WEBVTT

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>> Our first speaker today is
Doctor Claudia Glunch she is a
field

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>> Is a professor of civil and
environment engineering and
associate vice Provost

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for faculty advance at Duke
university she hold secondary
appointments

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in the necklace school of
Department and bio

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medical engineering sheet join
the Duke faculty in 2004. after

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obtaining her PhD from the
University of Texas at Austin.

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Currently she serves as a
associate director for the Duke,

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Michael bio center and
investigating

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an engineering bio macro. This
is between Duke and North

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Carolina State University. Her
research bridges environmental
engineering

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and molecular biotechnology.
Currently her research

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focus includes investigating the
impacts of emerging contaminants

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on the environmental pions and
end up of and

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efficacy, studying microbial

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and developing innovative water
treatment technologies.

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Claudia, we are excited for your
presentation we

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will turn it over to you.

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>> Thank you, for the
introduction.

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Thanks to the organizers for
inviting me today to talk

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about some of our works in
precision bioremediation. So, as
we have mentioned

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I am a environmental engineer by
training,

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and a lot of the research that
we do in my lap, incorporates

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concepts from molecular biology
and some of the fundamental
sciences.

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So what I will do today is talk
a little bit about

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and blend some more applied work
we have

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done was some of the more
fundamental research that we
have done in our

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lab.

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So, I think all of you that are
listening and today on today's

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webinar, being that it is on
Bioremediation you should be

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familiar that there is a fact
that there are many hazardous
waste sites

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that are in it distance are in
existence U.S. and worldwide, in

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the U.S. alone there are 4700
waste sites that have been
identified,

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it is staggering to think that
nearly 53 million Americans

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live within three miles of a
major hazardous waste sites,
even

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though we have made lots of
progress in the area

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of cleaning up the environment,
we still have a lot of problems

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that we need to solve. So there
is many

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technologies that have been
developed, throughout the years,
to remediate

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hazardous waste site, you will
recognize some of the

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strategies, soil and beeper
extraction, excavation,

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capping, and just to name a few.

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Some of the issues, associated
with these is that they

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can often be, very intrusive on
the environment to themselves.

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And it can cause lots of
long-term issues for those

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sites. So, we have spent a lot
of time thinking

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about what are some Constitution
Bioremediation that

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are less intrusive, and could be
more sustainable in the
long-term.

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So I'm showing a figure appear
of one of the would

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say classic compounds retreat
using Bioremediation tri-core

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ethane.

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It's very straightforward, we
are utilizing microbes that
exist, in

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the subsurface, to consume the
pollutant, and transform it into

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a more benign form of the

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parent compound. There are
inherent challenges with
Bioremediation.

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The first one being, very slow
degradation, if

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not stimulated properly or even
absent of degradation, at

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the right microbial community is
not present at the site.

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In the field of Institute bio
mediation and Bioremediation,
there's several

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approaches that can be taken,
you can do bio stimulation

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it is achieved by adding
specific nutrients to the

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subsurface. To get the
indigenous microbes that

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are already present at your
site, those can then do their
job,

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and transform the contaminants
into these non-toxic

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and products that we are hoping
to obtain.

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If there are no existing
microbes present at the site, an
alternate

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approach is bio augmentation.

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I/O augmentation, consists of
adding foreign microbes,

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to the subsurface. To then
perform,

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those same features.

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So bio augmentation, would be
for instance if researchers or
company

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has a fleet of microbes that are
known

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to degrade particular compounds
than those organisms

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can be inputted into that
environment to create that
degradation the

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biodegradation process.

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In some cases, we can combine
bio stimulation and bio
augmentation.

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That has been shown to result in
better biodegradation,

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it is clear, when we do bio
stimulation,

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and bio augmentation, it is
still very much a black box, and
we need

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to get a better understanding on
what is happening, at the site
and

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each site has very specific
characteristics, that weld the
Tate how successful

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this process will be.

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In many cases, the amended
microbes especially

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in the case of bio augmentation,
those organisms are not able to

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propagate, and that is because
they are

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unable to compete for important
nutrients and resources under
the

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given microbial ecological
conditions at those sites. So,
we clearly need

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to have a better understanding
of all of those factors. So,

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one of the approaches, we have
worked with

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in our lab, I will run through
three different approaches, that

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you will see increasingly
incorporating a

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mixed into the printed. The
first approach we have,

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that we devise is called somatic
bio augmentation. So

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genetic bio augmentation, as the
name applies, is relying on

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some exaggerated strains.

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So I was explained by
augmentation where we keep these
exalted units

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organisms and we add them to the
subsurface. When we do that,

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those zones are unable to
survive.

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So with the approach of genetic
bio augmentation, what we

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are doing is relying on extra,
zonal plasmids,

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that contain the important genes
needed for the degradation. So
you

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may have heard of Gene transfer
and particular

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conjugation and in the context
of antimicrobial resistance
where

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plasma plasmas are transferred
between

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organisms, they are promiscuous,
and they can enter into other

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cells through conjugation. In
that application, this is
something where

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we are trying to control the
transfer of the plasmas

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in our application we are trying
to promote the transfer of these

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plasmas that contain them for
the interest. Conjugation

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for those that are not familiar,
this is a physical

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process that occurs when a
pillar is formed between a cell

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that contains a fertile plasmid,
between a donor and recipient

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cell. There's a fickle --
physical bridge that's formed

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between these two cells, these
plasmids can then be
transferred,

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into a donor into a recipient
cell.

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That is present at the site. By
doing or utilizing this
technique

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we can actually take advantage
of existing microbes that are
present

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at the site, which then remove
this need for the

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exactness strained long-term
survival.

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>> Push the wrong button.

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>> Hi Claudia yes I think I need
to re-upload the slides, so it
will

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take a minute to get them back
up.

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>> Okay sorry.

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>> No problem.

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Thank you.

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so in genetic bio augmentation
what

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we are doing, is adding the
exactness strained which is a
donor cell that

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contains plasmid, and taking
advantage of

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the existing indigenous microbes
at this sites.

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So the donor cells, transfer
through conjugation,

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and forms trans conjugation
which is shown here

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in purple. The trans country can
then should be able to convert

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the contaminants into these
non-toxic products. As I

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mentioned, the advantages of
this would be,

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the donor cell does not need to
survive for the bioremediation

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to occur, also, the hypothesis
that you would need less foreign

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microbe addition to the sites.

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So, we utilize the system that
was developed eye Doctor foreign

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-- out of the Danish Institute,
the

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system utilizes a toluene that
is tied with

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green flowers and protein the
way that the system works is
that the

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green fluorescent protein is
under the

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control of a promoter that is
repressed, in the donor cell.
Once transferred

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to the recipient cell, that cell
does not have that protein does

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not express the repressor
proteins so it makes it

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very straightforward to be able
to monitor for the present

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of trans country can in your
sample.

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So, we had cells that

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had the plasmid, we transferred
the plasmid

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into E. coli cells, as you can
see these E. coli cells, the
trans

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conjugate cells now have this
green fluorescent protein that
you can

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easily identify, through a
microscope.

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Unfortunately, even though these
organisms had

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or had all of them they needed
they had taken at these plasmids

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we discovered, even though the
few

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to know control could degrade
it,

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notified by the line with the
triangle,

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are trans conjugate could not
degrade plasmid in fact,

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you can see from the shape of
this curb curve a it was a flat
line.

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So, that was an important lesson
for us,

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the fact that these plasmas were
being transferred, and the cell

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had the genetic material needed
it, that this conjugal transfer

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was not the same as a functional
phenotype. With the

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goal of designing bioremediation
technology, that improves the
degradation

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for and dealing with the issue
for

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us. It is clear, for bio
augmentation to work

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it needs to necessitate two
important factors, one is are
functional phenotype.

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That they actually do function,
and high

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conjugal transfer rates, so that
the plasmid is act truly picking

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up. We were able to trick the
system,

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to activate this pathway.

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We were able to do that in our
trans

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conjugate. So we tried a variety
of different simulations,
additional

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carbon forces in this particular
case I'm showing

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a figure that shows as a
function of glucose addition
increase

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in toluene degradation and
enzymatic activities

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for those particular pathways.
But, as you note from the bar

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on the far right-hand side, for
pseudo-motive pew dodo,

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on the right-hand side of your
screen you can tell, the

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activity is much higher than any
of the trans confidence,

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that are denoted as having
increased activities. So, we

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later discovered through variety
of different studies that, this

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was an artifact. It was linked
to a simulation

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of some of the stigma factors
and it was really

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a fortuitous a curve a as
opposed to us fixing it that
pathway.

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>> So, we went back to the
drawing board

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and we really try to understand
this, what we did discover is
that,

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there is actually a very big
impact of what

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the makeup is of the genetic
material of the plasmid relative
to you the

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parent cell that is living in
there so E. coli, the E. coli
genome

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has a 50% GC content where the
suit among

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Haddad 60% contact -- content

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so it's very close to the donor
cell. In fact, what we did

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find was that there was a study
that showed when they

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screened hundreds of different
bacteria that contained plasmid,

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it was a very high correlation
between the GC content

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of the cells, genomes and
plasmids.

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You can see in the figure, there
is a straight line and it's

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almost a 1-1 correlation between
the plasmid

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GC content and the host GC
content. This gave us clues in

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terms of what to start looking
for,

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to match the plasmid that we are
trying to conjugate,

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and a recipient style that we
would want to take up

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this plasmid.

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>> So we tested this hypothesis
and

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me new that this plasmid had a
59% GC content,

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so we looked for a variety of
different bacteria is,

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that we click conjugate, or we
could develop trans conjugate
with this

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plasmid, I am showing here a bio
dramatic

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tree, with a variety of
different trans conjugate, that
we did developed.

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You can see there is a range of
GC content, ranging from

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50% E. coli strains, that we
started our study with,

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all the way to a couple of
pseudo-moan related organisms
with the 60%

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or higher GC content similar to
our donor

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strain.

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We did find is that there was a
high correlation between both
the

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genomic GC content and filed
genomic as well

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as activity. So what this figure
is showing on the primary

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X access, the genomic content
and the secondary access,

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keeping in mind that

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smaller the number, the higher
relatedness exists between the

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donor and the recipient, then on
the Y access is the decorative

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-- degradation.

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There was a very high percentage
of

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or as your GC content becomes
closer, to the genomic content
of your

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donor, you can start seeing the
activities start to increase.

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Similarly, as your related mess
starts

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decreases so does your activity.

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Again this give us some
indication as far as what
organisms to target,

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for this genetic bio
augmentation approach.

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So, we did follow up with some
foil column experiments that

304
00:18:53.934 --> 00:19:00.934
were closer mimicking closely
mimicking activities, that might
occur at

305
00:19:00.934 --> 00:19:08.000
an actual sites. That, right
here, we had swell columns

306
00:19:08.000 --> 00:19:14.000
with a variety of different
soils, and also microbial
mixtures

307
00:19:14.000 --> 00:19:20.000
and then monitored it them over
time the presence of

308
00:19:20.000 --> 00:19:26.000
the trans conjugates, as well as
the activity itself.

309
00:19:26.000 --> 00:19:31.000
So we have some controls, and
autoclaved material, with
strains

310
00:19:31.000 --> 00:19:36.000
as well as subsequent carbon
additions

311
00:19:36.000 --> 00:19:42.000
and then some mixed communities
of the nonsterile soil with

312
00:19:42.000 --> 00:19:45.000
those glucose inputs.

313
00:19:45.000 --> 00:19:49.000
We then followed over time we
were interested in

314
00:19:49.000 --> 00:19:54.934
understanding with the select
pressure, what would happen

315
00:19:54.934 --> 00:19:56.934
to the microbial community, and
the

316
00:19:56.934 --> 00:20:01.934
degradation of the ability of
that micro or resulting a
microbial

317
00:20:01.934 --> 00:20:08.000
community. But we did find is
that, degradation did increase,

318
00:20:08.000 --> 00:20:13.000
over time and once we removed
the selective

319
00:20:13.000 --> 00:20:18.000
pressure, that microbial
community, did come back to
where it was initially.

320
00:20:18.000 --> 00:20:24.000
So, this first approach that

321
00:20:24.000 --> 00:20:30.000
I described demonstrates it is
possible to induce these
horizontal

322
00:20:30.000 --> 00:20:36.000
gene transfers of plasmid,
however, to obtain

323
00:20:36.000 --> 00:20:39.000
the functional phenotype is
really what we are after four

324
00:20:39.000 --> 00:20:44.000
bioremediation really requires
either in our

325
00:20:44.000 --> 00:20:50.000
case a loss or when we are the
glucose, and saw an increase in
functional

326
00:20:50.000 --> 00:20:53.934
genotype and degradation of
toluene, or a

327
00:20:53.934 --> 00:21:01.934
knowledge of the host bio
dramatic relatedness. This often
becomes

328
00:21:01.934 --> 00:21:08.000
an issue, when we are working
with unknown organisms.

329
00:21:08.000 --> 00:21:12.000
Which we often are at the sites
that we are

330
00:21:12.000 --> 00:21:18.000
working with. So this leads me
to the second approach that
really,

331
00:21:18.000 --> 00:21:22.000
it relies on using these
next-generation sequencing

332
00:21:22.000 --> 00:21:27.000
approaches. So, we

333
00:21:27.000 --> 00:21:33.000
had a Superfund center that is
directed by Rich and Heather at
the

334
00:21:33.000 --> 00:21:37.000
Nicholas school here, and I
serve as the

335
00:21:37.000 --> 00:21:46.000
PI on the engineering project,
the work we do,

336
00:21:46.000 --> 00:21:49.000
as part of the center is largely
focused on a couple of sites

337
00:21:49.000 --> 00:21:52.934
here in North Carolina as well
as in Virginia. So, the super
funded

338
00:21:52.934 --> 00:21:59.934
site there are over 1300
priority Superfund sites which
are detected

339
00:21:59.934 --> 00:22:04.934
on this figure, and pointing

340
00:22:04.934 --> 00:22:09.000
out one of them we spent a lot
of time working out. The
contaminants

341
00:22:09.000 --> 00:22:17.000
of intros for the existing work
has been polycystic carbon

342
00:22:17.000 --> 00:22:23.000
this class of compounds is often
or has been identified to

343
00:22:23.000 --> 00:22:28.000
be hazardous, and adverse health
effects, some of these

344
00:22:28.000 --> 00:22:33.000
compounds have been shown to be
toxic genic, Gino toxic,
carcinogenic,

345
00:22:33.000 --> 00:22:38.000
you name it, there are some of
these compounds

346
00:22:38.000 --> 00:22:42.000
just have been found to have
these effects.

347
00:22:42.000 --> 00:22:48.000
>> So these compounds are often
found in content mixtures,

348
00:22:48.000 --> 00:22:52.934
and the sites we have been
interested in,

349
00:22:52.934 --> 00:22:57.934
are those that have been with
treatments, activities, so

350
00:22:57.934 --> 00:23:04.934
this figure or this picture
here, demonstrates and shows

351
00:23:04.934 --> 00:23:08.000
where the forest has been
treated and you can see over
here on the

352
00:23:08.000 --> 00:23:14.000
left side, after it has been
treated.

353
00:23:14.000 --> 00:23:19.000
So it has these pH, they are
organic compounds and has used
huge rings

354
00:23:19.000 --> 00:23:25.000
demonstrated up here, in these
models. The pH

355
00:23:25.000 --> 00:23:33.000
is naturally and tiring --
tarring.

356
00:23:33.000 --> 00:23:37.000
>> The two sites that we have
worked on, one is the Republic
previous

357
00:23:37.000 --> 00:23:43.000
otic site at the Elizabeth River
appeared it is shown

358
00:23:43.000 --> 00:23:46.000
up here, in Virginia.

359
00:23:46.000 --> 00:23:51.934
This particular site, was
operated from the

360
00:23:51.934 --> 00:23:56.934
from the 1930s to the 1970s, it
was a site where

361
00:23:56.934 --> 00:24:01.934
there was with the treatment, in
this particular site seeps into

362
00:24:01.934 --> 00:24:08.000
the Elizabeth River, near
another site we worked on

363
00:24:08.000 --> 00:24:12.000
Atlantic words in the second
site that we work

364
00:24:12.000 --> 00:24:15.000
at is the whole come creosote
facility.

365
00:24:15.000 --> 00:24:21.000
This site is located in Yadkin
Bill North Carolina.

366
00:24:21.000 --> 00:24:27.000
The site was listed as a
national priority,

367
00:24:27.000 --> 00:24:30.000
site in 2012.

368
00:24:30.000 --> 00:24:36.000
This site also was used for

369
00:24:36.000 --> 00:24:40.000
wood treatment, but has a
different characteristic in

370
00:24:40.000 --> 00:24:45.000
terms of it is not on the coast,
it's since

371
00:24:45.000 --> 00:24:49.000
it's in the title influences
like we see in our other

372
00:24:49.000 --> 00:24:53.000
sites and they are not present
here. We were interested in
going

373
00:24:53.000 --> 00:24:57.000
to the sites and really starting
to look at what are the
microbial

374
00:24:57.000 --> 00:25:03.000
fingerprints at these sites, and
looking for correlation between

375
00:25:03.000 --> 00:25:09.067
the microbial fingerprint, and
the bile chemical fingerprint

376
00:25:09.067 --> 00:25:12.067
at the sites.

377
00:25:12.067 --> 00:25:16.067
We are particularly interested
at looking

378
00:25:16.067 --> 00:25:22.067
at some of these cross kingdom
interactions

379
00:25:22.067 --> 00:25:28.067
and how can we take advantage of
fungal abilities

380
00:25:28.067 --> 00:25:34.067
to degrade very complex and
larger molecules compounds like
this

381
00:25:34.067 --> 00:25:40.067
hydrocarbon to transform them
into metabolites that

382
00:25:40.067 --> 00:25:45.067
earn more bioavailable --
available to bacteria. So what
we have done,

383
00:25:45.067 --> 00:25:50.067
as we went and we collected
numerous samples from each of
these

384
00:25:50.067 --> 00:25:55.000
sites, we ran through some
next-generation

385
00:25:55.000 --> 00:26:01.000
sequencing, analysis of the
variety of different target, for

386
00:26:01.000 --> 00:26:08.000
both fungal and bacterial
counterparts,

387
00:26:08.000 --> 00:26:12.000
we use this information, we
decipher this information to
look for potential

388
00:26:12.000 --> 00:26:19.000
target for bioremediation. We
have looked at the 18

389
00:26:19.000 --> 00:26:23.000
as LSU, we looked at the IGS
region,

390
00:26:23.000 --> 00:26:31.000
several IGS regions as well as
other regions for bacteria. I
will

391
00:26:31.000 --> 00:26:36.000
show a couple of analyses that
we have done on these. So the
general

392
00:26:36.000 --> 00:26:41.000
concept, for performing an
applicant-based

393
00:26:41.000 --> 00:26:46.000
community analysis is similar
regardless of the target that

394
00:26:46.000 --> 00:26:51.934
we use, you go to your site,
extract and collect

395
00:26:51.934 --> 00:26:57.934
samples, extract DNA, amplify
particular regions of

396
00:26:57.934 --> 00:27:02.934
that DNA, and run through
several

397
00:27:02.934 --> 00:27:09.000
pipelines, to look for and
classify your

398
00:27:09.000 --> 00:27:17.000
reach to be able to see who is
present at your site.

399
00:27:17.000 --> 00:27:23.000
So some of the findings we
found, this is

400
00:27:23.000 --> 00:27:33.000
data that my student did a
number of

401
00:27:33.000 --> 00:27:35.000
years ago that is on the sites
in Virginia we were interested,
and

402
00:27:35.000 --> 00:27:40.000
understanding what does this
microbial fingerprint for fungi
looks like

403
00:27:40.000 --> 00:27:44.000
when you increase pH
concentration? this

404
00:27:44.000 --> 00:27:48.000
figure right here, shows what
that fingerprint looks

405
00:27:48.000 --> 00:27:53.934
like with increasing pH
concentration, as we move
towards the right,

406
00:27:53.934 --> 00:27:59.934
it shows the relative abundance
of fungal phyla.

407
00:27:59.934 --> 00:28:03.934
One of the interesting takeaways
is this

408
00:28:03.934 --> 00:28:09.000
from this is as we increase the
pH concentration, we start to

409
00:28:09.000 --> 00:28:14.000
see an increase in the dark
purple fungi, that fall under
the my coda

410
00:28:14.000 --> 00:28:18.000
phylum.

411
00:28:18.000 --> 00:28:23.000
This gives us some targets on
what might be some of the fungi
we can

412
00:28:23.000 --> 00:28:28.000
target to carry out the first
steps in the bio Deckard patient

413
00:28:28.000 --> 00:28:34.000
pathways of interest to us.
Similarly, for back

414
00:28:34.000 --> 00:28:40.000
here ER, we can run through a
similar analysis

415
00:28:40.000 --> 00:28:44.000
and this was work that was
performed by a student of mine
Lauren Redford,

416
00:28:44.000 --> 00:28:47.000
at the whole come creosote site,
this is showing

417
00:28:47.000 --> 00:28:52.934
relative abundance, and
different arterial phyla,

418
00:28:52.934 --> 00:28:57.934
we can further break it down
based off of this information,

419
00:28:57.934 --> 00:29:03.934
and really dive into specific
types of bacteria's.

420
00:29:03.934 --> 00:29:10.000
So based off of this, we have
developed kind

421
00:29:10.000 --> 00:29:15.000
of a how-to guide, how would you
carry this out your

422
00:29:15.000 --> 00:29:23.000
site? all the way from looking
at or isolating your

423
00:29:23.000 --> 00:29:27.000
new to psych acid or looking at
this relationship and
identifying

424
00:29:27.000 --> 00:29:32.000
what might be particular strains
that you can target for

425
00:29:32.000 --> 00:29:36.000
genetic bio augmentation. And
bio stimulation. I

426
00:29:36.000 --> 00:29:43.000
would encourage if you want more
information, to I will reference

427
00:29:43.000 --> 00:29:47.000
the paper we just published in
the Journal of hazardous
material

428
00:29:47.000 --> 00:29:50.000
on this topic.

429
00:29:50.000 --> 00:29:52.934
So, the next step is being
really aware

430
00:29:52.934 --> 00:29:57.934
we are spending a lot of time
it's really just trying to
decipher this

431
00:29:57.934 --> 00:29:59.934
very complex information.

432
00:29:59.934 --> 00:30:03.934
And we obtained it from the next
generation sequencing data set.

433
00:30:03.934 --> 00:30:10.000
With the idea, that
functionally, these communities
have lost

434
00:30:10.000 --> 00:30:15.000
a similarity.

435
00:30:15.000 --> 00:30:18.000
But when we start looking at
taxonomic differences, there is
a lot of diversity.

436
00:30:18.000 --> 00:30:24.000
So really trying to get at this
question of

437
00:30:24.000 --> 00:30:27.000
how can we ask the question
about the function

438
00:30:27.000 --> 00:30:31.000
which is really what we are
after, based after her base off

439
00:30:31.000 --> 00:30:34.000
of this information that we
have.

440
00:30:34.000 --> 00:30:39.000
So we are working on developing

441
00:30:39.000 --> 00:30:42.000
some approaches that really take
this information that we get
from

442
00:30:42.000 --> 00:30:51.000
the taxonomic subscription and
break it down into modules,

443
00:30:51.000 --> 00:30:53.934
and to start really
understanding how these
different modules are

444
00:30:53.934 --> 00:30:59.934
put together is through an
organism you know it should be,
it might

445
00:30:59.934 --> 00:31:08.000
work synergistically together or
competing

446
00:31:08.000 --> 00:31:13.000
to get some information about
each of these and how each of
these sites

447
00:31:13.000 --> 00:31:19.000
can be broken down more
systematically. A lot of the
work

448
00:31:19.000 --> 00:31:25.000
we are doing right now, is
looking at exchange ability of
these modules,

449
00:31:25.000 --> 00:31:28.000
trying to really understand the
correlation and connection
between

450
00:31:28.000 --> 00:31:32.000
the different parts of these
microbial communities. So, this
is really

451
00:31:32.000 --> 00:31:36.000
where we are heading.

452
00:31:36.000 --> 00:31:42.000
We are currently running some
experiments, looking at with

453
00:31:42.000 --> 00:31:47.000
pH, without and how these
communities change over time.
That way we can

454
00:31:47.000 --> 00:31:52.934
start to get at the question,
and build on the particular

455
00:31:52.934 --> 00:31:55.934
modules.

456
00:31:55.934 --> 00:32:00.934
So with that, I will start my
presentation and I have

457
00:32:00.934 --> 00:32:03.934
a lot of people I need to
acknowledge.

458
00:32:03.934 --> 00:32:09.000
This is a picture of a couple of
my research group site different

459
00:32:09.000 --> 00:32:14.000
points of time. But Daniel
Reggie

460
00:32:14.000 --> 00:32:17.000
appear has done a lot of the
work that is related to the
mouth of

461
00:32:17.000 --> 00:32:26.000
mathematical modeling that's
underway, and savanna

462
00:32:26.000 --> 00:32:31.000
and Lauren Redford did a lot of
the bacterial work, Lauren has
done

463
00:32:31.000 --> 00:32:38.000
a lot of the funds alert.

464
00:32:38.000 --> 00:32:41.000
Or work rather. I would like to
acknowledge our funders NIEHS,
and

465
00:32:41.000 --> 00:32:43.000
some of the earlier work that I
presented as well the genetic
bio

466
00:32:43.000 --> 00:32:47.000
augmentation was funded from the
national science foundation and

467
00:32:47.000 --> 00:32:50.000
my collaborators on this
project. With that I would

468
00:32:50.000 --> 00:32:52.934
be happy, to answer any
questions.

469
00:32:52.934 --> 00:32:54.934
Thank you.

470
00:32:54.934 --> 00:32:59.934
>> Excellent. Thank you for a
great presentation we do have
some questions

471
00:32:59.934 --> 00:33:01.934
that have come in.

472
00:33:01.934 --> 00:33:02.934
Let's read some of those to you.

473
00:33:02.934 --> 00:33:08.000
The first question wants to
know, how does a GC content
increase the

474
00:33:08.000 --> 00:33:12.000
functionality? does it modify
any gene functional units such
as promoter?

475
00:33:12.000 --> 00:33:15.000
coding sequencing etc.?

476
00:33:15.000 --> 00:33:23.000
>> That is a great question, the
way it increases the
functionality,

477
00:33:23.000 --> 00:33:29.000
is through it's actually because
of the

478
00:33:29.000 --> 00:33:34.000
tRNAs. So when different
organisms have different makeup
of tRNAs

479
00:33:34.000 --> 00:33:39.000
that they use, it's really more
through

480
00:33:39.000 --> 00:33:44.000
the ability to make more of the
right proteins is how we get the

481
00:33:44.000 --> 00:33:48.000
functionality. So that would be

482
00:33:48.000 --> 00:33:51.934
if we are transferring plasmas
between different organisms
where there

483
00:33:51.934 --> 00:33:55.934
might be different machineries
in the different

484
00:33:55.934 --> 00:34:00.934
organisms in our particular case
with the E. coli, what was
happening

485
00:34:00.934 --> 00:34:07.000
is there was a effect that was
linked to

486
00:34:07.000 --> 00:34:13.000
the sigma factors that were
involved, that were needed

487
00:34:13.000 --> 00:34:19.000
for the regulation of that
particular system. So it was a
fortuitous reaction

488
00:34:19.000 --> 00:34:23.000
a guests. To the system.

489
00:34:23.000 --> 00:34:27.000
>> Okay great. I want to remind
everyone, you

490
00:34:27.000 --> 00:34:32.000
can submit questions using the
Q&A pod in the lower right-hand
corner.

491
00:34:32.000 --> 00:34:37.000
That that any time during the
presentation.

492
00:34:37.000 --> 00:34:40.000
Please we do encourage you to's
keep submitting your questions
there

493
00:34:40.000 --> 00:34:43.000
should be time at the end if you
think of something as a move
into

494
00:34:43.000 --> 00:34:48.000
the next presentation. But,
while we are waiting, for more

495
00:34:48.000 --> 00:34:52.000
questions I want to offer up
people on the phone mind if you
would like

496
00:34:52.000 --> 00:34:57.000
to ask a question you can take
your phone off mute by pressing
pound

497
00:34:57.000 --> 00:34:59.000
six. You can see who you are and
your organization and

498
00:34:59.000 --> 00:35:05.067
ask your question. Remember you
can renew it your phone by
pressing*six.

499
00:35:05.067 --> 00:35:11.067
's or anyone on the phone, that
wants to

500
00:35:11.067 --> 00:35:18.067
ask a question?

501
00:35:18.067 --> 00:35:23.067
>> Okay, we will keep you in
mind and

502
00:35:23.067 --> 00:35:25.067
we will move along I know the
majority of the presentation is
on Adobe

503
00:35:25.067 --> 00:35:30.067
could -- connect and we had a
couple questions come in so we
will move

504
00:35:30.067 --> 00:35:35.067
on here this participant, states
that the

505
00:35:35.067 --> 00:35:42.067
transfer of catabolic to Rand --
random recipients carries the

506
00:35:42.067 --> 00:35:46.067
risk of co-carrying
antibacterial resistant genes
like kanamycin.

507
00:35:46.067 --> 00:35:52.000
Which is already a huge probe --
problem -- problem

508
00:35:52.000 --> 00:35:55.000
and asking what your idea is?

509
00:35:55.000 --> 00:36:00.000
>> That's an important point to
make in our case the plasma we

510
00:36:00.000 --> 00:36:11.000
were transferring does contain
the kanamycin resistant gene. So

511
00:36:11.000 --> 00:36:13.000
this would not be necessarily a
plasmid that would be used in
this

512
00:36:13.000 --> 00:36:17.000
particular setting but there's a
number of metabolic plasmids,
naturally

513
00:36:17.000 --> 00:36:21.000
occurring, CAD -- catabolic
plasmid that you not

514
00:36:21.000 --> 00:36:28.000
contain those antibiotic
resistant genes so it is really

515
00:36:28.000 --> 00:36:35.000
variable depending on the plasma
that you are aiming to transfer.

516
00:36:35.000 --> 00:36:40.000
>> Okay. We have another
question here. You want

517
00:36:40.000 --> 00:36:42.000
to know if you have any
suggestions on how to anticipate
or prevent

518
00:36:42.000 --> 00:36:46.000
accumulations of toxic
metabolites such as

519
00:36:46.000 --> 00:36:49.000
mono correct?

520
00:36:49.000 --> 00:36:51.934
>> Yes, that is also a great
question.

521
00:36:51.934 --> 00:36:56.934
It is really the more we know
about the pathways,

522
00:36:56.934 --> 00:37:01.934
the more we can think about what
are

523
00:37:01.934 --> 00:37:08.000
the right modifications and
interventions to implement at a
given site.

524
00:37:08.000 --> 00:37:11.000
So far bio chloride
specifically, if you're

525
00:37:11.000 --> 00:37:19.000
thinking about the microbes that
are needed to further breakdown

526
00:37:19.000 --> 00:37:24.000
as seen for instance in
understanding how those
organisms can either be

527
00:37:24.000 --> 00:37:29.000
stimulated or allowed to compete
more effectively,

528
00:37:29.000 --> 00:37:35.000
under the actual conditions at
any given site. So it boils

529
00:37:35.000 --> 00:37:40.000
down to understanding, in that
more complex

530
00:37:40.000 --> 00:37:44.000
microbial community, how these
specific targeted organism

531
00:37:44.000 --> 00:37:49.000
of interest can function
optimally.

532
00:37:49.000 --> 00:37:54.934
That way it can achieve the
function in this particular
case.

533
00:37:54.934 --> 00:37:58.934
>> Okay. Great. Another question
we received is, this person

534
00:37:58.934 --> 00:38:02.934
want to know what are the
biggest technical challenge to
field implementation

535
00:38:02.934 --> 00:38:08.000
of precision for bioremediation?

536
00:38:08.000 --> 00:38:15.000
>> There are clearly a lot of
challenges, that are left for us
to be able

537
00:38:15.000 --> 00:38:17.000
to do this in a systematic
fashion.

538
00:38:17.000 --> 00:38:25.000
The number one, challenge is
really the depth of our
databases.

539
00:38:25.000 --> 00:38:30.000
We are relying on databases to
identify the

540
00:38:30.000 --> 00:38:36.000
organisms that are present at
the site, at those databases are
not

541
00:38:36.000 --> 00:38:42.000
deep enough meaning we cannot
identify the organisms,

542
00:38:42.000 --> 00:38:48.000
then we cannot design or create
optimal implementation,

543
00:38:48.000 --> 00:38:54.934
not at that particular site.
That is a big issue,

544
00:38:54.934 --> 00:38:58.934
I would state that bacterial
databases are a little bit
deeper than the

545
00:38:58.934 --> 00:39:01.934
RKO or the fungi databases.

546
00:39:01.934 --> 00:39:06.000
>> So this is a problem, we do
run into. As

547
00:39:06.000 --> 00:39:11.000
more people, are in this line of
work. Those databases are likely

548
00:39:11.000 --> 00:39:17.000
to deepen. The second challenge,
is really we

549
00:39:17.000 --> 00:39:25.000
are often chasing very rare
pathways so we are often looking

550
00:39:25.000 --> 00:39:30.000
for a needle in a haystack.

551
00:39:30.000 --> 00:39:32.000
When you couple that with maybe
not even knowing, what you are
looking

552
00:39:32.000 --> 00:39:37.000
for in the first place, points
to the fact that

553
00:39:37.000 --> 00:39:43.000
we need more information again
as we build these models

554
00:39:43.000 --> 00:39:45.000
and we deepen our databases we
will be able to

555
00:39:45.000 --> 00:39:51.000
do that in a more systematic
fashion, to identify this

556
00:39:51.000 --> 00:39:55.934
as well as the final challenge
that I will mention at

557
00:39:55.934 --> 00:39:59.934
least in this context, is the
site we are working at,

558
00:39:59.934 --> 00:40:03.934
and this is one of the big
differences when we are working
between a lab

559
00:40:03.934 --> 00:40:10.000
or a real site, is the high
level of had a red naivety

560
00:40:10.000 --> 00:40:20.000
-- had a 90 --

561
00:40:20.000 --> 00:40:22.000
>> We might think that we have
particular microbiology but if
you go if you

562
00:40:22.000 --> 00:40:25.000
feel over you might actually
have a very different microbial
composition

563
00:40:25.000 --> 00:40:32.000
so really, getting at this
question, is a important
challenge. As well

564
00:40:32.000 --> 00:40:39.000
as understanding even the how
far do you need to

565
00:40:39.000 --> 00:40:42.000
collect, what's the size of the
courageous, for sampling that is

566
00:40:42.000 --> 00:40:47.000
needed. All of those are
important features.

567
00:40:47.000 --> 00:40:51.934
>> Okay, I want to thank you for
your great presentation, I think

568
00:40:51.934 --> 00:40:56.934
we will move on to the next
presenter.

569
00:40:56.934 --> 00:41:02.934
All know we have another
question Holland.

570
00:41:02.934 --> 00:41:06.000
This person broke and I am
working on a remediation project
where we

571
00:41:06.000 --> 00:41:10.000
have some challenges in buyer
remediating weather -- weathered
crude oil,

572
00:41:10.000 --> 00:41:13.000
most contaminants present RC
40+.

573
00:41:13.000 --> 00:41:18.000
Which are asked the teen

574
00:41:18.000 --> 00:41:22.000
and resin can you suggest
strains of microbial species
that has a

575
00:41:22.000 --> 00:41:29.000
genomic the genomes capable of
bio degrading such resistant
compounds?

576
00:41:29.000 --> 00:41:33.000
>> So, if that person is
submitted the question,

577
00:41:33.000 --> 00:41:37.000
could send me an email, I would
be happy to talk with them

578
00:41:37.000 --> 00:41:43.000
off-line, depending on the
approaches, the approaches we
are relying

579
00:41:43.000 --> 00:41:48.000
on our thinking about what are
fungi that would be able to
degrade those

580
00:41:48.000 --> 00:41:55.934
into compounds that then the
bacteria

581
00:41:55.934 --> 00:41:57.934
can attack but then it would be
optimal to think about at that
particular

582
00:41:57.934 --> 00:42:02.934
site, who are the microbial
players that are present? as
opposed to

583
00:42:02.934 --> 00:42:09.000
putting in exaggerated strains
that might

584
00:42:09.000 --> 00:42:16.000
not compete. I would be happy to
talk off-line about that.

585
00:42:16.000 --> 00:42:18.000
>> Thank you. just a reminder to
our participants, you can find
the

586
00:42:18.000 --> 00:42:22.000
presenters contact information
on the main seminar page where
you

587
00:42:22.000 --> 00:42:25.000
registered for today's live
event.

588
00:42:25.000 --> 00:42:29.000
>> Thank you Claudia. I think we
will move on to the next
presenter.

589
00:42:29.000 --> 00:42:34.000
That's Julian Schroeder.

590
00:42:34.000 --> 00:42:38.000
Doctor Julian Schroeder, is a
Novartis chair and distinguished

591
00:42:38.000 --> 00:42:42.000
chair in the book biological
science at the University of
sciences in

592
00:42:42.000 --> 00:42:48.000
San Diego is also codirector of
the Center for you -- food

593
00:42:48.000 --> 00:42:52.934
and fuel for the 21st century.
He received his PhD working with
her

594
00:42:52.934 --> 00:42:56.934
when they hair of the Max
Institute for biophysical
chemistry.

595
00:42:56.934 --> 00:43:02.934
In Germany. And was a Von
Humboldt postdoctoral

596
00:43:02.934 --> 00:43:07.000
at UCLA, for medicine. He has
pioneered

597
00:43:07.000 --> 00:43:11.000
characterizations of plant
channels that led to models of

598
00:43:11.000 --> 00:43:14.000
their unique functions and plant
biology. He also pioneered
functional

599
00:43:14.000 --> 00:43:20.000
characterizations of genes
including plant ions transfer

600
00:43:20.000 --> 00:43:22.000
proteins and their roles and
stress current.

601
00:43:22.000 --> 00:43:26.000
Its present research has a
justified research -- mechanisms
and genes

602
00:43:26.000 --> 00:43:31.000
that allow plants to have
environmental stresses in
particulars responses

603
00:43:31.000 --> 00:43:36.000
to elevated to CO2. drought, and
heavy metal stress.

604
00:43:36.000 --> 00:43:41.000
Julian I think we are turning it
over to you now.

605
00:43:41.000 --> 00:43:43.000
>> Thank you. can you hear me?

606
00:43:43.000 --> 00:43:46.000
>> Yes you some good.

607
00:43:46.000 --> 00:43:49.000
>> Great.

608
00:43:49.000 --> 00:43:52.934
Thank you Michelle, and thank
you to Heather and Sarah and
Bill in

609
00:43:52.934 --> 00:43:57.934
the Superfund team for the
opportunity to present

610
00:43:57.934 --> 00:44:03.934
our research. I will be speaking
to you about

611
00:44:03.934 --> 00:44:10.000
toxic, heavy metals as well as
arsenic, the metal and arsenic.

612
00:44:10.000 --> 00:44:14.000
We of course, heard very nicely
from Claudia just now about

613
00:44:14.000 --> 00:44:20.000
how microbes can be used to
degrade organic toxins,

614
00:44:20.000 --> 00:44:28.000
of course in the case of these
metals and metal Lloyd,

615
00:44:28.000 --> 00:44:33.000
we do not have microbes that can
do nuclear fusion to modify
these

616
00:44:33.000 --> 00:44:36.000
non-toxic compounds so this is a
challenge. As you can

617
00:44:36.000 --> 00:44:42.000
see in the flight, there are
many sources of

618
00:44:42.000 --> 00:44:48.000
these toxic exposed industrially
including on the top left

619
00:44:48.000 --> 00:44:53.000
even pesticide use as I may
mention again later. And
treatment of woods

620
00:44:53.000 --> 00:44:57.000
through arsenic and so on. There
are so on and

621
00:44:57.000 --> 00:45:03.000
particularly in the case margin,
not -- natural

622
00:45:03.000 --> 00:45:09.067
areas were these are found. So,
obviously we have many Superfund

623
00:45:09.067 --> 00:45:14.067
sites in the U.S., we have a
national priority

624
00:45:14.067 --> 00:45:19.067
list of toxins if we look here
on the right-hand

625
00:45:19.067 --> 00:45:22.067
side, we will see that some of
the highest

626
00:45:22.067 --> 00:45:27.067
priority toxins fall into these
categories of heavy metals. In
the

627
00:45:27.067 --> 00:45:33.067
metal lead Carson -- arsenic.

628
00:45:33.067 --> 00:45:35.067
How can it be removed?

629
00:45:35.067 --> 00:45:40.067
applications and research have
shown that plants provide tools

630
00:45:40.067 --> 00:45:46.067
that can be used for removal of
heavy metals.

631
00:45:46.067 --> 00:45:51.067
First of all how are these heavy
metals and arsenic to get

632
00:45:51.067 --> 00:45:58.000
up at the plate? research and
several areas have found the
nutrient transport

633
00:45:58.000 --> 00:46:01.000
trends ports.

634
00:46:01.000 --> 00:46:06.000
From their plants can
translocate these metals into
aerial portions

635
00:46:06.000 --> 00:46:09.000
implants are particularly strong
mechanisms for

636
00:46:09.000 --> 00:46:15.000
detoxification and accumulation
of these metals.

637
00:46:15.000 --> 00:46:18.000
There are a number of species
that plants that are called
hyper

638
00:46:18.000 --> 00:46:24.000
accumulators because they can
accumulate large amounts of
these

639
00:46:24.000 --> 00:46:32.000
metals and arsenic often times
these plants are quite

640
00:46:32.000 --> 00:46:35.000
small in stature. Obviously it
would be really interesting

641
00:46:35.000 --> 00:46:40.000
to know the molecular mechanisms
and networks pathways by which
plants

642
00:46:40.000 --> 00:46:44.000
can detoxify and over accumulate
these metals

643
00:46:44.000 --> 00:46:48.000
in order to then implement these
for example bioremediation

644
00:46:48.000 --> 00:46:51.000
trees or plants. I want to give
you some examples,

645
00:46:51.000 --> 00:46:56.934
of major detoxification methods
for all of the toxin I mentioned

646
00:46:56.934 --> 00:46:59.934
earlier.

647
00:46:59.934 --> 00:47:04.934
That sort of goes back to a lot
of this research that done

648
00:47:04.934 --> 00:47:13.000
in the lab rat like my colleague

649
00:47:13.000 --> 00:47:16.000
asked if they are the fruit fly
at the plant world meaning it's

650
00:47:16.000 --> 00:47:20.000
very useful for genomic
approaches and discovering gene
functions.

651
00:47:20.000 --> 00:47:24.000
What we have here is the mutant
and wild type plants grown under

652
00:47:24.000 --> 00:47:27.000
controlled conditions.

653
00:47:27.000 --> 00:47:31.000
Let me see if I can get the
printer on under controlled
conditions.

654
00:47:31.000 --> 00:47:35.000
You look very similar. However
if we expose these plants for
example

655
00:47:35.000 --> 00:47:40.000
to cadmium, you can see how this
mutant is

656
00:47:40.000 --> 00:47:47.000
highly sensitive to it while a
other plan is less sensitive. We
into

657
00:47:47.000 --> 00:47:51.934
other groups identified the gene
is responsible for this cadmium

658
00:47:51.934 --> 00:47:54.934
detoxification. I should say Men
resistance in and it turns out,

659
00:47:54.934 --> 00:48:00.934
using various several
approaches, it turns out that
the

660
00:48:00.934 --> 00:48:07.000
underlining gene, encodes hold
on I'm giving

661
00:48:07.000 --> 00:48:14.000
my pointer. A enzyme called Fido
cute incentive.

662
00:48:14.000 --> 00:48:22.000
So what is it due. It uses
glycol to Psion as a substrate

663
00:48:22.000 --> 00:48:26.000
and glutathione is a short
peptide consisting of three
amino acids.

664
00:48:26.000 --> 00:48:35.000
This Fido one is called
apartheid but sensitizes long
chains of

665
00:48:35.000 --> 00:48:39.000
amino acids that are rich in
Sistine, so glutamate existing

666
00:48:39.000 --> 00:48:43.000
glycine. Or this can be 2-11
copies this

667
00:48:43.000 --> 00:48:49.000
these are long peptides that are
rich in these peptides these
Philo

668
00:48:49.000 --> 00:48:54.934
Q attends are particularly good
at finding heavy metals, and

669
00:48:54.934 --> 00:48:57.934
also arsenate.

670
00:48:57.934 --> 00:49:06.000
So to expand on the slide we
just saw,

671
00:49:06.000 --> 00:49:09.000
we have plants grown in plates
under controlled conditions,

672
00:49:09.000 --> 00:49:15.000
or in the presence, of arsenate,
mercury, or cadmium.

673
00:49:15.000 --> 00:49:22.000
If we hone in here for example,
if you look

674
00:49:22.000 --> 00:49:27.000
at this mutant that is deficient
in this Philo Q attends and
things,

675
00:49:27.000 --> 00:49:33.000
and in the presence of arsenic
it is highly sensitive

676
00:49:33.000 --> 00:49:36.000
and we see it highly sensitive
in the presence of mercury and
cadmium.

677
00:49:36.000 --> 00:49:42.000
While if we look at the wild
type of plants, they are

678
00:49:42.000 --> 00:49:44.000
relatively resistant.

679
00:49:44.000 --> 00:49:51.000
The single gene plays a role in
resistance to all of these
toxicants.

680
00:49:51.000 --> 00:49:56.934
What is the underlining or how
can we further explain this?

681
00:49:56.934 --> 00:50:02.934
and understand this?

682
00:50:02.934 --> 00:50:06.000
so when a heavy metal such as
cadmium enters the cell, it
lines

683
00:50:06.000 --> 00:50:11.000
to these Fido Q attends very
tightly.

684
00:50:11.000 --> 00:50:15.000
In fact the Sistine's are very
important for this binding.

685
00:50:15.000 --> 00:50:23.000
Our next step in detoxification,
is that these Fido Q attend
complexes

686
00:50:23.000 --> 00:50:25.000
are transported into plant
molecules. The transporter

687
00:50:25.000 --> 00:50:32.000
had been unknown, though
research a number of years ago
suggested,

688
00:50:32.000 --> 00:50:38.000
the transporter may be a, ABC
transporter.

689
00:50:38.000 --> 00:50:42.000
So, not many advances have been
made

690
00:50:42.000 --> 00:50:48.000
in collaboration, we did more
experiments.

691
00:50:48.000 --> 00:50:53.934
And it was suggested that it was
an ABC

692
00:50:53.934 --> 00:50:59.934
transfer what is the underlining
gene?

693
00:50:59.934 --> 00:51:03.934
that is a challenge in plants as
we can see on the left, even on

694
00:51:03.934 --> 00:51:09.000
the plant rabbit offices there
are over 120 ABC transporter
genes making

695
00:51:09.000 --> 00:51:15.000
it very difficult to identify
the property. In our research

696
00:51:15.000 --> 00:51:21.000
on Philo T Lytton synthase, we
and others found that the

697
00:51:21.000 --> 00:51:27.000
deficiencies in code Fido Q 14.

698
00:51:27.000 --> 00:51:32.000
so these plants also can
accumulate file --

699
00:51:32.000 --> 00:51:43.000
Fido Q attends invades.
Interestingly, one of them has
only 11

700
00:51:43.000 --> 00:51:45.000
so we use that one is a model so
we can identify that can are
those

701
00:51:45.000 --> 00:51:51.000
kind of transporters rather.
That's in collaboration with
Rick

702
00:51:51.000 --> 00:51:53.934
Ricardo.

703
00:51:53.934 --> 00:51:59.934
And another researcher in Korea.
This led to two

704
00:51:59.934 --> 00:52:07.000
parallel advances. We were able
to identify

705
00:52:07.000 --> 00:52:10.000
ABC transporters that mediate
this response of transporting
toxic and

706
00:52:10.000 --> 00:52:15.000
or heavy metal, and arsenic Fido
Q attend product

707
00:52:15.000 --> 00:52:20.000
and then sequestering them away
we also identified genes in the

708
00:52:20.000 --> 00:52:23.000
plant Arabidopsis.

709
00:52:23.000 --> 00:52:29.000
Let me show you what that looks
like. In a rabbit.

710
00:52:29.000 --> 00:52:32.000
That's here in the next slide.

711
00:52:32.000 --> 00:52:38.000
-- Not in a rabbit, in
Arabidopsis.

712
00:52:38.000 --> 00:52:43.000
Without toxicants, we knocked
out

713
00:52:43.000 --> 00:52:47.000
one gene we identified, or the
other gene, the ABC transporter
family,

714
00:52:47.000 --> 00:52:50.000
or a double mutant.

715
00:52:50.000 --> 00:52:55.934
If you expose these plants for
example, to and arsenic based
pesticide,

716
00:52:55.934 --> 00:53:01.934
disodium ethanol, you see the
route wild type plant

717
00:53:01.934 --> 00:53:06.000
can withstand it because they
can detoxify the arsenic to the

718
00:53:06.000 --> 00:53:09.000
pathway and sequester it.

719
00:53:09.000 --> 00:53:19.000
The single knockout mutants in
these genes are also feeling

720
00:53:19.000 --> 00:53:21.000
-- fairly resistant because
there are two genes. If you
knockout to

721
00:53:21.000 --> 00:53:26.000
gene to huge sensitivity. You
can also do experiments rather
than

722
00:53:26.000 --> 00:53:29.000
adding this pesticide, directly
adding

723
00:53:29.000 --> 00:53:31.000
arsenic in this case, you see
how particularly the double
knockout

724
00:53:31.000 --> 00:53:35.000
is sensitive, but it's very
difficult to distinguish the
wild type and

725
00:53:35.000 --> 00:53:40.000
single mute. So, in summary,
what we have shown

726
00:53:40.000 --> 00:53:45.000
here is that important genes the
Philo

727
00:53:45.000 --> 00:53:50.000
synthase to combine

728
00:53:50.000 --> 00:53:54.934
heavy metals in the transporters
have been identified. I will get

729
00:53:54.934 --> 00:53:59.934
back to these later in my talk,
I

730
00:53:59.934 --> 00:54:06.000
want to switch the subject of
today mainly genomic and methods

731
00:54:06.000 --> 00:54:12.000
to identify metrics

732
00:54:12.000 --> 00:54:18.000
and Bioremediation.

733
00:54:18.000 --> 00:54:22.000
To address or address functional

734
00:54:22.000 --> 00:54:25.000
redundancy when my referring to?

735
00:54:25.000 --> 00:54:30.000
with functional redundancy?

736
00:54:30.000 --> 00:54:36.000
>> When the first plant genome
was sequenced, this goes back 20

737
00:54:36.000 --> 00:54:40.000
years ago, it became immediately
clear that plant genome

738
00:54:40.000 --> 00:54:49.000
genomes have a larger family so
they have gene copies of a type

739
00:54:49.000 --> 00:54:54.000
of gene. I gave you the example
of the ABC transporter. Where

740
00:54:54.000 --> 00:55:06.067
we have 20 ABC transporter
genes.

741
00:55:06.067 --> 00:55:09.067
That is a major issue in plants
if you want to discover Jean and

742
00:55:09.067 --> 00:55:13.067
Manatt molecular functions,
there are multiple genes in
redundancy

743
00:55:13.067 --> 00:55:18.067
that makes this difficult and
they showed you and

744
00:55:18.067 --> 00:55:26.067
the ABC transporters were single
mutant barely showed any
phenotype.

745
00:55:26.067 --> 00:55:32.067
In the model plant or lab plant,

746
00:55:32.067 --> 00:55:41.067
there are 29.000 encoding genes.

747
00:55:41.067 --> 00:55:51.067
But only 10% may have been

748
00:55:51.067 --> 00:55:54.000
identified in the nurse higher
ones were you can knockout to
genes

749
00:55:54.000 --> 00:55:58.000
like I showed you. We shown
functional phenotypes to maybe
15% of those

750
00:55:58.000 --> 00:56:01.000
29.000 genes.

751
00:56:01.000 --> 00:56:16.000
This is a challenge which we
posed ourselves

752
00:56:16.000 --> 00:56:24.000
so, genome

753
00:56:24.000 --> 00:56:40.000
wide while not targeting off
targets.

754
00:57:33.000 --> 00:57:39.000
>> So the combinations using 2-5
genes

755
00:57:39.000 --> 00:57:45.000
that would be silent. Using this
approach, we were able to target

756
00:57:45.000 --> 00:57:46.000
combinations of 18.000 genes.

757
00:57:46.000 --> 00:57:50.000
So that all the gene families
because you need to conserve
nucleotides

758
00:57:50.000 --> 00:57:55.934
without off target. Of course,
this is a resource that many
other

759
00:57:55.934 --> 00:57:59.934
labs are using, and we have set
up this database at UCSD where

760
00:57:59.934 --> 00:58:04.934
people if they have certain
genes, they

761
00:58:04.934 --> 00:58:11.000
can go in there and see if they
identified probe appropriate
ones.

762
00:58:11.000 --> 00:58:17.000
That's fine, but 2 million,

763
00:58:17.000 --> 00:58:23.000
as too many an order to do
genetic screening for mutants

764
00:58:23.000 --> 00:58:31.000
that are more tolerant are more
sensitive to toxicants.

765
00:58:31.000 --> 00:58:37.000
Among these 22 Felix selected

766
00:58:37.000 --> 00:58:40.000
22.

767
00:58:40.000 --> 00:58:46.000
So Felix synthesized 22.000.

768
00:58:46.000 --> 00:58:51.000
that also targeted these 18.000
genes in combination, that's

769
00:58:51.000 --> 00:58:58.934
a lot to screen for we divided
them into 10 sub- libraries.

770
00:58:58.934 --> 00:59:04.934
Let's say you are looking at
ones that may give you

771
00:59:04.934 --> 00:59:12.000
resistance or accumulation to a
metal so one library does
predicted

772
00:59:12.000 --> 00:59:16.000
DNA-binding and RNA binding.
Another sub library, target gene
families

773
00:59:16.000 --> 00:59:21.000
of unknown function.
Particularly interest to
discover new mechanisms.

774
00:59:21.000 --> 00:59:25.000
So, let me sort

775
00:59:25.000 --> 00:59:31.000
of give you a example of how
these work here. In this
diagram,

776
00:59:31.000 --> 00:59:37.000
this cartoon defines red boxes
are five highly homogenous

777
00:59:37.000 --> 00:59:39.000
genes.

778
00:59:39.000 --> 00:59:45.000
Let's just assume if you
knockout this gene alone this
kind of covers

779
00:59:45.000 --> 00:59:50.000
for each other, genetic
redundancy or overlapping
function

780
00:59:50.000 --> 00:59:52.934
so here is the microneedles.

781
00:59:52.934 --> 00:59:57.934
They knock them down if you give
us a phenotype but not

782
00:59:57.934 --> 01:00:02.934
everyone is a highly efficient
when it is designed

783
01:00:02.934 --> 01:00:08.000
on a scale even though we try to
develop the high binding ones.
Very

784
01:00:08.000 --> 01:00:20.000
often we have a second one by
the other nucleotides.

785
01:00:20.000 --> 01:00:26.000
Here's another one.

786
01:00:26.000 --> 01:00:38.000
This should have robustness.

787
01:00:38.000 --> 01:00:44.000
So Felix in the team, design
needs.

788
01:00:44.000 --> 01:00:52.934
We did proof of concept screens.

789
01:00:52.934 --> 01:00:58.934
So we were able to isolate
receptors in a

790
01:00:58.934 --> 01:01:02.934
short time, they have been
searched for, for several
decades.

791
01:01:02.934 --> 01:01:09.000
That was a proof of concept
since they were isolated 10
years

792
01:01:09.000 --> 01:01:10.000
ago.

793
01:01:10.000 --> 01:01:22.000
We have done different survival
species in the case of heavy
metals,

794
01:01:22.000 --> 01:01:25.000
here's an example of a mutant
that we identified on the
left-hand side,

795
01:01:25.000 --> 01:01:31.000
we see the wild type, and this
one on in the absence of a
toxicant.

796
01:01:31.000 --> 01:01:41.000
In the presence of arsenic, this
artificial one, grows better

797
01:01:41.000 --> 01:01:46.000
than the wild type. So it is
more resistant.

798
01:01:46.000 --> 01:01:50.000
So what genes, among the 22 or
18.000 genes are targeted this?

799
01:01:50.000 --> 01:01:54.934
it happens to be three phosphate
transporters.

800
01:01:54.934 --> 01:01:57.934
That intuitively, it makes sense
because, as I mentioned earlier,

801
01:01:57.934 --> 01:02:02.934
how is arsenic or arsenate taken
into roots? there

802
01:02:02.934 --> 01:02:08.000
phosphate transporters. If you
knocked down some phosphate
transporters,

803
01:02:08.000 --> 01:02:17.000
we are further investigating
this.

804
01:02:17.000 --> 01:02:21.000
So, the first step while
identifying and confirming,

805
01:02:21.000 --> 01:02:25.000
we identified the micro
substance or sequence that tells

806
01:02:25.000 --> 01:02:29.000
us with the potential targets
are, we ran treat we

807
01:02:29.000 --> 01:02:32.000
re-transform other plants to see
if we can confirm the phenotype

808
01:02:32.000 --> 01:02:37.000
and that's the first step shown
in the slide. What you can see
here,

809
01:02:37.000 --> 01:02:46.000
is we re-transform, let me see
my pointer.

810
01:02:46.000 --> 01:02:51.934
So I re-transform this and we
see

811
01:02:51.934 --> 01:03:00.934
this compared to the wild -type
control.

812
01:03:00.934 --> 01:03:03.934
I will get back to this in a
minute, I want to show

813
01:03:03.934 --> 01:03:09.000
you another example of a
interesting artificial micro RNA
we isolated

814
01:03:09.000 --> 01:03:13.000
many ones, using a number

815
01:03:13.000 --> 01:03:18.000
of approaches some showing you a
few examples. Here is another
interesting

816
01:03:18.000 --> 01:03:24.000
example. In the absence of a
stress, the plants wild type

817
01:03:24.000 --> 01:03:28.000
and other gross similarly well
if

818
01:03:28.000 --> 01:03:32.000
you look on the right-hand side
in the present of arsenate, this

819
01:03:32.000 --> 01:03:37.000
line carrying this one is
actually the roots are growing
longer than

820
01:03:37.000 --> 01:03:42.000
the wild types interestingly for
this line,

821
01:03:42.000 --> 01:03:46.000
if we analyze the sensitivity to
cadmium, we see more sensitivity

822
01:03:46.000 --> 01:03:54.934
in the wild type and then
arsenic we see more root growth.

823
01:03:54.934 --> 01:03:58.934
So let's dig in a little bit
here and that's what's going on.

824
01:03:58.934 --> 01:04:04.934
This micro RNA, is predicted to
target.

825
01:04:04.934 --> 01:04:09.000
This micro RNA targets

826
01:04:09.000 --> 01:04:14.000
16 that's a little bit unusual
because, 96% of micro

827
01:04:14.000 --> 01:04:21.000
RNAs that are in our libraries
target 2-5 genes. That has a

828
01:04:21.000 --> 01:04:26.000
advantage once you identify the
candidate target genes, you have

829
01:04:26.000 --> 01:04:27.000
a easier time narrowing down.

830
01:04:27.000 --> 01:04:32.000
The more targets you have the
more work you have so six genes,
are

831
01:04:32.000 --> 01:04:38.000
related as shown here by these
trends

832
01:04:38.000 --> 01:04:47.000
mutants and their related, we
can also using

833
01:04:47.000 --> 01:04:51.067
technology which ones are most
likely to be knocked down.

834
01:04:51.067 --> 01:04:54.000
And ones that are less likely.

835
01:04:54.000 --> 01:05:00.000
These are two different gene
family family so we knocked

836
01:05:00.000 --> 01:05:04.000
out members of this family and
that family to investigate the
response.

837
01:05:04.000 --> 01:05:09.067
So using the crisper approach
knocked out three

838
01:05:09.067 --> 01:05:13.067
out of four of the family
members if you knockout

839
01:05:13.067 --> 01:05:20.067
off for what we see over
controlled conditions

840
01:05:20.067 --> 01:05:28.067
when we look at arsenate we see
we see a

841
01:05:28.067 --> 01:05:34.067
longer root growth. This is
similar to the phenotype

842
01:05:34.067 --> 01:05:39.067
although as I said,

843
01:05:39.067 --> 01:05:49.067
we did knock out the sea before,
we are reconfiguring that
phenotype

844
01:05:49.067 --> 01:05:55.000
these are transcription factors
what are their targets?

845
01:05:55.000 --> 01:06:03.000
one message to do that, and
target that, as a method of

846
01:06:03.000 --> 01:06:08.000
variant use in a rapid Austin us
--

847
01:06:08.000 --> 01:06:12.000
rabbit offices a rabbit offices
-- if

848
01:06:12.000 --> 01:06:14.000
we look at this.

849
01:06:14.000 --> 01:06:20.000
>> For the CBS transcription
members we see moderate

850
01:06:20.000 --> 01:06:23.000
ones.

851
01:06:23.000 --> 01:06:28.000
These are putative candidates
but this might explain

852
01:06:28.000 --> 01:06:34.000
why these mutants affect the

853
01:06:34.000 --> 01:06:46.000
phenotypes.

854
01:06:46.000 --> 01:06:51.934
Show some data on this. We
shirts -- we first asked in the

855
01:06:51.934 --> 01:06:58.934
wild -type. What you can see in
the wild type it's

856
01:06:58.934 --> 01:07:02.934
expressed at a certain level, if
you expose your plans to arsenic

857
01:07:02.934 --> 01:07:08.000
it is reduced which makes sense
because, plans to take up our
arsenate

858
01:07:08.000 --> 01:07:17.000
through phosphate. If we look at
this one,

859
01:07:17.000 --> 01:07:23.000
even before after you expose it
to arsenic authority down.

860
01:07:23.000 --> 01:07:28.000
So it is responsible for

861
01:07:28.000 --> 01:07:30.000
that. If you knock it out, is
slower.

862
01:07:30.000 --> 01:07:36.000
In the presence of arsenic it
doesn't get further reduced.
This

863
01:07:36.000 --> 01:07:40.000
might in part explain, the
increased root growth in the

864
01:07:40.000 --> 01:07:43.000
presence of arsenic that we
identified.

865
01:07:43.000 --> 01:07:47.000
That is one gene, what if we
look at the other ones?

866
01:07:47.000 --> 01:07:51.934
this other one, that we had not
down, you don't see a clear
difference.

867
01:07:51.934 --> 01:08:03.934
In the crisper. In this one,
there may be a difference.

868
01:08:03.934 --> 01:08:08.000
If we knocked down all three of
these, we see a phenotype. If we

869
01:08:08.000 --> 01:08:09.000
knockout one we do not.

870
01:08:09.000 --> 01:08:13.000
Meaning, these genes and
phosphate reporters, can
contribute to the

871
01:08:13.000 --> 01:08:17.000
phenotype of phosphate, and on
the expression level

872
01:08:17.000 --> 01:08:20.000
it appears that this is
important.

873
01:08:20.000 --> 01:08:24.000
All of these transcription
reporters that

874
01:08:24.000 --> 01:08:27.000
is a hypothesis.

875
01:08:27.000 --> 01:08:36.000
Let me show you another example
of this type of research.

876
01:08:36.000 --> 01:08:42.000
Another words remember our
transcription factor, targets
for

877
01:08:42.000 --> 01:08:48.000
CBS genes into ERS 30 S genes.

878
01:08:48.000 --> 01:08:53.934
So this one knocked out to ERS
34-35 genes

879
01:08:53.934 --> 01:08:59.934
and created a double mutant.

880
01:08:59.934 --> 01:09:01.934
In the absence of stress there
may

881
01:09:01.934 --> 01:09:03.934
be a affect but it's not very
dramatic.

882
01:09:03.934 --> 01:09:06.000
Now we are looking at the
cadmium response. Remember the
artificial

883
01:09:06.000 --> 01:09:08.000
line is more sensitive to
cadmium, this double mutant, we
are seeing

884
01:09:08.000 --> 01:09:11.000
an enhanced sensitivity of
growth.

885
01:09:11.000 --> 01:09:15.000
And may not be identical at this
point to the micro RNA that

886
01:09:15.000 --> 01:09:20.000
target genes but certainly, it
is important it is a similar

887
01:09:20.000 --> 01:09:24.000
and more important or a
important phenotype and these
are all

888
01:09:24.000 --> 01:09:29.000
transcription factors. Which
genes might they impute?

889
01:09:29.000 --> 01:09:33.000
she had an interesting idea.
With previously published and
others

890
01:09:33.000 --> 01:09:38.000
have micro array data where they
have

891
01:09:38.000 --> 01:09:42.000
asked which genes does Me and
strongly reduce cadmium? you get
along

892
01:09:42.000 --> 01:09:48.000
list of genes from that and then
we can use

893
01:09:48.000 --> 01:09:54.934
another approach to ask which
DNA, elements in the genome, do
these

894
01:09:54.934 --> 01:09:56.934
two transcription factors find?

895
01:09:56.934 --> 01:10:01.934
then we can do a correlation
between the binding of DNA in
the cadmium

896
01:10:01.934 --> 01:10:04.934
expression so interestingly when
she did this and it may be hard

897
01:10:04.934 --> 01:10:11.000
to read, the top teen in the
list, it shows the strongest
cadmium

898
01:10:11.000 --> 01:10:15.000
induction is a nitrate
transporter called net nitrate
transporter 1.8.

899
01:10:15.000 --> 01:10:23.000
The reason that is interesting,
is

900
01:10:23.000 --> 01:10:24.367
in the previous superfund,

901
01:10:24.367 --> 01:10:27.000
supported research, we
identified this transporter

902
01:10:27.000 --> 01:10:29.000
as playing a role in cadmium
sensitivity, and their

903
01:10:29.000 --> 01:10:34.000
links nitrate transporter in the
plant. I will not go into this
but

904
01:10:34.000 --> 01:10:39.000
a former postdoc in my lab, did
identify this, he set up

905
01:10:39.000 --> 01:10:44.000
his own lab where we completed
this Eddie, what you can see
under controlled

906
01:10:44.000 --> 01:10:48.000
conditions, it doesn't show much
difference.

907
01:10:48.000 --> 01:10:52.934
In the presence of cadmium, and
nitrate we see this slight
sensitivity.

908
01:10:52.934 --> 01:10:57.934
So there may be again this
correlation where we are
identifying

909
01:10:57.934 --> 01:11:02.934
punitive elements of network.

910
01:11:02.934 --> 01:11:06.000
More work is needed in the early
days of this research. I want to

911
01:11:06.000 --> 01:11:08.000
show you some examples where we
find transporter genes and also

912
01:11:08.000 --> 01:11:13.000
transcription factors, that may
be linked to that and we are
also

913
01:11:13.000 --> 01:11:18.000
following up on that. Let me
summarize as part of my talk,

914
01:11:18.000 --> 01:11:21.000
there are very large gene
families and many large gene
families in

915
01:11:21.000 --> 01:11:27.000
plants disproportionately to
protect organisms. That has
limited gene

916
01:11:27.000 --> 01:11:30.000
it discovery and forward genetic
screening of

917
01:11:30.000 --> 01:11:35.000
important genes in plants. We
are applying, and we have
developed

918
01:11:35.000 --> 01:11:40.000
a Omics resource, in our
screening for

919
01:11:40.000 --> 01:11:46.000
new mutant and arsenic in
cadmium responses. This Omics
research

920
01:11:46.000 --> 01:11:49.000
is good for our research and
other

921
01:11:49.000 --> 01:11:54.934
laboratories restraint he use it
as well. We provided

922
01:11:54.934 --> 01:12:00.934
the Arabidopsis so that now,
earlier this year

923
01:12:00.934 --> 01:12:07.000
other ones can pursue the
screening as well. We can I

924
01:12:07.000 --> 01:12:11.000
tend to find important genes
within the large gene families

925
01:12:11.000 --> 01:12:22.000
found found in plants.

926
01:12:22.000 --> 01:12:24.000
And the rest of my talk because
obviously we are interested and

927
01:12:24.000 --> 01:12:26.000
I'm sure the listeners are
interested in questions of
bioremediation I

928
01:12:26.000 --> 01:12:32.000
want to mention some basic
science we are doing, and I
wanted to relate

929
01:12:32.000 --> 01:12:33.000
that to the fields.

930
01:12:33.000 --> 01:12:38.000
So, in other words, translating
from the lab

931
01:12:38.000 --> 01:12:43.000
to the field of course we are in
early days, as I mentioned
already

932
01:12:43.000 --> 01:12:47.000
earlier my

933
01:12:47.000 --> 01:12:50.000
laboratory has an interest, and
how plants respond to drought.
In

934
01:12:50.000 --> 01:12:54.934
fact some years ago we
identified a first plant mutant
we could

935
01:12:54.934 --> 01:12:57.934
show that's more tolerant than
its wild type control and these

936
01:12:57.934 --> 01:12:59.934
plants have been watered for 26
days in the mutant is more
assistant

937
01:12:59.934 --> 01:13:02.934
to drought this particular
mutant also has other phenotypes
that we

938
01:13:02.934 --> 01:13:09.000
further characterized, then the
court lab. I

939
01:13:09.000 --> 01:13:14.000
was very excited, when Arena
Meyer at the superfund center,

940
01:13:14.000 --> 01:13:22.000
and Arizona and then I started
talking about

941
01:13:22.000 --> 01:13:26.000
possible interactions as Reyna
presented on Monday in this
webinar

942
01:13:26.000 --> 01:13:31.000
series, she is very interested
in plants that can survive on
toxic

943
01:13:31.000 --> 01:13:37.000
low pH and toxic metals in arid
and

944
01:13:37.000 --> 01:13:40.000
semi arid regions.

945
01:13:40.000 --> 01:13:46.000
Like the iron King mine and
Reyna's

946
01:13:46.000 --> 01:13:51.934
laboratory has identified and
characterized plants

947
01:13:51.934 --> 01:13:53.934
that can grow under these
conditions, as presented on
Monday, this is

948
01:13:53.934 --> 01:13:57.934
important because also the
groundcover of resistant plants
will reduce

949
01:13:57.934 --> 01:14:08.000
the ice it will reduce the toxic
and respiratory

950
01:14:08.000 --> 01:14:16.000
diseases and so on another
disease is linked to that.

951
01:14:16.000 --> 01:14:17.000
So, we have started a
collaboration.

952
01:14:17.000 --> 01:14:20.000
In this case, an important
aspect of these plants is that
they can

953
01:14:20.000 --> 01:14:24.000
survive these conditions. As
well as grow,

954
01:14:24.000 --> 01:14:27.000
but not only that Reyna's engine
-- laboratory is interested in
the

955
01:14:27.000 --> 01:14:36.000
micro biome and how that plays a
role in this resistance. So,

956
01:14:36.000 --> 01:14:40.000
so there has been other

957
01:14:40.000 --> 01:14:46.000
laboratory researchers that have
grown plants in

958
01:14:46.000 --> 01:14:50.000
the presence of these microbes
that

959
01:14:50.000 --> 01:14:58.000
are characterized through
genomic methods at the same
time,

960
01:14:58.000 --> 01:15:02.000
chiefly isolated messenger RNA
samples from different tissues
under

961
01:15:02.000 --> 01:15:04.000
these conditions.

962
01:15:04.000 --> 01:15:10.067
I should also say the RNA
experiments there's been 48
samples

963
01:15:10.067 --> 01:15:18.067
thus far where as sequence and
Ron Evans at the

964
01:15:18.067 --> 01:15:24.067
and then Alexandria as well as
Chris a member of our superfund

965
01:15:24.067 --> 01:15:30.067
has helped with these large
datasets.

966
01:15:30.067 --> 01:15:33.067
Is ongoing analysis as we
recently obtained this data,

967
01:15:33.067 --> 01:15:40.067
this is a slide that Reyna
showed from the analyses so what
we

968
01:15:40.067 --> 01:15:45.067
are looking at here, our plants
treated with 10% compost

969
01:15:45.067 --> 01:15:50.067
versus 20% compost in different
replicas and roots. You can

970
01:15:50.067 --> 01:15:55.000
see genes that are clearly
reproducibly induced under the
higher

971
01:15:55.000 --> 01:16:06.000
compost treatments than the
lower compost treatment. And
other genes

972
01:16:06.000 --> 01:16:08.000
that are repressed or less
expressed

973
01:16:08.000 --> 01:16:11.000
under the higher compost
treatment compared to the lower.
Of course

974
01:16:11.000 --> 01:16:15.000
there are many genes that are
being identified in

975
01:16:15.000 --> 01:16:18.000
parallel, in Lane is Robert
Barbara Torre is looking at the
micro biome

976
01:16:18.000 --> 01:16:23.000
of these different plants at the
same time, we can compare these

977
01:16:23.000 --> 01:16:27.000
to our identified genes and gene
that we are identifying in

978
01:16:27.000 --> 01:16:31.000
our artificial screens which is
ongoing work that

979
01:16:31.000 --> 01:16:36.000
Reyna did present this Monday
and I wanted

980
01:16:36.000 --> 01:16:38.000
to refer to it.

981
01:16:38.000 --> 01:16:40.000
>> In the last part of my talk I
wanted to mention another aspect

982
01:16:40.000 --> 01:16:48.000
of toxicants and we as human are
exposed to them

983
01:16:48.000 --> 01:16:54.934
so this is from consumers or
from a few years ago, at that
point

984
01:16:54.934 --> 01:16:56.934
some of the listeners may recall
it was found that these
concentrated

985
01:16:56.934 --> 01:16:59.934
apple juices, and grape juices,
that we give our children to go

986
01:16:59.934 --> 01:17:06.000
to school, contained elevated
levels of arsenic.

987
01:17:06.000 --> 01:17:14.000
So these toxicants arsenic, or
lead, can make it into our food
chain.

988
01:17:14.000 --> 01:17:18.000
One question is how does

989
01:17:18.000 --> 01:17:22.000
that happen and how what can we
do about it? it's not only

990
01:17:22.000 --> 01:17:26.000
fruit juice, and also for
example some of our staple
crops, rice as

991
01:17:26.000 --> 01:17:32.000
an example, race grain, the
seeds and rice

992
01:17:32.000 --> 01:17:37.000
they are very good at
accumulating arsenic in cadmium.

993
01:17:37.000 --> 01:17:42.000
For rice grown in the U.S.,
particularly in southeastern
U.S. that's more

994
01:17:42.000 --> 01:17:47.000
humid and well-suited for rice
growth.

995
01:17:47.000 --> 01:17:50.000
We have elevated arsenic in the
soil, where does that come from?

996
01:17:50.000 --> 01:17:55.934
it comes from in part those
arsenic containing pesticides
that are been

997
01:17:55.934 --> 01:17:59.934
used in the past and cotton that
was grown and treated

998
01:17:59.934 --> 01:18:02.934
with those pesticides as well as
his natural reserves of arsenic

999
01:18:02.934 --> 01:18:09.000
so plants are very good at
accumulating arsenic in their
seed in

1000
01:18:09.000 --> 01:18:18.000
their studies on human exposure.
That's through race.

1001
01:18:18.000 --> 01:18:22.000
As I mentioned we discovered
mechanisms,

1002
01:18:22.000 --> 01:18:28.000
by which plants take up,
sequester, and

1003
01:18:28.000 --> 01:18:34.000
translocate these toxic ones.
Can we use these

1004
01:18:34.000 --> 01:18:35.000
in the laboratory?

1005
01:18:35.000 --> 01:18:38.000
to work on engineering
approaches?

1006
01:18:38.000 --> 01:18:44.000
in the terms of how these
toxicants enter the

1007
01:18:44.000 --> 01:18:47.000
roots I mention they do this by
nutrient

1008
01:18:47.000 --> 01:18:51.934
transporters. Is very difficult
because your clip

1009
01:18:51.934 --> 01:18:56.934
your plants require nutrients
and there's other genes in
parallel.

1010
01:18:56.934 --> 01:19:02.934
One question was can we at least
as proof, you some

1011
01:19:02.934 --> 01:19:08.000
of our discoveries and others in
the field to sequester

1012
01:19:08.000 --> 01:19:13.000
those? or expel them? that way
they do not end up in the
grains? to

1013
01:19:13.000 --> 01:19:19.000
do this we need one that can
target roots,

1014
01:19:19.000 --> 01:19:27.000
we have used, a rabbit obsess --
Arabidopsis

1015
01:19:27.000 --> 01:19:32.000
reporter, but less so the aerial
parts of the plant. The big

1016
01:19:32.000 --> 01:19:36.000
one was to find a promoter in
ricin through a collaboration
which showed

1017
01:19:36.000 --> 01:19:43.000
a very strong promoter that
targets race roots but not the
aerial section.

1018
01:19:43.000 --> 01:19:46.000
Can we actually do this is it
realistic?

1019
01:19:46.000 --> 01:19:51.934
this is laboratory?

1020
01:19:51.934 --> 01:19:55.934
if we identify mechanisms, their
genetic variation that exists in

1021
01:19:55.934 --> 01:20:00.934
the yields could be leveraged,
to target such genes either
through

1022
01:20:00.934 --> 01:20:06.000
genetic modification but also
through advanced molecular

1023
01:20:06.000 --> 01:20:08.000
breeding.

1024
01:20:08.000 --> 01:20:11.000
Through the revolution in genome
sequence knowledge can be
targeted

1025
01:20:11.000 --> 01:20:17.000
in non-GM oh approaches
potentially to do this. There is
evidence

1026
01:20:17.000 --> 01:20:23.000
and rice in the case of cadmium.
Can we, sequester

1027
01:20:23.000 --> 01:20:26.000
and keep toxicants out of the
area?

1028
01:20:26.000 --> 01:20:30.000
we have previously demonstrated
this in

1029
01:20:30.000 --> 01:20:34.000
a different project I will
briefly mention, one

1030
01:20:34.000 --> 01:20:40.000
of the metals that toxic to
plants, less so to us, to

1031
01:20:40.000 --> 01:20:45.000
a degree, are sodium ion, when
we irrigate

1032
01:20:45.000 --> 01:20:49.000
crops, sodium salts builds up in
soils and sodium particularly
toxic

1033
01:20:49.000 --> 01:20:51.000
and moves in to the leaves.

1034
01:20:51.000 --> 01:20:56.934
So plants and evolution, there's
a mechanism

1035
01:20:56.934 --> 01:21:00.934
that does protect them from
this. The way

1036
01:21:00.934 --> 01:21:07.000
this works is that sodium
together with the work with
nutrients

1037
01:21:07.000 --> 01:21:11.000
and sodium reaches leaves. As
water and nutrients

1038
01:21:11.000 --> 01:21:15.000
I will moving, you

1039
01:21:15.000 --> 01:21:21.000
have these H KT transporters
that pull the sodium out, and
then retain

1040
01:21:21.000 --> 01:21:25.000
them and then it sequestered or
expel

1041
01:21:25.000 --> 01:21:31.000
or exported. Let's say you knock
out the single gene and

1042
01:21:31.000 --> 01:21:34.000
Arabidopsis? what happened if
you knock it out? and you water
the

1043
01:21:34.000 --> 01:21:37.000
plants once was sodium chloride?

1044
01:21:37.000 --> 01:21:41.000
one can withstand it because
they keep the sodium in its
roots.

1045
01:21:41.000 --> 01:21:44.000
That is interesting, as I
mentioned, now if we look

1046
01:21:44.000 --> 01:21:50.000
at variation, that actually does
occur in nature, breeders

1047
01:21:50.000 --> 01:21:53.934
in Australia,

1048
01:21:53.934 --> 01:21:58.934
and in other regions have looked
at rice and wheat and it turns
out,

1049
01:21:58.934 --> 01:22:06.000
majors sought -- salt, has it in
wheat and rice they

1050
01:22:06.000 --> 01:22:09.000
use this mechanism to keep
sodium out of the leaves. The
presently

1051
01:22:09.000 --> 01:22:18.000
breeders are using this to
improve we, and rice, in the

1052
01:22:18.000 --> 01:22:20.000
field through non-GM operating
approaches

1053
01:22:20.000 --> 01:22:25.000
this research started to this
basic gene discovery. That the
wheat and

1054
01:22:25.000 --> 01:22:27.000
rice.

1055
01:22:27.000 --> 01:22:30.000
We've done this in my laboratory
but also the breeders
contribution

1056
01:22:30.000 --> 01:22:35.000
at the very important as well.
Back to heavy metals. We work

1057
01:22:35.000 --> 01:22:41.000
on various laboratories to look
at various

1058
01:22:41.000 --> 01:22:43.000
nutrient transporters mechanisms
have a enter the plant and
mention

1059
01:22:43.000 --> 01:22:47.000
the Fido queue attends an ABC
transom porters are transferred
meters there's

1060
01:22:47.000 --> 01:22:51.934
other pathways he can be
sequester there's a number of
genes

1061
01:22:51.934 --> 01:22:54.934
in this network, that have been
identified, that we are
identifying.

1062
01:22:54.934 --> 01:23:01.934
So we are presently
investigating in Arabidopsis and
rice, which

1063
01:23:01.934 --> 01:23:06.000
combinations of genes in which
genes can be used, to reduce

1064
01:23:06.000 --> 01:23:09.000
the transfer of these toxicants.

1065
01:23:09.000 --> 01:23:14.000
That to the grain as a example

1066
01:23:14.000 --> 01:23:17.000
of rice. So this is ongoing
work.

1067
01:23:17.000 --> 01:23:26.000
We actually have some transgenic
plants that we are going up to
collect

1068
01:23:26.000 --> 01:23:28.000
some information on that were
transformed with the right roots
promoter and

1069
01:23:28.000 --> 01:23:31.000
targeting different genes that
were tested with the help of Pam
Ronald

1070
01:23:31.000 --> 01:23:35.000
at UC Davis that works on rice
and transform these for us.

1071
01:23:35.000 --> 01:23:40.000
This is ongoing work, obviously
it is a challenge. That

1072
01:23:40.000 --> 01:23:44.000
is because the question is which
combination of genes might be
suitable?

1073
01:23:44.000 --> 01:23:50.000
that type of knowledge as they
arty emphasized can then be used

1074
01:23:50.000 --> 01:23:53.934
to look for these genes that
exists naturally for breeding,
or for also

1075
01:23:53.934 --> 01:23:59.934
genetic modification what are
the other.

1076
01:23:59.934 --> 01:24:03.934
The approaches to the genomics
revolution to identify them and

1077
01:24:03.934 --> 01:24:09.000
being able to make such
improvements through breeding,
it has been become

1078
01:24:09.000 --> 01:24:14.000
very powerful these days. So I
would like to finish by
acknowledging,

1079
01:24:14.000 --> 01:24:20.000
I think I mentioned, see you --

1080
01:24:20.000 --> 01:24:26.000
my two colleagues as well as
Felix

1081
01:24:26.000 --> 01:24:32.000
and my hope of propagation and
pilot screens and

1082
01:24:32.000 --> 01:24:36.000
other present and former lab
members who now have

1083
01:24:36.000 --> 01:24:40.000
their own group and new
positions. We

1084
01:24:40.000 --> 01:24:46.000
have collaborated in this work
with Ron Evans as I mentioned,

1085
01:24:46.000 --> 01:24:52.000
you have other collaborations I
did not

1086
01:24:52.000 --> 01:24:56.000
mention today including, some
others that I can presented

1087
01:24:56.000 --> 01:25:06.067
another time. So thank you very
much for your attention and
time.

1088
01:25:06.067 --> 01:25:09.067
>> Thank you for your
presentation it was excellent.
We have a number

1089
01:25:09.067 --> 01:25:13.067
of questions coming and I want
to encourage

1090
01:25:13.067 --> 01:25:16.067
the participants to continue to
submit questions via the Q&A
pod.

1091
01:25:16.067 --> 01:25:20.067
That's in the lower right-hand
corner of the screen. So I will
just get

1092
01:25:20.067 --> 01:25:22.067
started with the questions.

1093
01:25:22.067 --> 01:25:28.067
We have a person, who is asking,
about the triple

1094
01:25:28.067 --> 01:25:32.067
CBS mutant that was resistant
arsenic three?

1095
01:25:32.067 --> 01:25:36.067
you identified phosphate
transfers which would be arsenic
transporters?

1096
01:25:36.067 --> 01:25:40.067
can you elaborate on that.

1097
01:25:40.067 --> 01:25:46.067
>> Yes, that's a very
interesting question. If we
expose plans to

1098
01:25:46.067 --> 01:25:55.000
arsenate, that can be taken up
by phosphate transporters.

1099
01:25:55.000 --> 01:26:01.000
Phosphate itself, is toxic as it
can interfere and compete

1100
01:26:01.000 --> 01:26:05.000
with arsenate is toxic as it
competes

1101
01:26:05.000 --> 01:26:09.000
with phosphate. At the same time
arsenate is produced to
arsenate,

1102
01:26:09.000 --> 01:26:12.000
that's actually quite important.

1103
01:26:12.000 --> 01:26:19.000
And that's because arson night,
can indeed be customized by the

1104
01:26:19.000 --> 01:26:24.000
Fido queue it in. That bind
arsenate I'm sorry arson night

1105
01:26:24.000 --> 01:26:33.000
and through the pathway that we
described earlier. So,

1106
01:26:33.000 --> 01:26:39.000
I hope you so both phosphate I'm
sorry both arsenate

1107
01:26:39.000 --> 01:26:44.000
and arsenate are toxic and
because we have

1108
01:26:44.000 --> 01:26:49.000
reductase that can convert the
one to the other inside the
plant,

1109
01:26:49.000 --> 01:26:54.934
you can have both of these
toxicants playing an important

1110
01:26:54.934 --> 01:27:02.934
role.

1111
01:27:02.934 --> 01:27:06.000
>> Okay.

1112
01:27:06.000 --> 01:27:12.000
Did a simple unit have

1113
01:27:12.000 --> 01:27:16.000
--

1114
01:27:16.000 --> 01:27:22.000
>> No, we have not generated a
quadruple mutant as a mention

1115
01:27:22.000 --> 01:27:26.000
because it would be lethal. To
this point we have not seen
that. But

1116
01:27:26.000 --> 01:27:29.000
this is ongoing research.

1117
01:27:29.000 --> 01:27:35.000
>> Okay.

1118
01:27:35.000 --> 01:27:38.000
>> At this point I think I will
see if there is anyone who is on

1119
01:27:38.000 --> 01:27:42.000
the telephone I want to ask a
question, and continue to
encourage folks

1120
01:27:42.000 --> 01:27:48.000
to submit more questions in the
Q&A pod.

1121
01:27:48.000 --> 01:27:51.000
While we check in on the folks
with the phone

1122
01:27:51.000 --> 01:27:53.934
if you're on the phone as a
reminder take your phone off
mute by press

1123
01:27:53.934 --> 01:27:55.934
pound six.

1124
01:27:55.934 --> 01:27:58.934
>> Go ahead.

1125
01:27:58.934 --> 01:28:08.000
>> I just might add to the last
question, so

1126
01:28:08.000 --> 01:28:11.000
in a given mutant background
whether mutant is sensitive to
say arsenate

1127
01:28:11.000 --> 01:28:15.000
or arsenate, also depends on the
nutrient content of your media.

1128
01:28:15.000 --> 01:28:19.000
So we use different approaches.
We use media

1129
01:28:19.000 --> 01:28:24.000
that's fairly rich in nutrients
which reduces your sensitivity

1130
01:28:24.000 --> 01:28:27.000
to some of these toxicants, but
we also develop a minimal media

1131
01:28:27.000 --> 01:28:31.000
which is perhaps more realist
for what plant six Aryans in the
oil.

1132
01:28:31.000 --> 01:28:35.000
There you haven't enhanced
sensitivity so when you

1133
01:28:35.000 --> 01:28:39.000
look at different mutant
combinations as the

1134
01:28:39.000 --> 01:28:42.000
question was this may depend on
the nutrient content. So this is

1135
01:28:42.000 --> 01:28:46.000
something we are still working
on at the time.

1136
01:28:46.000 --> 01:28:51.000
>> Okay, thank you for
elaborating on that.

1137
01:28:51.000 --> 01:28:54.934
>> Is anyone, who was on the
phone that wanted ask a
question?

1138
01:28:54.934 --> 01:28:58.934
will just pause for a second to
see if

1139
01:28:58.934 --> 01:28:59.934
anyone speaks up.

1140
01:28:59.934 --> 01:29:04.934
>> It seems like the majority of
our participants, use the online

1141
01:29:04.934 --> 01:29:12.000
streaming but we do like to
check in.

1142
01:29:12.000 --> 01:29:16.000
>> Okay, I am not seeing any
more questions

1143
01:29:16.000 --> 01:29:19.000
coming in from the audience.

1144
01:29:19.000 --> 01:29:24.000
I do not know, if our presenters
have any

1145
01:29:24.000 --> 01:29:29.000
await one just came in. Let me
go look.

1146
01:29:29.000 --> 01:29:35.000
Okay this person says drought
resistant through regulation

1147
01:29:35.000 --> 01:29:49.000
and methodological changes, how
is drought resistant express.

1148
01:29:49.000 --> 01:29:51.934
>> Drought resistant is quite
complex in general there are
different mechanisms

1149
01:29:51.934 --> 01:29:54.934
that can contribute drought
resistance.

1150
01:29:54.934 --> 01:30:02.934
The question was model responses
so plants lose

1151
01:30:02.934 --> 01:30:08.000
over 90% of their water through
stomata. So that's an important

1152
01:30:08.000 --> 01:30:11.000
role in reducing or slowing
water loss

1153
01:30:11.000 --> 01:30:13.000
but there are other very
important traits of weight to
drought tolerance

1154
01:30:13.000 --> 01:30:18.000
and some of these are actually
the ability of roots

1155
01:30:18.000 --> 01:30:21.000
to grow deeper into soils to
seek water that would be deeper
and soiled

1156
01:30:21.000 --> 01:30:29.000
there are protective mechanisms
because, dehydration

1157
01:30:29.000 --> 01:30:34.000
and the osmotic stresses lead to
that and it can kill cells so
plants

1158
01:30:34.000 --> 01:30:41.000
mount resistant through genes
where

1159
01:30:41.000 --> 01:30:44.000
biophysical mechanisms are
completely understood but
drought resistance

1160
01:30:44.000 --> 01:30:50.000
is quite a complex trait. In the
case of the Fido

1161
01:30:50.000 --> 01:30:51.934
stabilizer plants, that Raina
Meyer has been working

1162
01:30:51.934 --> 01:30:56.934
on, these are plants that can
with and the

1163
01:30:56.934 --> 01:31:02.934
very low acidity not only
drought as well of these taxes
-- toxic

1164
01:31:02.934 --> 01:31:09.000
soils and the toxic metals in
those soils

1165
01:31:09.000 --> 01:31:13.000
and can grow, and general and
Casey's plants

1166
01:31:13.000 --> 01:31:18.000
obviously also need to have a
genetic makeup that can
withstand the aerator

1167
01:31:18.000 --> 01:31:20.000
or semi arid conditions in those
regions. Our

1168
01:31:20.000 --> 01:31:25.000
interest in this case, is how
these plants respond with
interest I should

1169
01:31:25.000 --> 01:31:30.000
save of rain is how these

1170
01:31:30.000 --> 01:31:32.000
plans were selected to grow
needs regions the question is,
how do

1171
01:31:32.000 --> 01:31:38.000
they Fido stabilize and grow in
the presence of the acidic pH,

1172
01:31:38.000 --> 01:31:41.000
and toxic metal so Raina could
do a better job at

1173
01:31:41.000 --> 01:31:45.000
that at the same time, very
interesting studies of how the
microbiota might

1174
01:31:45.000 --> 01:31:49.000
play a role in facilitating not
and regulating the pH but that's

1175
01:31:49.000 --> 01:31:53.934
research and Raina Meyer's
laboratory so the main thing
here is

1176
01:31:53.934 --> 01:31:57.934
plants that Raina has previously
characterized, can grow under
these

1177
01:31:57.934 --> 01:32:03.934
air ride sorry arid and semi
arid conditions.

1178
01:32:03.934 --> 01:32:11.000
>> Okay there's another question
for you.

1179
01:32:11.000 --> 01:32:13.000
Has any of your work looked at
arsenic sequestration

1180
01:32:13.000 --> 01:32:16.000
from arsenic contamination in
groundwater?

1181
01:32:16.000 --> 01:32:21.000
>> Yes. We ourselves have not
worked on that,

1182
01:32:21.000 --> 01:32:25.000
it is interesting that some
plant species, have an amazing
ability

1183
01:32:25.000 --> 01:32:31.000
to hyper accumulate arsenic, so
it was shown a number of

1184
01:32:31.000 --> 01:32:37.000
years ago, some frond species
can accumulate arsenic in

1185
01:32:37.000 --> 01:32:43.000
their fronds, a thousandfold
higher then in this case the
soil that

1186
01:32:43.000 --> 01:32:49.000
they grow in. Can you use
plants, to remove arsenic

1187
01:32:49.000 --> 01:32:53.934
from waters? obviously you would
need those waters to be standing

1188
01:32:53.934 --> 01:32:57.934
so the plants have time, I think
this point was emphasized,

1189
01:32:57.934 --> 01:33:03.934
possibly on Monday that or
actually think

1190
01:33:03.934 --> 01:33:10.000
it was mentioned for
bioremediation you do need time
in order

1191
01:33:10.000 --> 01:33:16.000
to remove say toxic -- toxicants
from soil McKay of waters

1192
01:33:16.000 --> 01:33:20.000
you would want some kind of
standing waters. To date plants
are used

1193
01:33:20.000 --> 01:33:25.000
to remove nitrates and
phosphates and so on from
standing water.

1194
01:33:25.000 --> 01:33:30.000
That is a first step to cleanse
waters.

1195
01:33:30.000 --> 01:33:35.000
I could imagine that would be
possible but if it were say a
flowing river,

1196
01:33:35.000 --> 01:33:41.000
then the tide would be another
issue.

1197
01:33:41.000 --> 01:33:44.000
>> That is a good point. I
remember a few years ago going
to a Superfund

1198
01:33:44.000 --> 01:33:49.000
site Virginia that was using the
using those funds and in sorry
ferns

1199
01:33:49.000 --> 01:33:51.000
the fronds in an apple orchard.

1200
01:33:51.000 --> 01:33:53.934
>> Yes.

1201
01:33:53.934 --> 01:33:59.934
>> I don't see any more
questions right now,

1202
01:33:59.934 --> 01:34:02.934
going to open it up if anyone
has questions for either Claudia
or

1203
01:34:02.934 --> 01:34:09.000
Julian you can submit the
question be sure to let us know

1204
01:34:09.000 --> 01:34:14.000
in your question who the
question is directed to and we

1205
01:34:14.000 --> 01:34:19.000
will read those allowed to the
presenters.

1206
01:34:19.000 --> 01:34:24.000
While we are waiting to see if
anyone submit any more questions

1207
01:34:24.000 --> 01:34:29.000
I will ask the speakers do you
have

1208
01:34:29.000 --> 01:34:45.000
any closing remarks? or last
things you would like to share
with us?

1209
01:34:45.000 --> 01:34:48.000
>> Okay.

1210
01:34:48.000 --> 01:34:50.000
I'm not seeing any more
questions coming in, I want

1211
01:34:50.000 --> 01:34:55.000
to thank both of our presenters,
for two wonderful presentations

1212
01:34:55.000 --> 01:35:00.000
today as I mentioned will be
archived.

1213
01:35:00.000 --> 01:35:03.000
I guess before we conclude I
want to make sure that everyone
is aware

1214
01:35:03.000 --> 01:35:09.067
and have seen the related URL
pod, it's on your screen above
the

1215
01:35:09.067 --> 01:35:15.067
Q&A pod it contains the seminar
resources,

1216
01:35:15.067 --> 01:35:17.067
linked to seminar feedback, and
a seminar resources page we

1217
01:35:17.067 --> 01:35:21.067
have assembled resources
directly applicable

1218
01:35:21.067 --> 01:35:24.067
to today's topic. These
resources will stay with the
seminar indefinitely.

1219
01:35:24.067 --> 01:35:32.067
You can access them after the
presentation.

1220
01:35:32.067 --> 01:35:35.067
We place a seminar in an archive
with the soundtrack so you can
access

1221
01:35:35.067 --> 01:35:38.067
the slides and hear the
presentation as it was given
today. Also and

1222
01:35:38.067 --> 01:35:40.067
not related URL pod you found it
you will find an online seminar

1223
01:35:40.067 --> 01:35:45.067
feedback form. Yes you consider
filling out the feedback form,
as

1224
01:35:45.067 --> 01:35:48.067
we do look at your comments when
we continue to try to improve

1225
01:35:48.067 --> 01:35:51.067
the content and delivery
mechanism.

1226
01:35:51.067 --> 01:35:55.000
We can also request a
confirmation email from the
feedback page, for

1227
01:35:55.000 --> 01:35:58.000
your participation in today's
event.

1228
01:35:58.000 --> 01:36:03.000
I also want to remind
participants various ways

1229
01:36:03.000 --> 01:36:07.000
to stay connected to you don't
miss any webinars. For more
information

1230
01:36:07.000 --> 01:36:11.000
on EPA's webinars refute -- go
to the website and you can
subscribe

1231
01:36:11.000 --> 01:36:18.000
to free monthly eat newsletters,
text directs and Facebook,
Twitter

1232
01:36:18.000 --> 01:36:23.000
and LinkedIn. We encourage you
to visit the SRP risk learning
page

1233
01:36:23.000 --> 01:36:30.000
for upcoming sessions. You can
also follow SRP on Twitter.

1234
01:36:30.000 --> 01:36:35.000
We want to encourage everyone to
join us on October 11 at 1 PM
Eastern

1235
01:36:35.000 --> 01:36:36.000
time, for session for the
series.

1236
01:36:36.000 --> 01:36:40.000
The third session will highlight
new and emerging tools for
improving

1237
01:36:40.000 --> 01:36:44.000
existing bioremediation such as
nanotechnology, novel filled

1238
01:36:44.000 --> 01:36:47.000
approaches and machine learning.

1239
01:36:47.000 --> 01:36:51.934
You will hear from Doctor
Alvarez from Rice University,
Laura

1240
01:36:51.934 --> 01:36:57.934
from the SRP funded small
business and of microbial
insight, and a

1241
01:36:57.934 --> 01:37:02.934
lien from the SRP funded small
business micro

1242
01:37:02.934 --> 01:37:07.000
V biotechnology. If you like to
join this webinar you can
register

1243
01:37:07.000 --> 01:37:10.000
now. You could not attend
archives we made available after
the event.

1244
01:37:10.000 --> 01:37:16.000
We encourage you to come back to
the stages for links to the
archives

1245
01:37:16.000 --> 01:37:18.000
after the webinars are held.

1246
01:37:18.000 --> 01:37:27.000
With that, it's time to clued
today's webinar, it's if you
submitted

1247
01:37:27.000 --> 01:37:30.000
a online question, and we do not
get to it, we will pass

1248
01:37:30.000 --> 01:37:32.000
those questions on to the
presenters so they can follow
up, if you want

1249
01:37:32.000 --> 01:37:34.000
or have additional questions
after the presentation you can
contact

1250
01:37:34.000 --> 01:37:39.000
the presenters via email, their
email addresses on the seminar
homepage,

1251
01:37:39.000 --> 01:37:44.000
on the main registration page
were

1252
01:37:44.000 --> 01:37:49.000
you signed up. I want to thank
our presenters, Claudia and
Julian for

1253
01:37:49.000 --> 01:37:54.934
their time preparing and sharing
the presentation without.

1254
01:37:54.934 --> 01:37:55.934
That concludes today's
presentation,

1255
01:37:55.934 --> 01:37:58.934
think you for your
participation.

1256
01:37:58.934 --> 01:38:01.934
Enjoy the rest of your day.

1257
01:38:01.934 --> 01:38:07.000
>> Thank you.