZahra’a Alatraktchi has a PhD in nanophysics and molecular biology. She has developed a tool that can capture and decode bacterial messages and thereby save lives.

Fatima AlZahra’a Alatraktchi – TEDx Talk TRANSCRIPT

You don’t know them. You don’t see them. But they’re always around, whispering, making secret plans, building armies with millions of soldiers. And when they decide to attack, they all attack at the same time.

I’m talking about bacteria.

Who did you think I was talking about?

Bacteria live in communities just like humans. They have families, they talk, and they plan their activities. And just like humans, they trick, deceive, and some might even cheat on each other.

What if I tell you that we can listen to bacterial conversations and translate their confidential information into human language? And what if I tell you that translating bacterial conversations can save lives?

I hold a PhD in nanophysics, and I’ve used nanotechnology to develop a real-time translation tool that can spy on bacterial communities and give us recordings of what bacteria are up to.

Bacteria live everywhere. They’re in the soil, on our furniture and inside our bodies. In fact, 90% of all the live cells in this theater are bacterial. Some bacteria are good for us; they help us digest food or produce antibiotics. And some bacteria are bad for us; they cause diseases and death.

To coordinate all the functions bacteria have, they have to be able to organize, and they do that just like us humans — by communicating.

But instead of using words, they use signaling molecules to communicate with each other. When bacteria are few, the signaling molecules just flow away, like the screams of a man alone in the desert. But when there are many bacteria, the signaling molecules accumulate, and the bacteria start sensing that they’re not alone. They listen to each other.

In this way, they keep track of how many they are and when they’re many enough to initiate a new action. And when the signaling molecules have reached a certain threshold, all the bacteria sense at once that they need to act with the same action. So bacterial conversation consists of an initiative and a reaction, a production of a molecule and the response to it.

In my research, I focus on spying on bacterial communities inside the human body. How does it work? We have a sample from a patient. It could be a blood or spit sample. We shoot electrons into the sample, the electrons will interact with any communication molecules present, and this interaction will give us information on the identity of the bacteria, the type of communication and how much the bacteria are talking.

But what is it like when bacteria communicate?

Before I developed the translation tool, my first assumption was that bacteria would have a primitive language, like infants that haven’t developed words and sentences yet. When they laugh, they’re happy; when they cry, they’re sad. Simple as that.

But bacteria turned out to be nowhere as primitive as I thought they would be. A molecule is not just a molecule. It can mean different things depending on the context, just like the crying of babies can mean different things: sometimes the baby is hungry, sometimes it’s wet, sometimes it’s hurt or afraid. Parents know how to decode those cries.

And to be a real translation tool, it had to be able to decode the signaling molecules and translate them depending on the context. Basically what the translation tool can do is to give us information on what the bacteria are doing right now and what they plan to do next.

Let me give you an example. Now I know we are not all micro-biologists here, but – and I have brought some bacterial data. They can be a bit tricky to understand if you are not trained but try to take a look.

Here’s a happy bacterial family that has infected a patient. Let’s call them the Montague family. They share resources, they reproduce, and they grow. One day, they get a new neighbor, bacterial family Capulet. Everything is fine, as long as they’re working together.

But then something unplanned happens. Romeo from Montague has a relationship with Juliet from Capulet. And yes, they share genetic material.

Now, this gene transfer can be dangerous to the Montagues that have the ambition to be the only family in the patient they have infected, and sharing genes contributes to the Capulets developing resistance to antibiotics. So the Montagues start talking internally to get rid of this other family by releasing this molecule.

And with subtitles: [Let us coordinate an attack.]

Let’s coordinate an attack. And then everybody at once responds by releasing a poison that will kill the other family. [Eliminate!]

The Capulets respond by calling for a counterattack. [Counterattack!]

With this molecule it makes all individual cells synchronized and respond at once. And they have a battle. This is a video of real bacteria dueling with swordlike organelles, where they try to kill each other by literally stabbing and rupturing each other. Whoever’s family wins this battle becomes the dominant bacteria.

And to be the true to the Romeo and Juliet story, sometimes bacteria even commit suicide. But I’ll spare you for that here.

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.

Each one of us will personally know how to deal with our own healthy bacteria and what to offer them to make them happy so they in return can protect and sustain our body.

And who knows? Maybe Google Translate will adopt this sooner.

So bacteria talk, they make secret plans, and they send confidential information to each other. But not only can we catch them whispering, we can all learn their secret language and become ourselves bacterial whisperers. And, as bacteria would say, “3-oxo-C12-aniline.”

Thank you.

 


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