To detect diseases earlier, let's speak bacteria's secret language

Bacteria "talk" to each other, sending chemical information to coordinate attacks. What if we could listen to what they were saying? Nanophysicist Fatima AlZahra'a Alatraktchi invented a tool to spy on bacterial chatter and translate their secret communication into human language. Her work could pave the way for early diagnosis of disease -- before we even get sick.

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.

(Laughter)

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 percent 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 focused 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.

And who knows?

Maybe Google Translate
will adopt this soon.

(Laughter)

Let me give you an example.

I've brought some bacterial data
that can be a bit tricky to understand

if you're not trained,

but try to take a look.

(Laughter)

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.

(Laughter)

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.

(Laughter)

And yes, they share genetic material.

(Laughter)

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.

(Laughter)

And with subtitles:

[Let us coordinate an attack.]

(Laughter)

Let's coordinate an attack.

And then everybody at once responds

by releasing a poison
that will kill the other family.

[Eliminate!]

(Laughter)

The Capulets respond
by calling for a counterattack.

[Counterattack!]

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.

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.

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.

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.

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?

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."

(Laughter)

(Applause)

Thank you.