(bright electronic music)
- My next assignment,
which I assigned to myself,
was to talk about some issues that arise
around the whole editing business.
And one of the first things
I'm gonna talk about,
and Jennifer introduced this very briefly,
is the fact that there are alternatives
to the standard
streptococcus pyogenes Cas9.
One of the first ones to be appropriated
was the staphorias Cas9.
This was promoted by
Ran in Feng Zhang's lab.
And one of the reasons that
they got interested in this
was because the pyogenes
Cas9 is a big protein,
1,358 amino acids.
I'm sorry, 1,368 amino acids.
It's a little cumbersome to work with
in some situations.
And the staphorias Cas9 is only
1,053 amino acids,
300 amino acids shorter.
People have also done a little bit of work
with this streptococcus thermophilus Cas9,
which is a little bit bigger,
70 or so amino acids bigger
than staphorias.
The Joung lab
isolated CRISPR RNA's and tracer RNA's
made themselves a single guide RNA
in the way that the Doudna
and Charpentier labs
had made a single guide RNA
for the strep pyogenes system.
They found that you needed a guide length
that's a little bit bigger.
The pyogenes system works quite well
with the 20 nucleotide guide sequence.
The staphorias system likes something
that's a little bit longer,
the guide sequence itself
a little bit longer
attached to this framework.
Recently Jungsu Kim's group
began working with an
even smaller Cas9 protein.
This is campylobacter
jejuni, and it's only
984 amino acids.
The coding sequence for it is under 3KB.
What they showed was that they could make
a single guide RNA using the same sort
of principles as for the predecessors.
And then they looked at guide links
and sort of similarly
to the staphorias system
guides of 21, 22, 23 nucleotides
seemed to work a little bit better
than the 20 nucleotide guide sequence
just this sequence that directs
the Cas9 protein to its target.
They also showed that
the jejuni Cas9 is quite specific
and it can be used in these
mouse myotubes
by injection into mouse muscle.
I'll talk about this again on Wednesday,
but one of the reasons for looking
for these effective shorter Cas9's,
is that a popular vector for gene delivery
in human cells and other mammalian cells
is the addeno-associated virus
and their vector's based on AAV,
but they have a limited capacity.
And if your Cas9 takes
up less of that capacity,
there's plenty of room
for a guide RNA sequence
and a promoter driving,
it's expression, a promoter
driving expression of Cas9
and then some other bits and pieces
all within the packaging limit
of addeno-associated virus
and then these are
addeno-associated virus delivery
to cells in culture and to living muscle.
Jennifer showed a later
version of this slide,
mine's from a review.
Eugene Koonin gets a lot of mileage
when more sequences are accumulated,
they make more sequence comparisons
and they expand the list
of these CRISPR systems
but even as of 2015,
they had identified
this type five within class two
where they're Cas9 like proteins
that are called Cpf1.
And Cpf1, Jennifer didn't
have time to go over it,
although she showed you a
slide that illustrated it.
Cpf1 has a bunch of differences
from the Cas9 system, which may be useful
for some purposes.
First of all, there's no tracer RNA.
There's a single RNA that's involved,
so you don't have to construct
a single guide RNA out of
CRISPR and tracer sequences.
It's just one RNA.
Although the Cas9 system
it's the five-prime-end
of the guide RNA, where
the guide sequence lives
and it recognizes the target.
In the Cpf1, it's the
three-prime-end of the guide RNA
that recognizes the target.
Instead of making blunt
cuts, it makes staggered cuts
that leave a four-base five-prime overhang
just like a lot of restriction enzymes.
People have considered that
this might have some advantages,
although I don't know
of anybody that's taken
advantage of those possible advantages.
The PAMs for the Cpf1's that I know about
are all AT rich rather than GC rich,
which has been true for some of the Cas9s
and the Cpf1's have been shown to have
quite high specificity.
This is just some more stuff.
This is again from Feng Zhang's lab
looking at guide links
and you can see anything from 18 to 22
is an effective guide
for in vitro cleavage.
There is very little
tolerance of mismatches
as you get close to the three-prime-end
of the guide sequence.
Sorry, to the hand proximal
or the five-prime-end of
the guide sequence itself
and they also demonstrated
activity in cells,
Cpf1's from two different bacteria
compared to the strep pyogenes Cas9.
This is a very functional
system that people have used.
One of the things that
people thought might limit
the utility of the original Cas9 system
was the fact that you had to have
a pair of G's in the genomic sequence
next to where the guide would bind.
So your target had to be defined
by a pair of G's.
But now, with some of these
other natural proteins,
the Cpf1 proteins that have
these AT rich PAM sequences,
the staphorias and campylobacter jejuni
have somewhat different guide sequence.
I hope everybody's familiar
with the nomenclature
that R means purine
and Y means pyrimidine.
For example, this jejuni Cas9 recognizes
A or G, T or C, then
AC, four base pairs away
from the sequence that is represented
in the guide sequence of the guide RNA.
And then, Keith Joung's lab
has gone about deriving Cas9's
from both staphorias and strep pyogenes
that recognize somewhat
different sequences.
Instead of GG, there's
one that recognizes GAG
and another one that recognizes GCG
and they made one from staphorias
that doesn't care about that G,
but only cares about purine and purine T.
And this is by in vitro selection.
So now you have a variety of PAM sequences
so you're less limited
in the genomic targets
that you can access.
Another thing that people
have done with Cas9,
mostly to improve its specificity,
is to break it into two pieces.
And you can break it
into an N terminal half
and a C terminal half and
express them separately.
The Douda lab actually
did this in a somewhat
more intentional way than just cutting it
N and C terminal.
You can set it up so that one of the,
sorry, so the association
between the N and the C
terminals doesn't happen except
in the presence of a small molecule
or some other initiating condition.
This is one, again, this
is from the Joung lab,
where rapamycin encourages
the dimerization
or the association of the
N and C terminal halves,
which then go to the
nucleus and have activity.
Now, the activity, the inherent activity,
is somewhat compromised compared
to the wild type N-type protein,
but the specificity is actually quite good
because there's nothing going on
until you introduce the rapamycin.
And I just gave you a list
of some of the other ones,
Luke was involved in one of these.
There's even a photo inducible
dimerization system
that has been developed.
So you can look those up on your own.
One of the things that people tend
to get freaked out about in
all of these editing technologies
is what's happening at
other places in the genome.
What are the off-target effects?
The concern about this was
stimulated and enhanced
by some papers that
were published in 2013,
just a little bit over a year
after the original Martin genic
Douda Charpentier publication
showing what you needed
in order to operate
the CRISPR system.
This is from Keith Joung's lab.
They have a situation where
they've got a single copy
GFPG in cells and then they're introducing
different guide RNA's
that will cut the GFP
and by nonhomologous end joining,
ultimately disrupt the
GFP coding sequence,
and they're just looking by
fluorescent activated analysis
what proportion of
cells are now GFP minus.
So it's just a disruption assay.
They've got three different guide RNAs
that have different GC contents
that are pretty good when
there's a perfect match between the target
and the GFPG and the guide sequence.
But then as you begin
introducing single based changes,
you find that if those
single based changes
are near the five-prime-end
of the guide RNA,
they have essentially no influence on this
or this guide RNA and its effectiveness.
Some greater influence on this one.
And even when you begin inducing pairs,
either adjacent pairs
or more distant pairs
of mismatches, you're still getting kind
of a lot of cutting with
some of these guide RNAs.
Then they went to endogenous genes
to look to see what's going on.
This is the target.
And in the genome of these human cells,
there are three different
types of human cells,
there are some sequences that are related,
but not identical to the intended target.
So let's look at U-2 OS cells.
They're getting 50%
mutagenesis in U-2 OS cells
at the design target.
But, they're getting a
similar, almost identical level
of mutogenesis at a target
that differs by three base pairs
and here's one that differs by four
that's giving an even
higher level of mutagenesis.
So, you know,
are we shooting ourselves in the foot
or some other more vital organ
when we are operating this system.
This is from the Feng Zhang
lab showing a similar thing.
It's a little difficult to
assimilate this right away,
but what they're showing,
when there's deep blue, that means
a single base change doesn't
interfere with cleavage.
And they're using similarly,
they're using a system
where they've got guide RNAs
that are mismatched to a single location
to a genomic target.
Single base changes
near the five-prime-end
of the guide RNA are
not inhibiting cleavage
very much at all.
Although, when you get closer to the PAM,
it makes a pretty big difference.
And this is again from the Joung lab.
If you put in more Cas9 and guide RNA,
this is using a plasma delivery system,
you get more indel formation
at the design target,
but you also get more at off target sites
and if you compare,
you make a specificity calculation
design target over secondary target,
the specificity goes down,
as you put in more of the nuclease.
- [Woman] So I assume
that people have shown
that that preference for the amino acids,
near the, sorry, (mumbles)
to PAM need to be stronger
is also mirrored in Cpf1, where
the three-prime and
five-prime are flipped?
- Yes, but Cpf1 actually, is less tolerant
of single base changes at either end
than the Cas9 system.
But it is true, I think it's at one end.
It might have been in a previous slide,
I just don't remember
right now how it goes.
You can look it up.
The harder you try to hit your target,
the more you're gonna hit the
secondary targets as well.
My view, all the way from the
time those papers came out,
little less than four years ago, has been
the CRISPR system actually
needs to have room
for mismatches.
And the reason for that is that,
when a virus infects the cell,
this cell has incorporated a sequence
from a relative of this virus
and now is protected
against that relative.
The infecting virus this time
may not have an identical
genomic sequence.
Viral sequences evolve
at a really high rate.
And so, you know, even keeping
coding sequences the same,
you can have redundant codons
that would have sequence changes,
and so if you were so specific
that even a single base pair of mismatch
between the CRISPR RNA that was generated
from this spacer --
Single mismatch between
this and incoming virus,
if that did away with your immunity,
you'd be in big trouble.
So mismatch tolerance I think is adaptive
in the CRISPR system
and we should expect it
to have less than perfect specificity.
Okay, we're gonna talk
about how much we care,
how we measure some of
the off target influences,
and what we do about it.
How much do we care?
Well it depends what you're doing.
If you're working in a model organism
and you're making new mutants
and you're gonna study gene function
through this mutagenesis,
you can make independent
mutations in the target
and study them to see if they
have the same phenotypes.
You can clean up the background
and a lot of genetic
organisms by out-crossing.
And you can complement your
mutant with a wild type gene
to make sure that the
phenotype you're studying
is do to that mutation
and not to something
that happened elsewhere in the genome.
If you're working with food organisms,
you're concerned about safety.
Both livestock and in plants,
you typically go through
a very narrow bottleneck,
you're essentially cloning the organisms
and then expanding them
through sperm donation
or seed propagation.
And so you can do whole genome sequencing
and look to see what else did we hit
when we were trying to hit gene X?
And when you go to clinical applications,
again, we're worried about safety.
And when people talk more specifically
about some of these clinical applications,
you can see that in somatic therapies,
particularly ex vivo
modifications of cells
that are then going to be
put back into a patient,
typically have a limited range
of off-target modifications
that might come up and bite you.
As long as you kind of have those covered,
you're probably in pretty good shape.
As you get to stem cell therapy,
somebody asked the question
earlier about stem cells.
Right now I'm more concerned
about the stem cells
than I am about the CRISPR
base modifications of them.
But that will change over time.
In germline therapies,
as Jennifer highlighted,
there are lots of reasons to be concerned
because you can't go backwards.
You're influencing an individual
and that individual's
ultimate offspring and
you could conceivably try to go back
and reverse the change using
the genome editing technologies again.
If there are multiple
things happening off target,
that could be a problem.
So how do we assess what's going on
at secondary targets?
And there are a bunch of ways to do this,
and I'm gonna
illustrate a few of them.
One thing that people will do
is to either make predictions
just based on sequence similarities.
I think that Jennifer
Listgarten will show you
that you can do something
beyond just looking
at sequence similarity.
Or you can use an in
vitro preference assay,
binding preference assay,
something like SELEX.
And then there a bunch of things.
This is another in vitro method
that David Booth's lab came up with.
There are a bunch of things you can do
that are, actually you're assessing
what's going on in cells.
One of these, who was
developing Keith Joung's lab
is Guide-seq.
If you read Keith Joung's papers,
you'll know that he develops a lot
of very, very useful tools in this area,
but none of them is developed
without a cool acronym.
I think they're probably developing
other technologies in their lab,
but they discard them if they can't think
of a cool acronym.
So this is GUIDE-seq,
and you have to go to the paper to see
what GUIDE stands for.
'Cause I don't remember.
It's actually a neat technology.
What they reasoned was,
when you make a break,
the ends will often go back together
and we even know that you can get an N
from one break to go together with an N
from another break.
What if we put in a little piece
of double-stranded DNA,
our double-stranded DNA
and we just look for end capture
of this double-strand DNA by
nonhomologous end joining.
And if there's enough of
that around in the cell,
instead of the two ends of the break
coming back together with each other,
they'll capture this oligo in-between.
So this is a 34 base pair,
a double-stranded ogligonucleotide,
it has five-prime phosphates,
which help with ligation,
but the ends are protected
chemically from degradation,
and it doesn't work well
unless you protect those ends.
So you're getting blunt
joints for the most part.
Now this is in the genome
only at sites where a break was made.
And so you can use this
as sort of a landing pad
for PCR and amplify the sequences
that are beside where that oligo
got incorporated in the genome.
This is just an elaboration of what you do
to get this ready for DNA sequencing.
When a break has been made,
what you should do is you should read out
of this oligo tag.
It can go in in either orientation,
read out of that oligo sequence
into genomic sequence in both directions
away from the the place
where the double stranded break was made.
And so this is a way of looking to see
where in the genome are
the double strand breaks.
And the data from an experiment
like this looks like that.
And there will a quiz on this.
What I'll do is I'll just
show you one example.
Not the best and not the worst example.
This is a side in the VEGFA gene.
And there is the target sequence up there.
And then these are the
number of sequence reads
from a GUIDE-seq experiment
for the intended target,
which is that black square.
And a bunch of other genomic sequences.
The horrifying thing from this is
that the intended target
is only number three
in this list.
There are two others have three.
This has two and this
has three mismatches.
That line right there
shows that any nucleotide
is acceptable between the
PAM and the guide sequence.
That was expected.
But these guys are being
captured more frequently
by this experiment than
the intended target,
which represents only
11% of all the reads.
So this again is suggesting
that there's a lot of
off-target cleavage going on.
Another procedure that's been used,
this has been adopted by the Joung lab,
although it was invented
by these characters.
And this is one where you --
Whereas the guide sequence is cumulative,
so the oligo is there,
the cleavage re-agent is
there over a period of time
and any break that's made
during that period of time
is a candidate for picking up the oligo.
This is one where the only thing
that's going into cells
is the cleavage re-agent.
Then you fix the cells and you capture
whatever breaks, whatever
the standing level
of breaks is at that time.
You capture those breaks by ligation
to a oligonucleotide, it's
a hairpin oligonucleotide.
I think it's this one.
You capture with a hairpin oligonucleotide
then you shear or cut again.
You capture these things
with magnetic beads
and then you ligate something
on to the other end.
So you've captured,
with this initial oligo,
you've captured an end that was made
by the cleavage re-agent in cells.
And the Joung lab has used this.
And these are just data from
the paper where they did that.
Where they're showing
levels of sequence reads
from that experiment at the design target
and some secondary targets.
And one of the things they
were trying to demonstrate
in this paper is that for
these particular targets,
the pyogenes Cas9 seem
to cut secondary sites
more frequently than the staphorias Cas9.
But that was a way of capturing
sites that have been cut by
either of these Cas9 proteins.
Capture them in that snapshot.
This is a system that was
developed in Fred Alt's lab
for a different purpose, but
then repurposed for this.
I'm not gonna go through the whole thing,
but what they're basically doing
is they're using the design target
kind of as a GUIDE-seq oligo,
to capture cuts that happen
at other sites in the genome.
So if you make two cuts in the genome,
as I told you, you get
these translocations.
One cut finding the other cut
and getting a translocation.
Well, in fact, that's
what they're depending on.
They're depending on these translocations.
Here's an example where the intended break
was made in a RAG a, inside a RAG1 genome
in chromosome 11.
And then, when they pulled out everything
that was connected to
the break at RAG1,
they found that there were sequences
from elsewhere in the genome
and that's what these little
fountains are showing.
If they're red, they're
pretty common translocations,
if they're just sort of beige,
just sort of yellowish,
then they're less common translocations.
But there are other
places around the genome.
So that's identifying secondary sites
that have been cut while the primary site
was also being cut.
This is a procedure that was developed
in Jungsu Kim's lab, in Korea.
What they do is they identify targets
for a particular Cas9 guide RNA complex.
By in vitro cleavage.
And they drive this really hard.
So take total cellular DNA,
cut it with Cas9
and a guide RNA
and then just randomly sequence
all of the molecules in the genome.
And for any site that's
susceptible to cleavage,
you shouldn't get any reads
that cross that cut site.
So you drive it hard, they
pick up the intended site
and they pick up secondary sites.
Then you take DNA from cells
that have been treated
with that guide RNA and Cas9 protein,
and you should generate
indels in some of the copies
of those sites and now the sequence reads
will go across instead of all stopping
at the break site.
You use that to identify targets
that Cas9 can cut, and apparently does cut
and mutate in cells.
So there are all these procedures.
Even before GUIDE-seq, people had used
a capture of a viral genome
an integration defective Lentiviral genome
who works by the same principle.
Some of less effective.
And each of these procedures
has some drawbacks.
They all seem to be
pretty good at identifying
secondary sites, but as I said,
GUIDE-seq and is true of this.
IDLV cature, they're cumulative,
so you may over emphasize
secondary targets
as the cells grow and
you continue cutting.
BLESS is just a snapshot, it
very depending on the timing
of the snapshot you take relative
to the introduction of the nuclease.
Digenome-seq requires very deep sequencing
and this HTGTS high through-put
genome translocation sequencing.
That depends on getting
at least two breaks
in the cell, so you
have to kind of over cut
in order to see anything.
They all have some drawbacks and someone
should sit down and compare these
with dozens and dozens of guide RNAs
to see if they all agree.
As an alternative to worrying about
and measuring these off-target effects,
people have also begun working
on improving the
specificity of the nucleases
so that you have less reason to worry
about off-target cleavage.
One of the early ways of doing this was
to turn Cas9
into a nicking enzyme.
Jennifer said that with
these anti CRISPR proteins
that interfere with the access
of the H and H domain to DNA,
that you actually inhibit
cutting of both strands.
But if all you do is you make
a single amino acid change
in the protein,
so that the H and H domain, for example,
is not cut, the other active site can cut
the guide strand and it
can cut the guide strand
wherever in the genome Cas9 has to bind.
So if you provide two guide RNAs,
targeted to opposite strands,
pretty close to each other,
the cell looks at this as if
it was a double-strand break.
A frank double-strand break.
But, what you've done is
you've squared the specificity
of the system, because now
both of these guide RNAs
have to bind in order to make the nick.
This improves specificity, and remarkably,
these guide RNAs combine the sequences
that are up to 100 base pairs apart
in the genome and still
have pretty good efficacy.
So there's a paper from
George Church's lab
that shows that you can
get end-joined mutogenesis
and hemology dependent repair
using these double-nicking systems.
This is a paper from Fung Zhung's lab
showing essentially the same thing.
And one of the things
they've done is that they've
shown that with the double-nicking,
that your specificity is better.
That the ratio of mutogenesis
at defined off targets --
On-target to defined off-targets
gets better with the double-nicking
that's in the shaded bars here
than it is with the double-strand break
inducing wild type Cas9 protein.
And this is just showing that you can go
some distance apart.
They looked at the
separation between these.
You can go some distance apart
and still get pretty good efficacy.
I should emphasize that single nicks
are not very effective.
Nancy Maizels at the
University of Washington
has shown that you can get some efficacy
out of a single nick, but it's not as good
as double-nicking.
This is looking at homologous repair.
I guess all of these are
looking at homologous repair.
And when you're doing double-nicking,
generating a five-prime overhang.
So you have a nick on one
strand, nick on the other strand,
if it's five-prime-ends
that are overhanging
between the nicks, that works
better for homologous repair
than the other way around.
And I can tell you why I
think that is, if you ask me.
Another thing that Jungsu Kim found
was that if you just put
a couple of extra G's
on the five-prime-end of the guide RNA,
that don't match the target,
it actually improves the
specificity a little bit.
This is just data from his paper
and I can tell you in a
minute why I think that works.
In Keith Joung's lab, said, well,
what if we shorten the
length of the guide sequence?
We just take a single guide RNA instead
of making the match to
the genome, 20 base pairs,
we make it 19 or 18 or 17.
It turns out for many of these guide RNAs,
going to 17 or 18 base pairs,
doesn't reduce the indel frequency,
but it can still support
homologous repair, but it
does improve the specificity.
This is showing for
single base mismatches,
full-length guide RNAs
versus truncated guide RNAs,
and this is for two-base mismatches.
And you can see that whereas
this guide RNA isn't very sensitive
to single-base mismatches, even
when you use a shorter one.
But this one is very much more sensitive
to single-base mismatches
and this one is quite a bit
more sensitive.
And even this one is much more sensitive
to two-base mismatches.
They also went and they did GUIDE-seq,
whereas intitially at
this particular target,
the design target was third best
and represented at 11% of the reads.
Now, with the 17-base guide RNA,
it's moved into first place and
represents 30% of the reads.
It's improving things.
I had talked a little bit about
donors that take advantage
of the displaces strand.
And Jennifer talked
about that a little bit.
In Slaymaker, in Fung Zhung's lab, said,
well, part of the energy
of binding of Cas9
and the guide RNA to the target
comes through non specific
ionic interactions between
positive charges on the protein
and negative charges on
this displace strand.
So if there's excess energy
at the design target,
that means the secondary sites
that are only partially matched
now have enough energy
to get bound and cut.
What if we reduce the
energy of interaction
to the displace strand?
And so there was no crystal
structure at that time
that actually showed where
the displaced strand is.
Now there is one, or at
least that structure.
But they thought they could identify
positively charged residues out here.
They were good candidates based
on the existing crystal structures.
And so they mutated them one at a time.
And looked to see what's
the efficiency of cleavage
at the design target and a couple
of identified secondary targets.
And they found ones that seemed to cut
and make indels in the design target
quite efficiently.
As efficiently as the wild type,
but significantly reduced the cleavage
at these secondary sites.
And they then began
combining these mutations.
They're substitutions of alanine,
uncharged amino acid for
positively charged lysines
or argons and they eventually came up
with enhanced streptococcus
pyogenes Cas9 1.1
that has three of these substitutions
and shows very high cleavage
at the design target
and very little at the secondary sites.
So that's a way to enhance specificity.
And this is just showing the specificity.
Here's 1.1 out here that is
and also here, is quite sensitive.
Remember blue indicates insensitivity,
white indicates sensitivity.
It's much more sensitive to
mismatches than the wild type
and varies to single mismatches
and very sensitive to
these double mismatches.
Keith Joung's lab had the same idean,
but they went for the
other strand of the target.
They went for the RNA-DNA hybrid
and there they actually
had a crystal structure
that showed the
contacts between the protein
and the DNA-RNA hybrid.
So they identified some residues
they thought they could mutate
that would reduce the binding energy
between Cas9 guide RNA
and the target
and they made mutations in those
and similarly to what
the Joung lab had shown,
they found increase specificity
by combining a number of these mutations.
These are again, alanine substitutions.
This is arginine,
asparagine, and glutamine
in this particular case.
They did Guideseek on these.
They didn't actually on
this paper do Guideseek
on the same target I showed you earlier,
but they did Guideseek on
a bunch of other targets
and show that.
Here's one where the wild type
showed pretty good cleavage
at the design target
but lots of capture of sequences
elsewhere in the genome
and this high fidelity one
version of Cas9 protein
picked up no sequences
from these other sites
using Guideseek.
And there are a bunch of
them where the specificity
was very greatly enhanced.
So you've got all of these methods
to improve specificity.
I think that the truncated guides
and I think it's also true of these
five-prime GG extensions
and these mutations in basic
or polar residues in the protein
may all work by eliminating
that excess affinity.
So if you bind the target more tightly
than you absolutely need to.
You've got this excess affinity,
which means the secondary targets,
although they have some
mismatches that reduce affinity,
they've still got that extra affinity
from other interactions.
If that's true, then
the on target affinity
is going to depend on the
sequence of the target
and the guide sequence.
So for very AT rich targets,
you may not be able to employ
some of these affinity reducing measures,
you'd be better off going a wild type.
But if you go to very GC rich target,
where the RNA/DNA hybrid
energy is still really high,
you may want to employ some
of these other approaches.
As far as I've seen, people who are doing
sort of routine use of the system,
have not started to employ
these high fidelity modifications.
They're still kind of going
with the basic system.
But as they get more
finicky, they may need to.
It's also true, as I think I said before,
that the Cpf1 homologue seem to have
higher inherence specificity.
So that's another thing you can do.
This is just showing,
this is comparing
well, there are a variety
of things going on here.
This is from Jungsu Kim's lab.
One of the things that's showing is
the off-target cleavage
can be very much lower.
I'm looking for one where
they actually had --
The comparison that they have done
with pyogenes Cas9 has
to do with doing a lot
of sequencing of the cut sites and making
a sequence logo for one
of the preferences of
the Cpf1s and Cas9.
And Cas9, obviously can tolerate
some substitutions,
particularly far from the PAM,
whereas the Cpf1's
again, far from the PAM,
they tolerate more.
But these are much more
well defined sequence logos
indicating higher specificity.
This is an interesting observation.
They set up a situation where
these two Cpf1's from
two different bacteria
and SP Cas9 actually
cut the same sequence.
Just based on the disposition of the PAMs.
And what they found was that
even between the two Cpf1s,
the indel signature
was somewhat different.
This Cpf1 preferred
making a 15-based deletion
with a minus six-base and
a three-base deletion,
whereas this one cutting the same target,
preferred the six-based deletion
and had a 16-based deletion
as the secondary preference
and little or none of
the three-based deletion.
So there's something about the
proteins, not just the
underlying sequence,
but something about the proteins
that's influencing the indel outcome
and then the pyogenes Cas9
cutting in the same site
had different preference.
And as I'd said before,
if you do the experiment multiple times,
you get the same preference
for the same protein
and guide RNA.
This is GUIDE-seq looking at Cpf1 homologs
and again, you're seeing situations where
they're getting very
little capture of breaks
at secondary sites in the genome.
One of the Cpf1's gives
a few other sequences.
This one's giving essentially none
and the reason that
there's just a single line
in many of these, they've
captured nothing but
the designed target, the Cpf1 cleavage.
If you're worried about specificity,
then you have things you can do about it.
First you can design a new guide RNA,
that's what people tend to do, initially.
You can use paired nicking,
you can use this mismatched GG extension,
you can make the truncated guides.
We actually, that one I
was here working at IGI,
we made a 17 or 18 based guide sequence
that worked very well.
You can use these Cas9 mutants
from the Zhung lab or the Joung lab Cpf1.
I'm gonna mention on Wednesday,
why using RNP delivery has some advantages
for specificity over some of
the other delivery methods.
There are a lot of things you can do
to enhance this specificity.
Since this lecture was called
editing issues, I'm gonna do something
like what Jennifer did very briefly.
I'm gonna emphasize a
couple of different thing
from what she talked about.
She was talking about the applications
that themselves and how they
would influence society.
What I'm gonna do is I'm gonna talk about
two issues that I think
are quite important
and these may come up again
on Friday, I don't know.
One is whether you're talking about making
changes, enhancements of
agrucultural organisms,
or changes in humans to fix them,
you have to decide at the
outset who needs to be fixed.
What are you gonna change?
What people are you gonna tell
need to be fixed 'cause they aren't right.
And then once you've decided what to fix,
how are you gonna distribute that fix
to the organisms, people
who need the most.
Even in the realm of agriculture,
I showed you an example of modifications
and Rita showed you an
example of dehorning cattle.
What other changes should be made
and are they changes that would benefit
farmers in Wisconsin, or are they changes
that should benefit farmers
in Sub-Saharan, Africa.
On the human side, some
of you may know this book
by Andrew Solomon, it's
called Far From the Tree
and it talks about people who are unusual
in various ways.
And one of the groups he talks about
are people of short stature.
Have genetic causes for being very short.
And he makes the point that many people
who were born with short stature
wouldn't change that, even if they could.
They're comfortable with who they are
and in many cases, wouldn't even want
a genetic change that would
make their children tall
instead of short.
So you have to be careful who you identify
as needing to be fixed.
In the realm of distribution,
when I was here on
sabbatical two years ago,
we worked on a project to fix the mutation
in sickle cell disease.
Well here we are, we're sitting up here
in Northern California and
there are sickle cell patients
in Oakland, maybe a few in Berkeley,
but the vast majority of
sickle cell trait carriers
and disease sufferers
are in tropical regions
in Africa and Asia and
if we develop this fancy,
expensive molecular technology,
how do we get it to the people,
the huge numbers of
people who really need it?
So these are issues that come up
sort of post technology.
What are you gonna do with these tools
that you now have?
So I just wanted to highlight that
and that I didn't want to show.
So questions about Cas9 variance
and off-target issues?
Yes?
- [Man] If I remember right,
for prop modification maybe to start
from a single (mumbles).
On genome sequencing you can,
otherwise people should use it all.
- Particularly for the
agricultural organisms,
plants and animals, the
phenotype is the key, right?
You want to test the product
and not care as much about the method
by which the product was generated.
So you can do whole genome sequencing.
There's a disadvantage to
doing whole genome sequencing,
there's no chance that the genome sequence
of one of those bulls I showed you earlier
is identical to his parent.
It's just not gonna be true.
Because of the number of cell divisions
and the mutations that accumulate
just during self proliferation.
So there are gonna be differences
and if you insist that
there be no differences,
you'd never do anything.
Whole genome sequencing can reveal things
that are pretty common
and look like things
that could have been generated
by off-target cleavage,
but you have to be
careful about what you do.
How many people notice this paper
that appeared in Nature
Methods a little while ago
that said, "Unanticipated, high levels of
"off-target mutogenesis."
You should ignore that
paper, absolutely completely!
Maybe those of you who haven't seen it,
it's in Nature Methods, June maybe?
May or June.
Very recent.
The first author is Schaffer.
So if you want to look it up,
I can give you the reference to it later,
if you're interested.
And maybe we should have a quiz later.
People need to tell me why
you should ignore this paper.
What's wrong with this paper?
Anyway, you should ignore it,
nothing it says is true.
Yeah.
- [Man] I was just wondering,
with the Cas9 variance, if
you look at the CRISPR rates
and the species, do you see,
like the guide sequences
longer or shorter?
If that was like the origin if adjusting
the guide sequence or do they
just (mumbles) force that?
- Did who group force it?
- [Man] People who study the variance.
Did they feel like, well,
I'm gonna try ...
- So, the CRISPR RNAs are
developed from a long transcript.
And when that transcript is processed,
you don't get the whole
of what we call spacer
in the final CRISPR RNA,
so you can't just look
at the size of the spacers
and from first principles
decide how much is going to appear
in the ultimate CRISPR RNA,
so they could've gone and looked
at the natural CRISPR
RNAs, but it was easier
to look at this in
a more technological setting.
It's also true that the
repeat sequence part of that
is eliminated during processing.
- [Woman] So because we know our sequence
of our guide RNA, theoretically,
bio (mumbling) you
should be able to predict
what the off-target sequencing would be.
Do we find that to be the case
when we compare it to
the empirical results
of of say, GUIDE-seq?
- There is an imperfect overlap
between predicted and
detected secondary targets.
I don't know that anybody really
knows the reason for that.
I guess it's on Wednesday
that Jennifer Listgarten
is gonna be here and
she's developed some tools
for predicting good targets.
You can maybe ask her that question.
But it us surprising that sometimes
a single mismatch doesn't appear
as frequently in the GUIDE-seq study
or one of these other studies
as a two or three base mismatch.
And I went and I tried to do something
looking trying to use
energetics to predict
what targets would be more susceptible
to mismatches than others.
There was an imperfect overlap of that
and experiment also.
So I don't think anybody really has
a great way of rationalizing that.
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