(soothing music)
- Just to give you guys a quick rundown,
my lab specializes in
the design and adaptation
of high-throughput quantitative approaches
to better understand
host-pathogen interactions
and how those interactions
lead to differences
in disease severity and treatment outcome.
So it's kind of like if a bug
is going around the office,
and you have a head cold,
Susan next to you,
she's just living her best life,
and Karen in the cubicle next
to her is out for a week.
Right now we tend to
think of these variations
as bad luck or as a
consequence of lifestyle,
some sort of moral failure
on behalf of those people.
And then, these variations,
rarely do we ever think of
in terms of the biology.
Karen always gets sick
is a fairly medieval way
of thinking about infectious disease,
but in truth,
that's how oftentimes we
think and talk about it.
These perceptions have dramatic impacts
on the products we make,
the research we do,
and what our priorities are.
So it's really important to
understand this variation
and what it means.
So with that in mind,
I just wanted to give you
a quick rundown on what
our lab is doing in regards to
trying to understand the human components
and how they relate to HIV infection.
So, HIV really needs
very little introduction.
There are currently over 37 million people
living with the virus worldwide,
with over one and a half
million new infections
last year alone.
The virus, if left untreated,
infects and depletes CD4-positive T cells,
causing acquired immunodeficiency
syndrome, or AIDS.
So while there are a large number
of highly effective
antiretroviral therapies now.
These therapies are not cures,
so once someone is infected
they're infected for life and subject
to the downstream of physical,
mental, economic, social
and legal consequences of that status.
So, that is not to, in any way,
belittle the amazing progress that
antiretroviral therapy has done.
Really it has changed
the face of an epidemic
from what once was a death sentence
to what is now a manageable condition.
In fact, if somebody is
on antiretroviral therapy
and they're successfully
suppressing virus replication,
they virtually cannot transmit the virus.
The World Health Organization has recently
adopted this as policy,
wherein if the virus is
undetectable in the blood,
it is untransmittable.
Undetectable equals untransmittable,
and that's basically the principle behind
the global effort to end the epidemic.
The idea that if we can
get enough antiretrovirals
to enough people we can stop
the virus from spreading.
So it's been the goal of UNAIDS to achieve
this 90, 90, 90 goal by today, by 2020.
Essentially,
to have 90% of the people
infected to be diagnosed,
90% of the people diagnosed on treatment,
and 90% of the people on treatment
to achieve viral suppression.
Sadly, we are nowhere near
actually achieving those goals.
Currently, it's estimated
that about 70% of the people
living with the virus are diagnosed.
Only 77% of which are on treatment,
82% of which have achieved suppression.
And while these numbers sound pretty good,
they result in really
dramatic failures on our part
to stop transmission of the virus.
So our original goal in 2020 was to have
less than a half million
new infections each year.
We're currently over a
million new infections a year
above that goal.
So all of this is just kind of to say,
that as amazing as
antiretroviral therapies are,
they're just not enough.
In the first place,
anybody who's infected and
on antiretroviral therapy
has to take a pill every single day
for the rest of their life,
which is vastly too expensive,
and just completely infeasible in many
resource-poor settings.
Furthermore, even if somebody
is on these kind of therapies,
these ART drugs are not perfect.
They do not restore
normal immune function,
nor do they completely fix
or repress virus replication.
This leads to systemic inflammation
and increased rates of neuro degeneration,
liver and kidney failure, cancer,
and a host of other comorbidities
that together decrease life span.
So, this is all basically to say that
to improve patient health
and provide a lasting
solution to the epidemic,
we really need to invest in new curative
and improved therapeutic solutions.
These interventions really need to account
for the host as much as they
account for the pathogen.
What I mean by that is, we
can no longer just think
of this virus as sitting in a
test tube and try to stop it.
We really have to think of the
virus as a two-part system.
It's virus and host.
That's the whole nature
of infectious disease.
So like all viruses,
HIV relies on components
of the host cell to replicate and survive.
HIV relies on hundreds of different genes,
proteins and metabolites in a human cell
to successfully replicate.
It turns an infected cell essentially
into a virus production factory,
and at the same time is able to inhibit
our immune defense with
only nine simple genes.
How do we do this?
How does the virus do it?
And can we use some of these pathways
to develop new therapeutics and new cures?
This is something that biologists
have been trying to
figure out for decades,
and that's actually the
most difficult part.
So we've been looking and looking
and trying to identify
all of the different genes
that HIV uses to infect a human cell
and we've been pretty poor at it.
So just about a decade ago,
so this was right in the
sort of heart of genome wide,
RNAi was the big thing on the scene.
Four big labs came up with four big papers
each looking genome-wide
using these RNAi platforms
to identify which genes
HIV uses to replicate.
They each identified in
these high profile papers
roughly 300 to 400 genes
that the virus required to replicate.
When you take those data
sets and overlap them,
they all agree on exactly zero genes.
So what's the problem?
This threw the field into a crazy tizzy,
because if these giant labs
can do these giant experiments,
genome-wide screens
and then agree on zero,
that's a problem.
So a bunch of post-mortem studies came out
trying to identify
exactly what went wrong.
Basically they came to two conclusions.
One, they relied on RNAi.
That's limited by transient activity,
off target effects, and
incomplete knockdown.
But then two,
these cell lines are not
authentically recapitulating
the intracellular environment
of the host cell that
HIV normally infects.
So, these are the cell lines
they used in these studies,
everything from HeLas and
hex to Jurkat T cells.
So just to emphasize this point,
now here are some EM images of a HeLa cell
in the upper left, a Jurkat
cell on the upper right,
and then a primary T cell
at zero, 24, and 48 hours
poststimulation.
HIV can really only infect that cell,
the activated T cell.
It doesn't naturally infect
HeLa cells or Jurkat cells.
But oftentimes we rely on them
as models in our research.
Even looking at the pictures of them,
you can see the gross
morphological differences
between these cells and
at the molecular level,
it's way, way ... the
differences are even greater.
So, based on all of this,
our lab teamed up with
Nevan Krogan and Alex Marcin at UCSF
as well as with Jennifer
Dowden at UC Berkeley
to develop a high-throughput
CRISPR-Cas9 platform
for editing in primary human T cells.
How this platform essentially works is,
we isolate T cells out of human blood.
These T cells are then activated.
In the meantime, we isolate
Cas9 protein in vitro
and synthesize guide RNA.
These can be complexed
together to form stable,
Cas9 Ribonucleoproteins.
These proteins then are
delivered to these cells
by electroporation to result
in a polyclonal knockout pool.
These can then be expanded.
You can verify the knockout at the genome
or the protein level
and then use them for
downstream phenotypic assays,
such as HIV infection.
So just to give you a quick
rundown on the platform,
cell number in a simple
little 96 well cuvette,
you can electroporate
somewhere between 100 thousand
and a million cells, 20
microliter reaction volumes.
Efficiencies, we're getting
near complete ablation.
You can do each one of these
in less than a couple hours.
You can do them 96 wells at a time.
We're currently doing 384 at a time.
Everything is commercially available.
Right now, we're using
the Amaxa Lonza platform,
but it's adaptable to any
electroporation device
you might have.
You can multiplex it, so
you can do up to quadruple
or quintuple knockouts if you wanted to.
Toxicity is fairly low.
You can freeze-thaw these
RNPs however much you like,
and the cost is relatively minimal.
So, some proof of principle.
So how HIV enters a
cell is it binds to CD4
as the main receptor.
Then it binds to a co-receptor,
either CXCR4 or CCR5,
depending on the strain.
So we decided to knock
out CCR5, the co-receptor,
just to show that this
could indeed be done.
So here, we're looking at
facts plots on the left,
staining for CCR5 on the
Y-axis and CD4 on the X.
As you can see in our normal T cells,
there's a sub-population expressing CCR5.
You treat with a control,
such as Cas9 only.
That's still there.
You treat with a CCR5 guide
RNAs and expression is ablated.
It does not impact the
cell service expression
of other markers, such us CD4 or CD25,
which is a marker of T cell activation.
Now, if you look over then
at infecting these cells,
such as in the blue histogram.
These are cells infected with
the CCR5-tropic HIV-1 virus
and you can see in our control reaction
we have complete infectivity
and then in our CCR5 knockout cells
we see almost no replication.
And this can be completely rescued
simply by changing the
envelope on the virus,
so it's not that these
cells are sick or dying,
it's truly because of
this specific gene edit.
This is great and we can
do this all one at a time
but what we really wanted to know is
can we do this hundreds
of genes at a time?
We decided to go ahead
and knockout roughly
430 HIV-human
protein-protein interactions.
This is just a network map
of the physical interactions
HIV is thought to make in a
human cell upon infection.
There are over 400 different genes
that the virus is supposed to
be able to physically bind to
but nobody has ever actually tested
to see if these were genetically required.
We decided to knock these all out
because they should be
functionally important.
We would expect them to have an effect.
This provides a mechanistic
handle for functional follow up
because we have physical
and functional genetic data,
but then also we wanted
to just ask the question
as mass spec becomes more and more used,
how many of these hits
are actually gonna prove
to be translatable?
This is what we decided to do.
This was basically our
experimental design.
We had 430 genes that we were
able to design guides to.
Three CRISPR RNA per gene plus controls.
Those were synthesized in 96 well arrays,
electroporated into activated T cells
from at least two to five donors per well.
These were then activated and expanded,
replica plated and then infected
with a GFP reporter virus,
which was monitored by automated
flow cytometry over time.
At the same time we collected
genomic DNA from these cells
and we used deep sequencing
to calculate our efficiency of knockout.
So just briefly regarding the efficiency.
How we did this was
we have bio-emphamatically
designed primers
flanking the target gene site.
We amplify across, add
index primers, purify,
do deep sequencing
and we end up with a lot
of reads kind of like
is shown in that cartoon on the right.
CRISPR-Cas9 relies on the
endogenous repair machinery
to repair those edits
but based on the reads you can calculate
that your percent insertion deletion
and your percent efficiency
of your guide cutting.
And we were able to get reads
for about 83% of our knockouts.
So you take all that data, look at it
and right here we're showing
the most efficient guide per
gene in pink in the histogram.
On average, we were able to
get a knockout efficiency
of at least 75% in our primary T cells
and if you look at the
variation donor to donor,
as shown in the dot
plot over on the right,
you can see that we have
very high correlation
moving from one patient to another.
You do see a few points
where one donor is way better
at editing than the other.
When you actually look
at the sequencing data,
that's usually because there's a snip
in the CRISPR binding site in one patient
but not the other person.
That's all well and good.
We have our efficiency data.
Now let's take a quick
look at our infectivity.
Like I said, we incorporated
the same six controls
on every single plate that we did.
Three non-targeting, which
are shown on the left.
We get very tight reproducible
rates of infection
and then three controlled genes,
which we know dramatically
decrease HIV reinfectivity.
They all look good days
three, five and seven.
Looking at the results
from every single guide
we identified many guides
that decreased infection,
a few that increased infection.
They stay relatively stable over time
as could be infected,
expected, not infected,
but then the cool thing is you
can take your efficiency data
and plot it on the x-axis
relative to your infectivity data.
So now the more efficient
the guide RNA is,
we're seeing more and more perturbations
to the infectivity of these cells.
Based on a number of titration experiments
that I can't go into right now,
we were able to ...
There we go.
Identify that we needed 30% editing
in order to see a phenotype in our assay.
So assigning a 1.5% FDR, we
can identify our true hits
which are shown here in blue.
If you take these guide RNAs then
and map them back to the gene level,
we identify 86 genes in this network map
that are essential for HIV replication
or roughly 20% of our HIV
protein-protein interaction map.
You can take that functional data
and now put it onto the physical map
and you have a functional
map of how HIV is replicating
directly in a primary T cell.
Of the 86 host factors identified,
roughly half have been validated
in previous mechanistic studies
but the other half had
not since been reported.
We've been slowly working through our way
and publishing on the mechanism of these
but we have been able to recapitulate
a lot of really well known biology.
For example, the HIV
transcription factor, Tat,
binds the P-TEFb complex
to stimulate transcriptional elongation
and of course, when we
knockout members of P-TEFb
we see decreases in infection,
we knockout negative regulators
of that, as shown in red,
we increase infection.
So, all very consistent.
One really surprising thing that we found
and one of the last
things I wanted to note
is we do see pretty dramatic
donor to donor variability
in specific host factors
meaning that this virus
is using different genes
in different people to get the job done.
For example, when we
look at this gene, FBXO6,
here we can see in four
different donors, A, C, D and E.
No difference between
the blue and black line
meaning no difference between
the non-targeting control and knockout
but in donor B,
for whatever reason this
gene is incredibly important
for this virus to get by
and we have absolutely no
understanding of this as of yet
but it's interesting and
important to consider
when we're beginning
to develop therapeutics
that would target any of these pathways.
Moving forward what we're
hoping to do with this
is now we're taking sequencing
data from these patients
and mapping single nucleotide variants
on to these important complexes and genes
and beginning to knock in
single nucleotides one at a time
to identify how these
variations in the human genome
can contribute to differences
in disease outcome.
With that, I just wanted to
leave you with this thought.
High-throughput gene editing
in primary human cells
is readily doable.
If your company is not doing this
to support their biological pathways,
identify their drug targets, et cetera,
they really should be
because primary cells
are very different than
your cell line models.
With that, just wanted
to thank people in my lab
who contributed, all our
collaborators and funders.
Thank you much.
- [Audience] (applause).
(soothing music)
