(air whooshing)
(upbeat music)
- Hi, everyone.
So this is the evolution
of infectious disease,
lecture number one and what we
are going to talk about today
is first an update on an ongoing pandemic.
I know that this is a topic
that everybody is thinking about nonstop.
So I just wanted to address it head-on,
first in these lecture
and then I'll move on
to the introduction of the larger course
that I'm teaching right now,
Evolution of Infectious Disease.
We are in the midst of a
pandemic, it is astonishing.
We have not seen anything
like this in over 100 years.
We don't know when it will end
and right now it's expanding all around,
especially the United States.
So we have, in the United States,
over 160,000 cases of COVID-19.
This is a stat that was from last night
and so I'm sure that we
have many, many more.
This map was from last night
and I looked at it this morning
in California as well about 7000 now.
So this thing is expanding,
it's growing exponentially.
It's affecting every
single one of the states,
the United States and it's
affecting the entire world.
I should note that as we're
starting these lectures,
I am going to try to give you
the most up-to-date
information on COVID-19
and so you'll see that a lot of the data
that you're about to look at
is actually from last night,
from things that people
published on their websites,
that are up-to-date bits of information.
So of course, COVID-19
is a global pandemic.
It's growing in a lot of countries.
Luckily, it's declining
in some countries as well.
This is just a map from the New York Times
that shows us which countries
is the disease spreading the fastest.
Right now the United
States is the fastest.
Actually, it's kind of off
the scale of this figure.
We are growing by tens
of thousands per day,
not just thousands per day.
And I don't know about you guys
but every day I wake up and
I look at the data to see
if this growth rate is
beginning to decrease.
So what you're looking at is a graph
where the x-axis is time and the y-axis
is the number of new cases
reported in the United States per day
and so for a long time,
we had this slow gradual
rise of the rate of increase
and now we have, tens of thousands,
20,000 cases being reported,
new cases each day.
And so, this is what we
call exponential growth
or it's nearly exponential growth
and throughout the course,
I will describe the map
behind exponential growth,
how it works and why this is so dangerous
for society to be experiencing a virus
that can spread its range so quickly.
So while things are very dark,
there is possibly some
hope in the near future,
that things are going to calm down
and things are even going to
calm down in the United States
where this disease is
spreading so quickly.
So, if you just kind of squint your eyes
and fit a curve to this line,
you could maybe convince yourself
that things are beginning to plateau,
although I could also fit
a a linear function to this
showing that they're just increasing
but there is some reason to
think that we're near the peak
and that the number of
cases is gonna subside.
Okay, so this is actually a data analysis
by one of my friends, Rob
Beardmore over in the UK.
And what he is pointing
out is that the pandemic
or the epidemics in
each of these countries,
actually, they tend to show a
less than exponential spread.
So everybody's talking
about exponential spread
and we'll get into the actual math
and the science behind exponential spread
but note that it's just very, very bad.
And what he is showing
in this plot actually,
and this is just a brand new paper
that's just freely available on GitHub.
And what he's showing is that actually,
most of the countries that
have had COVID-19 for a while,
the rate at which COVID-19
is spreading is actually
below exponential and that rate
is decreasing through time.
So the y-axis, this
deaths per day per capita,
and the x-axis is day,
so that's progression
and that's days since
the first death reported
in the country caused by COVID-19
And let's not worry too much
about what the y-axis says
just a higher value on
the y-axis is worst off.
But the way that you read this graph is
that if you have exponential growth
with this transformation on the y-axis,
it would give you just a
straight line across the graph.
So just the straight line across here.
But if you don't have exponential growth
if you're actually expanding
less than exponential
and so relatively slow for what
the virus could possibly be doing,
then you have these slopes that go down.
And of course we know China
is a great example where
we know that they had a terrible outbreak,
it spread like crazy and they
all quarantined themselves
and especially in regions
that were hotspots.
They did a lot of testing,
they quarantined people
that they knew were infected,
they looked at the network of people
that they interacted with
and quarantined those people
and they got the local epidemic
in China under control.
And so that's the pattern
that we want every country
to have and that we want
the globe to have as well.
And so one problem though,
with this pattern is
that this is the line
for the United States
and you see that it does
not have a sloping pattern,
it is much more flat,
if you do the analysis.
So that's what these numbers are here for,
this section of the graph
and I should let you know
that I love data (laughs) I'm a scientist
and so a lot of this
class is just going to be
looking over these data, the
data directly from papers
and interpreting them and
understanding what's going on.
And some of the data that you'll be seeing
in the COVID-19 section
are so raw and so new
that some of the figures
will be actually hard to read
because they haven't
actually been published
and perfected for journals
but these are just scientists
trying to get this data
out as fast as possible.
Okay, so these are just telling us
what line is associated with which country
and then these deltas,
this is just how different
the growth rate is compared
to an exponential growth rate.
So exponential is like as fast
as you possibly could grow
and how different is this
pattern compared to exponential.
And so what you see here is that
the United States does
in fact have some delta,
some slowing down, it's
negative, so some slowing down
of the growth rate compared to exponential
but actually compared
to the other countries,
we're not doing as well,
our number is much, much, much lower.
So hopefully in the near
future, just a few weeks ago,
we really started to
practice social distancing
and this line will begin to fall down,
just like the rest of the countries.
So the other thing that
Rob Beardmore did is
that he actually created
a mathematical model
and later in the term,
we will go over the math
that underlies predictions
for the number of mortalities
during an epidemic and whether or not
an epidemic will spread.
These are called the SIR models.
So don't worry about the details now
but you will learn them later on
but he then made predictions
based on fitting these models
to the data and so what
he's plotting here now
and sorry, there's a lot of
overlap in the labels here
but what he has on the x-axis is days,
so in cumulative deaths.
So this is the number of
deaths caused by COVID-19
and all of these various countries.
And so the points are actual data
and then the lines are his
mathematical predictions.
And so the encouraging part
of these predictions is
that he actually thinks
that there's gonna be many,
many fewer deaths than many of the
classic mathematical models predict.
So yesterday on the news,
Trump said something about
100,000 to 200,000 deaths
in the United States.
And actually, if his models are correct,
it's going to be just thousands
to maybe tens of thousands
and not hundreds of thousands.
So all of these mathematical
models make assumptions
and it's really hard to know
which one is actually correct.
All of these models
predict that things like
social distancing and
quarantining are important
to keep these numbers down
and so this prediction
here is with the fact
that we are socially distancing ourselves
and we are quarantining ourselves,
we are taking lots of precautions
not to spread COVID-19.
You can look at all of
these different trajectories
for different countries,
we're pretty high up there.
Other countries are predicted
to have more mortality
and, yeah, I don't know.
This is just a little
bit of encouraging news
that the mortality, the number of deaths
might not be as high as we first thought.
Okay, there's other things
really encouraging news as well
and so this is data just from last night.
It's really interesting
device, instead of data
and actually company.
So this is a thermometer here.
This is a smart thermometer
that connects to the internet
and it sends data to Kinsa health
and so they've designed
these smart thermometers
and people all over the country
have these smart thermometers
and are regularly taking
their temperatures.
And so I think they've been
using these thermometers
for a couple years and
they have algorithms
that are smart and can figure out
what are sort of normal
patterns of increases
in the average person's body temperature
and decreases in the average
person's body temperature.
And so, during flu season
there's a very typical rise
in people's temperature and then a decline
in people's temperature and so they keep
really great data on this and they have
very good mathematical
models or learning algorithms
that allow them to be able
to predict what is normal.
And so when they see the real
life-data being abnormal,
then they know something has changed.
Maybe there is a new virus like COVID-19
caused by SARS CoV-2 spreading
around the United States
and so, let's start over here at this data
and then I'll tell you what
this graph is showing us
and I'll tell you why
it's all very positive.
Okay, so during a typical flu season,
you have data that would
follow this kind of trend here.
So this is the expected line
and you can see the
observed up to this point,
fall right along the expected line.
So everything was going normal,
people were getting the normal flu
and having higher temperatures
and this flu season was behaving
just like it normally does.
Then, once we began to have
this COVID-19 outbreak,
we saw massive deviations
from what was expected
and so that made sense.
We have a new virus and lots
of people are catching it
and the virus causes a fever
and so their temperatures
are being raised.
And then what is really encouraging is
that our social distancing
and all the struggle
that we're going through is
actually having a real effect
on this data here.
So I think that this data
is sort of the first,
it's the canary in the coal
mine but in a good direction.
That things that we're doing are changing,
our behaviors are actually working
and so the percent of people that are ill,
that have high temperatures
is actually plummeted.
And so what this is telling us,
that our social distancing is
probably having two effects.
It's beginning to influence
the spread of COVID-19
and hopefully slow it down
but it also is probably
inhibiting the spread of normal influenza
and so actually, fewer people
in general are getting fevers.
So we'll see what happens with this data.
It's a little early to interpret it
and I probably just overinterpreted it,
but we'll keep an update on this
and we can see what it tells us.
It's really fascinating.
It's really cool to see that,
we could all change our behaviors
and have such a dramatic
effect, this is nationwide.
And so what this map is showing us here is
it's a seven-day trend of illness,
and we can see that it's decreasing
around the United States.
So social distancing is
really having an effect.
It's probably actually
not just limiting COVID-19
but limiting influenza and other viruses
that would cause us to have fevers.
So that's really encouraging.
I looked at this map about a week ago
and it was bright red or
maybe a little bit longer
than a week ago but
recently it was bright red.
So this is really good news.
Okay, so the other bit of real-time data
that we're getting is data from GISAID
and this is a group that
collects genomic data,
it collects it for lots
of different viruses,
lots of different diseases
but it's collecting a ton of thousands
and thousands of genomic
sequences of SARS CoV-2.
So I'm trying to be careful
and when I use the phrase SARS CoV-2
that's the strain of virus
that is spreading around the globe.
The actual disease is called COVID-19.
So Just like HIV causes the disease Aids,
SARS CoV-2 causes the disease COVID-19.
In the next lecture, I will
go over a lot more about
what are Corona viruses?
What is SARS?
What is SARS CoV-2?
And so that you sort of
have a better framework
to understand what's going on but for now,
I just wanna sort of glance over some data
and point you in directions
of really cool websites
and real-time data so that you can begin
to track this stuff on your own.
And I also wanted to just
sort of entice you with
showing you figures and
showing you types of data
that might be really
confusing to you right now
but by the end of this course,
you'll absolutely be
able to look at these,
know what kind of algorithms produce them,
know what this means and really understand
how to interpret it and even help inform
other people about this stuff.
Okay, so this is genomic data.
So this is one of the first
epidemics where in real-time
we have advanced sequencing,
DNA sequencing technology
that allows us to rapidly
sequence whole genomes of SARS,
and then upload that data
and this data gets uploaded.
And this group, nextstrain.org
analyzes the data almost immediately
and then makes all of
these different plots
that we can learn about the
evolution of this disease
and the spread of this
disease around the globe.
So if you're interested
definitely go to nextstrain.org,
they have lots of ways to
interact with this data
to look at things and you'll
see here this is not a video
but on their website, you can hit play
and what it does is it shows you exactly
how this disease spread around the globe.
And that the way they're
able to figure that out
was by constructing this phylogeny.
Here, you could think of this as
these are evolutionary relationships.
You can also think of
this as just kind of like
a family tree of viruses.
So each of these points
is a different virus
and the connections between the points
describe how they're
related to one another.
Okay, so, we will be building phylogenies.
We'll use phylogenies to be
able to make maps like this
and what this is, down here,
you'll be looking at lots
of data like this as well.
This is the genome of the virus lined up,
from the first position to the
last position of the genome
and what this is showing us here are
where are mutations
occurring in the genome
and this peak here is very interesting.
This is the host recognition protein.
You'll hear a lot about
host recognition proteins
because this is what I
study in my own research
not on corona viruses but other viruses
and we actually have very
similar patterns sometimes.
We see these big spikes in the evolution
that's happening here.
This means that the virus is evolving
to better recognize its new
host, its new host is us.
That's what it suggests.
I haven't looked at the data explicitly.
So later on in the term,
we will get more into the exact data
and be able to figure out
whether or not natural selection
is acting on this protein or
any other protein of genome
to promote its evolution
and promote its adaptation to humans.
Right now, the evidence that the virus
is adapting to us is pretty slim.
I wanna step back for a second.
I am very excited about
teaching this class.
I'm also just excited in general
about just how amazing
science is right now.
We have all of this real-time
data just streaming in
and people are sharing it.
There's this whole revolution
right now happening where
many of the journals are
making any publication
related to COVID-19 free
and available to everybody.
People are making their data
freely available as well.
Normally, these things are kind of,
people hold on to their data
so they can write their paper first.
Or people or journals trying to make money
off of publications and so they put up
a paywall behind their articles.
BUt right now all of this
stuff is freely available.
The New York Times has dropped its paywall
for COVID-19 related subjects
so that people can look at these plots,
even if they don't have a subscription.
All of this information
sharing is really powerful
and it's really gonna transform
the way that we do science
and it's amazing to be able
to watch this pandemic unfold
and get all of this data and learn from it
and in real-time react to
it and hopefully it helps us
stop the spread of this pandemic.
So I'm just really excited
that people have come together,
at least scientists in such a great way
to share all these resources.
So that's what scientists are doing.
So what should you be doing right now?
And so I just wanted to reinforce this.
I mean, you have to stay home.
You have to limit the
amount of interactions
that you have with other people.
That is going to save lives.
That's gonna hopefully stop
you from being very ill.
So please, please, please stay at home.
That's the most effective way
to deal with this pandemic right now.
And I would suggest also,
with the knowledge that you're
learning from this course,
remind people why it's
important to stay at home
and politely remind them,
nobody likes to be nagged
but I do find myself kind
of in a reverse position
with my parents where, as a teenager,
they would be telling me what to do
and to stay at home and so forth.
And now I find myself telling my parents
how important it is to stay at home,
not to go to the local
pub and things like that.
So yeah, stay at home and encourage people
to follow that direction.
I would suggest take one day at a time.
This is going to be a long period
where we have to do social distancing.
People are talking about June,
maybe we'll have some relief in the summer
because of the climate being warmer
and the virus not being
able to spread as well
but we actually don't know that yet
and so we might have some
relief in the summer.
It's seems that the major
search of the pandemic
in the United States will
peak probably mid April
and then hopefully drop back down
but even when this
pandemic drops back down,
we're not gonna be able
to just immediately
start life up again.
We'll have to sort of
take inch by inch measures
to sort of step outside
of our house a little bit,
maybe go to work when there's
not so many people there.
I think in my lab, once things
calmed down a little bit,
we will have people
come back into the lab,
right now it's shut down but
we'll have them work in shifts,
they don't overlap with each other.
We're not sure exactly what will happen
but at the moment, we have
to take one day at a time,
prepare mentally for a long
haul of social distancing,
and learn how to be adaptive.
Learn how to use Zoom
and things like that.
So I'm very sorry about all of this
but it's critical to stop
the spread of this pandemic
and I also wanna say, study
hard and stay focused.
I've been working very hard
and it's kept my mind off
of what's happening around
us and I have to say
that while working hard can be stressful,
it's not as stressful as dealing
with everything that's happening.
So distract yourself,
listening to podcasts.
So there's this question
in the news right now
of whether or not we
should shelter-in-place.
The idea is that sheltering in place
has huge economic consequences.
We're not out spending
money and we're not at work
or many of us are not at
work and so what that means
is that we're really hurting our economy
and having a bad economy
can have really bad side effects as well.
And so what I wanna
convince you of right now
is that is a false dichotomy.
That actually if we
don't shelter-in-place,
and this pandemic gets out of
control in the United States,
our economy is gonna
be much more worse off
than if we do shelter-in-place.
Sure, we don't spend as much money,
hopefully people can maintain their lives
and their jobs and the
government is helping out
with that right now but
the ramifications of
if we let this pandemic get out of control
are much worse off and
so this is what people
have been saying for a while,
it's the alternative
argument against this,
we should not shelter-in-place idea.
But there's actually
data that we can look to
that support this claim.
Okay, so you might have heard
about the 1918 flu pandemic.
Usually I talk about
this later in the class,
we have a lecture on influenza
and I just want to sort of
give a brief little
intro to it right here.
So this is mortality
rate per 100,000 people
on the y-axis and on the
x-axis we have years.
And so a long time ago,
life was a lot rougher,
people died of infectious
diseases at a much higher rate
and we get modern medicine
and so people are not dying
from infectious diseases as much.
We have better medicine
and better hospital care,
certainly the advent of antibiotics
and also vaccines helped
drop this number down a lot
but you can see this
incredible spike around 1918.
This was the last pandemic
that we experienced at this scale
and there was just an
incredible increase in mortality
caused by that influenza strain.
So 100 years ago is very
different than life is now
but I think we can use how
people responded to that pandemic
to learn a little bit about
this current pandemic.
And so, I'm not the
only one that thinks is.
Actually this guy, Emil Verner
thought that before I did.
He is an MIT professor, economist
and they looked back
at data on which states
within the United States
responded to this pandemic
by sheltering in place versus which cities
didn't do social distancing
and then they saw,
whether or not that affected
their rate of mortality
and yes, it certainly did.
If you sheltered in place,
you had fewer people
that were sick and fewer people that died
and then the last thing they did, though,
is they said, well, the cities
that sheltered in place,
did their economy suffer more or less
than the cities that
didn't shelter-in-place?
And what they found is actually that
the cities that sheltered in place,
they had less mortality
and the changes employment
were not as bad.
What I wanna say here is that
the x-axis are the mortalities
and the y-axis are changes in employment
and the way that you can interpret this is
that higher values are good
and lower values are bad.
And so what it's showing here
you have these different cities, okay?
And the cities are labeled red and green.
The green ones are the ones
that actually did shelter-in-place
and the red ones are the ones
that did not respond as well
and so what we see is that there is
sort of a clustering of green over here
and a clustering of red over there.
And then there's this
overall relationship,
where cities that were hit the hardest
also the change in employment,
so the drop in employment was the hardest.
I actually grew up in Pittsburgh.
So it seems like my people
were just really stubborn
and did not shelter in
place and did not respond
and both the mortality rate
was really high and also,
their economy really
suffered around that time.
So it's much better to respond to this.
It's a win-win situation
where our economies
won't suffer as much and we will also just
have fewer people that
die from this disease.
Now, I'm going to start
to move more towards
the typical first lecture
that I would give in the class
and I wanna start this out by saying,
should we have anticipated
a global pandemic?
And the answer is yes.
And so these are slides I would
normally give to my lecture.
It's actually very depressing right now
to be giving you slides.
Basically, I started out
my lectures by saying
that we have altered the globe in ways
that actually set up kind
of the perfect petri dish
for a new virus to emerge
into the human population
and then spread around the world.
And so what are the
factors that people look at
and are sort of the ways that
we have changed the globe
to increase the chance of these pandemics?
So the first factor is
that, so I should say that,
where new viruses come from,
it's not that they just
sort of come out of nowhere.
They jump from one species
to another species.
So we know about the
swine flu and the bird flu
and now COVID-19,
COVID-19 comes from bats,
swine flu and bird flu,
obviously come for those animals.
But there's lots and lots
of mammals around the world
and birds and other
animals that have viruses
and have a huge diversity of viruses.
And these viruses have some
potential to cross species
and jump into the human population.
Obviously, it's relatively
rare and it's even rare
for a new virus to jump
into human population
and then to begin spreading
from human to human.
We'll talk about how this
process works in later lectures
but there's always a risk
and when you're interacting with animals
that this sort of this junk could happen.
Now, of course, we all
interact with animals
and you don't need to
sort of change your life
because of that but as we
interact with natural areas
and animals more and more,
we are enhancing our chances
for a virus to jump from a
species like a bat to humans,
just like SARS CoV-2 did.
And so our encroachment on natural areas,
the increased production of livestock.
We have all of these
farms all over the world,
and humans are eating more and more meat.
And these farms are a
reservoir for viruses
that could jump into our populations.
And also things like
the exotic animal trade.
This is a way that animals
can contribute viruses to us as well.
So just our increased
encroachment on nature basically,
is really enhancing the potential
for these pandemics to happen.
So next, we have these
huge cities and more
and more people around the
globe are living in cities
and that's a good thing.
As we have more and more people on Earth,
we have to live more and more efficiently.
We have to preserve the
resources that we have.
We have to burn less fossil fuel,
we have to use less energy and as a whole,
cities are much more efficient.
However, they do have the side effect
that we have in cities,
we have people interacting
closely with each other,
each individual in the city
encounters hundreds of
other people every day.
And so in these kinds of situations,
we have a lot of potential
for a viral spread.
And so, if a virus does jump
into a human population,
even if it's not very good
at spreading between humans,
it can get a foothold in
these urban environments
because there's so much opportunity for it
to spread from one individual to the next.
And so really you can get
the beginnings of an epidemic
happening in these local
cities and the virus
sort of adapting to humans
and beginning to spread
better in human populations.
The third factor is that
we have global travel
and so this is also a good thing.
It's good that we have a global society
that we have people
moving around the world.
This helps with just
diminishing the differences
between us and promoting harmony on Earth
but it does have this consequence
that if there's a local
epidemic, say in Wuhan
and people leave that area
and go to other areas of the world,
then they're going to
spread that epidemic.
Certainly, it's not just
China, it's not just Wuhan.
In the United States, we
had a swine flu outbreak,
I think in 2009 and that spread
around the world as well.
It was not as deadly as SARS is
and so we didn't have to suffer
as much as we are right now
but it can happen anywhere
and spread around the world
and while certainly
global travel has benefits
it has this cost as well.
So those three features of
how the globe is changing,
has really set us up for pandemics.
We knew that this was a problem.
We had in the United States
pandemic teams assembled.
Lots of different agencies
have invested money.
I wish we were a little bit
more prepared but we didn't know
that this was bound to
happen at some point.
So what are possible solutions
to these three problems?
So basically, encroachment
on natural areas.
We should preserve natural areas.
We should try to stay
away from those forests.
There's a lot of other
benefits to not destroying
natural areas besides
not opening ourselves up
to emerging diseases.
Urbanization, as I said,
it's really important for us
to live efficiently and
cities help us do that.
But we can design cities in better ways,
so that the spread of disease
is at least slowed down.
So things like
automatically opening doors.
So you don't have to touch door handles,
that's an engineering strategy
that minimizes the potential
for passage of pathogens from
one person to the next person
and so there's lots of smart ways
that we haven't even come up with
where we can engineer cities.
So that will be a topic that
we go over later on as well.
How to have smarter architecture
to limit the spread of pathogens
and limit their evolution.
Okay, in global travel, I don't know.
I mean, I think we're
all beginning to learn
how to live without global travel
and we're communicating through Zoom
and we have these lectures online
and so maybe we'll learn to
minimize our global travel.
That would be good for the environment,
because we won't be producing
as much carbon dioxide
as well from airplanes.
So I think maybe we'll find
ways to still have harmony,
still have these interconnected societies
that share knowledge and resources
but not have to actually
physically go to as many places,
although, we all love to travel
and it's nice to experience
cultures firsthand.
We knew that this pandemic
would happen eventually
and it's really grim that it has happened.
But I do think we'll go
over ways of stopping it
throughout this course.
So hopefully, we'll sort of
end on a more positive note
but before I get positive,
there are other looming crises
as well that we'll talk about
and so this is just a graph
of a type of antibiotic resistance gene
called a beta lactamase and
it's the spread of this gene
and what we can see is that
that gene is spreading exponentially.
And this is happening in
every country in the world
and so what this crisis
is it's much more slow
than the current crisis
we're going through.
But basically, bacteria are
evolving antibiotic resistance
and so we're no longer
able to use the medicines
that we used to use to
treat bacterial infections.
And so there are even some cases
where people are beginning to die
from bacterial infections
that used to be treatable.
And so I think it's important
that we all focus on COVID-19 right now
but there's also these
other looming threats
that we know are happening,
just like we knew
that a pandemic could possibly happen
that we need to learn about
and we need to stop
before they too happen.
So I will go over the evolution
of antibiotic resistance
and also new strategies
for dealing with antibiotic resistance.
So I just wanna point out that these two
are emerging diseases and
antibiotic resistance.
At their core, they are
problems caused by evolution.
This virus has evolved in a way
so that it can jump
into human populations.
Bacteria are evolving in a way so
that they are no longer
sensitive to the medicines
that we treat them with and
so, really to understand
two of the biggest problems
I see humans facing,
certainly climate change and other things
are huge problems as well
but in terms of medicine,
two of the biggest
problems that we're facing,
are being caused by the
evolution of microbes.
And so I wanna stress how important it is
for people to research
the evolution of microbes
and for people like yourselves
to be learning about
the evolution process in the
context of microorganisms.
Okay, so now what I wanna do is go over
just some of the questions
that we'll be answering
with the material in this class.
And so one, like I just said
is we're gonna understand
the antibiotic resistance
or how antibiotic resistance evolves.
And so here are just two figures
that maybe don't make any
sense to you right now.
One is a protein that
mutations tend to occur
in this protein that confer
resistance to penicillin
and then the second image
is two bacteria sharing DNA
with one another and they're sharing
antibiotic resistance genes.
So two mechanisms for
antibiotic resistance
are both mutations and
horizontal gene transfer.
We'll dig into those
much more later in class.
We'll also talk about how
viruses jump between species.
We actually know a lot about
this in terms of bird flu
and that's what this figure is showing you
is a host recognition protein of bird flu,
like that S-protein I pointed
out earlier for COVID-19.
So this is just the overall
structure of influenza.
This is the protein that evolves.
Here's just sort of zooming in
on the region of the protein
that's evolving and it evolves in ways
that can then attach to
human cells and infect them
and so we know a lot about
gain-of-function in influenza
and there are certainly studies
on other corona viruses,
I don't think as many on SARS
and certainly not this SARS CoV-2
but when we get to this
lecture on host jumps,
I'll be sure to give you the
most up-to-date information
on how SARS CoV-2 spread to humans.
So gain-of-function evolution
is mainly what my lab actually works on,
and so if you're interested
in what my lab works on,
here's a New York Times
article from my PhD research
and here's a more recent
article from research at UCSD
about another professor at
UCSD, Katie Petrie's work.
Okay, so we are going
to learn about evolution
and we are going to apply
it to many problems.
One of the ways that you can apply
an evolutionary understanding
is to be able to track
the spread of diseases and
to determine things like,
where did the disease originate?
What are the mechanisms
allowing them to spread?
We can predict where they're headed
and we can even develop
methods to stop their spread.
So really understanding
how they're evolving
as they're spreading can
help us really actually
begin to intervene in the pandemic spread.
We will track, we will learn about
how to track pathogens locally.
So, within hospitals or
between different people
isolate in a single region.
We will also learn how to track pathogens
as they move around the world.
This is from Nextstrain,
this is how the early stages
of SARS CoV-2 spread around the world
and we will even look into understanding
how we can predict where epidemics
are likely to emerge in the future.
And so, this is a plot
from EcoHealth Alliance
as published in 2017 in
Nature Communications.
What this group of researchers
did is they figured out
what are the key variables that are likely
to lead to pandemics.
So, like we talked about
earlier in this lecture,
things like human density,
human interactions
with biodiversity and also another one
that we haven't talked about
is whether or not people
are experiencing bad
effects from climate change
that could also help contribute
to new emerging diseases.
And so they put these variables together,
they're able to measure human density
and biodiversity and so
forth and then they can make
this heat map of where we expect
future pandemics to occur.
So this is the site
obviously of where SARS CoV-2
emerge from, we don't
expect that the new disease
could just come from Asia
but we have hotspots in North America,
even down here in Southern California
and around New York and
hotspots of West Africa.
This is around the region where
the 2014, 2015 Ebola outbreak happened
but certainly Europe has
some hotspots as well.
So, it can happen in basically
most of our countries.
It's a probabilistic thing.
This is not telling you
exactly where it will happen
but if we know the
factors that do contribute
to emerging diseases in the next pandemic,
hopefully we can go to these regions
that actually begin to intervene.
So we'll talk a lot
about emerging diseases
and how to predict where
they're gonna happen
and how to stop.
We are going to learn
about how natural selection
acts on viruses and
we're gonna learn about
how it can actually act to
reduce the variance of viruses
and that often viruses, when they begin
to infect a new species start
out as being very deadly
and then they co-evolve with that species
and eventually wind up
being much more benign
and so hopefully, that
is what's going to happen
with SARS CoV-2 that maybe
the future strains of it
will evolve to be less deadly
and be something more
like normal influenza
or hopefully even like the common cold.
And so this is just a figure,
don't worry about these
variables but this is an equation
that we'll learn about
and it predicts that
under certain circumstances,
you'll actually get natural selection
that favors less virulent pathogens
and also less infectious pathogens.
And so hopefully, the
conditions are right,
so that future strains of
CoV-2 will be less deadly.
We're gonna talk a lot
about antibiotic resistance
and we're also gonna talk
about multidrug resistance
and that problem and how we can address it
using things like drug cocktails.
And so this is just a
life cycle of SARS CoV-2
and I just have this up here
because I want to point out
that there's a lot of effort right now
in trying to get new drugs that can attack
the replication of this virus
and certainly there's
going to be a huge rush
to begin using the first drug that we get.
And there's some reason
to do that for sure.
I mean, there's obvious
reasons to use the drug
if it's effective and it
doesn't have side effects
but there's also reasons to
be a little bit cautious.
And it's that viruses like this virus,
have a relatively high mutation rate
and so if it's relatively
easy for the virus to mutate
and to be resistant to the therapy
then within a couple
days, you can begin to see
these escape mutants that
can avoid the therapy
and then spread and then the therapy
that we worked so hard to
develop, it's just not effective.
So hopefully we have a
couple of different therapies
that come online around the same time
and hopefully there's no bad interactions
between those therapies and we
can use them in combination.
So I'll teach you about mutation rates.
I'll teach you about
why drug cocktails work
based on mutation rates
and how they they work.
And then we'll we'll think
about smarter designs
to drug administration so that
you don't get resistance evolving.
That basically we challenge the virus
with too many different
challenges at the same time.
So that they can't evolve resistance.
Okay, one of the lectures
that a lot of students
really enjoy is one that we teach about
new emerging biotechnologies
that can be used
to help fight the evolution
of antibiotic resistance
or the evolution of pathogens in general.
And so this is a figure from a paper
that was recently
published, this is from UCSD
and it's this larger
group, headed by Ethan Beer
and Victor Nizet and you
don't have to look at this
and understand what's going on.
This is a series of genetic elements
that they put together
where these genetic elements
when you put them into a bacterium,
they reverse the bacteria's ability
to be antibiotic resistant
even if you start out with
antibiotic resistant bacteria,
they will be resensitized to antibiotics
and then you can wipe
them out with antibiotics.
So it's a really effective strategy
to be able to resensitize bacteria,
and so that we can continue
to use the antibiotics
that we already have.
So that's an interesting lecture
and we'll talk about
the mix of biotechnology
thinking about evolution,
how to reverse evolution.
One of my favorite lectures also is about
using evolutionary biology to predict
what future strains of
influenza will look like
and then to be able to
better develop vaccines
to deal with those future strains.
So basically every year
when you get a flu shot,
what has gone into that is people have
picked out a couple influenza strains
that they think will
circulate in the coming year
and then they make a vaccine
for those influenza strains.
But sometimes they get that wrong
and the reason why they get it wrong is
that influenza between the years evolved
in ways that they didn't
anticipate and so when that happens
then we have a really bad flu season.
So there's a huge push to be able
to anticipate viral evolution
so we can make things
like vaccines and other treatments
that are sort of one
step ahead of the virus
and are able to act on that future strain.
So it's really important to be able
to predict the evolutionary process.
To do that, of course, you have to know
about the evolutionary process
and how it actually works.
So the beginning of our lectures
will be focused on understanding
that evolutionary process,
understanding just the
details of how mutation
and natural selection and so forth works
and then we'll begin
to apply those lessons
later on in the lectures.
Another reason that's
really important to study
the evolution of infectious diseases,
is that helps us understand
our own evolution.
Everybody knows this, it is
pretty widely known though,
that humans, 8% of our
genomes is actually composed
of viral DNA and that
there's actually some genes
that we use, functions for like childbirth
that were originally genes,
that were viral genes
that integrated into our genomes
and the virus infected us
and then were passed on
from one generation to
the next and actually,
we evolved to use those
genes for core functions.
Most of this DNA is just junk DNA.
We don't use it for anything
but it's really fascinating
that a large fraction of
our genome is viral DNA.
We'll learn about how that
happens later on in the course
but so viruses are actually
directly influencing
our evolution as humans by contributing
genetic information to us.
But I think the more intuitive way
that viruses shape our evolution is
that they are a major dominant predator.
They cause mortality even in modern times.
A lot of people obviously die from viruses
or other pathogens.
This is malaria, which is actually
a single cellular eukarya and
we know that these pathogens,
put selective pressure on
us and cause us to evolve.
Often in intro biology classes,
you learn about how malaria
selects in Sub Saharan Africa
for a mutation that causes
cells to be sickle-shaped.
This mutation, if it's
in a person's genome
and its heterozygotic,
it's actually beneficial
because it doesn't have
this deleterious effect
of creating sickle-cell
shaped red blood cells
but it does alter the cell in a way
that this malarial parasite
which infects red blood cells
is less able to infect and
so it provides resistance
to those people but it turns
out that if that allele
is homozygotic in a person
then it creates the sickle-cell
shaped cells, it's very bad
and it can cause a lot of
pain and premature death.
But what I'm just pointing out
is this is a classic example,
where we have evidence that our pathogens
are applying pressure
and selecting for alleles
that otherwise seemed like
they would be deleterious
when removed from our
population but they persist
because of this ongoing
pressure on our populations
to be resistant to things like malaria.
So that's a classic
example of how pathogens
are shaping human genetic
variation and our evolution.
But it turns out in recent years,
as our data is getting better and better,
as we have more and more genome sequences,
we can begin to look at,
what other mutations in
our genome are likely there
because it gives us an advantage
in dealing with our viruses.
And this incredible study
that we'll talk about
came out a couple years ago,
where they found that 30%
of the adaptive amino acid
changes in our genomes
can be attributed to avoiding viruses.
That viruses are our main
selective agents and has
really shaped a lot of the
genetic variation in our genomes.
So we'll have a lecture
also on coevolution
between pathogens and us and how pathogens
are directly influencing our own evolution
and the genetic variation on populations.
So I think I'm making the
case here that it's important
to study the evolution
of infectious disease
and there is a growing realization
that this is important.
There's lots of books that have come out
in recent years about this.
However, there's just not as much progress
as I would have hoped on.
And one of the statistics
that's always kind of disturbing
is that, if you look at
faculty and medical schools,
rarely are those faculty
evolutionary biologists.
And so this is actually old data
but this still stands today,
where somebody looked
at the faculty members
of a bunch of different medical schools
and plotted just a histogram.
So this is just number of faculty
that are evolutionary biologists
and this is per medical school
and so many medical schools have zero.
Sorry, let me go back
and explain this better.
This is a histogram.
This axis is number of medical schools,
not number of faculty.
This is the axis of
number of medical schools
and this is the number of faculty
that are evolutionary biologists
at those medical schools
and so there's one medical
school that's the outlier
that has eight faculty that
are evolutionary biologists
but most medical schools this data set
of 33 medical schools, something like 28
of the 33 medical
schools have zero faculty
that are evolutionary biologists.
So this is just a reminder that
it's still not widely accepted,
that the understanding
the evolutionary process
is important for understanding
how to treat diseases.
But I will certainly
make the case throughout
that it is important.
What is really funny to me is that doctors
or biomedical researchers
tend to even avoid
using the word evolutionary biology.
So what we're comparing here are studies
that were published in
evolutionary journals
and studies that were republished
in biomedical journals
and a synonym for evolution
is often emergence.
Now, this is a lot sloppier of a term.
Emergence just means
that it kind of arises,
doesn't arise from a genetic
mutation, isn't an adaptation.
That's not captured in this term here
but the term evolution means
that there's a genetic
change that is inherited,
that causes an evolutionary change
and so it's very specific.
And so if you are talking
about genetic mutations
and evolution, you should
use the word evolution
'cause it's more precise
but what we see is
that biomedical journals,
people that are publishing them
avoid using the word evolution
and they substitute in emergence.
And so it actually appears
that there's an aversion
to even using the term evolution.
But I think it's obviously very critical.
There are centers that are being developed
to study evolutionary biology
that are actually in medical schools.
This is the logo for the first center
that opened in a medical school
to study evolutionary biology.
This is at the University of
Pittsburgh, founded in 2017.
So my hope with this course is just that
everyone listening understands
how important it is
to understand the evolutionary process,
to understand disease and if
you are going on to careers
in biomedical research or in medicine,
then you are always thinking about,
how things might be changing,
how things might be mutating
and to develop strategies
that will help mitigate the bad effects
of evolution of resistance
and evolution of
pathogenicity and so forth.
(upbeat music)
