- The topic for the
lecture today will be...
We wanted you to take a deep dive
into some of the latest Bio
Technologies around COVID-19
So far goes for testing, socially matter
while introducing you
we give our introduction
to Serology testing for COVID-19
and then the second half,
we'll have Stanley Qi,
who is a professor in BioEngineering
and he'll talk about using
CRISPR for size COVID-2.
So we're very happy to have
Sheldon training us here
and giving the first presentation.
So he is also a co-habitor of mine
and he is a platform group
leader at the (mumbles)
where he is doing a lot
of interesting work.
Computational, microscopy,
computational imaging
and more recently on applying
this technologies to COVID-19.
So we look forward to your presentation.
Thanks for joining us.
- Thank you James and
thanks for having me.
Can everybody hear me just fine?
- Yes we hear it.
- So as James mentioned
our a main research
focuses computational imaging
and we image living systems,
live cells and tissues,
and a couple of months ago, we got drawn
into the field of testing for COVID-19
and today I'm going to share
with you some background
on serological testing and
specifically the approach
we are taking of
multiplexing the serological
analysis of several antigens
to improve the accuracy
of detection.
So first 10 to 15 minutes,
I would just like to give you a background
on why serological tests are important,
what I'm currently
prevailing serological tests
and what other basic
concepts for the kinds
of serological tests we are doing.
So if you think about
the process of infection,
when a person gets
infected and they initially
have any symptomatic, they go
through any symptomatic phase
where the virus grows in the body.
So this blue curve here
shows the title of the virus,
post infection and as
our immune system reacts
to the presence of the virus,
the first type of antibody
that's made by overactive
immune system is IGM,
and as its starters begin
to increase after a while
another type of antibody IGD
its starters begin to increase,
and at that point, IDG is(mumbles),
if its the right type
of IDG if the inhibition
produces antibodies that
can neutralize the virus.
That's helpful with the patients
and the virus type just begins to fall.
So that's really the time
course of spread of the virus
and the growth of the
antibodies in a human
and the Corona Virus looks like this.
This is a beautiful drawing
from David Goodsell,
and many of you would know that
it's called the Corona Virus
because of the spike floatings that create
a corona around the virus.
It's about 150 to 160
nanometers in diameter.
It's a spherical bios particle.
Antibodies are about tenth of the size
and the way the antibodies
defeat the virus
is that they recognize the spike floating
on the surface of the virus
that prevents the propagation
of the virus and also
the ELISA antibiotics,
ELISA, an immune response,
so our immune system
can clear the viruses.
So, for us to assess where the we are,
whether a patient or a
person is going to recover
from the infection, it's really important
to mention the titre of the antibody.
And this titres are measured in blood.
So serological tests are blood tests
and the number of reasons you would want
to have serological tests.
You maybe very familiar
with the type of PCR test
that are being done to detect
the titres of the viruses.
You can detect the virus only
when the infection is current,
and also only symptomatic
individuals show up
at the clinics end and
take their side to PCR test
and they are given their side to PCR test.
So, there is a need to
cover larger set of patients
who maybe asymptomatic
and also there is a need
to detect past infection
and current infection.
So serological tests allow you to assess
the state of the infection,
even when somebody
doesn't have an active infection.
Its known the RT-PCR tests
are prone to false negatives ,
so this tests can...
So as the confirmatory test also
and reduce the false negative rates.
When somebody has the antibodies,
its known the immunity
lasts for a while at least a few months
when the antibodies laying
in the long range is not clear yet,
but having the test, you can
assess if frontline workers
have the immunity and they
can safely return to work.
So that's one of the
driving process behind
rapid development of serological tests.
As we think about opening up the community
and resuming the public
activity, It is really important
to assess what fraction
of the community is immune
and if the community has
reached the hard immunity
to defeat the current rate
of application of the virus.
Convalescent plasma is actually
useful for work treatments.
Convalescent plasma is
plasma found in patients
who have been infected and recovered
and with serological test,
you can confirm the titres
of antibody titres and use
that plasma for treatment
and severe cases.
There is a lot of value in having accurate
and scalable serological tests.
- Shall we have a question from Adi?
- Yes.
- [Adi] Yeah I had a question
about the plasma donations.
I've heard kind of a lot of fuzz
in the popular science community
about doing plasma donors,
but I was curious if the
antibodies aren't being produced
by the persons internal immune system,
then whether they can
be long term effective?
- So is your question " Do
antibodies last longterm
within plasma?"
- [Adi] Yeah and whether if
they're not lasting longterm
whether they can still be
effective at treating Covid 19.
- They can be effective. I
don't know all the carriers
because I'm actually an optics expert
and computational expert.
I've just started
thinking about immunology
since two three months.
So with that caveat, I know of
studies where Ebola patients
are treated with convalescent plasma,
and I know clinical studies
where convalescent plasma
has shown a improvement in symptoms
in patients who are being
treated for COVID-19.
And there are biological,
there are papers that show
that you can prevent the
entry of the virus into cells
using convalescent plasma.
There is a very recent pre print,
if you send me an email
I'll be happy to point out,
which shows that if you
take convalescent plasma
from the patient, you
can use it to prevent
the virus from entering the cell.
So, if it can work in a culture,
there is a likelihood that the antibodies
that you intravenously
provide to the patient
also can neutralize the virus.
There are a number of questions of course,
about where is the virus doing most damage
and can antibody reach the
organs where it actually
needs to reach or does the
body have to synthesize
antibodies you know?
So, yes, there are a number of questions
but it has been shown to be
effective in several cases.
- [Adi] Okay, thank you so much.
- Great. So now you know once the value
and the need for the artificer test
and serological tests become apparent
number of entities
started making this test
and there was a time where CDC and FDA
were very stringent
about approving the test
and there was a time when
they were a little lags
and especially for serological
tests, a lot of bogus
and inaccurate products came into market
and actually flooded the community.
A lot of folks bought this
kits that you can test
at home kits that you can get
from manufacturers in China.
But these were not backed by any data,
they were not validated properly,
their access was spotty.
There was even news on
the topic of validation,
I think UK lost close to 5 billion pounds,
buying tests that are actually not useful.
So there is clearly need
for further research
and further development
in serological tests.
And the streaks are from two months ago
and actually I got really
interested in working
on this problem.
And it struck me that folks
were working on laboratory tests.
They were doing a lot of manual work,
and there's a clear lack of automation.
For example, this individual
finds itself rushing
to read complaints about
laboratory testing.
Although they're putting 16 hours a day.
And another physician points
out that in the absence
of testing, they are not able
to make informed decisions
about who gets to come to
the hospital and who doesn't.
So, that motivated us to work on this
and my colleague John Pack
had already started thinking
about this and making
proteins for serological tests
and he has been providing this
proteins to a number of labs
and it has been used in many studies.
While this was happening, a
couple of key papers came out
and this is an example of
how the open publication,
how pre prints and how rapid dissemination
of scientific work has a positive impact.
So a group at Mount Sinai
Florin Kramer's group,
they have a serological
test that's based on ELISA
and we'll talk about what ELISA is,
And showed that it's quite specific
and that set off a chain reaction
and a lot of folks realized
that you can implement this test
quite inexpensively and at scale.
- Sheldon, can you say a little bit
of what's the main difference
with this different serologic testing.
Why did some of the tests not work?
Are they testing for the wrong antibodies?
Or was it manufacturing?
- Yeah, so I'm actually going
to get to the point right now.
So, there are three major types of assays
and these three essays
are in increasing order
of importance or value of information.
So the tests that had
not been very accurate
or lateral flow assay is where
some antibodies are spotted
on a column and then you
place a drop of blood
and through capillary action
that flows through the channel,
and if you have binding
against that antibody,
you get an optical readout to
get a colorimetric readout.
This test can fail for
a number of reasons.
The antibodies can degrade over time
and what is spotted?
Which antigens are used?
It's not clear and they haven't gone
through strict quality control.
There is a very nice pre print
from Alex Marson's group
which compared the sensitivity
and specificity of number
of lateral flow assays
against the using the the RT
PCR test as a ground truth
and using the ELISA as a benchmark,
and Yeah, I'd recommend
looking at Alex Marson's,
paper and website to understand
what's the accuracy of
this lateral flow essays.
These are qualitative essays,
easy to manufacture and mass,
and of course very easy to use
because you just take a drop of blood
and put it on the column.
So, now there is another type
of assay which is more quantitative.
It says staple in an immunologic
community called ELISA.
It's also an assay which finds antibodies
that bind against in an antigen.
Now, we should make a distinction
between binding of the
antibodies and immunity.
Not all antibodies can't
provide or confer immunity.
Antibodies need to neutralize
the virus to provide immunity.
So, these antibody binding
assets need to be complemented
with virus neutralization assay,
where you really measure
if specific antibodies
or specific serum which
has a complex mixture
of antibodies, neutralizes
the spread of the virus.
And we'll be talking about ELISA
it's possible to benchmark
ELISA against virus
neutralization assay and
several groups are working on it
to first use virus neutralization assay
to assess which serum neutralize the virus
and then use ELISA and
then use those serum
to see how ELISA detects
antibodies in those Sera.
So ELISA can act as a useful proxy
once validated for neutralization.
Now, we're going to talk about
few antigens and antibodies,
so I want to just highlight viral proteins
and the antibodies.
The antigens to which the community
has found that humans make many antibodies
are the nuclear protein
which is in the nucleic acid
of the virus it encapsulates the genome
and against the spike protein,
and on the surface of the spike protein,
there is a domain called
receptor binding domain,
which is the domain that facilitates entry
of the virus inside the cell.
So, antibodies that
bind the nuclear protein
may not neutralize the virus,
but antibodies that bind
the spike protein and RBD
(mumbles)
and utilize blockers
because prevent the entry
of the virus into the next cell.
And the antibodies have this
structure they look like a y
and there are a few
different kinds of antibodies
as I briefly mentioned, IGM's
are the ones to be produced
early in the infection
and IDG are workhorses
and once the immune system is primed,
a lot of IDG'S produce and
that's the type of antibody
that begins to clear the virus.
Now, the way ELISA works is this.
ELISA is an Enzyme Linked
Immuno Sorbent Assay.
What you do is you coat a
surface, which may be a plate
or a slide with an antigen,
you bring in an analyte which
is the serum in this case,
so a patient serum and
if the patient serum
can recognize the antigen,
if the antibody in the patient's serum
can recognize the antigen it will bind
and this hexi-domain it's a
conserved domain of antibodies
which you can detect
with a secondary antibody
from let's say mouse, which
will recognize FC domain
of a human antibody.
And in that secondary
antibody you have a hook
to capture an optical probe.
So essentially, this is
a cascade of reactions
by which you are converting
a concentration of antigen
into an optical density measurement
into absorption measurement.
So, and then one can read out these
developed plates through a
simple transmission microscope
where you measure the
absorption on the spots.
Now, to make the measurement quantitative,
what's done is that you
dilute this Sera serially.
You have the samples and
you do a serial dilution
one to five for example, and
measure the optical density
for each of the serum at
a few different dilutions.
At a very low dilution,
when the concentration
of antibody is very high dilution
or very low concentration,
you don't have much antibody
bound to the antigens and
you get low optical density
and as the antibody concentration goes up,
the signal begins to increase
and then at some point
you have saturation.
And by looking at this transition
point, you can assess 50%
but looking at the point
at which you get 50% optical
density in this dynamic
range, you can assess
the titer of antibody
against specific antigens.
So that's how ELISA works
and these are prevalent
serological tests that are based on ELISA
or similar methods.
A commercial test that's
being used at UCSF
is Abbott's serological tests.
It uses a nuclear protein
to detect the antibodies.
It has a specificity of 99.4%
and sensitivity of 93.8%.
And it's important to
realize this is two weeks
after onset of symptoms
because the antibodies stick;
it's an IGD test and iGD's take some time
to appear in the serum.
Krammer's ELISA test using RBD's
is quite a bit more
specific and I'm yet to
really understand what's the sensitivity.
The important thing is that
these are first pass test,
the ELISA test with a nuclear protein
and RBD receptor binding
domain are first pass test,
and you can see that the specificity
is of course not hundred percent,
so you might have false positives
and you rule out this false positives
by using the spiked protein
and the reason for this to pass test
is the ease of producing these proteins.
Nuclear protein and RBD can be
produced in large quantities,
whereas spiked protein,
it's harder to produce
because it's a big protein
and making recombinant spike takes work.
So this two pass assays have been the norm
at this point for epidemiological studies.
I don't have as much information
about what's being used
in clinic at Stanford,
but James, do you have
something to add here?
Do you know--
- I'm just gonna ask as
I say for the apex tests,
where they use the the M protein,
do they use the entire protein
or they use what they have
like different peptides
instead of proteins, different
parallel antigens to test?
- Yeah, I'm not 100% sure, I
would think they use the whole
recombinant protein, but I'm
not hundred percent sure.
So there is also a question
of whether one should use peptide
or full protein because
antibodies recognize sequence
and confirmation both.
And it's generally better
to use full protein
so that you have a more
accurate interpretation
of the reactivity to the virus.
Now, this is an example of
how and measuring antibody
titers against two or
more antigens is useful,
and this notion extends to other types
of measurements of immune status.
If you wanted to assess if
the patient has antibodies
that can cross react with COVID-2,
but they might not be exposed to COVID-2,
you would want to have antigens
from common cold Corona
viruses in the same assay
So as a result, there has been
an interesting multiplexing
ELISA measurements,
against several antigens,
and that's the essay we
have been working on.
And it has three components.
It's a highly collaborative project.
The three components of this
platform are an antigen array,
a low cost plate reader,
and finally an open source
serological analysis pipeline.
So I'm going to walk through
these three component.
And the places where we see
these being really useful
are in epidemiological analysis,
where you can initially screen
against all three antigens
in a single pass and do it rapidly
and eventually assess for cross reactivity
by using multiple antigens.
Each of this spot is an antigen.
and we're also developing this
for low resource settings.
In partnership with Gates
has supported several grand
challenge awardees in Africa,
in Asia and in those places
running this ELISA test,
it's really challenging,
and now they're seeing the
first wave of infection.
And we want this platform to
be to be scalable, and useful.
As John has been signing up
this proteins to other groups,
the common ask they
have is what can be used
to read the plates.
So, with combination of this
inexpensive plate reader
that we are building in
collaboration with Mario Prakash
and this bi-converse
analysis pipeline we have,
we are going to meet this need.
As I mentioned, it's a team effort.
Everybody in my lab is
contributing to this effort.
We recruited a student from
Stanford from Steve Queensland.
He's from Applied Physics program.
He's helping build a major
brand greenhouses leading
the epidemiological studies at UCSF,
and he's been part of two
large studies, one in Bolinas,
one in Michigan.
John and Eric are the
protein gurus on the team.
Manu's lab built the squid
platform for their research
and Which is what we are adapting now
for reading the plates.
The idea of antibody arrays,
ELISA's with antigen arrays
originated with Krista who was at Mile Hub
and now she's at immune work
and she is doing a short tour of duty
to help with this project.
So an antigen area looks
like this in every well
of a 96 well plate.
You have many spots, each of
which has different antigens.
This is an example where the
area has receptor binding
domains pipe protein, nuclear
protein, as well as controls,
which is actually very valuable,
because this controls allow you
to do quantitative analysis.
So, there are some controls
which are fiducials
simply to recognize the
orientation of the area there.
(mumbles)
this is an antigen that
will recognize antibodies
produced by flu vaccine.
So it lets you assess if an individual
who was showing symptoms,
do they have a flu?
If the flu like symptoms
or do they have COVID-19.
So, and you can spot every well
of 96 Well with this added,
and the Edit has
replicates of the antigens
which allows you to
make robust assessment.
This is what an image
looks like once the ELISA
has been run and the
plate has been developed.
This few spots are fiducials,
the blue spots are fiducials,
this is a positive control
and these are four copies
of the same antigen.
And we read this plate
through this inexpensive
plate reader for reference, you
can look at the screwdriver,
it's a very compact imaging system.
It's built of very inexpensive parts.
In total it costs less than $700, $800.
We use Jetson nano to
control the microscope
and to run the analysis on board.
So nothing very complex in this case,
but still really scalable and very useful.
can be another--
- What special channel...
- Yeah.
- So the sample from one
patient would typically
just go to one well?
Or would you also put at
it in multiple wells for?
- Yeah, thanks for asking the question.
So the sample from one patient
would be serially diluted.
There are two types of
measurements you can do.
You can...
If you want to screen many patients,
many individuals, you
would choose one dilution
that you will optimize.
That's what we have just done
is to optimize a serum dilution
and dilution of secondary antibodies.
And you can do a first pass screen
with one serum dilution,
and in that case you can analyze
about 8084 samples in one pass.
If you want to be quantitative,
you would do serial dilution
as I was mentioning before,
so that you can see the change
in the optical density
as a function of dilution
of the serum, and that
would let you assess
the antibody titer.
And we are currently
choosing the concentrations
of antigen, secondary
antibody and serum dilution,
so that in majority of the cases
we can do a large scale screen
and in majority of the cases,
we can provide confident, set of positive
or set of negative result.
Yeah, so for quantitative analysis,
you would want to do
dilution for qualitative,
yes no answer you don't
need to do dilutions.
Once these images come in, we
put it through this pipeline.
It's on gate hub it's open source.
Anybody who is interested
in this is welcome
to take a look, use and contribute.
The pipeline takes this
images of 96 well plates,
some metadata and extracts
the optical density
on each of this spot.
And once this optical
density is extracted,
we put it through an
analysis part of the pipeline
and visualization part of the pipeline.
And the key measurement
is again optical density
versus serum dilution.
This is what the data
looks like on the y axis.
Here we have optical density,
x axis is serum dilution on large scale
and these curves are four different serum
and this sera have been tested
to be COVID positive or
negative with RT PCR.
So we have the ground truth
for evaluating this test
as the RT PCR test itself.
And you can see that you
don't see much change
in the optical density
as the concentration
of negative sera increases
and as the concentration
of positive sera goes up,
you see this transition
from low optical density
to high optical density.
- What is this
with this type of data you--
- What is spike 62.5 or so? (mumbles)
- Yeah. So, these are concentrations,
nanogram per microliter,
62.5 nanograms per microliter
125 nanograms per microliter.
These are concentrations of antigens
that are spotted in the area.
And ideally what we would like to see
is the transition from
quantitative analysis,
we would like to see the transition from
very low signal to very high signal,
and for that one needs to optimize
the concentration of antigen,
the dilution of the serum
and the concentration
of secondary antibody.
So, there are three
concentrations to optimize
and here in this specific array,
the RBD was spotted at
multiple concentration spike.
You can see 62.5 and 500 nuclear protein
was spotted at two
different concentrations,
spike was spotted at two
different concentrations.
And the effect is that as the
antigen concentration goes up,
the optical density increases
because you're capturing
more of the antibody
from the serum.
So, if I have higher concentration
of spike in a given spot,
it will capture more of the antibody
from the patient serum which
captures more of the secondary
and then you get darker spot.
And it can happen at at
the lowest serum dilution
that we use in the experiment.
If that makes sense.
- Thank you.
- So, we get lot of this
measurements of 40 versus dilution
and then what we would like to figure out
is what's an optimal concentration
of the antigen spike,
See what's the optimal
value of serum dilution,
and what's the optimal Secondary,
we haven't talked about secondary,
but the same concept extends.
Whether the optimal concentrations
of these three reagents
that are part of this reaction,
so that you have the
most discriminatory power
for epidemiological studies.
And we are looking at
the outer seekers here,
by changing the threshold
of optical density
at which we call a serum
positive or negative.
You change the threshold and
you can make this plot of true
positive rate versus false positive rate.
I should emphasize that this evaluation
is only as good as the
accuracy of the RT PCR test
and it's something that we
haven't heard about is that if we
know that the RT PCR has
certain amount of false negative
rate, how do you take that
into account as we evaluate
the serological test.
And based on this, ROCNL analysis,
we would like to have the highest
and most robust classification accuracy.
And, so this is where
the work is right now
and again we welcome contributions.
There are quite a few areas
where we immediately need help
we can use your help
and there are some areas
which will become important
as the data begins to roll in.
So for low resource settings
and even for biological labs,
who are not used to imaging,
we would like to integrate
the imaging and analysis
on the Jetson nano on cuttlefish.
So, if somebody is interested
in imaging automation
and online analysis, we
have a project we can offer
to integrate the control
software for the squid platform
and the bi Server.
If someone is interested in dissemination
of this multiplex assays,
building copies of this instrument,
just pushing forward with
scaling of this platform,
that also is valuable.
And I can see the need
for data curation methods,
data visualization methods,
as Brian runs the tests
with this platform.
They're in the process
of running about 150 sera
as a next validation experiment,
and at that point we'll
be writing a pre print.
And after that, depending on
the results of the Validation
test, this will scale up to 6000 samples.
And I also see an
interesting opportunity here
because there are this
diversity of serological tests.
And they carry different
types of information
with different accuracy.
There is a need to intersect
the qualitative tests
and more accurate and
quantitative multiplex tests.
You can't do multiplex
quantitative tests all the time,
but you can do it some of the times,
and of course, you can
do qualitative tests
for a much larger number of samples.
So having principled
methods to some data driven,
some based on understanding
of the reaction kinetics
to intersect this data that I think will
provide more insights
and it's an interesting area to pursue.
Now, that's the end of the...
I have one last minute.
So this is all I had to say
about serological tests.
And our main area of work
before this is computational imaging.
And what we work on is
imaging primary samples,
clinically relevant samples
like human brain tissue.
We developed microscopes as we, model them
so we have furthered
model of how we are making
the measurement, we
use convex optimization
to invert this data and
extract measurements
from the images and we employ
deep learning approaches
to then analyze this
high dimensional data.
And you know, close the loop.
And we have just begun
to pilot the experiments
where we can access how the infection
can affect specific cell types.
And this is very much in the early stages
and as a result we are recruiting.
We have an opening for
a research associate
who is interested in
automation and Biology.
So, if you or your friends are interested,
please reach out to me.
And with that, I want to
finally thank this whole team
It has been an amazing experience
to get into this new field
and contribute something to this effort
and thanks for your time.
- Thank you very much Sheldon.
That was very informative.
Any questions?
So I have one question as
well about the sensitivity
testing in especially physiology.
If you would put it in test for people
who show very mild symptoms
or even decent vanadic.
So do you have many samples
that's for USS or for testing?
- Very easy to combine
because there are bent sera
from before COVID-19.
If you meant that those who
don't have any symptoms?
Or did you mean those
who had mild symptoms?
- Both.
- Good.So those who have no
symptoms that are now exposed
to COVID-2, those are easy to
find because there are bent
sera from a year ago, and since
Corona Virus did not exist
before then, we can be confident
that we have the true negative sample.
Now for asymptomatic individuals,
that part is trickier
because the asymptomatic,
they are unlikely to come up
positive on the RT PCR test
and if they don't come up
positive in RT PCR test,
and you'll see the serological test
whether you see presence
or absence of antibodies,
you cannot be quite confident.
But in fact I think
that's the test population
for this kind of assay.
You can evaluate this specificity
sensitivity of the assay
with true positives and
true negatives patients
who had been infected
and who whenever infected
and that gives you enough
confidence in the sensitivity.
And also--
- That gives you the confidence
of the sensitivity index strings.
- A very strong(mumbles) you give.
How would you figure out the sensitivity
for this in the middle range
for the more ambiguous individuals.
- So the level to use that is time
is that because it takes a
while for antibody titers
to build up, you can sample the serum
from patients at different
time and so that doing
the time cross can give you
handle on the sensitivity
to really look early phases.
And my understanding in
the immunological assay
in that those who are asymptomatic
somehow have cross reactive T cells...
There are some interesting
papers that point out
that the T cells that
are produced in response
to common cold corona viruses
can recognize COVID-2.
So, it's an interesting puzzle
why some have no symptoms
and some have serious symptoms.
From the technical point
of antibody testing
I think the time cause sampling the serum
at different times gives you one lever.
- That makes sense.
Great thank you so much Sheldon.
That was pretty terrific.
- Thank you.
- Thanks, and then we
have our next speaker,
Professor Stanley Qi, who is the professor
in BioEngineering department here.
Hey Stanley.
- Hey James.
- Yeah, so he's been doing
really interesting work
on actually applying
genome editing so CRISPR
technologies to SARS COVID-2,
so he's gonna tell us about
some very cutting edge, very
latest developments there.
- All right. I'm going to share my screen.
Okay can you see my screen?
And can you all hear me well?
- Yes.
- All right.Thank you so much
for having me join this class.
As James mentioned, I'm a Bio Engineer
and what we work on used
to be train (mumbles)
and also synthetic biology and in response
to this SARS COV-2, we've
been developing a strategy
that based of CRISPR and
we explore the strategy
for broad spectrum antivirals meaning
not only for this
particular virus SARS COV-2
but also we coat so the
strategy can be probably
Used for many types of virus.
So let me start my lecture
by asking this question.
I hope to sync together with you.
What might be the best solution
for the next viral pandemic?
So if we think about that,
in the past 20 years,
we experienced a number of
epidemics right? (mumbles)
Starting with 2003 SARS 2
and following there is 2009,
H1N1 and then 2015, we've been worse
and in between there
are also thicker virus,
there is (mumbles), some sort of Ebola
or very distal virus in
certain areas of the world.
So if you think about that,
are we prepared for all
the viral pandemic?
So first from this class
and also from many resource nowadays,
I feel like everyone is
becoming a virologist this days.
Which is ripped right? (laughing)
And let's attack this
options, vaccine of course.
This Y option, antibodies
and also small molecules.
But then the question is,
if our (mumbles) potential
solution, if you get the metric molecules,
yes we may be able to fight the virus.
But then, why we are not
perfect? That's my question.
Okay, so I will start here
in terms of pandemics.
Not only virus cause
pandemics but also bacteria.
And in fact, in human history,
pandemics caused by bacteria
is even worse than virus.
You probably know in
the recent human history
in the past 100 years,
Cholera like pandemic
which is really bad.
Cholera killed many of our people.
And also, almost 1000
years ago black death,
likely caused by a particular bacteria,
yersinia pertis believed
to be propted from rodents,
mice to humans killed many
people almost 100 million people.
Which is really bad.
But then in recent
years, we start to notice
it's harder for bacteria
to truly cause pandemics.
Of course I would say
its harder not impossible
because there is the all
drug resistant bacteria
that treat here as antibiotics.
Of course this is really,
I'm trying to think
through the line.
If you compare all
bacteria induced pandemics,
for example, if this pandemic
was not caused by COVID,
but instead caused by
another type of E.coli,
which may kill people
but easier like airborne
or food borne but will be
able to invent antibiotics
to kill this bacteria before
it even becomes pandemic?
But why antibiotics are good
as rampant to fight pandemics.
I want to highlight three features
because this three features
are linked to our sync
spectrum, why we want better antivirals.
And what we want answered
by the antivirals.
The first feature I want to
highlight is broad spectrum.
So if we look at many antibiotics
that we've been using this days,
of course penicillin were
the very adequate right?
But also there are many other generations
of different things like
ampicillins, tetra cyclin
and you'll notice there are very many
different species of bacteria.
I'm talking about families,
talking about orders
and different bacteria not
just one specific bacteria.
Because for each pandemic,
it will be caused
by a particular bacteria very likely.
But your solution should not only focus
on that particular string.
Otherwise your solution is too specific
and won't be effective for the next one
because it's hard to predict
what will be the next
bacteria causing pandemic.
Same thing for virus, we
cannot predict the next virus.
Will there be another
type of corona Virus?
Or if there will be a
totally different virus.
But for antibiotics,
they were useful to kill
many different bacteria which is great.
It means if you have a solution here,
very likely you may use that again
even in the future for another species.
Second I want to highlight off the shelf
and also people call it over the counter.
Means you don't need to take
a long time to guard it.
It's already manufactured.
It's already sitting in the warehouse
or in pharmacies somewhere.
or you need to look for it,
take it out and give it to people.
And what is most important
for pandemic, timing right?
So you talk about the huge difference
between a one year response
to, one month respond to,
one week response, if everything
off shelf we are fine.
And finally, rapid research
and development and designable.
And well actually designable,
which is always a goal
of synthetic biology and also a major goal
of bio engineers especially working
with cells very unpredictable systems.
But we want to make the system definable.
It's very challenging because
it's very complicated systems.
So hard to design a molecule,
so hard design a cellular system.
But still we want this
solution to be definable.
For example. I want ask this question.
Nowadays, everyone's
talking about how long
they take to get the vaccine.
Of course, we see recently very exciting,
promising development
of the certain vaccines
like from the maternal, the
mRNA vaccine past phase one.
But also you hear the news
that they haven't disclosed the data yet.
And also, it's really amazing
45 people have gone out
and hey buddy, I don't know,
we need to look at the data.
But really, what normally
people predict for vaccine
is 18 months, because it's not a totally
a defendable process,
you need to do screening,
you need to do a lot
of testing to make sure
this deactivate virus or this component
or virus actually can induce immunity?
And how do you know?No
way, You have to try it.
You have to pass that in animal
and then human touch a long time.
What if we can have another solution
which we can easily design
and test in eight weeks?
Imagine this now everyone
worked from home.
Everyone's doing remote work.
If it were not because of
this 18 month prediction,
if we originally in March,
we said, within eight weeks,
we're going to get a vaccine for everyone.
I don't think we need to,
if I call this my trouble
as of today, right?
For everybody, maybe
you'll be already enjoying
the beautiful summer already.
So that's basically my point here.
I want to start here to see okay,
now what happened to the antivirals?
Let's see, vaccines and antibodies.
Are they prospects of spectrum?
By definition, they are
very narrow spectrum.
The almost super specific
to target a specific
antigen vaccine right?
So if you use a flu
vaccine, it's in largely
only works for flu or
very close by strains.
Even a different strain
of flu, it may not work.
Not now to see like Coronavirus.
I mean speak of that in remdesivir,
which is a small molecule drug
which is actually interesting
if you think about.
Of course they have been approved by FDA
as a one way to give for
very severe patients,
but I also haven't seen the data yet,
but in principle if remdesivir
targets or replicates RDRP,
which is common to many RNA virus
but originally it was developed for Ebola,
but it didn't work that well.
And then people found it
actually has some broad spectrum
and hyper activity against many RNA virus
who uses enter, which is
the interesting thing.
So the concept can be
borrowed here say okay,
we probably need more solution molecules
or modality like this,
so be expect for both
spectrum and off shelf
vaccines definitely not off shelf.
Whenever there's a new virus come out,
you need to take time to develop new one
or a new antigen come out, you
need to develop antibodies.
People advancing technology
to shorten this cycle
but by nature, this still takes some time,
and also did not first,
it's not possible to decide right now.
So to best find the next pandemic.
So that's why I want to start here.
So we need antivirals.
Ideally, I'd hope if broad
spectrum is off shelf
or almost off shelf
means like almost there,
but you just need to change a
small compete of commodities.
It takes a few weeks, that's fine as well,
and also develop with a few weeks.
That's why we've been exploiting CRISPR
for such an antiviral.
For this lecture, I want
to tell you why we think
CRISPR might be such a solution,
but we don't 100% Sure,
because it's such an early stage,
and it's just a new type of modality
that people haven't had that yet
in any of our history
for infectious disease.
Before I jump to the CRISPR,
is there any comments?
Or before I move on?
Questions needs to clarify?
Also I know like you, many of
you are engineering students?
So maybe that's why it's great
to have this conversation
because my background was in Phoenix,
so I learned about it.
That's why it's an interesting
thing from different angle,
you're gonna find something you--
- So we have a question from Syrum.
- Please.
- [Syrum] I'd appreciate
if you could clarify
how exactly can you
create entire antivirals
that cover a broad spectrum.
So I thought that you have all
these like antiviral drugs will
target like a very specific
like protein or biological element?
- That's great question and
then also some something
I want to discuss and why
CRISPR would be suitable
because if we want to
target broad spectrum,
we need to find common
features of the virus,
let's say why antibiotics
are pretty broad spectrum.
Why you can use this antibodies
to kill this string bacteria
in our next species of bacteria?
Because a lot of them
target a common machinery
used by bacteria, but not by human.
For example, bacteria
in the bacteria case,
Nobody really targeted the cell wall
or bacteria because cell wall
or bacteria is so different
from human cell memory. Right?
So you just prevent the cell
wall from being synthesized.
So that's why it works for
so many different bacteria
before virus we need to
think about that right?
What is common to virus.
I'm not saying it can feel all virus,
but at least it can kill many many virus.
A lot of virus, they
all share common thing.
For example, Coronavirus
there are 3000 Coronavirus,
stats and the source code to and immerse,`
they are really similar.
Their similar is like
770 something percent
from genome side and
also the the mechanism
they use to infect humans is
extremely similar, I would say,
although the exact sequence though exactly
like a protein for entry into cells,
maybe some particular
kinetics are different, right?
So, there's a lot here, but
they all share something common.
So basically, we'll talk about that next.
Something broad spectrum
should be targeting
some very common things.
Okay, how about this?
Let me move on to talk about CRISPR
because I don't know how
many of you are familiar
with CRISPR but you probably definitely
heard about CRISPR in the recent years.
There are a lot of great
things about CRISPR.
There are some things bad about CRISPR,
there's a lot of things
about CRISPR.(laughing)
Gene editing is an interesting field
(coughing)
because it has a great
potential for gene therapy,
a lot of genetic disease
that people cannot even cure.
So why CRISPR?
Okay, so CRISPR stands for
long bioinformatics name,
Clustered Regularly Interspaced
Short Palindromic Repeats
I didn't even type here
because I don't think you
need to memorize this name.
You just need to remember
CRISPR and name is so long
is not given by a biologist.
It was given by a bio
Informatician who first discovered,
looking at the sequence the
genome sequence or E.coli,
And find there are a lot of short
palindromic repeats sequence
in the E.coli genome.
They didn't know what they do,
but its cluster is regularly inter spaced
short palindromic repeats
so that's the description.
But later to like 20 years
for Biology to understand
what is indeed CRISPR? Why
bacteria have this system?
Why a human doesn't have a system at all,
if natural and all our system in bacteria.
So when we think about bacteria,
there are single cell organism.
Most bacteria just have
a single cell right?
And they still exposed in the environment.
Some of the leaving our guts,
some of them leaning in the soil,
some living in hot spring,
but they are pretty
much exposed to the wild
and they expose themselves
to a lot of foods.
And among the many bad
things, bacteria phage,
bacteria virus, bacteria classmates,
they all may lead directly
injecting their material.
Either DNA or RNA genome into the cells
to kill these bacteria.
And bacteria, they grow really fast.
But it's also because
they only single cell.
They have very limited immunity to fight
and back CRISPR is one immunity they use.
So for example, how it works.
Do you see the picture showing
the flow of how the CRISPR works?
Can you see my cursor here?
So this is a bacteriophage.
It looks like a phage right?
So injects it's genome EA.
A lot of bacteria really fight DNA virus
because in natural environment,
DNA virus for bacteria
is more stable than RNA virus,
but for humans the difference
is yes, we do have a lot of
the virus like HPV (mumbles)
HIV or Dino virus have EMRs
but a lot of RNA virus
cause us big trouble
like HIV, Ebola, dongers, Zika, cromials,
they all are RNA virus.
somehow RNA a virus evolved faster
and also can help better
invade our human immune system.
But bacteria find a lot of this virus.
They also fight antivirus,
I'll talk later.
So this DA virus this red
line is a piece of DNA.
Somehow, bacteria house this CRISPR.
The CRISPR consists of two parts,
why it's called attach gene,
means CRISPR associated genes
is being called proteins.
Another crafter is if CRISPR locus,
if they don't encode any protein,
if you know the central dogma
become RNA become protein,
but these guys don't encode proteins.
They only encode RNA.
So the black diamond here are repeats
30 base pair very short
because repeat, repeat, repeat.
But between the repeats
are also short sequence
like a 30 to 35, sometimes
20 base pair pretty short.
Then each color means
a different sequence,
every color is different,
but in history when people
look at them, when they are aligned
these colored box sequence
to sequenced bacteriophage
now different, wow, the perfect match.
For example, each box may
match to a different virus.
So it's kind of like a genetic memory
that is encoded on bacteria to stay long.
But how it happens is because
bacteria somehow knows
how to take piece of the
sequence from the bacteria phage
and we're writing and call
the recombination process
to write the sequence into its own genome.
And from that moment, any
offspring come from these bacteria
will (mumbles) inherit the sequence.
They will have a permanent memory.
Become like a tape permanent writing
the sequence into their own genome.
Then that is called acquisition.
Means now I acquire a memory
and this memory it can
close a sequence perfectly
complimentary to the viral sequence.
Now I know this is a bad guy.
So how it works. So afterwards,
you know this CRISPR cast
genes they'll become proteins,
if they fix this CRISPR
locus will become RA.
It first become a very long stretch.
This whole locus is one piece of RA,
but each fragment is pretty small.
Then we have a process
called RNA processing
into very small segments.
But each segment includes
a different sequence.
It's very modular, it's like a
code like that instruct them.
Okay, so each one's really
target a different sequence,
and then these CRISPR
protein come to bind to them,
and then we have this pairing
just a bind to TC by 2G to
search for any other virus.
For example, the green
one, also in (mumbles)
will find a virus with the
green color sequence to cure it.
But sometimes there are
some phage that not(mumbles)
to the bacteria.
So they will leave them alone
because they're also called pro phage,
means they are good for
bacteria so they'll keep them.
So if you look at that,
this is the natural
antiviral system in bacteria.
The interest fact about
that is very efficient.
Because like I said,
bacteria grow really fast.
They need to make sure the host process
happens very quickly,
almost within minutes,
and they cannot afford hours
like what our bodies do.
They need to finish to
kill their fold in minutes
or even seconds and also
they are very specific
because they need to
kill their specific phage
they don't kill other phage
and also they don't damage
their own general DNA
because if they sync up
DNA very dangerous things.
And also, it's easily
adaptable and definable
because all you need is to read,
design these different boxes
to let it target a different sequence.
So engineers like design, right.?
So if anything we can
design we can control it.
So you must have heard about CRISPR cas9.
So why present here, this
is worth what we know
about cas-9 and these cas-9 leads
to gene editing because you
can really easily design
this code here and then pair with cas-9
which is our DNA targeting Oregon,
or a DNA cleaving system.
Also, biologists call them nuclease.
So, you pair them together,
you can use them to cut a piece of DNA.
That's called gene editing,
because you can modify
the human DNA not only the smarter DNA.
That's how people repurposed
this anti viral system
for editing the human genes
or any genes like any more
in plants in other micro microbes,
and also my lab developed
this nucleus (mumbles)
we mutated last night.
We don't cut DNA we're using to recruit
a different molecule like to turn
on or turn off a gene,
almost like a gene switch.
So that would be called gene regulation
so we don't modify DNA sequence.
We modify how these genes
DNA become a protein process.
So now I'd like to talk about Cas 13.
Cas 13 is a totally different system,
come from cas-9, if not
Oregon in DNA target
it's already got an RNA already.
The reason is there are
some bacteria also have some
bad infos, they are RNA virus.
And there are several different species
or test 13 it could be
13 A or B or C or D.
So they are they have
different protein sequence.
So we may name it for
you readers by alignment,
you'll see all this looks very similar.
They feel they belong to a same molecule,
or this looks really different
though they belong to different market
but they still somewhat similar, right?
So that's why you do some
a,b,c,d's subcategory distinction.
How it works if we use
particular this cas 13D.
The reason we use this Cas
13D is because it's compared
to ABC, It's really small.
It's almost a smallest if
only it's 930 amino acid.
It means it's only 2.8 KB in length.
it's one of the very small protein
compared to most CRISPR proteins
mandatory for putting like four or five KB
in DNA sequences are very big.
What it does is quite simple.
It use a guide RNA like here,
a repeat and also a spacer.
This spacer, you can rewrite
the spacer to any sequence,
but sometimes you will divide
them to match to a target RNA,
and maybe bind to it.
It just activates it's Rnease activity
means it will cut this RNA.
So it chopped off this array into pieces
and prevents RNA from functioning.
For example, if it's a virus RNA,
it will prevent from replicating.
If it's an mRNA, it will prevent
that from being translated into proteins.
(Coughing)
That is how the CRISPR
Cas 13 it still works.
So you don't need to think too much
about the biochemical BT.
Just think about, is a GPS Okay,
it's a GPS but because the
address is here in this guide
in this particular 22
base pair of sequence,
you can easily rewrite that
and synthesize a new array.
And then these cas 13 is a GPS, right?
you that address that it knows how to use
this address to search
for a particular target,
and when they find a target,
they kill the target, they cut it.
It's very specific.
It doesn't bind other molecules.
That's why we actually
Start with this thing.
Two years ago, we were supported
by these DARPA prepare
program in Stanford learning
it's really pre emptive expression
of protected legal and response elements,
and you can find
information on this website.
The point of that is we try to define
a new set of modalities
that can be effective
against certain threats
to the human society,
and they're appropriate at
the moment and there are three
threats and the each team will
take on a different threat
kinda like a challenge
right? You challenge yourself
how to solve it and then
that threat will be a virus.
Even two years ago DARPA said
there may be sometime in
the future we will face
another virus pandemic infecting
all people in the world
and how can we counteract that quickly?
A second would be radiation
they also say Yeah,
sometimes you know nuclear
power plant may be leaking
or maybe blow may explode
or there may be a war
relating radiation, how can
we using some bio methods
to remedy this radiation.
Third would be super toxic chemicals
because nowadays people
use a lot of pacified
and a lot of compounds
become more and more toxic
and very hard to get clean
them from environments
and even tiny bit amount
to human and animal,
so the question is, how can
we can we conquered that?
So basically, you take on the
challenge you find for new
genetic methods thinking
about which gene you target
to get rid of a virus.
What is your molecule you should use,
and thirdly how do you
formulate into a clinical setup?
And how collaborators why is David Lewis
and Murray as actually
as a scientist in my lab,
who managed this project.
So what it's like, here's
what we think I think
since you guys all been
learning SARS COV-2 or biology,
so this is a brief picture of what happens
when a virus infecting human cell.
SARS COV-2 virus, it has
had RNA genome inside
it also has like a surface protein.
Normally people talk a
lot about spike protein
is a viral protein, and it
binds to a human H2 receptor
and it triggers entry.
At the same time, the
pancreas too is a protein
so we'll have cleavage
and allowing this virus
to enter and then afterwards
while RNA released
into human cells, like
human long cells RV cells.
And then after that, the viral
genome will be replicated
because this RNA virus is
a positive sense genome
RNA a single strand.
It will translate a few proteins,
a polypeptide protein
and then some protein
include a polymerase to
replicate that and then there
is probably stripe you can make a trend
and this native strength and
Gino become also produced
a lot more complex
genome for packaging in.
At the same time, they also
make a lot of viral mRNA
to produce a lot more proteins,
like nucleo capsid proteins,
by protein, membrane
protein, envelope protein,
who together help package
this mRNA into a particle,
and then this virus released
to infect more cells.
So this is the general brief
picture of what is the life
cycle and the model work
or antibody or vaccine
targeting either spike
protein, or these proteins
or this human surface protein.
But we won't say okay,
Beside that, can we target
what's inside the cells.
Why do we do it? Because
although many virus
will frequently change
the different protein
and different receptor
for entry into cells,
Almost all Coronavirus
have this common replication procedure.
They use the same molecule,
same procedure to replicate
themselves and to package them.
But if we can find a
solution to target here,
we essentially find a
broad spectrum solution
for all Coronavirus, while
if you find an antibody
to block spike or humans
to, another virus may mutate
the spike protein to find a
new receptor to increase cells,
So you're not really finding
a broad spectrum solution.
So what we think is if
it is our CRISPR cas13
and the guide RNA to
target this genome RNA,
or a viral genome, we
will degrade its genome
and also prevent the genes brushing.
And ultimately, maybe It'll
prevent viral replication
and the packaging and their release.
That's why we call the PacMan,
it's called a prophylactic,
antiviral prophylactic
means preventing, it means if your cells
at the same time infected by a virus,
early stage, not too late.
Now say you already
have like a Tondo virus,
because at the moment,
then that will make harder
for this system to clear
by either early stage.
If we give some patients the system,
it may be really effective.
That's why we thought that.
So by bioinformatically, we did some work.
That's also bioinformatics
is a theoretical basis.
Why this system is a broad spectrum.
So first we look at
all promo virus genome.
There's 3000 published, several
of them infecting humans.
All of the rest come from animal host.
We aligned this SARS COV-2 and immerse
and then we've defined this
CRISPR sequence between
a 22 nucleotide sequence
and across the whole genome
to see which region is highly conserved
among all this virus, because
we will have targeted sequence
rather than the highly variable region.
Common sequence targeting
would be the basis foundation
for the broad spectrum targeting.
And then we also analyze off targets
because you want your system
to target only virus not human genes.
Because equally possible is
a poorly designed gallery,
we also target the human
mRNA and cause degradation
of that mRNA, and flitch off targets.
And also we want to make sure
these RNA's are expressed,
which is a technical thing,
and also we analyze efficiency
because the question different
sequence have different ATCG
compensation and different
folding structure
where it's more effective.
Rather than you'd have 1000 guys
to find the best one
because it will be very slow
and not definable, can
we predict this one might
be the best or this one
will be a second best?
I've only (mumbles) that will shorten
the development time from months to weeks.
That's exactly what we did.
We did a pan virus alignment,
we did off target analysis,
and also we made sure
they can be expressed and
then we analyzed efficiency.
So this is a Coronavirus General okay?
Almost 30,000 bases and it has many genes,
and so when we aligned the conservation,
100% means perfectly conserved
among all Coronavirus almost.
And then you find there are
some regions very conserved.
We choose to the most
conserved region is actually
this replicates RNA
dependent RNA polymerase.
And this nuclear calculator
is much better than spike
and also some other very variable regions.
And the truth is region two at the target
to design guide RNAs.
And long story short, they
synthesize these fragments
into a expressible piece
of RNA in human cells
so we can't have the target efficiency
because we need to target RNA not DNA.
so we synthesize DNA but we make sure
they can be expressed inquiry.
This is actually worth value in January
because at the moment,
we didn't have any live virus in the lab.
So what is like for the technic system?
It actually has two vectors.
Why it's called expressor Cas
13 protein fused with color,
fluorescent protein.
So we know whenever you see a red color,
this is the red color with a cherry color
and then you know Cas13 is there.
The second is expressing this guide RNA,
you can define this color to be mRNA,
so you can easily change this RNA
to target different regions.
So what our experiment is
we have a long axio felt
because the virus infect human long cells.
We put a pull out guide.
Do any of you have you any question?
- Yes. Daniel has a question.
- [Daniel] Hi Stanley, so it
seems like one one bottleneck
for this CRISPR approach, Is
that you have to get enough
cells to actually have
this CRISPR mechanism
in order to clinically have an effect.
And why this would actually
be implemented in the clinic
and how you would scale
this for different viruses
that infect different cell types?
- Okay, good question.
The question really is
length of delivery, right?
How do you deliver CRISPR
because this CRISPR system
is a intracellular mechanism.
It works inside cells, it
doesn't work outside cells
unlike antibody; antibody,
you just need to inject
to the surface of cells in a block.
But this one works inside cells,
which is also consistent
with what the natural
functional prefer, right?
You can call it by bacteria
marketing inside bacteria cells.
But for this case,
the question is how do
we DNA work inside cells?
This is not a common question
for CRISP for this Pac Man,
but also a common problem
for all gene therapy.
If you know all gene therapy requires
UT delivers certain proteins or RNA,
or DNA inside patient cells
to change code or DNA.
For example, sickle cell anaemia,
you need to deliver molecules
there to change a beta
globin gene sequence
to deliver them inside
the bone marrow cells
or like cystic fibrosis you need
to divert the thing into the human cells.
So we've been what we've been
doing is we've been utilizing
the available delivery
molecules modality like a lipid
lipisomes like nano particles.
Certain types of what
they call shuttle peptide,
which is a protein help
material get inside cell
because their chemical
property really helps the cell
to uptake these molecules inside them.
And so we are using vitamin
D magnesium to test PacMan
to move in the into the
pre clinics and clinics.
It's not an easy question.
I should really say delivery
is the major challenge
for all gene therapy.
But someday we're going to solve it
because without solving it,
gene therapy won't be even be a therapy.
But we share the same problem,
so we are utilizing their...
We really share this common part with them
so we're really testing
all established methods into therapy.
- [Daniel] Is there a reason why,
take this sort of approach
where we want to deliver to,
for example, lung cells
rather than on immune cells
to directly produce targeting antibodies?
Like a targeted immune approach?
- Right, so different cells
have different delivery modalities.
For example, some liposome,
lipid nanoparticles
you can engineer their
surface to encode certain
like an antibody to also
make sure particular cells
will only uptake those nanoparticles.
So if you do a longtail
specific nanoparticle
you'll mostly go too long,
but some only go to labor.
So that is a general hope here.
Of course, it doesn't
work very effectively
and also specificity
sometimes it's a challenge.
But for us specificity
is not a major concern
because our system even
it gets into a messy
you won't get into long
tail, but you end up getting
into the kidney cells and not
get to anything kidney cells,
you just will be degraded after a while.
- [Daniel] I See thank you.
- That's a whole Of course, I
will say we are not there yet.
But that's why delivery we are testing.
(coughing)
So also I would say our
test if we have delivered
this into this cell culture
because it's much easier
delivering to cell culture.
You can use many possible methods to,
you recall transfection just
means you deliver this DNA
using liposomes to get into the cells
which liposomes means lipid particles,
and then you challenge
them with either virus
or encoding the sequence and
then re merge the expression
and also RNA is built.
And here I will go quickly.
So basically we identify
different pools or galleries
like a (mumbles) for galleries
but into these different locations
or the other RDRP 2G group
to target different regions
because this is worth the work
that originally we need to see.
What's the principle of cutting.
So we tag them, we find some
cause work better than others.
And then later we isolated the best pool.
For example from this pool four,
if I know this one works really well.
So now let's isolate them
into individual wells.
And by testing individual
one we can find some works
really efficiently like this one,
number 16 can repress this RNA load
and also associated
fluorescent protein by 98%.
The challenge I will say like
because we are not really
a biology lab, we were
like a gene editing lab,
and so we didn't have this virus
but we do have our collaborator
who worked on influenza.
Influenza, Like I said
is another RNA virus
and also we tried to
target since last year
so we have this H1N1 virus in the lab,
is different from (mumbles) virus
because it has eight genomes,
each genome is smaller,
but there are eight but
still with the same strategy,
we say let's define our
edge to target each fragment
and we did a screening to
say, for each fragment,
which one we'll get you better repression?
For example number six.
If you require that you
reduce the RNA load virus,
because if it's virus or virus low to 28%
and we use two different
nearly numerology,
you will talk about infectivity,
because it can be heavily
infected or lightly infected.
People use this MOI called
multiplex to infection,
generally means how many
viral particles each cell got.
MOI Equals 2.5 means every cell
on average got 2.5 particles a virus.
So the more you got, the more
severe infection you get.
So you actually see if you
have a very bad infection,
the efficiency decreased from
28% to 48% remaining virus,
but it's still repressible, It's good.
So that's why for influenza,
we cut it for either infection,
we actually can repair that.
So we are now testing all of ours.
So about why I believe is broad spectrum.
Let's come back from
the computational side,
the theoretical prediction side.
So what we think is, for
example, you have many virus,
strings, and each string may be a totally
different phylum or it's mutant imitation.
And then each RNA may target some regions
because of binding specificity.
But if you have enough of them,
you will be able to cover all of them.
So it's actually an algorithm,
become an algorithm to define
a small set of RNAs
that target all strains.
So if it works beautifully
as what I described,
just by predicting guys to target virus,
then targeting broad spectrum virus
become an algorithm prediction question.
We did that practice, for
example, this is all Coronavirus
consisting of more than 3000 coronavirus,
and they have different groups
alpha, beta, gamma, delta.
And it's also the seven virus,
infecting humans are
highlighted somewhere here,
or they are related strings.
And then we designed a weird computation.
We designed six guide RNAs
that theoretically can cover all of them.
The reason we device six
is because we did an algorithm prediction.
What is the minimum number of
guys that can target maximum
on the number of virus?
If you use one, the best
situation we can target is 40%.
But if we use two, we
can target up to 50%.
But then if we keep adding
this number up using six,
we can target more than 91%.
And calculation shows if you want
to target 100% sequenced
Coronavirus, you'll need 22.
So this is a prediction,
and also even within this SARS COV-2
and people keep sequencing
new sequence right here.
I mean, this data we're starting March,
so there were only 1000.
We have now have like a more than 10 times
of these numbers, having
updated my slides.
So basically, my point is,
if you design a guide RNA,
where we align them to all this,
you'll find it actually target
99.6% using the single guide.
It means there's still some
small number of mutation
like 0.4% of the sequence actually mutate
with a single mismatch here,
but that's why we are using a pool right?
We are using a pool, we
are using two or three guy,
because if you use two guys
you will significantly
reduce the mistargeting.
Same thing for influenza.
So now two final slides.
My say is how do we
think it may be useful?
Because I will say for
this pac man to be useful,
we have a very early stage.
No product like this is
before we are just exploring
whether or not if it's useful,
and we imagined if it is useful,
it will be a nebulizer system
or maybe a nasal spray,
that you can deliver this PacMan in a form
like a protein form or RNA form
into the long on the aerobic cells,
and because they are packaged
inside the nanoparticles,
they will diffuse and penetrate
into the aerobic cells
and they will get in there
and to help people clear
the virus in their aerobic cells.
And the different from gene editing
is these are not modifying any DNA
and also because there
using protein or RNA,
they don't stay there for
a very long period of time,
I think there is a model to
predict that it may stay there
for weeks to a month.
But afterwards, because
those cells will replicate,
will clear out some protein
arrays, it will be degraded,
but it'll give people protection
for a few weeks to a month,
and imagining in a pandemic like this,
if we were able to give
everyone like this for months,
I'm pretty sure this is social
distancing a virus itself.
The virus will significantly propagation
there will be reduced and eliminated.
So the challenge like you
got ask three questions.
The first one is we are
testing biosafety level three,
which is a very high level
to test all these libraries.
Second is really delivery.
And we are testing
different methods to deliver
either peptides or maybe nanoparticles
or using other virus,
like AAV is widely Use for gene therapy.
And we are also testing
animals by using which animal,
hamster, ferrets, mice, very
complicated animal models.
And the fourth is how can
we target all RNA virus
like HIV Ebola or Vika or theoretically
any potential virus in the future.
So I think I run out of
time, but I don't know
if there are time for questions?
If there are, I'll be
very happy to answer.
- Great, thank you very much,
Stanley. Yes. Any questions?
So I have one question regarding
the efficiency that we need.
So, what fraction of the
relevant cells in the body
do you think needs to
have this construct inside
in order to to really be effective
to prevent the virus spreads?
- That's a very good question.
So according to the current model,
of understanding a virus,
when a virus got inside
the human body, it
usually infects the nasal
or this upper respiratory
tract part of the tissue.
They don't directly get...
Although people know this
virus may infect brain cells,
may infect kidney, may infect all cells
cause diarrhea cause confusion,
but those are not the entry point.
Virus probably get into us
by using mouth-nasal cells.
So following into that argument,
if they were able to get
PacMan into the same trajectory
as those initial contact cells like nasal,
upper respiratory or even not long,
because long is a
secondary logale patient,
because their model seen as
the first replicate a lot
in nasal and then the virus be
in read into the lung later,
something similar like that.
So I think if we can
target the early part,
the spot infected by virus,
we may already get relatively
efficient elimination
because all the string
theories, I'm not saying Pac Man
will replace the human immunity,
Pac Man is to delay to
reduce the viral load
to provide enough time for human body
to develop their own immunity.
Because if you think about if I initially
received 100 particles, it may kill me.
But if I received only one virus particle,
I will give my body more time to respond
and develop antibodies.
And also same thing for elder people.
Why elder people have a
higher likely solitary
likely because their immune
system responds slower,
or some or many of them
are immune deficient.
But if if we give them more time,
if we buy more time for
them, it may help them a lot.
- Great other questions?
- [Woman] I just wanted to say thank you
so much for your talk, this
was really interesting.
- Thank you.Like I said, I'm
not trained as a biologist.
I really don't bother
by complicated biology
or biochemistry or biophysics there.
What I think is all we
try to simplify things
because our trained endo
physicist and the latter
became a bio engineer,
the engineering side.
Sometimes I think I hope
some engineering approach
or the goal here to simplify,
because engineers try to simplify things.
We try not to make things
unnecessary complicated.
We try to make things
simple and while useful.
So that's why we try to simplify
a lot of complicated Biology there.
(mumbles)
Of course sometimes it may not be helpful
because we really need to
consider the complexity.
But sometimes, it may work (laughs)
If it works that will be amazing,
that's why, I felt in
about some early work here
and hopefully, I think we've
shared something in common.
(laughing)
- Yes very interesting, that's terrific.
But thank you so much Stanley,
this really a terrific presentation
- All right, thank you for having me.
Happy to have more discussions.
- All right, that's great.Have
a good weekend everyone.
