- My name is Dr. Ethel Gordon.
I'm a member of Salem State Biology faculty
and treasurer for the Darwin Festival committee.
It is my pleasure to welcome
the Salem State University community
and our guests from the local area and beyond.
We are happy to welcome some of our area
high school students and STEM students of any age.
Before we begin, I'd like to remind you that we will have
questions and answers at the end of the talk,
and you will see on the bottom of your screen
a box that says Q and A,
you can write your questions at any time,
and Dr. Ogbunu will be happy to answer
as many as possible at the end of the talk.
So again, it is my honor today to introduce
our afternoon speaker Dr. C. Brandon Ogbunu.
Dr. Ogbunu is Assistant Professor
in the Department of Ecology and Evolutionary Biology
at Yale university.
He is a computational biologist whose research
investigates complex problems in epidemiology, evolutionary
and population genetics and evolution.
His work utilizes a range of methods
from experimental evolution, to biochemistry,
mathematics and evolutionary computation.
He has published on topics as varied
as the evolution of anti-microbial resistance,
virus evolution and also on health policy.
In addition, he runs a parallel research program
at the intersection of science, society and culture,
where he writes for publications including
"Scientific American," the nonprofit magazine, "Undark"
and the "Boston Review."
He gives public lectures and creates media
in this program as well.
Dr. Ogbunu's talk is entitled
Environment times everything interactions,
from protein evolution to the stories of us.
It is supported by an honorarium
from Bruce Perry and the campus center.
Please give a warm Salem State welcome
to Dr.C.Brandon Ogbunu.
- Great, thank you for that kind introduction.
I am so excited to be here.
From what I understand,
Professor Jablonski spoke this morning.
And that is to say that, that's a challenging act to follow,
it's an understatement.
I'm a tremendous fan of Professor Jablonski.
So, I'm gonna do the best I can after talking
after a legend but I think we're gonna be able
to have hopefully some fun today
and talk about some interesting ideas.
The title of my talk,
E by everything interactions from protein evolution
to the stories of us.
And I promise you that will make sense as we talk.
There's gonna be a theme that we're gonna use,
we're gonna introduce it early,
and we're gonna kind of run this throughout
my research program and one of the themes
is going to be kind of the way that I think about my actual
work in the laboratory and the work that I'm publishing
and the relationship between that
and the work that I'm doing kind of in life.
And these things are not kind of
as separate as one often thinks.
That scientists are part of a functional world
and that the ideas that we have can interact with the way
we live and the way we treat each other, et cetera.
I wanna thank a whole host of collaborators and mentors
and friends and people who have supported
various parts of this work.
Mentees are now mentors, that's the way this profession
works and I wanna make sure they are all acknowledged.
Some of these characters you might meet along the way,
but I wanted to make sure people were probably acknowledged.
Similarly, I wanna make sure all of the institutions
who supported this work in its various iterations and forms
through the years are properly acknowledged
and want to thank them for their administrative
and financial support through the years.
Absolutely, I wanna thank Professors Fisher, Gordon,
for your support and the rest of the Department of Biology
at Salem State for the kind invitation
and for facilitating my participation
which I'm honored by.
Again, all of the sponsors and organizers
of this great festival.
Really, really a unique and important event,
one of the most interesting of its kind.
So congratulations to all of you for putting
on another successful version of this
and I'm really, really thrilled to participate.
And perhaps last but not least, perhaps last but most,
all of you for taking time out of your afternoon
to listen to what I have to say.
Talking about what my lab does.
And I think, am like I'm one of these people
who every five years, I like to rebrand myself, right?
Not because I'm an actual company, but because I think
every few years I get different things to think about.
And I think one of the things that I've been trained
in a lot of different things, formally in population,
genetics and epidemiology
and mathematical modeling, experiment evolution.
But I like to brand myself differently.
It's kind of I like to brand myself
by the ways I like to think about nature.
That's actually how I would prefer to brand myself.
And really the thing that I'm interested in
is how interactions between actors and the environments
shape disease dynamics, right?
That's really, really kind...
I call myself a non-linearity nerd,
as in, you put one thing and another thing together
and something surprising happens,
in biology and the way that creates disease.
That's how I think about what my interests are.
And that really does run through my research program.
And I think really, really one of the things
in the title of the talk, right,
Environment by everything, is kind of considering carefully
what this notion of an environment is, right?
When we think about what an environment is
in biology and in society, right?
So, when a biologist is talking about the environment,
they're usually thinking about several things,
several types of definitions
and different types of biologists, right?
Think about it and apply it in their work a different way.
So for the individuals who are familiar
with the field called quantitative genetics
or population genetics, right?
Or if you're really a super nerd and you've heard
of this concept called plasticity, right?
You might be thinking about an environment this way.
Don't let the equations or anything like that throw you off.
All it means is this.
Let's just say you have person and one person two
or tree type one and tree type two and basically
you have hot and cold temperature and that different people,
some of you individuals in the audience
are warm-weather people,
some of you are kind of cold-weather people.
Some of you are morning people, some of you are not.
Some of you like to thrive at night, some of you don't.
The point is that organisms differ in the way
that we perform based on some kind of environment.
And that environment again, could be time of day,
it could be temperature, it could be a type of nutrient,
it could be a type of infection with something, right?
So the idea here is that,
that's one way we consider this environment,
it's thinking about the way different organisms
of different kinds or different mutations
of an organism, function across different environments.
For those of you that might be more informed,
we call this graph something
called a norm of reaction, right?
And for the high school students you can write that down.
Sound very smart in class, talk about a Norm of Reaction.
It basically demonstrates how an organism
performs across different environments.
On the right here is just an equation that people use,
where the information from this graph is used.
So that's one way a biologist think about the environment.
What's another way that the biologists
think about an environment, right?
Maybe something called micro-environment.
Now that is a protein, it's enzyme there, okay.
And that's not so much like, environment like time of day,
you're talking about proteins
at a very, very small scale here.
We're talking about things like temperature,
so not unlike we experienced temperature.
Do you think the enzymes in your body
that break down food might function differently
at different temperatures?
If you go to extreme cold, you think that might affect
their ability to break down food or absolutely, right?
I think the body kind of has this very, very sophisticated
machinery to make sure that,
you might have here homeostasis,
we have these things in place to make sure things
are functioning at a level and our temperature
stays at a level.
Because getting too cold and getting too hot
is bad for the way that we function.
So, even within ourselves, the way that proteins are folding
is gonna be very, very, very specific
to a certain environment.
But that's one way we often think about an environment,
is at a very micro scale, small, small, small, small scale,
and things that that does to us.
Another way we might consider environment, right?
Is this one.
I can have you guess about what this is a mapping of,
what we could probably guess,
it's kind of like either temperature you just heard
this from Professor Jablonski, right?
In terms of UV radiation, right?
Because as you see here, it's gonna be red,
it's gonna correspond to a temperature here.
And of course, this correlates in some ways
with UV radiation.
But the point is, we could mean something global.
We could mean kind of like the global sense.
And we're talking about climate change
and these big processes that apply broadly
across hundreds and thousands of miles.
That's another way that a lot of us
think about environments.
And I think environmental sciences, for example,
looking at how climate change influences,
kind of the population of a species of bear
is often times thinking about it this way,
the environment has kind of a global process, okay.
That's another way that biologists think
about the environment.
But another way we think about an environment, is this.
And what's that?
So that's Queensbridge housing projects.
That's the largest, I think still the largest
public housing projects in the country.
I'm not from there, but I am from New York city,
and I did spend time growing up in public housing.
And so the idea here is, this is different.
This is kind of where you live,
and who you live with and who you're connected to
and who you talk to and where you work
and where you go to school.
This is another important aspect of what our environment is.
And it turns out that, right?
This one, is connected to this one.
And what am I looking at here?
This is in the 1930s, okay.
This is the 1930s, what are we noticing here?
Right?
This is segregation.
This individual is drinking out of a water fountain
that is relegated to individuals of a certain color, okay.
This is the United States, all right.
So the idea here is, this is another way
that people think about environments.
This is kind of a historical thing.
That's not so historical, right?
'Cause it obviously has a role
for the way the world is lived today.
But the idea here is we can think
about environments, many, many ways.
We can think about it with regards to quantitative genetics.
We can think about it with regards to physiology,
as in the things going on within the body
and the way the body is kind of maintaining temperature
and things of that nature.
We can think about it in terms of climate,
that's the way we consider the environment.
We can think about our environment
as like who you actually live with
and where you live and what neighborhood that is.
And then we can think about it
as what are the forces that influence where we live
and why do we live on this side of the town
and not that one?
And why do these people live on this side of the town
and not that one?
All of these things are important actors,
when we kinda of thinking about a biological phenomenon,
especially one like disease.
They're all important actors, right?
And I think my work really kind of invokes
all of these different factors in different levels,
in different parts of my research program, right?
Now I can't ask everything and some of these things
I'm obviously better trained to aggressively pursue.
So the talk you gonna get today
is mostly in these realms, right?
In terms of the basic science
that I do in my laboratory and a little bit of this.
But I think these other realms like this local,
lived and corporeal, this historical,
structural and psychological.
People in high school, corporeal,
it's a good word, write it down.
These are other aspects that my research program
is currently interrogating in the context
of looking at disease, right?
And in looking at life.
And so that's what I'm gonna talk to you about today, right?
When I say environment by everything interactions,
my point is, there's not a problem in biology, none.
With one of these factors isn't actually influencing
the biological phenomenon of interest.
I don't care if you're interested in trees
or plants or human disease or rabbit reproduction
or photosynthesis, something in here is influencing that
and I think this influences biological phenomenon
more than we think and in ways that may surprise us.
Okay, right.
And so we'll talk about today, right?
Mostly is the evolution infectious diseases,
like I told you.
Evolution at the level of genes and proteins,
and mostly I'll be talking about how micro-environments
influence adaptive evolution, okay?
So what we'll get then, right, we'll do something today,
the way we'll kind of proceed is we'll start with a problem
like anti-microbial resistance in malaria.
Malaria is a disease that isn't so much a problem
in the United States but remains a very, very large problem
throughout the world, in Asia
and certainly in Sub-Saharan Africa.
And of course it is kind of transmitted
by the anopheles mosquito.
And of course there's a lot of resistance to the drugs
that we use that were once effective
and still are in some places, right?
So we'll take this problem, we'll kind of think about
how to think about antibiotic resistance in general,
so not just malaria, but we'll take that to thinking about
kind of problems and how proteins evolve
and we'll do that to think about big questions in evolution.
So, we'll start with an individual problem
and how that's influenced by the environment.
And then we'll walk that to hack,
ask some bigger questions about who we are, right?
Alright and so I use proteins.
Now, most of you when we think about proteins
that our daily life and our dietary life,
we're thinking about,
how much protein is in my protein shake?
Or am I getting my 20 pounds of protein?
Again, I know when I was in high school
and I heard there were some high school students here,
I was a little bit of a jock
and I played high school football.
And I was just like obsessed with just...
I thought, mostly incorrectly,
that if you just like feed your body protein,
you'll grow big muscles and you'll be in the NFL.
Doesn't work that way.
But the point is protein consumption is very, very important
for kind of maintaining all this function
that the body is, right?
So the proteins are a class of biomolecule
and they're really responsible
for lot of the kind of acting within ourselves.
A lot of the switches, a lot of the things
that break down enzymes, a lot of the things
that perform functions, are protein.
And so we'll talk about kind of individual proteins today.
Right and we'll talk about these things,
kind of, proteins can be considered two things.
Proteins can be considered, right?
They're real physical things,
I mean, these are made of kind of atoms and the things
you learn in chemistry with rows electrons and all of that.
That's real, that happens here and we also study that.
But today we're gonna use a simpler way of thinking
about proteins, we won't go into the electron level.
I'm not gonna make you invoke your high school chemistry,
your college chemistry knowledge really.
Thinking about a protein kind of as a collection
of information and when we think about it that way,
I think a lot of different questions arise
that make it easier to understand, right?
So the way it'll go, we'll talk about protein evolution,
we'll talk about reverse evolution,
and then we'll talk about this last part.
So, on protein space.
Now, junior people in particular, okay,
I don't mean young, but junior,
it doesn't matter how old or young you are chronologically,
but wherever you are in your scientific training,
I'm gonna tell you a really, really useful piece of advice.
You should be a historian of your own field.
If you're interested in marine biology,
read the history of marine biology.
How did the ideas come up?
How did we arrive at those ideas?
Who did them first?
Why do we measure things this way or that way?
It's a very important thing.
If you're interested in a botany,
if you're interested in medicine, right?
And the reason why I say that, it's not just because I want
you to spend kind of needless time reading,
it's because when you study the history of a field,
you identify some ideas that may be wrong
or need improvement or on the positive side,
things that are like inspiring that other people
have forgotten or aren't being kinda used properly
or maybe can give you a new framework
for thinking about your work today.
You think just because work happened a long time ago
that somehow they were less intelligent than you are.
That ain't true, okay?
They thought just as clearly about nature
as you did if not more,
the difference is they just didn't have
the technology that you have, right?
This story here is about how I started reading historically
about my field and it ended up
changing my career forever, right?
Another cool thing about reading about history is,
this is not just true for science.
You ever read a history book and you identify people
in history that you're kind of like?
You're like, I'm kind of like this person,
I mean or you wish you were like this person
but there's strands of their personality.
I just read a great biography of Frederick Douglas
and of course, I'm not saying I'm Frederick Douglas,
my goodness, one of the greatest Americans ever.
But what I'm saying is there's threads of the way he thought
about society and himself and I was like,
I really do feel that way.
And so that it's also important to do for science.
And for me, the figure that I discovered in reading science
was somebody named John Maynard Smith.
Now John Maynard Smith, was an Englishman, brilliant.
And as somebody who thinks a lot about the world
the way that I do and I've kind of adopted
John Maynard Smith as somebody that I really admire, okay?
And what did John Maynard Smith do?
When I was in graduate school, I read this important paper.
Now, don't worry about reading about it now,
it's called Natural selection
and the concept of a protein space.
Now mind you, at this time
I was not studying proteins, at all.
I was not studying proteins at this time.
I was actually studying microbes,
but I read this paper anyway
and it had a very big impact in my life.
Note the date 1970, okay.
It's 2021 now, but 2020 was his 50th anniversary.
So I ended up writing a reflection on this, right?
In genetics, right?
A very kind of nice journal.
I wrote a reflection on the importance of this paper.
And like I told you, again, I am not a historian.
I'm a population geneticist who studies proteins.
So why am I doing a historical take on this?
Well, doing the history of this paper gave me real ideas.
And so what I discovered a couple of things, okay?
About kind of the origin of this paper.
It turns out that this paper was a rebuttal.
So the paper here was a rebuttal,
to a paper that was published in 1969
by somebody named Frank.B.Salisbury.
Now, Frank B. Salisbury was a well-regarded plant
physiologist, very well-regarded.
And what Frank. B Salisbury said is the following,
"if life really depends on each gene being as unique
"as it appears to be, then it is too unique to come
"into being by chance to mutations."
This is also published in "Nature" one of the most
prestigious journals in the world, right?
And basically what Frank. B.Salisbury said.
is that natural selection can't really solve
a lot of complex problems, right?
That there's gotta be some other force.
If that sounds fishy to you stay with me.
John Maynard Smith, read this paper and was like, no, no.
I mean, I don't know if John Maynard Smith
said that, because I haven't met him or asked him
but I'm imagining to myself that John Maynard Smith
was like, no, no, no, no, no, no.
And John Maynard Smith said the following, right?
If you notice the first line, "Salisbury has argued
"that there is an apparent contradiction."
He basically says, you're wrong
and I'm gonna demonstrate why you're wrong.
And the model of protein evolution I want to discuss
is best understood by analogy with a popular word game.
I don't know how many of you all are gamers.
I'm a huge gamer, we'll get to that later.
I love board games, I love video games
but one of the most important papers ever
in evolutionary biology, didn't really have any equations
in it even though it was written
by a brilliant mathematician.
And it used a child's game to explain in a central concept.
And I don't know how many of you have played word ladder
but this is what word ladder is like, right?
In word ladder, what do you do?
In word ladder, you're basically changing one letter
at a time, you wanna go from word and you wanna to gene.
and you wanna change one letter at a time.
Now, what is the winning pathway?
The winning pathway is the one where all the words
make sense along the way, which one would win here.
If I was in person, I'd like call on you
and maybe embarrass people, I'm just kidding.
But the point is, I'm not gonna do that here.
You all are so smart, you probably already landed here.
That's the winner.
Why is that the winner?
That's the winner because all the words
are meaningful words.
Look at this.
That's a word that's meaningful.
Werd, not really.
Wene, not really, right?
Gord, hmm, not really, right?
Wond, sure, wone, nope.
All these other ones have a word in the pathway
that does not make sense or you know
it doesn't really make sense.
Whereas this one, all of the words make sense.
Now, what was John Maynard Smith trying to say, okay,
John Maynard Smith was making a very important point.
He said that adaptive evolution works this way.
It does not need to solve word to gene in one step.
It is solving it bit by bit by bit by bit.
Evolution's working slightly and it's searching a space
and that's how it identifies, long-term solutions.
The only thing that has to happen to solve a big problem
in evolution, one thing has to happen in order to solve
a big problem in evolution that the things along the way
have to make sense.
So as long as the thing, it makes sense,
meaning what that the organism functions.
So as long as the thing that maybe we're evolving
a new protein, maybe we're evolving a new enzyme,
maybe we're evolving some kind of pigment.
We talk about professor Jablonski's work earlier.
Maybe we're talking about the genes having to do
with skin pigmentation or eye color or what have you.
The only thing that has to happen in order
for you to get from one color to a much different color
is that the things along the way have to make sense.
And this is the way adaptive evolution
actually works, right?
And we'll see that, okay?
What happened to this debate?
So John Maynard Smith goes on to one of the most
illustrious careers he was already famous at that time.
Goes on one of the most illustrious
careers ever, ever, okay?
You ask different people in different fields,
they'll tell you a different John Maynard Smith.
Some of you all are really, really...
Some of you are game theory nerds, right?
People who might've heard a game theory,
might've seen a beautiful mind, right?
Where with John Nash, where he came up
with kind of some of the basic tenants of kind of,
how game theory is applied and economics...
The mathematical foundations of game theory,
which were applied to economics
ended up winning a Nobel prize in economics.
John Maynard Smith was one of the people
who brought it to biology, okay?
So my point is, John Maynard Smith was a brilliant person
who thought really broadly across disciplines.
What happened to Salisbury?
Who was well-respected, not long after that paper
got published, he didn't rebut, didn't really fire back
at John Maynard Smith.
He ended up starting to write books like this, right?
And then of course his Magnum Opus.
So you can see the beginnings
of creationist logic that he was trying to hide
in that scientific paper that John Maynard Smith rebutted
I must say the one book that Salisbury did publish
that I am fond of, or rather, not so much fond of
but I am kinda intrigued by and I'll empathize
with, is the one on UFO's, that one I'll give him, okay?
That one I'll give it, right?
But the rest of the stuff, again, was trying to kind
of hide some creationists ideas and evolution.
So really it's this protein space concept
that John Maynard Smith mentioned
is a very, very, very rich concept.
A lot of ideas in modern biology, sophisticated
and hyper nerdy ones, live within that basic word game.
That basic word game of going from word to gene
you can get a lot of concepts.
There's this thing called the fitness or adaptive landscape
that some of you might've heard of that falls
directly out of that.
That predated protein space.
But my point is from protein space
you can get a lot of cool things.
And that's really what my research program did.
I think I learned protein space first.
And then I learned all of these sophisticated ideas,
but one of the cool things is pay most attention
to this last thing.
The thing about protein space going
from word to gene is that also implies
that evolution might be able to be predictable.
If we know what all the words are and we know which words
make sense, what if that's true for proteins?
We can maybe predict the steps
that evolution will take, right?
So you can conceivably turn evolution
into a predictive process, right?
So let's think about that for a second.
Say, for example, we talked about this.
And this is basically the same word game
but just kind of reorganize it to a different graph, right?
Word to wore to gore to gone to gene,
remember all those paths, all that is in here.
So this is basically the same thing I just showed you.
This is the exact same John Maynard Smith example
but reorganized, right?
What if I said, we can apply some of these to real
biological data sets, right?
So for example, instead of a word with letters,
instead of this being W, O, R, D, what if I was talking
about an enzyme?
And instead of four letters, these were four mutations
that were associated to resistance to a drug, right?
And what if I told you there's a problem that looks exactly
the same and there is one, resistance to drugs
in malaria parasites.
This rises from four mutations, okay?
So you can take this exact same game, word to gene
and make it into this different framework,
these are four amino acids, substitutions.
And you know, you'll look up
what they are but that's not the point.
The point is, you know, these are four amino acids.
This is kind of their natural existing form,
this is the resistant form.
We can actually look that way.
So for example, we can take this exact map
that I told you is a basic word game.
And we can basically do this.
You have the problem of anti-malaria resistance.
It's amazing, sometimes biology just works in your favor.
I never would have stumbled on this framework,
if I was not reading broadly early in my career.
And I saw this and I said, wait a minute,
this is the exact same problem.
So once you have a problem like this
you can begin to think about things in a different way.
You can think some questions about antibiotic resistance.
You can say, all right, well, is it predictable?
If I know that I can go from word, to wore, to gore,
to gone, to gene, maybe there's something similar
for resistance and if we can predict resistance
then maybe we can treat it better, right?
And really what are the forces that are shaping right now?
Don't get caught up in the big word, trajectories,
the paths, right?
I already told you that it's meaningful words
but what are the extra forces that are shaping it?
So what are the forces?
We said, meaningful words, word, that makes sense.
Wore, that makes it, gone, gene, that makes sense.
But what if I kinda gave you a little bit
of a wrinkle right now?
There's still a little bit of a wrinkle in here, right?
What if I said that John Maynard Smith was not being
culturally sensitive to himself, because in British English
this is not the only way to win this game.
This is American English, in British English.
G, O, R, D, is an acronym because you know they throw
that little, oh, on in British English.
I don't know why they do that.
But you know, they throw this little, oh, on,
which means that G, O, R, D, is a word
in which case that's an accessible pathway.
So there's two different pathways meaning,
what is meaningful, what evolution can do
is going to be dependent on the context
and the environment that you're in.
You can't simply say that, there's one
route through evolution.
What's gonna happen depends on your environment.
It's going to depend on your environment, okay?
Now what are those environments?
We use this British English example to say that, okay, well,
what's meaningful in British English is different, right?
But what would be the environment
be in the context of antibiotic resistance?
What would it mean?
Well, we're talking about an antibiotic resistance.
We're talking about several things.
We're talking about the type of drugs you use,
how much of that drug is in your bloodstream.
So all the people, there's like a million people
in this seminar, which is amazing and very intimidating.
How many people in here?
Let's just see, oh, whatever.
I used to have the number, but whatever, it doesn't matter.
I think it was 130 something or something crazy like that.
The point is, if you take all the people
in this talk and I give you all the same drug
maybe even let it be aspirin.
Do you think you're all going to have the same amount
of aspirin in your bloodstream in two hours or two days?
Of course not.
That's why I have these people here,
you know I'm from New York city
and it's kind of crazy seeing this now, right?
Given where we are.
But the point is, humans are kind
of genetically diverse in a lot of ways.
And so the point is we metabolize drugs differently.
So my point is that there's a malaria parasite
in this person and they take a drug
and this person they take a drug and this person
they take a drug, that malaria parasite
will experience different things in different people.
And if the environments are different
then maybe we need to be asking questions
about how that influences how we move
through the game, right?
So how do we do this?
Don't get confused here, it's just a combination
of experimental stuff and computational stuff.
And my work's here.
So basically what I do is I engineer actual proteins
in the lab with all the words, think about this as the word
to gene, you engineer all the mutations
into the protein of interest, right?
There'll be the one that we use to treat with drug, okay?
You engineer all those, then you grow all of those different
variants, all these words in a range of drug concentrations,
different environments, you grow them in a bunch
of different environments and then you use simulation,
this is where computer science comes in.
Your computer science can tell you, right?
The organs can tell you kind
of what's gonna be likely to happen, okay?
So we can do it this way.
We can ask questions like this.
We can say, all right, well maybe in drug one or drug A,
you get this path, maybe in drug B
you get this one and maybe in drug C, you get this,
this is a perfectly reasonable question.
And so we can ask these questions
about how drug context influences the way evolution works.
The same way I just told you
that American English looks this way
and British English gives you another way.
Conceptually, it's the exact same thing
as John Maynard Smith said in 1970
that again applies to a word game, okay?
We can ask that question and we did.
So we run simulations and I think if you're...
Let's focus on the left, I just put the right there,
just because some of you wanna see the,
computer nerds wanna see the actual simulation.
This is an example of what a simulation looks like.
So basically when you treat with no drug,
you have all, you have a simulation where you have
like a million of these and they replicating and mutating.
So it's like almost like a computer game, right?
And this way we're gonna treat with no drug.
And in that situation, this one wins.
That's the one you're seeing at a hundred percent.
One point is a hundred percent, right?
But if you treat with a low amount of a drug
called Pyrimethamine, look what happens,
evolution moves here.
If you treat with an intermediate amount,
evolution also moves there.
So good, okay, we treat with a low amount,
we treat with an intermediate amount.
we get the same solution,
But when we treat with a high amount, what do we get?
A different solution.
Now that's for a drug called pyrimethamine.
I don't if I have anti-meds in the audience
but pyrimethamine is very similar to another drug
we use to treat malaria called cycloguanil, okay?
We do the same thing for cycloguanil, okay?
But in cycloguanil, low amounts of cycloguanil,
get you here.
Intermediate cycloguanil gets you there
and high amounts of cycloguanil gets you here.
So putting everything together what do we have?
This is kind of pyrimethamine one drug,
this is a very similar drug called cycloguanil.
The colors mean different solutions.
So basically at low drug, you get one solution, at high drug
you get another and low concentration of cycloguanil,
you get one solution and intermediate high, you get another.
And so what did we just discover?
The direction of evolution depends on the type of drug
you're using and the concentration of drug
that you're using.
And this is knowledge that we don't really appreciate
right now, right now you come in with a certain infection,
they give you drugs.
That drug isn't tailored to you,
that drug isn't tailored to what's inside your body.
And I'm not saying that's wrong, based on the knowledge
that we've had but the future of treatment of things
is going to be a lot better, right?
Like the personalized paradigm, as in, Professor Gordon,
Professor Fischer and me, the drugs should be tailored
to very specific things about the way our bodies work
but kind of what is the, you know, if say there's a good
drug to treat some kind of infection we have,
the drugs should be tailored
to the particulars of us in the drug.
And we need to understand this information.
The other thing is how resistance happens
is gonna kind of be dependent
on these things about us, right?
But more broadly if you wanna make any predictions
in evolution and we tried do this climate change,
this has come up in COVID, which is an area
of my research, right?
In terms of kind of being able to predict
what's gonna happen and when and how, you know,
why it's been so difficult to understand?
The reason why it was difficult to predict the evolution
of the strains, is because COVID-19 is not one disease,
it's a bunch of micro outbreaks in a bunch
of different places, where people are connected differently,
live differently, are structured differently, have access
to different things, have access to different healthcare.
And that's gonna influence again, right?
The way that it evolves, which is why of course
it's been challenging to keep track of, right?
So that's another example,
another unfortunate example, okay?
So I talked to you just now
mostly about evolution of resistance.
Now some of you are very smart, okay?
And you heard that and were like, all right, cool.
What next?
How do we actually treat this more intelligently?
What do we do next?
And there's a lot of ways that we can actually
you know, that have been proposed
and are being used to treat.
One of the things, one of the strategies we kind of thought,
you know, it's still on the table
but we kind of thought historically might be a good way
to get rid of antibiotic resistance is,
what if we just took drug away and let evolution
evolve back to the treatable form?
And then we can treat the treatable form.
That seems like a pretty reasonable
and smart way to go about doing things, right?
So you kind of were but here's the question
is evolution really reversible?
Turns out there's a big literature on this, right?
It turns out, there's a big literature on this, right?
About reverse evolution.
Do things happen the way they move forward?
And it's kinda like a philosophical thing almost, right?
About the ability to undo things right?
And it turns out this Dollo character has a lot.
Now Dollo was talking about from paleontology,
I do not study paleontology, okay?
I mean, I love it and it's cool.
And I'm into all that, I'm into Jurassic park
as much as everyone else is but I do not study that.
But this idea is a profound one.
"An organism is unable to return even partially,
"to a previous stage already realized in the ranks
"of its ancestors."
Meaning you ain't getting back.
If you evolve a function, it's very, very difficult
to unevolve that function.
And so Richard Dawkins was right about this, he said,
"It's just a statement about the improbability of following
"the exact same evolutionary trajectory twice."
Or basically, if you hard for you to do one thing one way,
it's gonna be really hard for you to do the exact opposite
thing in the other direction but that's Dollo's law.
And so my point there's law that exists that says,
evolution reversibility is rare.
What I sought to do, was test this but for the problem
I just showed you.
So here we go, right?
And if you really think about this, because again, right?
So here we go.
Let's do the same pathways, it's a very similar enzyme,
but in reverse, that is, we have the resistant variant here.
Our starting point now is the most resistant one
and we wanna get back to the word, right?
So now we're at gene and we wanna go to word, right?
We're at gene and we wanna go to word, now according
to Maynard Smith's game, that should be reversible.
You can go back because meaning in that situation is simple.
But biology we're gonna find works differently.
So what I did was I did the same thing I did before
I engineered all of these in the laboratory, I grew them
up and basically measured their kind of protein function
across a bunch of different environments, right?
And I ran the same simulation that we talked about.
So, I mean, we're all experts now.
So I basically said, all right, let's start the simulation,
this one is the most resistant one.
I'll start it with treating with a high amount of drug.
But again remember, my goal is to reverse from gene to work.
And so at high drug, this one wins.
And this is pyrimethamine, when I go to lower drug
the population moves here, one mutation closer.
So it's like gene, then gone, right?
I think that helps, right?
And you can see the simulation here,
if you really wanna see it.
I go even to no drug.
So I start here and I use no drug
and I'm still trapped there.
I would expect that if I'm using no drug,
remember the first time we talked about this result,
this one is the one that's favored with no drug.
So why can't I get back?
So I said, okay, well there's gotta be something wrong
with my work, right?
So I basically sat there, I was a lot more careful.
So I said, alright, this is what I'll do.
I'll start the population evenly distributed here,
that means, let's just say, I got a million here,
a million here, a million here, a million here
and they're all mutating.
And I said, alright, because I've given this a push
I've started giving it a head start, I'll be able
to get here, right?
With no drug.
And what happens, none of them get there.
In no simulations did I ever get back
to this, what we call wild type, which is the one
that I would expect to grow the best in no drug, right?
And why is that?
Why is evolution irreversible?
Why can't I go back?
And the reason why I can't go back
is because there are things called compensatory mutations.
Now, what are those like?
Let's think about some analogies for those.
What those are is, right?
If I'm on one side, if I have a word here
that is struggling and the best version is all the way
on the other side, in order for me to get there, right?
Again, if this one is the best one,
these have to be incrementally a little bit better
in order for me to get here.
But what if I have a version of a word
that grows pretty well when there's no drug
and grows pretty well when there is drugs, right?
And it turns out this one mutation
gives you pretty good growth with and without drug
and because of that, it prevents
you from getting back across, it just to kinda go here.
It turns out anytime you get this kind of end mutation here,
anytime you get it, you have a hard time
crossing back across.
So the part of the key here is that the results
are not surprising because reversing resistance
has been very challenging.
Look at this, long-term persistence of resistant
Enterococcus species after antibiotics to eradicate.
Persistence of antibiotic resistance...
We are having a very hard time in nature
getting rid of resistance, that's bad news.
That means once you have a resistance in high numbers,
there are mutations that kind of keep it there
even if you take drug away, so the only wake thing
we can do now and we're doing this right?
We can do two things.
We can either be more responsible with the drugs
that we're using, which is one thing we've started to be.
I think that is one that people have learned, A.
Or B, we can come up with new drugs
that maybe target the resistant forms.
And that's actually a frontier in medicinal chemistry.
And that's some work that we're doing.
How can we kind of identify ways
to treat the resistant forms, right?
Again, frontiers of the way we treat things, okay?
So what I told you so far, right?
We've gotten a lot...
I mean, this is some interesting things.
I took you from a historical thing, right?
That John Maynard Smith came up as a rebuttal.
We took that concept, we wielded it to understand
how antibiotic resistance, anti malaria resistance evolves.
And then we added this dimension of the environment,
if the drug environment looks this way or this way
you're gonna get different solutions, right?
Which is a very, very kind of powerful point,
that kind of influences how we think
about antibiotic resistance.
Then I said, all right, well reversal, right?
We used the kind of same word analogy
think about going from gene to word
and it turns out it's really, really hard
to do that in evolution.
And we demonstrated why that is the case, okay?
But in the last part of this conversation we're gonna have,
we're gonna talk about why it is.
We're gonna talk about the other kind of dimensions
of the environment, okay?
The ones that kind of influenced my ontogeny
another good word, high school students.
How I got to where I am, okay?
And I think this is an important part of the environment
because this one does not function at a molecular level,
this one is funky at a macro level but it influences
the work that I do and why I do it and how I do it, okay?
That's what we'll talk about here, alright?
The role of the scientist in society,
my own attempt to reconcile environment culture
and the moment.
And this is why I do stuff like this, right?
Because this is part of my job.
And I take a lot of pride in this part of my job, okay?
So I told you about this, right?
We talked about housing projects here,
we talked about kind of the history of this country
and the way this was part of this country
and I talked about what do these have to do with my work?
Oh, you just told me all this nerdy stuff
about molecular evolution, oh come on,
what does that have to do with your work?
Well, number one, I think this work on the other side
of my research, I think about the way pandemics spread.
This is very directly related to my work.
And what we find is that when we actually
think about kind of the way people live
and how they live well, that's connected to this, right?
How housing was built and who had access
to where that persists to date.
And that has history.
So if I'm interested in the way COVID is spreading
in certain communities, well I have to understand
all these things.
So my point is this actually directly feeds into my work.
But more than that, it feeds into how I think
about what my role is in society, right?
Because when I go, why am I doing what I'm doing?
I'm doing it because of this woman.
It's my mother, okay?
Fannie Louise Bennett, right?
And unquestionably, I mean, that is an amazing afro.
Okay, I'm sorry, alright.
I don't have, I didn't get...
I'm certainly not as good looking.
I don't have this great hair, but most importantly
I'm not anywhere near as smart.
If she had my opportunities
she would be a better scientist than I was.
She grew up and she's from Baltimore as a little girl,
she experienced the end of Jim Crow, visiting her family.
And she said, she didn't understand why she had to sit
on the back of the bus.
This is my mother, right?
So what I'm saying is in a different context,
she would have been way better than I was.
So my charge in this profession
is to identify Fannie Louise Bennett's
and give them an opportunity.
And the way that I've chosen to do that, right?
Is to kind of speak out on things
and that means talking about the relationship
between stuff like this and stuff like that, okay?
Because it is these forces that were responsible
for her being unable to be in my seat.
She should have been a National Academy's member now.
She was a teacher and that's about as far as women
her color went in that day, okay?
And so it is my job to do this and it's my job further
to talk about the relationship between this and this.
This is my job as a scientist
to articulate all these things.
So how do I do that?
I tell stories, okay?
And I talk about the experiences.
So I tell some stories, number one and number two,
I write, I love to write, another thing my mother
was good at and better at than I was, I write.
And I write for a lot of things,
I'm fortunate to be a columnist at "Wired,"
where I write a lot about live experiences, alright?
And you know, just recently I kind of wrote this.
This is my most recent piece where I actually
took the stuff that you just heard about disease
and I actually made an analogy for a lot of the problems
we're experiencing, right?
People like this, I mean, you know,
regardless how you feel about politically,
the point is I try to be smart and careful and creative
with about the way I kind of write and talk about society.
But more than that, I think about what my mother's influence
was, I do this kind of work in part because it is my job
to identify other people and inspire other people.
Because when I was a young person, right?
I thought about this type of work
and I would think about, why should we do this?
Does it distract from my real work?
And how does it detract?
Well, number one, what is real work?
This work formally helps my research.
I have built bridges scientifically because of this work.
But more importantly, this is my way of paying it forward
because I would not be a scientist,
if it was not for my mother
and I would not be a scientist if it was not for somebody,
you might've heard named Stephen Jay Gould,
who wrote and when I was in college, I had never ever
I barely taken biology in high school.
I think I like slept, dude.
I was not a good high school student, but I read.
And when I read Stephen Jay Gould essays
in natural history, I was like, this is brilliant.
Steve Jay Gould wrote, I'm a sports fanatic.
He wrote one of the best essays ever about baseball,
about Joe DiMaggio's 56 game hitting streak.
He's also a Yankees fan from New York.
So you see a little Yankee thing there.
Maybe that's gonna get me deleted from this call,
I know we were in red sock country.
I'm nice, I promise.
The point is this is the kind of stuff
that kind of inspired me to be a biologist.
And so all of these efforts, both to pay homage
to my mother, identify people with her
and to kind of pay back people like Stephen Jay Gould,
who inspired a generation of people like me.
This is as much of a part of my research,
as everything else that you've heard.
And with that I'll end and I'll take some questions.
Thank you.
- Thank you very much, Dr. Bruno
for a wonderfully engaging talk, sharing
and into splicing biology, genetics, and culture.
My first question to you is from one of my colleagues,
Dr. Amy Sprinkle, who's a microbiologist.
What is your reaction to the recent announcement
that AI has solved protein folding?
Do you use AI in your work studying protein space?
- Great question.
So, my reaction, well..
It's funny because I'm currently writing,
I'll make this relevant...
I'm currently writing a 20 year, scientific American is
running a series of essays on the 20th anniversary
of the announcement of the human genome project
but then that's the completion of the draft
of the humans genome which was 20 years ago.
And since then, I've always leaned towards being skeptical
of things until they actually deliver me something, right?
So I think right now it a very, very kind of...
It's cool, the example problems that they've demonstrated
are very, very interesting but yet deliver me a drab first,
deliver me some kind of fundamental understanding
and then we'll talk.
But right now it's a gadget and I think it allows
us to explore certain things.
But I think I'm very, very, very skeptical.
You know, it's not so much skeptical.
I wanna see it deliver before you're gonna get me.
But I think it's a cool technology.
I do use some AI and machine learning
in my work in various ways are kind of like lowercase AI,
not big time, little stuff.
But I'm excited about the applications,
but like the notion of having solved
to the protein folding, like no,
I think let's all slow our roles a little bit.
- Dr. Ogbunu, I really appreciate your willingness
to connect science and the humanities and that we are all
in this as a way to try to understand things.
And I'm in all of your work, thank you.
That's wonderful.
- It's okay.
- A student has asked when organisms evolve
antibiotic resistance, is it similar to how we develop
antibodies when a foreign entity is detected
in our body or do they evolve proteins
and enzymes that break down the antibiotics substance?
- That is a well-informed, one of those questions
that reveals the question asker
knows what they're talking about.
So let's get to the first part of that.
Is it like the generation of antibodies?
I mean, I've made fun of immunologists for 20 years, right?
I'm like, y'all don't know what you're talking about.
It's just a mess.
And I think, they've had their moment here
and in COVID because ,boy, have they risen to the occasion.
And I think in part is, because I think the process
of antibody generation just used to look so messy.
I mean, I used to look at an immunology textbook
and it used to look like,
I don't even know what the language is.
I mean, you pick a confusing textbook
and it'd be like, what is this?
What are these cytokines?
There's no theory here at all.
It's just like a bunch of kinds of things spinning out.
I'm like, let me get this out of my face
because there's no logic here, right?
I think that's no longer the case.
And I think the process of antibody generation
is very, very evolutionary.
So the process is the right 'cause there's a selection
process, so at a fundamental level there is a lot
of similarity about the process with which we generate
antibodies and antibiotic resistance.
So number one, I think at a level the processes
are very, very similar absolutely.
And the people who actually are studying this had a kind
of careful even mutation by mutation level, the way I am.
So that's number one, number two you said
something else very intelligent, which was
about microbes that break down and that's also true, right?
That's also true.
So the mechanism of resistance that I was talking about
that's only one mechanism of resistance.
And that is you have an enzyme
that blood needs to metabolize and you treat it
with drug and then mutations, what do they do?
They just close off that site
and the drug can't get in anymore.
That's the mechanism I'm talking about
but there are many other mechanisms
through which a drug have efflux pumps.
Sometimes they have pumps where it's like the drug
comes in and the microbes just like, bye, peace.
That's different.
Now fundamentally I think as a process level,
it's very, very similar
but mechanistically it's different.
So the things that I talk about still apply
but they may be applied differently in some ways.
So, these are excellent questions.
- Dr. Ogbunu hare's another very topical question
given the COVID environment we're in.
And the question reads,
have you applied your work on protein space
on the Sars-Cov-2 protein and what does that entail?
- Excellent, excellent question.
So I think most of my work in Sars-Cov-2,
until pretty recently was more a mathematical modeling
and more of the high scale side, I actually haven't done
but I am getting into molecular work now.
And to give you another example
of how the public work helps the science,
I wrote an article about COVID in the "NBA for ESPN", right?
I'm being a hundred percent serious.
And that is how I have my new collaboration
because somebody saw me articulate the issues,
with controlling COVID and they were like,
well, these ideas are good.
(laughs)
You wanna collaborate?
I was like, yeah.
So what I'm saying is, right?
So the point is what are the questions to be asked?
And I think they are powerful and important
and they relate to the problems with COVID right now.
And what are they?
This protein, this spike protein,
everybody's kind of favorite or least favorite
depending on how you think about a protein
in the world right now.
That is basically the one that's underground mutations
in all the strains and will continue to undergo mutations.
One of the fundamental questions is can we construct
a word to gene map for that spike protein?
And do we understand what all the different mutations.
Now it's gonna be a lot more than four mutations.
We're talking about dozens of mutations.
So there's gonna be a huge map
but the conceptually it's similar,
conceptually it's similar.
Is there a route that this thing
can take to evade community, right?
So my point is the questions are being asked.
Like the people who are doing it
aren't phrasing it that way.
But the questions are being asked that way.
And I am similarly interested in that.
There's a technique called deep mutational scanning.
And with deep mutation scanning is you can basically
take a protein and like create,
like tens of thousands of different variants
and measure them in some kind of phenotypes.
So in my case, it would be like, how will you grow in drug?
But the phenotype might be, how will you bind an antibody?
And from that, you can create a mapping
and you can basically say, oh okay, this one,
this one, this one, this one, this one
this one are gonna be a problem.
Oh, let's sequence and watch out for those.
And we can have something called a watch list,
an epidemiological watch list where we're sequencing
in nature and we're like, oh, maybe this one here
isn't a problem yet but if it gets this one mutation,
this one's gonna be a problem.
Let's mobilize resources here.
So this is where we wanna go with this,
we wanna make surveillance informed
by details of that protein space, right?
And I think this is what I hope,
this is what I wanna do with my work.
when we get these samples, I'm getting samples.
I can't say which.
From one of the leagues, from one of the sports leagues,
I'm getting samples from one of the sports leagues.
It's pretty cool.
- Nelson, do you have other questions?
- There's none from the audience but I just wonder
about, do you see this being a continual race
or do you see that there being some way to actually
instead of just recognize that spike protein
are there ways to attach things to it, to disable
kind of thing, because we got to get rid of this.
I mean, this is an awful thing.
- Excellent, you're totally right.
So a couple of things, number one, I mean
the best way to have dealt
with this would have been to have this under control
So you didn't give the opportunity for this to happen.
- Yes.
- If we had control of this thing, like we just,
if you give evolution enough chances
I mean, we got a lot of examples of that
you know what I'm saying?
It's just rolling.
It's had all these chances.
I mean, it's been partying, it's just infecting
all these people were like, okay, let's roll this dice.
Let's roll this dice and this came up ACEs.
It's still very rare.
Changes, things happen.
It took a while, but it still happens
if you give them all these opportunities to replicate.
So that's what's happened, right?
So that's too late for that one.
We can't actually prevent that from happening now
'cause it's already happening.
So your question is what to do now.
And I think we have to get back into the drug business.
I think vaccines are definitely the best way,
the best way to really, really kick a viral epidemic
or pandemic in the teeth is with a vaccine.
So I think we got to really, really
do that so we can prevent harm in as many people
as possible in a short amount of time, that's clear.
But, we have to kind of get back into the drug business
and the drug business basically say, all right, well
we know what the spike protein looks like.
Do we have a molecule that can get in the way?
And the answer is we have candidates.
We have antivirals, there's the T20 fusion inhibitors
for HIV, which you know.
We have very effective drugs for HIV.
They do a lot of things, but one of them,
it just blocked HIV from blinding to T-cells.
So we have drugs that do this in other viruses.
I think we've kind of like, the attention's gone away
from drugs because of our investment in the vaccine
which is appropriate.
But I think long-term, we do wanna get in the business
of being able to kind of...
I mean, frankly, the same problem I outlined,
we wanna be able to identify what the proteins look like,
what their phenotypes are and be able to like target
certain things with drugs.
So I think conceptually it's very similar
to some things I outlined.
So yes, I think we definitely wanna get into that business.
- [Nelson] Cool.
- Just a final question to you, Dr. Ogbunu
before I pass you back to my colleague, Ethel Gordon,
you were speaking about different scales of environment
the micro scale and then you went up to describe
your mother's history.
If we go up to the planetary scale
the South African variant, do you think there's evidence
and your gut feel that very different national environments
will lead to different ways of these variants mutating?
- So the answer is yes
but I think the national scale might be arbitrary.
I don't know if that's the meaningful way
to organize that information, well in some ways, right?
It is in some ways 'cause like places that control the virus
a certain way certainly have a different experience.
So I think that's true.
So national boundary is useful in that way.
Here's what I think is much more important
it's just how people are living, right?
If you're living in a lot of people that could be in Brazil,
that could be in Brooklyn,
that could be in Johannesburg, right?
The virus experience is just seeing
a lot of homo sapiens living together.
If you have age structure in a certain place.
So my point is there's a lot of ways that national stuff
or state stuff influence the trajectory of evolution
but it's not like anything essential to Connecticut
and it's not like in Massachusetts.
It's about the way people are structured in Connecticut
or the way we chose to control it in Connecticut
relative to Massachusetts.
I don't remember how we're doing relative to one another.
I think we're a little bit better but whatever.
And I'm just teasing.
Obviously, I gotta live for Massachusetts,
Anyway, yeah, yeah but that's a great question.
- Thanks very much, I'll pass it back to Dr. Gordon.
Thank you for a lovely talk.
- Thank you.
- Thank you, Dr. Ogbunu, it's been a pleasure.
Thank you for very insightful and interesting perspectives
on these important questions.
Given us a lot to think about.
And I'd like to thank all of our participants,
all of our audience members,
whether from Salem State Community
or the larger community and our high school students
and STEM students across the area.
So thank you again for participating with us
in this 42nd Annual Darwin festival
and thank all of our guests as well.
- Thank you Dr. Gordon, thank you all for your kindness
and generosity and for the audience members.
I appreciate you attending.
- Thank you.
- Okay.