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- Good afternoon everyone

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and thank you, Dr. Scottgale.

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Welcome everyone to our final talk

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of the 43rd Darwin festival.

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And just to remind you
today is alumni day.

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Traditionally, over the last 43 years,

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the final day of the talk is alumni day.

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Some of you in the
audience may not be aware

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why do we always have this in February.

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It's to time the talk
with Darwin's birthday,

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which this year didn't quite overlap

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because his birthday's tomorrow

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and most of us perhaps

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would be doing something
else on a Saturday.

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Just a quick reminder, before
I introduce an alumnus,

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Mr. Peter Shearstone.

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That if you're a student, a bio student

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in the, Mya Hall building.

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After this talk, if you wanted
to come down to the lab,

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Mya Hall 414,

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I've got some little surprises
that I'll be giving away.

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With that let me introduce
Mr. Peter Shearstone.

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As I said, Peter is an alumnus.

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He graduated in 1989 through
our biology department.

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He joined Thermo Fisher Scientific in 2018

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as vice-president for global
quality and regulatory affairs,

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and is responsible for leading

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the company's global QARA team,

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which consists of over 8,000 employees.

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Thermo Fisher Scientific is headquartered

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in Waltham, Massachusetts,

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and is the world leader
in serving science.

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Their mission being to enable customers

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to make the world a healthier,
cleaner, and safer place.

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As I said, Peter graduated from bio,

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our biology department in
1989 with a BS in biology

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and through his company,
Thermo Fisher Scientific,

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they are providing support

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for the Darwin festival
over the next five years,

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enabling us to provide these talks

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to the broader north
shore of Massachusetts.

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Peter, thank you very
much for joining us today.

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- Dr. Fisher, thank you so much.

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It's an honor.

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And I had to do some quick math

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to realize that when I was at Salem,

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I was attending the sixth and
seventh and eighth and ninth

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annual Darwin festival.

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So obviously completely
thrilled and honored

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to be here today and to provide
support for the festival.

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Really honored to introduce you all

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to Dr. Laranjo and to Sydney.

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And let me just give you a quick,

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some quick comments on both of them.

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Dr. Laranjo is an assistant professor

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at Salem State University
biology department.

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She's in her third year
with the university.

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She obtained her
undergraduate biology degree

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from UMass Lowell.

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So another state college grad.

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And joined Salem state from Brandis

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where she completed her work on her PhD.

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Born and raised in Brazil,

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she came to the us when she was 15

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and whilst working on her
bachelor's degree at Lowell,

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worked in a neurobiology lab,

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was part of a national
science foundation research

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experience for undergraduates program REU,

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and internship, rather, excuse me.

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And that's what inspired her
really to pursue her PhD.

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Whilst at Brandis, she
did quite a bit of work

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and research in a genetics lab,

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studying a specific
class of mutation created

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during DNA replication.

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And in her lab at Salem State,

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she's very fortunate to work
with a team of incredible

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students who are just
as curious as she is.

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And I will say personally,
curiosity is what you know,

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gets many of us started, I
think on the science track.

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Her goal and the team's goal
is to continue their work,

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to test FDA approved drugs
for their ability to cause

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specific types of DNA mutation

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using bacteria as a model organism.

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And they're also collaborating

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with Framingham State University,

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which is also very good to see for see.

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For Sydney, she's a 2021 Salem
State graduate in biology.

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She's also a graduate
of Malden High School

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and was a research assistant
at Tufts University,

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actually during her senior
year of high school,

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where I suppose Sydney,

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that's probably where you got the bug,

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like many of us did.

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You've been a resident
assistant at Salem State

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as well as a supplemental
instructor in organic chemistry.

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I wish I had known you back in 1985,

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when I took organic chem,

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you could have probably
helped me a bit there.

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And she's worked with Dr.
Laranjo on her honors project

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and is co-presenting our topic today,

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which is investigating FDA
approved anti-tumor drugs

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for effects on template-switch
mutagenesis or TSM in E coli.

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So please join me in welcoming
Dr. Laranjo and Sydney.

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Thank you.

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- Thank you so much for
such a nice introduction.

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And start over here.

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And good afternoon everyone.

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I am very happy, very excited to be here,

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sharing with you a bit about our work.

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And as was mentioned,

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we're gonna talk a little bit
out FDA drugs and the effect

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on a specific class of mutation.

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But maybe just, maybe you
don't think about cells

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and DNAs every day.

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So I wanna give you a little
bit of a background reminder.

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So maybe you've seen a picture
like this in your fifth

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or sixth, seventh grade biology class,

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or even in a intro bio class.

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We know here we have a cell,

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the smallest structural and
function unit of an organism.

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We learn in school that
inside of the cell,

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we have the nucleus and in the
nucleus, we have chromosomes,

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which are lots of DNA
wrapped around proteins

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in a way that will fit inside of the cell.

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When we look into DNA,

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we know that DNA is composed
of two chains that coiled

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around each other.

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They form what we call a double helix,

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and that carries the genetic instructions

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that is used for growth,
development, functioning,

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reproduction of all known organisms.

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But sometimes they make
mistakes and those mistakes,

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we call DNA mutations
and any of these mistakes

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have the potential to
change the genetic code.

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So you can see here that a lot can happen,

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and there's so much going on in the DNA

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that it opens up good amount
of field for us to study,

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and a lot of questions for us to ask.

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I have here simplified what
we call a replication fork.

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So if you look it over here,

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we have our DNA and our
DNA needs to be replicated

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because if it doesn't
replicate the cell will die.

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And it needs to replicate.

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And it opens, the two strands opens up.

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And at that point it's
called replication fork.

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It doesn't really look like
any fork I have at home,

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to be honest, but this
is what people named it.

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And you can see that there is
some specific characteristics

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of this replication and fork.

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We know that it has directions,

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and this is very important
because it allows us to track,

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which strand is which.

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And we give names to them,

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one, the leading strand and
the other, the lagging strand.

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So the leading strand is also known

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as the continuous strand.

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The replication process
here is gonna be smoother.

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The lagging strand is still
going to be replicated,

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but other players are gonna
have to come and join us.

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But when you look at the DNA and you look

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at the replication process,

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here is a very simplified cartoon,

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but you can see that there
is a lot of players here.

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So all of this players
here brings the question,

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how does all work?

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How do they all work together?

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And how do they work to be doing
what they're supposed to do

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and not have that many mutations?

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So the first time that I
got interested in the topic,

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I was in high school,

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and I just kept thinking
things have a place to go,

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they have a function to do,

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and why are some things
going out of control?

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So you also call that a
perfectionist mentality,

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which is bad and good.

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But I took the good part of it,

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and I decided to use to
investigate how the DNA uses

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its resources to be perfect,

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to do what is supposed to do,

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to replicate in the way
it's supposed to do.

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But it also brought up the question,

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what if you make a mistake?

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We all make mistakes our
DNA is gonna make mistakes.

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How is it going to get fixed?

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And that is where my passion
for DNA mutations is started.

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And it's interesting
because as Peter mentioned,

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I worked in a neurobiology lab.

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I truly enjoy my time there,

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but it was a totally different project.

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And I just wanted to bring this
up because sometimes you are

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not going to get the opportunity
to work on the thing you

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are passionate about right away.

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But take, take any opportunity
and make the best of it,

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use those skills.

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So then when it comes the
time that you can study

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what you want, you are ready for it.

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So that's what I did. But
let's talk about mutations.

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I started out wondering how
often these mutations happen.

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Is it something that
the DNA has to do at it

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on a daily basis or every so often?

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And that's not something

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we think about it all the time, right.

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But actually they happen much
more often than we think.

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For example, the human genome is estimated

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to go through 10 to the
16 power DNA mutations

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in a single day.

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Of course, a lot of them
are going to be corrected.

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Most of them otherwise, death and diseases

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will happen much more often.

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But even though they have
great mechanisms to fix,

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it is still estimated that
we have around 8,000 human

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diseases caused because of DNA mutations.

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So think about it like this.

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If you are an undergrad
and you are working

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for or five years in your degree,

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that estimates you to have
about two times 10 to the 19th

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power of mutational events
during your college career.

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So that is a lot of
mutations that can happen.

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And this number can be
increased by lack of nutrients.

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If you don't have health,

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food habits by stress
and so on, lack of sleep.

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And if you are professor,

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I wanted to think about every
10 years of you teaching.

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Every 10 years of you
teaching is estimated

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that in that timeframe,

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you're gonna go through 3.7 times 10

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to the 19th power of mutation.

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And also this number can be increased

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by stress, lack of sleep, and so on.

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Now maybe you'll like, yeah,

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those are a lot of zeros
afterwards, a lot of numbers,

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but that's a lot of
years and a lot of days.

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Well, just during this talk
that I'm giving to you, our DNA,

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mine and yours can go through up

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to 4.2 times 10 to the
14th power mutations.

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My point is mutations happen very often

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and that number just keep going up,

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but there are different
sources of DNA mutations,

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and that is why it's
difficult to study them.

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If there was one source,

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we could all study that together

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and we could make that work.

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But a lot of things can
cause DNA mutations.

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Some of those sources are
external and others are indulgent.

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So for example, a viral infection,
radiation, chemotherapy,

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things from the outside
that will affect your DNA.

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And when we talk about radiation,

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we talk about daily radiation too.

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For example, if you are
flying a lot in airplanes,

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you are exposed a lot more to radiation

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than if you're don't, if you were here.

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But there are other sources
that are inside of the cell,

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such as replication, stress,

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and other things that
happen in a regular basis.

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But the source that I was interested,

256
00:11:51,280 --> 00:11:53,250
it's actually not really talked about.

257
00:11:53,250 --> 00:11:56,110
And people are studying more and more now,

258
00:11:56,110 --> 00:11:59,080
but it's not in the diagrams
as you can see here.

259
00:11:59,080 --> 00:12:02,070
And it, we don't know much about it.

260
00:12:02,070 --> 00:12:04,630
So I fixed this diagram. There you go.

261
00:12:04,630 --> 00:12:07,450
We're gonna talk about
secondary structures

262
00:12:07,450 --> 00:12:10,460
as a source of DNA mutation.

263
00:12:10,460 --> 00:12:14,410
So secondary structures are
shapes that the DNA takes

264
00:12:14,410 --> 00:12:17,080
and they can make funny figures like this.

265
00:12:17,080 --> 00:12:20,940
We are gonna focus on this
type of structure here.

266
00:12:20,940 --> 00:12:24,010
It's called a hairpin loop,
'cause it looks like a hairpin.

267
00:12:24,010 --> 00:12:27,340
And as you can see, we are
always gonna look at directions,

268
00:12:27,340 --> 00:12:30,430
but this is a shape that the DNA can make

269
00:12:30,430 --> 00:12:32,713
based on how it is formed.

270
00:12:33,880 --> 00:12:37,930
So I'm going to explain to you
how these shapes are created

271
00:12:37,930 --> 00:12:41,840
by giving you an example of
something that you'll probably

272
00:12:41,840 --> 00:12:46,240
know more about it than
DNA, and which is tacos.

273
00:12:46,240 --> 00:12:50,010
Taco is a very, very delicious, it's great

274
00:12:50,010 --> 00:12:52,030
and what does that have to do with DNA?

275
00:12:52,030 --> 00:12:55,060
Well, if you look at this taco cat,

276
00:12:55,060 --> 00:12:57,670
the spell backward is also taco cat.

277
00:12:57,670 --> 00:13:00,420
That's a palindrome that
we talk about here, right.

278
00:13:00,420 --> 00:13:03,200
But in DNA, the basis of DNA,

279
00:13:03,200 --> 00:13:07,820
we use letters for those to
identify each phase and they can

280
00:13:07,820 --> 00:13:11,270
also be in a setting that they are going

281
00:13:11,270 --> 00:13:15,770
to look like palindromes
or quasi, semi-palindromes.

282
00:13:15,770 --> 00:13:18,320
So the letters can be
organized in the same way

283
00:13:18,320 --> 00:13:21,570
that it's going to be the
same letters backwards or not.

284
00:13:21,570 --> 00:13:26,360
And those structures have been
linked to forming hairpins.

285
00:13:26,360 --> 00:13:29,990
So regions of palindromes in the DNA

286
00:13:29,990 --> 00:13:33,523
form the structures that we call hairpin.

287
00:13:34,860 --> 00:13:36,650
And if you think about it,

288
00:13:36,650 --> 00:13:40,230
there's different forms of
hairpin, there's different sizes.

289
00:13:40,230 --> 00:13:42,110
But if you have one in the cell,

290
00:13:42,110 --> 00:13:44,210
you wanted to replicate your DNA.

291
00:13:44,210 --> 00:13:47,430
And as I mentioned, you
want to replicate the DNA

292
00:13:47,430 --> 00:13:49,120
exactly like it was before.

293
00:13:49,120 --> 00:13:50,950
You don't wanna add any mutations.

294
00:13:50,950 --> 00:13:52,270
You don't wanna delete anything.

295
00:13:52,270 --> 00:13:54,840
You don't wanna change
the genetic configuration.

296
00:13:54,840 --> 00:13:59,710
You wanted to have this hairpin
be duplicated and so on.

297
00:13:59,710 --> 00:14:01,620
You want all the same thing.

298
00:14:01,620 --> 00:14:03,370
And in order for you to do that,

299
00:14:03,370 --> 00:14:06,330
you're going to look at
one of them as a template.

300
00:14:06,330 --> 00:14:08,870
So this is going to be my hairpin template

301
00:14:08,870 --> 00:14:13,160
and the cell is gonna wanna do
over and over the same thing.

302
00:14:13,160 --> 00:14:16,420
Sometimes if you change the template

303
00:14:16,420 --> 00:14:18,300
you can have problems.

304
00:14:18,300 --> 00:14:20,080
I'll give you an example.

305
00:14:20,080 --> 00:14:22,840
Imagine you are in an
art class and you receive

306
00:14:22,840 --> 00:14:26,900
an assignment and your
assignment is to draw a horse.

307
00:14:26,900 --> 00:14:30,950
You receive from the professor,
a template of a horse.

308
00:14:30,950 --> 00:14:32,410
And you know, you're
gonna draw this horse.

309
00:14:32,410 --> 00:14:34,440
You know, you can see everything there.

310
00:14:34,440 --> 00:14:36,250
You're like, okay, I have the template.

311
00:14:36,250 --> 00:14:37,890
I can just trace it.

312
00:14:37,890 --> 00:14:39,680
This is what I'm going to do.

313
00:14:39,680 --> 00:14:43,730
And then you leave your assignment
at a table and maybe you

314
00:14:43,730 --> 00:14:45,960
have a toddler at home like me

315
00:14:45,960 --> 00:14:48,160
and you forget that you
leave your assignment there.

316
00:14:48,160 --> 00:14:50,480
The toddler comes and it changes

317
00:14:50,480 --> 00:14:52,820
that template to something similar.

318
00:14:52,820 --> 00:14:56,010
A horse is nice, but how about unicorn?

319
00:14:56,010 --> 00:14:58,830
Very similar, very similar to a horse,

320
00:14:58,830 --> 00:15:01,360
but not the initial one.

321
00:15:01,360 --> 00:15:04,020
So now instead of using
the horse as the template,

322
00:15:04,020 --> 00:15:05,930
you ended up using the unicorn.

323
00:15:05,930 --> 00:15:08,720
You submit this assignment
and it doesn't matter how nice

324
00:15:08,720 --> 00:15:13,190
this your unicorn looks. It
is not what you was asked for.

325
00:15:13,190 --> 00:15:15,690
Same thing goes with the hairpins.

326
00:15:15,690 --> 00:15:19,660
These hairpins have been
associated with a mutation that it

327
00:15:19,660 --> 00:15:23,000
changes from a quasi palindrome

328
00:15:23,000 --> 00:15:25,330
to a perfect palindrome.

329
00:15:25,330 --> 00:15:26,730
What does that mean?

330
00:15:26,730 --> 00:15:30,370
It means a region of the
DNA that formed this hairpin

331
00:15:30,370 --> 00:15:34,980
had an imperfect hairpin,
a quasi almost palindrome.

332
00:15:34,980 --> 00:15:37,240
Something happened, a mutation happens

333
00:15:37,240 --> 00:15:38,900
and it makes it a perfect.

334
00:15:38,900 --> 00:15:40,860
And just because it's
perfect and it's nice,

335
00:15:40,860 --> 00:15:44,850
like the unicorn does not
mean it's what we wanted to.

336
00:15:44,850 --> 00:15:47,300
So even though it's a perfect palindrome,

337
00:15:47,300 --> 00:15:49,060
that is a mutation.

338
00:15:49,060 --> 00:15:52,186
And this mutations
correlated with the structure

339
00:15:52,186 --> 00:15:55,306
can have very implications,

340
00:15:55,306 --> 00:15:59,020
many implications in
human health and so on.

341
00:15:59,020 --> 00:16:00,090
So I'm gonna tell you,

342
00:16:00,090 --> 00:16:03,590
how do we believe that
this mutation happens

343
00:16:03,590 --> 00:16:07,393
going from a quasi palindrome
to a perfect palindrome.

344
00:16:09,130 --> 00:16:13,000
So this model was first
proposed by Ripley in 1982.

345
00:16:13,000 --> 00:16:14,870
So not that long ago.

346
00:16:14,870 --> 00:16:19,070
Here we have a DNA strand and this shapes,

347
00:16:19,070 --> 00:16:22,740
they pretend that each one
of them is a base of the DNA.

348
00:16:22,740 --> 00:16:27,150
And we have a region of
an imperfect hairpin here.

349
00:16:27,150 --> 00:16:28,560
Our DNA opens up,

350
00:16:28,560 --> 00:16:31,440
and DNA polymerase is
gonna come to replicate

351
00:16:31,440 --> 00:16:32,713
that strand of the DNA.

352
00:16:33,660 --> 00:16:36,920
It's going through and
then for some reason,

353
00:16:36,920 --> 00:16:40,810
when you reach the region
of an imperfect hairpin,

354
00:16:40,810 --> 00:16:45,010
the polymerase can momentarily
disassociate from the fork.

355
00:16:45,010 --> 00:16:47,360
It doesn't run away
and go to other places,

356
00:16:47,360 --> 00:16:48,790
but it stays nearby.

357
00:16:48,790 --> 00:16:52,240
But it's no longer
attached to this new strand

358
00:16:52,240 --> 00:16:54,490
that it was just making it out.

359
00:16:54,490 --> 00:16:56,910
And it's no longer
attached to the template

360
00:16:56,910 --> 00:16:59,683
that it was using it to
make this new strand,

361
00:17:00,550 --> 00:17:03,290
because this is a region of a hairpin,

362
00:17:03,290 --> 00:17:06,640
because of the configuration
of how the basis work.

363
00:17:06,640 --> 00:17:11,150
This strand can now fold on itself

364
00:17:11,150 --> 00:17:13,910
because of its nature of the basis.

365
00:17:13,910 --> 00:17:15,710
When it folds on the
top and on the bottom,

366
00:17:15,710 --> 00:17:18,850
we also call this a cruciform structure,

367
00:17:18,850 --> 00:17:23,260
but you can see here, it
starts to fold on itself.

368
00:17:23,260 --> 00:17:25,530
And at this point, or at some point,

369
00:17:25,530 --> 00:17:27,170
the polymerase has to come back.

370
00:17:27,170 --> 00:17:29,400
It has to finish replication.

371
00:17:29,400 --> 00:17:33,910
But at some frequency
instead of the polymerase,

372
00:17:33,910 --> 00:17:36,150
returning to your original template

373
00:17:36,150 --> 00:17:39,180
and continue to making this new DNA.

374
00:17:39,180 --> 00:17:43,670
At some frequency, it can bind
to the new strand instead.

375
00:17:43,670 --> 00:17:48,310
And now it's going to use the
new strand as the template.

376
00:17:48,310 --> 00:17:52,070
So as if the bottom is the
horse, the top is the unicorn.

377
00:17:52,070 --> 00:17:53,560
You're trying to make the horse,

378
00:17:53,560 --> 00:17:55,850
but now you went to the wrong one.

379
00:17:55,850 --> 00:17:58,960
And now it's gonna use the new
template as much as it can.

380
00:17:58,960 --> 00:18:00,730
Because this is a new strand,

381
00:18:00,730 --> 00:18:03,940
you can see it's we don't
have any more bases here.

382
00:18:03,940 --> 00:18:07,700
So it's going to go and
replicate and then, okay.

383
00:18:07,700 --> 00:18:09,590
What do I do now?

384
00:18:09,590 --> 00:18:14,030
Polymerase will have to jump
back and switch template again

385
00:18:14,030 --> 00:18:16,850
in order to proceed with the replication.

386
00:18:16,850 --> 00:18:19,453
So this is the second template switch.

387
00:18:20,300 --> 00:18:23,540
This forms where we know this,
the cruciform is structure.

388
00:18:23,540 --> 00:18:27,760
That repair mechanisms are
gonna come here and help resolve

389
00:18:27,760 --> 00:18:31,850
this structure and allow the
DNA to proceed the replication.

390
00:18:31,850 --> 00:18:34,050
But once all of that happens,

391
00:18:34,050 --> 00:18:38,210
we will ended up with
template switch mutations.

392
00:18:38,210 --> 00:18:41,670
We started with an
imperfect, a quasi palindrome

393
00:18:41,670 --> 00:18:45,080
and we ended up having
a perfect palindrome.

394
00:18:45,080 --> 00:18:47,940
So even though it's
perfect, it is a mutation

395
00:18:47,940 --> 00:18:49,943
and that is not great.

396
00:18:50,790 --> 00:18:53,570
These mutations and
clusters of these mutations

397
00:18:53,570 --> 00:18:56,968
have been seen from bacterias to humans,

398
00:18:56,968 --> 00:19:00,800
which suggests that it's
a conserved mechanism.

399
00:19:00,800 --> 00:19:03,860
So in bacteria, in some
regions of the DNA,

400
00:19:03,860 --> 00:19:06,630
that more mutations
happen more than others.

401
00:19:06,630 --> 00:19:08,700
We call those hotspots.

402
00:19:08,700 --> 00:19:12,510
We can see 500 times more
template switch mutations

403
00:19:12,510 --> 00:19:14,410
than in other locations.

404
00:19:14,410 --> 00:19:16,490
We also looked at in yeast and in yeast,

405
00:19:16,490 --> 00:19:20,680
we see that it's induced during
double strand break repair.

406
00:19:20,680 --> 00:19:24,120
And if you break your double
strand DNA, you have to fix it.

407
00:19:24,120 --> 00:19:26,859
If you don't fix it,
the cell's going to die.

408
00:19:26,859 --> 00:19:27,692
And in humans,

409
00:19:27,692 --> 00:19:30,490
a few diseases have already
been associated with them.

410
00:19:30,490 --> 00:19:33,782
53 related cancer edemas,

411
00:19:33,782 --> 00:19:37,828
and the most known one is
the osteogenesis imperfecta.

412
00:19:37,828 --> 00:19:41,610
So we are still understanding
exactly what cascade

413
00:19:41,610 --> 00:19:43,240
of events is set.

414
00:19:43,240 --> 00:19:45,740
But we do know the
template switch mechanisms,

415
00:19:45,740 --> 00:19:49,730
do play a role into
all of these mutations.

416
00:19:49,730 --> 00:19:51,400
So at this,

417
00:19:51,400 --> 00:19:53,470
at the time I was at Brandeis University

418
00:19:53,470 --> 00:19:55,160
doing my graduate work

419
00:19:55,160 --> 00:19:58,920
and I had the honor to work on creating

420
00:19:58,920 --> 00:20:02,390
and testing quasi palindrome
mutation reporters.

421
00:20:02,390 --> 00:20:05,480
So reporters is something
that it's gonna tell you

422
00:20:05,480 --> 00:20:06,950
what it does, right.

423
00:20:06,950 --> 00:20:11,580
So we tested many and we
were able to find reporters.

424
00:20:11,580 --> 00:20:12,850
They are very specific.

425
00:20:12,850 --> 00:20:15,423
We created them high accuracy,

426
00:20:16,500 --> 00:20:20,779
and we made reporters that
will tell us how often,

427
00:20:20,779 --> 00:20:24,200
what is the rate of quasi palindrome

428
00:20:24,200 --> 00:20:26,440
temple switch mutations.

429
00:20:26,440 --> 00:20:31,300
So this is how Dr. Lovett
designed these reporters.

430
00:20:31,300 --> 00:20:32,660
She chose her favorite gene.

431
00:20:32,660 --> 00:20:35,470
So that stands for your favorite gene.

432
00:20:35,470 --> 00:20:38,680
So she chose her favorite
gene and she found a region

433
00:20:38,680 --> 00:20:41,590
that already had a
perfect palindrome there.

434
00:20:41,590 --> 00:20:46,300
So we added four base pairs
into each side of the hairpin.

435
00:20:46,300 --> 00:20:48,560
So in this case here,
I only have a one side,

436
00:20:48,560 --> 00:20:50,580
but we did a reporter for the leading

437
00:20:50,580 --> 00:20:52,070
and the lagging strand.

438
00:20:52,070 --> 00:20:53,963
So because it has directions,

439
00:20:54,970 --> 00:20:57,580
we could make a reporter for each side.

440
00:20:57,580 --> 00:21:01,080
So once we put this four
base pair insertion here,

441
00:21:01,080 --> 00:21:02,870
this gene is no longer active.

442
00:21:02,870 --> 00:21:05,670
So the cell, the colony,

443
00:21:05,670 --> 00:21:07,130
the culture that we're gonna played,,

444
00:21:07,130 --> 00:21:09,580
can only survive at specific conditions,

445
00:21:09,580 --> 00:21:12,980
if something happens and
make this gene good again.

446
00:21:12,980 --> 00:21:17,420
And the only way for us to
remove these four base pairs here

447
00:21:17,420 --> 00:21:19,380
in this gene, in E coli,

448
00:21:19,380 --> 00:21:22,300
is if it goes through a
template switch mutation.

449
00:21:22,300 --> 00:21:26,100
So we did that in E coli and in yeast.

450
00:21:26,100 --> 00:21:27,940
In yeast we use directory,

451
00:21:27,940 --> 00:21:30,860
and today I'm not gonna be
talking about this data.

452
00:21:30,860 --> 00:21:35,160
We stay with E coli and
we did this research

453
00:21:35,160 --> 00:21:37,990
where we could see that
the reporter is very,

454
00:21:37,990 --> 00:21:40,770
very accurate in such a
way that all of the times

455
00:21:40,770 --> 00:21:43,090
that we've lost this four base pairs,

456
00:21:43,090 --> 00:21:44,540
the only way possible

457
00:21:44,540 --> 00:21:47,990
is actually going through
a template switch mutation.

458
00:21:47,990 --> 00:21:50,210
So the assay works like this.

459
00:21:50,210 --> 00:21:53,030
We could take, in both
systems we're very similar.

460
00:21:53,030 --> 00:21:54,820
So that's why I wanna just show it here.

461
00:21:54,820 --> 00:21:56,610
But we inoculate the colonies

462
00:21:56,610 --> 00:21:59,450
with appropriate media for each organisms.

463
00:21:59,450 --> 00:22:04,050
And then we either didn't do
any treatment in each of them,

464
00:22:04,050 --> 00:22:08,500
but we also later on treated
with some drugs, the culture,

465
00:22:08,500 --> 00:22:11,590
and then we played it
in a specific plates.

466
00:22:11,590 --> 00:22:13,570
So we played it in plates

467
00:22:13,570 --> 00:22:16,150
that would give us the
total population count.

468
00:22:16,150 --> 00:22:18,290
So for bacteria, it's the lb

469
00:22:18,290 --> 00:22:21,840
and for yeast, we had a
selective marker LEU2.

470
00:22:21,840 --> 00:22:24,140
So in this plates here,
everything would grow.

471
00:22:24,140 --> 00:22:25,650
We had all the nutrients,

472
00:22:25,650 --> 00:22:27,610
everything is gonna be happy and grow.

473
00:22:27,610 --> 00:22:30,053
So we know how much we have it.

474
00:22:31,100 --> 00:22:33,150
And we also played in specifically plate,

475
00:22:33,150 --> 00:22:35,620
and this place are called selective plate

476
00:22:35,620 --> 00:22:38,540
because they selected for the mutation.

477
00:22:38,540 --> 00:22:42,450
Because only the cells that
went through a template,

478
00:22:42,450 --> 00:22:46,230
switch mutation and activated
that gene that we deleted

479
00:22:46,230 --> 00:22:50,400
is now going to, the gene
that we made it inactive.

480
00:22:50,400 --> 00:22:53,870
It's now going to grow.

481
00:22:53,870 --> 00:22:55,970
So here will be a LacMin.

482
00:22:55,970 --> 00:22:59,270
So there is no lactose
here unless lacZ gene

483
00:22:59,270 --> 00:23:02,050
is activated again, the
cells are not gonna grow.

484
00:23:02,050 --> 00:23:04,080
And the same here was for the URA.

485
00:23:04,080 --> 00:23:06,970
So URA was inactivated,

486
00:23:06,970 --> 00:23:09,943
as soon as mutation happen
in now activates back in.

487
00:23:11,350 --> 00:23:13,980
We did this multiple,
multiple, multiple times,

488
00:23:13,980 --> 00:23:15,780
and we got some good data.

489
00:23:15,780 --> 00:23:19,780
We published few papers on
this, and we looked at it,

490
00:23:19,780 --> 00:23:22,580
how things inside of the cell

491
00:23:22,580 --> 00:23:25,390
actually prevent this class of mutation.

492
00:23:25,390 --> 00:23:28,170
So we looked at single
strand binding proteins.

493
00:23:28,170 --> 00:23:29,710
We looked at polymerase,

494
00:23:29,710 --> 00:23:32,930
their mutants and saw
that the cell itself tries

495
00:23:32,930 --> 00:23:34,410
to fix this problem.

496
00:23:34,410 --> 00:23:36,610
As I mentioned, many mutations happen,

497
00:23:36,610 --> 00:23:39,230
and we do have things
in the cell that says,

498
00:23:39,230 --> 00:23:41,450
hold on, let's fix it.

499
00:23:41,450 --> 00:23:45,948
But we also noticed that without
some abilities of the cell,

500
00:23:45,948 --> 00:23:49,670
and without some proteins
of the cell working well,

501
00:23:49,670 --> 00:23:52,720
this type of mutation
happens much more often.

502
00:23:52,720 --> 00:23:54,880
And this case here
would be the polymerase.

503
00:23:54,880 --> 00:23:57,760
So we notice that if we
mess up with the polymerase,

504
00:23:57,760 --> 00:24:00,980
now we have more time
for this event to happen

505
00:24:00,980 --> 00:24:02,970
and we see more mutations.

506
00:24:02,970 --> 00:24:05,080
Here in the bottom are
some of the yeast genes

507
00:24:05,080 --> 00:24:07,950
that I screen to see if we would increase

508
00:24:07,950 --> 00:24:10,890
or decrease the frequency of mutation.

509
00:24:10,890 --> 00:24:12,363
And we worked a lot of that.

510
00:24:13,640 --> 00:24:15,470
And then that's when it came the idea.

511
00:24:15,470 --> 00:24:19,740
What if we treat the cell with chemicals

512
00:24:19,740 --> 00:24:24,380
that will make DNA replication be stalled,

513
00:24:24,380 --> 00:24:27,460
or that will interfere
with DNA replication.

514
00:24:27,460 --> 00:24:32,460
Since that our hypothesis is
that if replication stops,

515
00:24:32,470 --> 00:24:34,340
the polymerase leaves the fork,

516
00:24:34,340 --> 00:24:37,400
there is more time for
this event to happen.

517
00:24:37,400 --> 00:24:40,393
Then we could be onto something here.

518
00:24:41,870 --> 00:24:44,630
So we decided to look at
DNA, protein crosslinks

519
00:24:44,630 --> 00:24:48,320
as how they are barriers
to the replication fork.

520
00:24:48,320 --> 00:24:50,210
So DNA proteins crosslinks,

521
00:24:50,210 --> 00:24:52,930
they can be produced
by natural metabolisms,

522
00:24:52,930 --> 00:24:55,490
and they haven't seen in all organisms.

523
00:24:55,490 --> 00:24:57,300
They are a barrier to the fork

524
00:24:57,300 --> 00:24:59,360
because they stop replication.

525
00:24:59,360 --> 00:25:01,530
And if they stop the replication,

526
00:25:01,530 --> 00:25:03,610
now we have a higher chance

527
00:25:03,610 --> 00:25:07,430
of the polymerase leaving the fork.

528
00:25:07,430 --> 00:25:11,580
So then the hypothesis will
be that DNA protein crosslinks

529
00:25:11,580 --> 00:25:14,610
will promote this class of mutation.

530
00:25:14,610 --> 00:25:17,790
There are many normal cellular enzymes.

531
00:25:17,790 --> 00:25:21,160
I have here cytosine
methyltransferase and topoisomerase.

532
00:25:21,160 --> 00:25:24,620
That they are known to
produce covalent complexes

533
00:25:24,620 --> 00:25:27,890
as intermediates in
the reaction mechanism.

534
00:25:27,890 --> 00:25:30,250
So the trapping of these intermediates

535
00:25:30,250 --> 00:25:33,750
would promote covalent
DNA, protein crosslinks.

536
00:25:33,750 --> 00:25:36,860
So I try with five-azacytidine,

537
00:25:36,860 --> 00:25:38,740
which is a anti-cancer drug

538
00:25:38,740 --> 00:25:42,800
and ciprofloxacin which is an antibiotic.

539
00:25:42,800 --> 00:25:44,270
But we also look at aldehydes,

540
00:25:44,270 --> 00:25:49,270
the formaldehydes knows to
fix and trap protein and DNA.

541
00:25:50,090 --> 00:25:52,550
When we look at the data for this,

542
00:25:52,550 --> 00:25:56,190
we saw that five-aza as well as the other,

543
00:25:56,190 --> 00:25:58,240
but I'm show you first five-aza,

544
00:25:58,240 --> 00:26:00,320
induces this class of mutation.

545
00:26:00,320 --> 00:26:02,420
So we can see here,

546
00:26:02,420 --> 00:26:04,880
the leading strand and the lagging strand.

547
00:26:04,880 --> 00:26:05,900
In the leading strand,

548
00:26:05,900 --> 00:26:09,530
we saw a 44 fold increase in mutations

549
00:26:09,530 --> 00:26:13,770
after treating the cells
with a sub lethal dose

550
00:26:13,770 --> 00:26:15,590
of five-azacytidine.

551
00:26:15,590 --> 00:26:19,290
On the lagging strand, we
saw a nine fold increase,

552
00:26:19,290 --> 00:26:21,780
which is still very significant.

553
00:26:21,780 --> 00:26:25,580
Here on the Y-axis, we have
the rates of mutations.

554
00:26:25,580 --> 00:26:28,450
The funny thing is that
this difference between 44

555
00:26:28,450 --> 00:26:30,570
and nine X was very expected,

556
00:26:30,570 --> 00:26:33,310
because in previous work we've shown

557
00:26:33,310 --> 00:26:35,930
that the lagging strand
has a lower number.

558
00:26:35,930 --> 00:26:38,620
Not because it happens less often,

559
00:26:38,620 --> 00:26:40,943
but because of all their proteins

560
00:26:40,943 --> 00:26:43,200
that are involved in the lagging process.

561
00:26:43,200 --> 00:26:44,750
And I'll be happy to answer questions

562
00:26:44,750 --> 00:26:46,810
if you have on that topic.

563
00:26:46,810 --> 00:26:48,640
But we see that, we see a pattern

564
00:26:48,640 --> 00:26:50,940
of five-aza induced mutations.

565
00:26:50,940 --> 00:26:51,900
And from now on,

566
00:26:51,900 --> 00:26:54,370
I'm only gonna show you data
from the leading strand,

567
00:26:54,370 --> 00:26:57,120
but the lagging strand also happens

568
00:26:57,120 --> 00:26:59,770
with the leading strand
being the strongest response.

569
00:27:00,740 --> 00:27:04,040
When I look at formaldehyde
and ciprofloxacin,

570
00:27:04,040 --> 00:27:06,730
I also saw a very large increase.

571
00:27:06,730 --> 00:27:09,040
If you look at this scale
here, this is a log scale.

572
00:27:09,040 --> 00:27:11,680
So 33 fold increase of mutations

573
00:27:11,680 --> 00:27:13,450
after treating formaldehyde,

574
00:27:13,450 --> 00:27:15,800
But with the antibiotics ciprofloxacin

575
00:27:15,800 --> 00:27:19,550
we saw over 670 fold
increase of mutations.

576
00:27:19,550 --> 00:27:21,650
This was by far the highest mutation

577
00:27:21,650 --> 00:27:23,930
that we have ever reported it.

578
00:27:23,930 --> 00:27:27,300
So this, it is very interesting

579
00:27:27,300 --> 00:27:31,670
because we need to more
fully categorize the system

580
00:27:31,670 --> 00:27:33,360
and know more about it and know

581
00:27:33,360 --> 00:27:36,350
the pathways and how
everything is happening.

582
00:27:36,350 --> 00:27:39,870
So at this point I joined Salem State,

583
00:27:39,870 --> 00:27:44,870
and that was awesome because I
talked to my graduate advisor

584
00:27:45,780 --> 00:27:47,630
and I said, I wanna continue doing this.

585
00:27:47,630 --> 00:27:50,970
And I wanna look at FDA approved drugs.

586
00:27:50,970 --> 00:27:52,810
And I wanna look at these drugs because

587
00:27:52,810 --> 00:27:55,590
they are well studied,
we know a lot about them.

588
00:27:55,590 --> 00:27:59,920
I can buy a lot of them
in bulk, in libraries.

589
00:27:59,920 --> 00:28:01,510
And I wanna know, like,

590
00:28:01,510 --> 00:28:05,620
are these drugs interfering
with temple switch mutation?

591
00:28:05,620 --> 00:28:10,480
So I joined Salem State
and I had the best time.

592
00:28:10,480 --> 00:28:14,840
I received a lot of
support from the faculty,

593
00:28:14,840 --> 00:28:16,660
from my supervisors, with the lab.

594
00:28:16,660 --> 00:28:19,980
They say let's do this. I'm so excited.

595
00:28:19,980 --> 00:28:23,690
And my first goal is to
find motivated students.

596
00:28:23,690 --> 00:28:24,850
So I need to find students,

597
00:28:24,850 --> 00:28:29,230
because it's not really super exciting

598
00:28:29,230 --> 00:28:31,440
to say, I'm starting from scratch.

599
00:28:31,440 --> 00:28:33,240
So we're gonna have to read a lot.

600
00:28:33,240 --> 00:28:34,710
We're gonna have to order materials.

601
00:28:34,710 --> 00:28:36,460
We're gonna have to clean up lab.

602
00:28:36,460 --> 00:28:40,080
We're gonna have to
order, order many products

603
00:28:40,080 --> 00:28:41,690
that we might not have it now.

604
00:28:41,690 --> 00:28:44,740
At the same time, my colleague Dr. Brown

605
00:28:44,740 --> 00:28:47,800
was so helpful in sharing
his lab space and say,

606
00:28:47,800 --> 00:28:50,440
I'm gonna make some
space for you. Come here.

607
00:28:50,440 --> 00:28:52,580
I'm gonna help you. It was great.

608
00:28:52,580 --> 00:28:56,120
It was the best welcoming I ever received.

609
00:28:56,120 --> 00:28:58,940
I got the students and I told them, look,

610
00:28:58,940 --> 00:29:02,326
we're gonna have to work from the bottom.

611
00:29:02,326 --> 00:29:04,126
And from now, from that time to now,

612
00:29:05,110 --> 00:29:07,820
I had a 11 students going through my lab.

613
00:29:07,820 --> 00:29:11,040
Out of those 11, eight graduated already.

614
00:29:11,040 --> 00:29:13,260
And some told me I wanna help,

615
00:29:13,260 --> 00:29:15,080
and I don't actually wanna do research.

616
00:29:15,080 --> 00:29:17,270
I wanna learn how to prepare media.

617
00:29:17,270 --> 00:29:20,130
I wanna learn how to write grants.

618
00:29:20,130 --> 00:29:23,970
I wanna learn how to read these
papers that you have to read

619
00:29:23,970 --> 00:29:27,550
in order to propose experiment.
So it was really nice.

620
00:29:27,550 --> 00:29:30,050
So I was like, okay,
find motivated students.

621
00:29:30,050 --> 00:29:33,920
I got that. And you're gonna
meet one of them today, Sydney.

622
00:29:33,920 --> 00:29:36,990
Here Sydney, Becky, they
just graduated from the lab.

623
00:29:36,990 --> 00:29:38,630
I had that down.

624
00:29:38,630 --> 00:29:41,440
Then I had some funding.
I had department funding.

625
00:29:41,440 --> 00:29:43,910
Very generously. I was
very happy about it.

626
00:29:43,910 --> 00:29:46,870
But I told my students, I
wanna work over the summer too,

627
00:29:46,870 --> 00:29:49,680
with just starting. I need a lot of work.

628
00:29:49,680 --> 00:29:51,063
Let's work over the summer.

629
00:29:52,160 --> 00:29:55,110
Myself, I knew that I could not afford

630
00:29:55,110 --> 00:29:58,930
working over the summer in
a lab unless I had funding.

631
00:29:58,930 --> 00:30:02,550
So I told them, let's apply
for funding over the summer.

632
00:30:02,550 --> 00:30:06,830
And I am very happy to share
that both of them got it each

633
00:30:06,830 --> 00:30:10,420
in one summer, the Kathy
Murphy summer research award,

634
00:30:10,420 --> 00:30:15,050
which was essential into
getting the work that they did.

635
00:30:15,050 --> 00:30:17,920
So we started, we did all the controls.

636
00:30:17,920 --> 00:30:19,090
We set up everything,

637
00:30:19,090 --> 00:30:22,020
and all of this a few
months have passed, right.

638
00:30:22,020 --> 00:30:24,230
We set up the controls.
We did all the testing.

639
00:30:24,230 --> 00:30:27,230
We wrote all the proposals.
Everything is good.

640
00:30:27,230 --> 00:30:29,800
Now we are ready to do the research.

641
00:30:29,800 --> 00:30:33,570
We are in lab, we are setting
up, we have everything ready

642
00:30:33,570 --> 00:30:36,500
and this is like March 2020.

643
00:30:36,500 --> 00:30:40,010
I am so happy. Let's do it.

644
00:30:40,010 --> 00:30:43,410
And, you know, what happen in March, 2020.

645
00:30:43,410 --> 00:30:45,549
The pandemic started.

646
00:30:45,549 --> 00:30:46,660
At that point,

647
00:30:46,660 --> 00:30:51,660
we had to get back to the
lab virtually and decide

648
00:30:52,030 --> 00:30:55,090
we need to do something. We
don't wanna stop the research.

649
00:30:55,090 --> 00:30:56,210
How can we adapt?

650
00:30:56,210 --> 00:31:00,837
How can we start working and
get something done before,

651
00:31:01,800 --> 00:31:05,000
going back to the lab. So
it was a big challenge.

652
00:31:05,000 --> 00:31:06,900
And I'm very, very happy

653
00:31:06,900 --> 00:31:09,540
to introduce to you Sydney Addorisio,

654
00:31:09,540 --> 00:31:11,050
that is going to be talking about

655
00:31:11,050 --> 00:31:14,410
what she did during this time.

656
00:31:14,410 --> 00:31:16,613
I'm happy to tell you
that she did return to lab

657
00:31:16,613 --> 00:31:21,613
once the lab was open, but
she did a lot of work remote.

658
00:31:22,100 --> 00:31:24,500
So Sydney did a lot of work
and she'll tell you about it.

659
00:31:24,500 --> 00:31:26,490
But she became a first author

660
00:31:26,490 --> 00:31:28,510
in the paper we published this year.

661
00:31:28,510 --> 00:31:31,200
She won the Kathy Murphy award.

662
00:31:31,200 --> 00:31:34,240
She presented her work
in multiple conferences,

663
00:31:34,240 --> 00:31:35,670
not only Salem State,

664
00:31:35,670 --> 00:31:38,840
but also at the Boston
Bacteria Meeting, SACNAS,

665
00:31:38,840 --> 00:31:41,670
Massachusetts Undergraduate
Research Conference.

666
00:31:41,670 --> 00:31:43,630
She's currently interviewing

667
00:31:43,630 --> 00:31:47,260
for graduate school and
she now works at Biogen.

668
00:31:47,260 --> 00:31:50,540
So I'm gonna let you know
now what she has done

669
00:31:50,540 --> 00:31:53,000
and what is the next
step into our project.

670
00:31:53,000 --> 00:31:55,680
Sydney the floor is yours.

671
00:31:55,680 --> 00:31:57,460
- Thank you, Dr. Laranjo.

672
00:31:57,460 --> 00:32:00,100
So as Dr. Laranjo started to talk about.

673
00:32:00,100 --> 00:32:01,770
You know, we got ready,

674
00:32:01,770 --> 00:32:04,570
we were geared and ready
to go in March of 2020,

675
00:32:04,570 --> 00:32:06,270
and then COVID hit.

676
00:32:06,270 --> 00:32:08,960
So as researchers, we said, you know,

677
00:32:08,960 --> 00:32:11,543
we can't stop here. We need to adapt.

678
00:32:13,660 --> 00:32:16,380
So first what we decided to
do and what we knew we needed

679
00:32:16,380 --> 00:32:18,790
to do was just read, read, read, read,

680
00:32:18,790 --> 00:32:21,110
and learn all about these different drugs.

681
00:32:21,110 --> 00:32:23,210
So this image is actually

682
00:32:23,210 --> 00:32:26,620
an image of the drug library
that Dr Laranjo purchased.

683
00:32:26,620 --> 00:32:29,530
And these were the drugs
that we could possibly choose

684
00:32:29,530 --> 00:32:32,530
to investigate for this
template switch mutation

685
00:32:32,530 --> 00:32:36,100
that Dr. Laranjo just explained.

686
00:32:36,100 --> 00:32:37,830
So we had to learn about
all the different drugs

687
00:32:37,830 --> 00:32:38,937
that we had available to us,

688
00:32:38,937 --> 00:32:41,270
and what their mechanisms of actions were,

689
00:32:41,270 --> 00:32:42,800
or how they worked.

690
00:32:42,800 --> 00:32:44,210
What kind of concentrations

691
00:32:44,210 --> 00:32:46,450
we wanted to possibly study them at.

692
00:32:46,450 --> 00:32:48,750
And we had to take all
of this information,

693
00:32:48,750 --> 00:32:51,630
put it all together to
formulate a hypothesis.

694
00:32:51,630 --> 00:32:54,330
So I came up with a
couple of different drugs

695
00:32:54,330 --> 00:32:55,690
that I wanted to look at,

696
00:32:55,690 --> 00:32:58,890
their concentration, studied
their mechanisms of action.

697
00:32:58,890 --> 00:33:01,140
And under the guidance of Dr. Laranjo,

698
00:33:01,140 --> 00:33:03,200
I was able to take the hypothesis

699
00:33:03,200 --> 00:33:04,960
and all of this information,

700
00:33:04,960 --> 00:33:07,740
formulate it into a
graduate level proposal

701
00:33:07,740 --> 00:33:09,430
with specific aims.

702
00:33:09,430 --> 00:33:11,250
And this is ultimately what became

703
00:33:11,250 --> 00:33:14,420
my honors thesis as an honors graduate.

704
00:33:14,420 --> 00:33:15,600
And on top of all of this,

705
00:33:15,600 --> 00:33:18,820
we were also learning the lab
techniques, the statistics,

706
00:33:18,820 --> 00:33:21,450
about how to calculate
mutation frequencies,

707
00:33:21,450 --> 00:33:24,190
and also how to present those statistics.

708
00:33:24,190 --> 00:33:25,820
You can do all the
statistics in the world,

709
00:33:25,820 --> 00:33:28,170
but if it doesn't make
sense, what's the point.

710
00:33:29,870 --> 00:33:31,880
But also just as a quick little plug,

711
00:33:31,880 --> 00:33:35,000
there was a period of
time that we couldn't be

712
00:33:35,000 --> 00:33:37,200
in the lab at all and it was so far out

713
00:33:37,200 --> 00:33:38,600
that we actually decided to take

714
00:33:38,600 --> 00:33:41,630
advantage of the opportunity
to not be in the lab.

715
00:33:41,630 --> 00:33:44,370
To collaborate with Dr.
Pena and her students

716
00:33:44,370 --> 00:33:46,390
at Framingham State University.

717
00:33:46,390 --> 00:33:48,610
And it was during this
time that we actually wrote

718
00:33:48,610 --> 00:33:52,530
a review article focusing on
oxidative stress and bacteria.

719
00:33:52,530 --> 00:33:54,420
I specifically was reading and learning

720
00:33:54,420 --> 00:33:59,420
about microbial genome regulation
under oxidative stress.

721
00:33:59,700 --> 00:34:03,100
And this process was absolutely amazing.

722
00:34:03,100 --> 00:34:05,320
I mean, I, as well as those other students

723
00:34:05,320 --> 00:34:08,010
got to learn how to
write scientific writing,

724
00:34:08,010 --> 00:34:10,920
that is actually publishable.

725
00:34:10,920 --> 00:34:14,579
Working with people, both
within a lab, people I knew,

726
00:34:14,579 --> 00:34:17,920
as well as people in a lab
that I'd never seen before,

727
00:34:17,920 --> 00:34:20,020
I'd never met them before.

728
00:34:20,020 --> 00:34:22,820
And being able to give
and receive feedback

729
00:34:22,820 --> 00:34:24,730
from people I haven't met before.

730
00:34:24,730 --> 00:34:26,600
And that was super important.

731
00:34:26,600 --> 00:34:29,710
And knowing that we had to
give feedback that was going

732
00:34:29,710 --> 00:34:33,720
to present us as strong scientific writers

733
00:34:33,720 --> 00:34:35,910
and present a really strong piece.

734
00:34:35,910 --> 00:34:38,980
And I am happy to say that this article

735
00:34:38,980 --> 00:34:41,220
has actually been accepted for publication

736
00:34:41,220 --> 00:34:43,540
in the journal, Fine
Focus as of the beginning

737
00:34:43,540 --> 00:34:45,030
of this year.

738
00:34:45,030 --> 00:34:46,660
And as the first author,

739
00:34:46,660 --> 00:34:49,470
I also got the kind of inside scoop

740
00:34:49,470 --> 00:34:52,340
on how the submission process works.

741
00:34:52,340 --> 00:34:55,580
I was able to kind of
be the liaison between

742
00:34:55,580 --> 00:34:59,630
the editors and everybody else,
fielding reviewer comments,

743
00:34:59,630 --> 00:35:02,280
kind of seeing that
sometimes daunting email

744
00:35:02,280 --> 00:35:03,870
with the long list of comments

745
00:35:03,870 --> 00:35:06,293
and having to deal with those as well.

746
00:35:09,090 --> 00:35:11,700
So going back to being in the lab,

747
00:35:11,700 --> 00:35:13,970
and once I was allowed
to be back in the lab.

748
00:35:13,970 --> 00:35:16,230
When I was looking at
this large array of drugs,

749
00:35:16,230 --> 00:35:19,120
I had to think about
where do I wanna start?

750
00:35:19,120 --> 00:35:21,880
There is a lot that goes
into DNA replication,

751
00:35:21,880 --> 00:35:24,050
a lot of proteins and components.

752
00:35:24,050 --> 00:35:27,330
And I had known that Dr.
Laranjo already had some work

753
00:35:27,330 --> 00:35:31,170
out relating to SSBs
as well as polymerase.

754
00:35:31,170 --> 00:35:35,610
But where did I want
to include my research?

755
00:35:35,610 --> 00:35:38,530
What did I want to investigate?

756
00:35:38,530 --> 00:35:41,523
And for me, that answer was topoisomerase.

757
00:35:43,180 --> 00:35:48,180
So topoisomerase are proteins
that actually relieve tension

758
00:35:48,310 --> 00:35:50,960
in the DNA, and there are
two types of topoisomerase.

759
00:35:51,862 --> 00:35:53,960
Topoisomerase one relieves tension

760
00:35:53,960 --> 00:35:56,250
by cutting one strand of the DNA,

761
00:35:56,250 --> 00:35:58,510
whereas topoisomerase two relieves tension

762
00:35:58,510 --> 00:36:00,133
by cutting both strands.

763
00:36:02,990 --> 00:36:05,380
So the first drug I decided
to look at from this drug

764
00:36:05,380 --> 00:36:07,300
library is CPT-11.

765
00:36:07,300 --> 00:36:10,800
And CPT-11 is a
topoisomerase one inhibitor

766
00:36:10,800 --> 00:36:13,180
that is also a chemotherapeutic.

767
00:36:13,180 --> 00:36:15,430
So it kills cells that are dividing a lot

768
00:36:15,430 --> 00:36:18,820
via this inhibition of topoisomerase one.

769
00:36:18,820 --> 00:36:21,950
So it's slightly in more
detailed mechanism of action

770
00:36:21,950 --> 00:36:23,520
is that it traps the DNA

771
00:36:23,520 --> 00:36:26,150
and induces protein-linked DNA breaks

772
00:36:26,150 --> 00:36:29,053
that the cell cannot survive
with, they're cytotoxic.

773
00:36:30,280 --> 00:36:32,170
Now it wasn't just about picking a drug.

774
00:36:32,170 --> 00:36:34,530
We also had to pick at what concentration

775
00:36:34,530 --> 00:36:36,440
I wanted to study these drugs.

776
00:36:36,440 --> 00:36:40,200
And I had picked 0.5 and 1.0 micromolar

777
00:36:40,200 --> 00:36:43,080
as literature sited these concentrations

778
00:36:43,080 --> 00:36:44,570
as being clinically relevant.

779
00:36:44,570 --> 00:36:46,703
So we figured we would start there.

780
00:36:48,800 --> 00:36:50,550
The second drug I decided to investigate

781
00:36:50,550 --> 00:36:52,470
was doxorubicin hydrochloride,

782
00:36:52,470 --> 00:36:55,000
which I will abbreviate
from here on is DOXSO.

783
00:36:55,000 --> 00:36:58,160
And this is a bacterial
topoisomerase two inhibitor.

784
00:36:58,160 --> 00:37:01,070
So cutting the two strands of the DNA.

785
00:37:01,070 --> 00:37:03,320
And this particular drug causes cell death

786
00:37:03,320 --> 00:37:05,340
because it stabilizes intermediates

787
00:37:05,340 --> 00:37:07,940
from the complex between
the topoisomerase two

788
00:37:07,940 --> 00:37:09,060
and the DNA.

789
00:37:09,060 --> 00:37:10,530
But overall, the main takeaway

790
00:37:10,530 --> 00:37:13,170
is that it is stopping replication.

791
00:37:13,170 --> 00:37:14,040
So for this drug,

792
00:37:14,040 --> 00:37:16,460
I decided to investigate
the concentrations

793
00:37:16,460 --> 00:37:21,050
at 0.025 and 0.4 micromolar.

794
00:37:21,050 --> 00:37:23,391
Now these two are quite far apart,

795
00:37:23,391 --> 00:37:25,815
especially compared to CPT,

796
00:37:25,815 --> 00:37:29,270
but it was found in
the literature that 0.4

797
00:37:29,270 --> 00:37:32,290
was a topoisomerase two
poison concentration

798
00:37:32,290 --> 00:37:34,890
without killing the cells
that we were working with.

799
00:37:37,220 --> 00:37:39,650
So just a reminder, Dr. Laranjo spoke

800
00:37:39,650 --> 00:37:41,210
on these mutation assays already.

801
00:37:41,210 --> 00:37:42,310
But just to remind you,

802
00:37:42,310 --> 00:37:45,701
the work I did was specifically in E coli,

803
00:37:45,701 --> 00:37:47,940
had nothing to do with yeast.

804
00:37:47,940 --> 00:37:52,680
And that the lb media allows
for all cells to grow,

805
00:37:52,680 --> 00:37:55,620
so that provided me with a total count.

806
00:37:55,620 --> 00:37:59,220
Whereas the LacMin media you see here

807
00:37:59,220 --> 00:38:02,543
allowed me to count only the
mutants to create a frequency.

808
00:38:04,980 --> 00:38:06,170
So in doing this research,

809
00:38:06,170 --> 00:38:08,640
there are three possible scenarios.

810
00:38:08,640 --> 00:38:11,610
Now the first thing is
that without any drug,

811
00:38:11,610 --> 00:38:14,200
there is always going to
be a baseline frequency

812
00:38:14,200 --> 00:38:15,270
of this mutation type.

813
00:38:15,270 --> 00:38:16,240
It is going to happen,

814
00:38:16,240 --> 00:38:19,720
whether I expose the
E coli to drug or not.

815
00:38:19,720 --> 00:38:23,240
And if we find that the
rate or the frequency,

816
00:38:23,240 --> 00:38:26,700
my apologies of mutation
is similar to the no drug,

817
00:38:26,700 --> 00:38:28,110
or is very close to it.

818
00:38:28,110 --> 00:38:30,310
This just means that we are
gonna have more work to do

819
00:38:30,310 --> 00:38:31,143
in the lab.

820
00:38:31,143 --> 00:38:33,543
More questions need to be
answered and investigated.

821
00:38:34,650 --> 00:38:37,470
The second scenario is
if we find that there is

822
00:38:37,470 --> 00:38:39,950
a three times or greater increase

823
00:38:39,950 --> 00:38:42,660
in the frequency of this mutation type.

824
00:38:42,660 --> 00:38:44,820
And as Dr Laranjo mentioned,

825
00:38:44,820 --> 00:38:48,700
there have been antibiotics
and chemotherapeutics

826
00:38:48,700 --> 00:38:52,170
that have been bound to express
this particular scenario.

827
00:38:52,170 --> 00:38:55,140
And if we found that the drugs
I'm investigating did too.

828
00:38:55,140 --> 00:38:57,150
This is a good thing, we're
adding to the literature,

829
00:38:57,150 --> 00:38:58,623
we're adding to that list.

830
00:38:59,680 --> 00:39:02,690
Now the third scenario is
if there was a three times

831
00:39:02,690 --> 00:39:06,400
or greater decrease compared to

832
00:39:06,400 --> 00:39:07,723
that baseline of mutations

833
00:39:07,723 --> 00:39:10,210
that are going to occur, regardless.

834
00:39:10,210 --> 00:39:13,980
Now this would be groundbreaking
and super fascinating just

835
00:39:13,980 --> 00:39:17,260
because there hasn't
been drug or antibiotic

836
00:39:17,260 --> 00:39:18,600
that has seen this pattern.

837
00:39:18,600 --> 00:39:20,850
So this would be
particularly groundbreaking.

838
00:39:23,242 --> 00:39:27,000
So this is my data. We're
gonna start with CPT-11.

839
00:39:27,000 --> 00:39:30,580
So we see that baseline when
there is no drug exposure

840
00:39:30,580 --> 00:39:33,150
whatsoever, the mutations are happening.

841
00:39:33,150 --> 00:39:35,270
And then once I expose the drug,

842
00:39:35,270 --> 00:39:39,100
the E coli to 0.5 micromolar,
we see a decrease,

843
00:39:39,100 --> 00:39:40,220
and it is quite large.

844
00:39:40,220 --> 00:39:42,650
We're seeing a 2.5 fold decrease.

845
00:39:42,650 --> 00:39:46,200
However, that threefold threshold
that I was talking about,

846
00:39:46,200 --> 00:39:48,100
that is what Dr. Laranjo and I decided

847
00:39:48,100 --> 00:39:49,490
we were going to stick to,

848
00:39:49,490 --> 00:39:51,900
to determine whether a
difference was significant.

849
00:39:51,900 --> 00:39:54,463
And as you can see, 2.5 is not three.

850
00:39:55,700 --> 00:39:59,040
And then when I expose the
E coli to 1.0 micromolar

851
00:39:59,040 --> 00:40:01,010
it decreased even a little bit more.

852
00:40:01,010 --> 00:40:02,130
Now you can round numbers,

853
00:40:02,130 --> 00:40:05,090
you can do all of that,
but we were very firm with

854
00:40:05,090 --> 00:40:09,140
our three time threshold and
2.8 while being very close

855
00:40:09,140 --> 00:40:10,880
is not quite there yet.

856
00:40:10,880 --> 00:40:12,810
So this means that we
still have work to do

857
00:40:12,810 --> 00:40:15,533
and there's still
questions to be answered.

858
00:40:17,510 --> 00:40:19,410
Now, looking at DOXSO, we again,

859
00:40:19,410 --> 00:40:21,450
we see the baseline mutation.

860
00:40:21,450 --> 00:40:23,640
It's going to happen regardless.

861
00:40:23,640 --> 00:40:27,332
Then if I look first at
0.4, which is in the middle,

862
00:40:27,332 --> 00:40:28,165
just so you know,

863
00:40:28,165 --> 00:40:31,210
this is the larger
concentration I'm showing first.

864
00:40:31,210 --> 00:40:35,100
There was a decrease just
like with CPT, but not as big.

865
00:40:35,100 --> 00:40:38,080
We're seeing a 1.7 fold decrease.

866
00:40:38,080 --> 00:40:41,190
But with 0.0, 0.025,

867
00:40:41,190 --> 00:40:43,500
we still see a decrease slightly less

868
00:40:43,500 --> 00:40:46,600
than with 0.4, 1.8 fold.

869
00:40:46,600 --> 00:40:50,420
So just like with CPT-11
more work needs to be done.

870
00:40:50,420 --> 00:40:53,453
There are more questions that
need to be asked and answered.

871
00:40:55,850 --> 00:40:58,070
So the conclusions from this study.

872
00:40:58,070 --> 00:41:01,350
Drugs have different mechanisms of action.

873
00:41:01,350 --> 00:41:03,530
And this is why I investigated

874
00:41:03,530 --> 00:41:05,600
a couple of different concentrations,

875
00:41:05,600 --> 00:41:07,320
because the concentration of a drug

876
00:41:07,320 --> 00:41:10,320
that you use will affect one pathway.

877
00:41:10,320 --> 00:41:15,320
So in a very abstract example,
DOXSO at concentration one,

878
00:41:15,770 --> 00:41:17,490
might yield mechanism one.

879
00:41:17,490 --> 00:41:20,390
Whereas a separate concentration,

880
00:41:20,390 --> 00:41:23,190
it might have a totally
different mechanism of action.

881
00:41:23,190 --> 00:41:24,820
So these different concentrations

882
00:41:24,820 --> 00:41:26,883
are going to impact different pathways.

883
00:41:29,800 --> 00:41:33,070
So a suggestion that we have
following the study would be

884
00:41:33,070 --> 00:41:34,210
to adjust the variables.

885
00:41:34,210 --> 00:41:36,730
Obviously one at a time in isolation,

886
00:41:36,730 --> 00:41:38,590
but changing the concentration perhaps.

887
00:41:38,590 --> 00:41:43,590
Maybe if we increased
that 1.0 CPT to 1.25,

888
00:41:43,890 --> 00:41:46,860
maybe that drop in mutation frequency

889
00:41:46,860 --> 00:41:49,060
would hit that three time threshold,

890
00:41:49,060 --> 00:41:51,550
or perhaps we leave the
concentrations the same,

891
00:41:51,550 --> 00:41:54,250
but increase the exposure
time of the drugs to E coli.

892
00:41:56,530 --> 00:42:00,670
So overall, all of, out of all
the drugs tested, not by me,

893
00:42:00,670 --> 00:42:02,310
but including all of the research

894
00:42:02,310 --> 00:42:05,110
that's been done out there, Dr. Laranjo.

895
00:42:05,110 --> 00:42:06,940
These were the only two that showcased

896
00:42:06,940 --> 00:42:09,310
a decrease in mutation frequency.

897
00:42:09,310 --> 00:42:10,300
Keeping in mind,

898
00:42:10,300 --> 00:42:12,740
it wasn't a significant in
the sense that it didn't hit

899
00:42:12,740 --> 00:42:15,590
that three time threshold,
but there was potential there.

900
00:42:17,050 --> 00:42:20,480
- And that was how we
got to those two drugs

901
00:42:20,480 --> 00:42:23,220
and then Sydney decided to graduate

902
00:42:23,220 --> 00:42:26,100
and pass the Baton to the next one.

903
00:42:26,100 --> 00:42:29,100
So I'm happy to tell you
that we do have students

904
00:42:29,100 --> 00:42:31,030
that will continue to work on them,

905
00:42:31,030 --> 00:42:35,660
but they also have all the
selection of drugs to keep going.

906
00:42:35,660 --> 00:42:37,440
And all of the other ones
that the other students

907
00:42:37,440 --> 00:42:41,410
have tested had actually seen
a slight increase or increase.

908
00:42:41,410 --> 00:42:42,950
But as Sydney mentioned,

909
00:42:42,950 --> 00:42:47,600
these are the only one that
shows a potential decrease,

910
00:42:47,600 --> 00:42:50,630
which that is gonna be
very cool if this holds,

911
00:42:50,630 --> 00:42:54,440
because it could be elucidating mechanisms

912
00:42:54,440 --> 00:42:56,350
to prevent this class of mutation.

913
00:42:56,350 --> 00:42:58,440
Because if the mutation is less,

914
00:42:58,440 --> 00:43:00,100
we are preventing this mutation.

915
00:43:00,100 --> 00:43:03,100
So we might understand
more about the mechanisms

916
00:43:03,100 --> 00:43:05,140
and how to prevent them.

917
00:43:05,140 --> 00:43:07,150
- And it could just mean
that the drugs I tested

918
00:43:07,150 --> 00:43:09,810
simply had nothing to, their mechanism,

919
00:43:09,810 --> 00:43:11,790
had nothing to do with topoisomerase

920
00:43:11,790 --> 00:43:14,180
in relation to this mutation type.

921
00:43:14,180 --> 00:43:15,474
- Absolutely.

922
00:43:15,474 --> 00:43:16,700
But with that,

923
00:43:16,700 --> 00:43:19,460
I do wanna acknowledge
some people and places

924
00:43:19,460 --> 00:43:21,350
that have given us support.

925
00:43:21,350 --> 00:43:23,560
I wanna start with my lab members.

926
00:43:23,560 --> 00:43:25,640
Here we have Sydney and
Becky that just graduated,

927
00:43:25,640 --> 00:43:27,830
but all of the past and current members

928
00:43:27,830 --> 00:43:30,240
have been honestly a blessing.

929
00:43:30,240 --> 00:43:34,040
I am so proud of all of you
and I can't wait to keep seeing

930
00:43:34,040 --> 00:43:36,380
you being amazing researchers.

931
00:43:36,380 --> 00:43:39,410
I wanted to thank the collaborators,

932
00:43:39,410 --> 00:43:42,760
people that I share lab with,
my colleagues, my department,

933
00:43:42,760 --> 00:43:44,330
my supportment system.

934
00:43:44,330 --> 00:43:48,300
The biology department as a
whole at Salem has been amazing.

935
00:43:48,300 --> 00:43:50,060
I wanna thank the Dartmouth
festival committee

936
00:43:50,060 --> 00:43:53,400
for allowing us to share this good news.

937
00:43:53,400 --> 00:43:56,100
And I wanna thank the Kathy Murphy award

938
00:43:56,100 --> 00:43:59,210
for funding Sydney's research.

939
00:43:59,210 --> 00:44:01,390
The biology research interest group,

940
00:44:01,390 --> 00:44:03,880
it's a great new group that
if you're not in it yet,

941
00:44:03,880 --> 00:44:04,873
you should join.

942
00:44:05,920 --> 00:44:07,317
Where we talk about research

943
00:44:07,317 --> 00:44:10,947
and we talk about
opportunities for research.

944
00:44:10,947 --> 00:44:14,543
And one of them, big one, is
by participating into SACNAS.

945
00:44:14,543 --> 00:44:16,980
So I wanna leave my plugging here.

946
00:44:16,980 --> 00:44:20,450
SACNAS stands for Society
for Advancement of Chicanos

947
00:44:20,450 --> 00:44:22,320
and Native American Science.

948
00:44:22,320 --> 00:44:24,890
And we are opening a chapter at Salem,

949
00:44:24,890 --> 00:44:27,710
as soon as we have enough
students sign up for it.

950
00:44:27,710 --> 00:44:30,620
But if you wanted to go to
conferences, participate,

951
00:44:30,620 --> 00:44:33,820
and you have any questions,
feel free to reach me.

952
00:44:33,820 --> 00:44:36,110
I wanna give a shout out to Dr. Lovett.

953
00:44:36,110 --> 00:44:37,510
She was my graduate advisor,

954
00:44:37,510 --> 00:44:40,500
and she's the one who taught me everything

955
00:44:40,500 --> 00:44:42,140
from the beginning of this project.

956
00:44:42,140 --> 00:44:45,240
And I wanna give a shout
out to her and Dr. Warnick.

957
00:44:45,240 --> 00:44:46,920
Dr. Warnick and Dr. Lovett

958
00:44:46,920 --> 00:44:51,280
were my mentors as an undergraduate
and a graduate career.

959
00:44:51,280 --> 00:44:52,830
So I wanted to leave this here

960
00:44:52,830 --> 00:44:55,540
because a lot of students are present.

961
00:44:55,540 --> 00:44:58,760
And I wanna encourage
you to find a mentor.

962
00:44:58,760 --> 00:45:01,490
You might not find a mentor that agrees

963
00:45:01,490 --> 00:45:03,110
with everything you wanna do.

964
00:45:03,110 --> 00:45:06,680
You might not find a
mentor that is perfect.

965
00:45:06,680 --> 00:45:09,520
People make mistakes,
mentors make mistakes,

966
00:45:09,520 --> 00:45:11,050
but they make such a difference.

967
00:45:11,050 --> 00:45:13,240
And because of these
two mentors in my life,

968
00:45:13,240 --> 00:45:15,610
my career changed completely.

969
00:45:15,610 --> 00:45:19,290
They guided me, they taught me
things I didn't know existed.

970
00:45:19,290 --> 00:45:20,800
They told me all those secrets

971
00:45:20,800 --> 00:45:22,650
that people don't talk about it,

972
00:45:22,650 --> 00:45:25,110
on how to get internships, how to apply,

973
00:45:25,110 --> 00:45:26,817
how to go out there.

974
00:45:26,817 --> 00:45:29,270
And me coming from another country,

975
00:45:29,270 --> 00:45:31,850
I didn't know the culture and
I didn't know a lot of things

976
00:45:31,850 --> 00:45:33,440
or not even how to start.

977
00:45:33,440 --> 00:45:36,440
And these two professors
have completely changed

978
00:45:36,440 --> 00:45:37,730
the course of my life.

979
00:45:37,730 --> 00:45:39,550
So if you haven't got a mentor yet,

980
00:45:39,550 --> 00:45:41,600
and you are interested in doing research,

981
00:45:41,600 --> 00:45:46,600
I wanted to encourage you to
find one and to get as much

982
00:45:46,620 --> 00:45:51,620
research experience as you are
able to and enjoy this path

983
00:45:51,950 --> 00:45:53,330
that science gives us.

984
00:45:53,330 --> 00:45:55,613
And with that, I will take any questions.

985
00:45:57,570 --> 00:46:00,980
- Thank you, Dr. Laranjo
and Sydney Addorisio.

986
00:46:00,980 --> 00:46:03,550
That was a wonderful talk.
That was really, really cool.

987
00:46:03,550 --> 00:46:05,040
And we've got lots of questions

988
00:46:05,040 --> 00:46:07,350
so I'm gonna get straight to it.

989
00:46:07,350 --> 00:46:09,550
Along those lines one of the questions is,

990
00:46:09,550 --> 00:46:10,890
is there any way a student

991
00:46:10,890 --> 00:46:13,890
could get involved with
something like this?

992
00:46:13,890 --> 00:46:17,120
- Absolutely. So I wanted to, if you,

993
00:46:17,120 --> 00:46:20,170
if the person who asked the
question is not involved yet,

994
00:46:20,170 --> 00:46:22,650
I wanted to encourage you to join

995
00:46:22,650 --> 00:46:25,250
the biology research interest group.

996
00:46:25,250 --> 00:46:27,830
So this is a group of professors

997
00:46:27,830 --> 00:46:30,410
who do research or are
interested in research,

998
00:46:30,410 --> 00:46:32,560
or are there to support research

999
00:46:33,580 --> 00:46:36,840
that we meet a few Mondays
throughout the semester.

1000
00:46:36,840 --> 00:46:39,070
And we talk about research opportunities,

1001
00:46:39,070 --> 00:46:41,500
and maybe we are not gonna have a spot

1002
00:46:41,500 --> 00:46:45,150
at that moment in our lab,
but we know someone who might,

1003
00:46:45,150 --> 00:46:47,670
and you can get
connections with other labs

1004
00:46:47,670 --> 00:46:49,610
and other opportunities as well.

1005
00:46:49,610 --> 00:46:51,040
So at Salem,

1006
00:46:51,040 --> 00:46:54,650
we do have the biology
research interest group

1007
00:46:54,650 --> 00:46:57,900
and keep an eye out. Dr.
Fischer sends an email

1008
00:46:57,900 --> 00:46:59,793
and he includes that on that email.

1009
00:47:01,570 --> 00:47:02,403
- Dr. Brown.

1010
00:47:03,408 --> 00:47:08,408
- Great. So I'll ask another
question from the audience.

1011
00:47:10,330 --> 00:47:14,550
Are these mutations
recognizable in humans easily?

1012
00:47:14,550 --> 00:47:19,490
Is it always a four based
pair addition or mutation?

1013
00:47:19,490 --> 00:47:21,300
- That's a great question.

1014
00:47:21,300 --> 00:47:24,440
So in humans, in order,
all of these mutations,

1015
00:47:24,440 --> 00:47:25,680
when they are first recognize,

1016
00:47:25,680 --> 00:47:28,410
the only way for us to
see is by sequencing.

1017
00:47:28,410 --> 00:47:30,470
We need to sequence the whole DNA.

1018
00:47:30,470 --> 00:47:34,360
In E coli, this is very
quick, it's $4 and it's quick.

1019
00:47:34,360 --> 00:47:36,180
We do that, we do it in an afternoon

1020
00:47:36,180 --> 00:47:39,260
and we have it by the
other day in the morning.

1021
00:47:39,260 --> 00:47:40,850
Here is the catch.

1022
00:47:40,850 --> 00:47:43,920
In E coli by adding these four bases,

1023
00:47:43,920 --> 00:47:47,800
the only way the mutation
happens that remove those bases

1024
00:47:47,800 --> 00:47:49,300
is by templates switch.

1025
00:47:49,300 --> 00:47:51,610
In yeast we had to do adjustments

1026
00:47:51,610 --> 00:47:54,200
because yeast has other mechanisms

1027
00:47:54,200 --> 00:47:56,040
that remove those four base pairs

1028
00:47:56,040 --> 00:47:58,150
that is not through temple switch.

1029
00:47:58,150 --> 00:48:00,400
So that was a challenge.

1030
00:48:00,400 --> 00:48:02,220
So that's an excellent question.

1031
00:48:02,220 --> 00:48:03,740
So these reporters,

1032
00:48:03,740 --> 00:48:06,170
we only have it in bacteria and in yeast,

1033
00:48:06,170 --> 00:48:09,490
but in order to recognize
this mutation in any organism,

1034
00:48:09,490 --> 00:48:13,143
we can only do by sequencing
the whole, the whole genome.

1035
00:48:16,600 --> 00:48:18,463
- Another question from the audience.

1036
00:48:20,070 --> 00:48:20,903
The person says

1037
00:48:20,903 --> 00:48:22,620
I think you're work is
incredibly fascinating,

1038
00:48:22,620 --> 00:48:23,453
but I'm wondering,

1039
00:48:23,453 --> 00:48:24,910
do you think natural
selection or evolution

1040
00:48:24,910 --> 00:48:27,703
has anything to do with
the way the DNA mutates?

1041
00:48:28,920 --> 00:48:30,580
- Yes. (laughs)

1042
00:48:30,580 --> 00:48:35,580
Yes, I do think so. Especially
because we know that,

1043
00:48:36,190 --> 00:48:37,660
we don't understand the whole mechanism,

1044
00:48:37,660 --> 00:48:41,010
but we know that anything that
will affect the replication

1045
00:48:41,010 --> 00:48:44,170
for will increase the
chances of mutations.

1046
00:48:44,170 --> 00:48:48,600
So we might not even be talking
about hairpin specifically,

1047
00:48:48,600 --> 00:48:51,600
but other parts of the DNA can be affected

1048
00:48:51,600 --> 00:48:54,810
that will cause replication
fork to be blocked.

1049
00:48:54,810 --> 00:48:56,170
And then coincidentally,

1050
00:48:56,170 --> 00:48:59,570
if that happens near
these palindrome mutations

1051
00:48:59,570 --> 00:49:02,530
who have more template switch
mutation, so absolutely.

1052
00:49:02,530 --> 00:49:05,190
And that is why it makes
us so difficult to study,

1053
00:49:05,190 --> 00:49:06,490
'cause we don't know everything

1054
00:49:06,490 --> 00:49:11,280
and we cannot control any
external environment yet, right.

1055
00:49:12,280 --> 00:49:14,060
So, excellent question.

1056
00:49:14,060 --> 00:49:15,450
- Can I add to that a little bit?

1057
00:49:15,450 --> 00:49:16,283
- [Laura] Yeah.

1058
00:49:16,283 --> 00:49:20,580
- Yeah. So that diagram too,
that we showed a couple times

1059
00:49:20,580 --> 00:49:24,180
showing like the topoisomerases,
the SSBs, the polymerase.

1060
00:49:24,180 --> 00:49:29,180
All of those are possible
targets that could possibly lead

1061
00:49:29,420 --> 00:49:31,720
to this template switch mutagenesis.

1062
00:49:31,720 --> 00:49:33,830
So anything that goes kind of array

1063
00:49:33,830 --> 00:49:36,343
in any of those regions might play a role.

1064
00:49:37,560 --> 00:49:38,573
- [Laura] Absolutely.

1065
00:49:42,020 --> 00:49:43,067
- Yeah.

1066
00:49:43,067 --> 00:49:45,440
So I have a quick question about

1067
00:49:45,440 --> 00:49:47,823
kind of directed to Sydney, I guess.

1068
00:49:48,900 --> 00:49:53,900
How, what ideas do you have
for how you would go about

1069
00:49:57,060 --> 00:50:00,837
trying to figure out whether the mechanism

1070
00:50:00,837 --> 00:50:04,963
of the effects that you saw
for CPT-11 and the DOXSO,

1071
00:50:06,021 --> 00:50:10,700
were actually related to
interfering with the topoisomerase

1072
00:50:10,700 --> 00:50:12,530
or if there's some other mechanism?

1073
00:50:12,530 --> 00:50:14,395
Like how would you, how
would you address that?

1074
00:50:14,395 --> 00:50:16,784
- Oh, I love this question. Okay.

1075
00:50:16,784 --> 00:50:21,784
So, first I just wanna say too,
with CPT, CPT is a pro drug.

1076
00:50:24,250 --> 00:50:28,850
So it requires a certain
metabolite that will allow for it

1077
00:50:28,850 --> 00:50:32,570
to be converted to a more active form

1078
00:50:32,570 --> 00:50:35,570
that, than it is without that metabolite.

1079
00:50:35,570 --> 00:50:38,060
So that would be kind of my
first step there is just to do

1080
00:50:38,060 --> 00:50:42,260
that first and see if the
mutation frequency changes.

1081
00:50:42,260 --> 00:50:44,170
And then after that, and this would apply

1082
00:50:44,170 --> 00:50:46,690
for both DOXSO and CPT.

1083
00:50:46,690 --> 00:50:50,720
If there was a way that
I could possibly track

1084
00:50:50,720 --> 00:50:53,350
the interactions with the drug

1085
00:50:53,350 --> 00:50:56,050
and the topoisomerase one or two,

1086
00:50:56,050 --> 00:50:59,050
whether that be through
fluorescence or, you know,

1087
00:50:59,050 --> 00:51:01,860
some possible way that I could track that,

1088
00:51:01,860 --> 00:51:02,730
that would be ideal.

1089
00:51:02,730 --> 00:51:06,760
Because as I mentioned different,
like DOXSO specifically,

1090
00:51:06,760 --> 00:51:10,890
has at least four different
mechanisms of action, right.

1091
00:51:10,890 --> 00:51:12,340
So it's important as you say,

1092
00:51:12,340 --> 00:51:16,430
to make sure that the concentration
I am using is inducing

1093
00:51:16,430 --> 00:51:20,590
that one specific mechanism of
action so that somebody could

1094
00:51:20,590 --> 00:51:23,610
say it is that one mechanism
of action that is causing it

1095
00:51:23,610 --> 00:51:26,253
and maybe not, you know,
one of the other three.

1096
00:51:27,290 --> 00:51:29,630
- Yes. So in her specific aims,

1097
00:51:29,630 --> 00:51:31,530
that was exactly aim three.

1098
00:51:31,530 --> 00:51:34,700
So we first we'll test it and
then once we see an effect,

1099
00:51:34,700 --> 00:51:37,250
we're gonna look at all the mechanisms

1100
00:51:37,250 --> 00:51:39,837
and modify specific
things on those mechanisms

1101
00:51:39,837 --> 00:51:42,210
and see if this mechanism is affected,

1102
00:51:42,210 --> 00:51:43,830
we should see a response.

1103
00:51:43,830 --> 00:51:45,730
So that's an excellent question.

1104
00:51:45,730 --> 00:51:46,563
- Okay.

1105
00:51:47,780 --> 00:51:52,390
I think this is another question
directed at, for Sydney.

1106
00:51:52,390 --> 00:51:53,410
Was it intimidating

1107
00:51:53,410 --> 00:51:55,910
to present your research
at different conventions?

1108
00:51:57,000 --> 00:51:59,260
- Absolutely. (laughs)

1109
00:51:59,260 --> 00:52:01,240
At first, absolutely.

1110
00:52:01,240 --> 00:52:02,073
And that's okay.

1111
00:52:02,073 --> 00:52:04,620
You know, I think especially
the first time, you know,

1112
00:52:04,620 --> 00:52:06,493
it is very nerve wracking.

1113
00:52:07,330 --> 00:52:11,000
But and I'm gonna plug SACNAS
again in here a little bit.

1114
00:52:11,000 --> 00:52:13,570
SACNAS was my first conference
that I presented at.

1115
00:52:13,570 --> 00:52:16,130
And that was a great format,

1116
00:52:16,130 --> 00:52:17,880
because even though it was virtual,

1117
00:52:18,920 --> 00:52:23,920
it was very collaborative. It
was very informative for me.

1118
00:52:24,850 --> 00:52:28,050
And a lot of the people
that gave me feedback,

1119
00:52:28,050 --> 00:52:30,200
it was all constructive criticism.

1120
00:52:30,200 --> 00:52:34,500
So there was nothing, you know,
I didn't feel like judged.

1121
00:52:34,500 --> 00:52:37,930
I didn't feel like I was ever wrong.

1122
00:52:37,930 --> 00:52:42,470
You know, it was people that
really just wanted to see

1123
00:52:42,470 --> 00:52:43,520
what came of my research.

1124
00:52:43,520 --> 00:52:45,280
They really wanted me
to grow as a researcher.

1125
00:52:45,280 --> 00:52:46,870
And one of the people that talked to me

1126
00:52:46,870 --> 00:52:48,380
was actually a cardiologist.

1127
00:52:48,380 --> 00:52:50,250
And he approached me and he said,

1128
00:52:50,250 --> 00:52:53,220
I have no idea what any of this means.

1129
00:52:53,220 --> 00:52:55,360
I have zero experience with it.

1130
00:52:55,360 --> 00:52:57,840
However, I do know what topoisomerase is.

1131
00:52:57,840 --> 00:52:59,740
And have you thought about this?

1132
00:52:59,740 --> 00:53:01,450
You know, so it's always nice

1133
00:53:01,450 --> 00:53:05,380
to present at conferences and
get some other perspectives.

1134
00:53:05,380 --> 00:53:07,350
- And Sydney said yes to all of them.

1135
00:53:07,350 --> 00:53:11,410
I kept sending it to her.
She said yes to all of them.

1136
00:53:11,410 --> 00:53:13,020
And this one in particular,

1137
00:53:13,020 --> 00:53:16,970
one thing that she didn't know
is that my previous advisor,

1138
00:53:16,970 --> 00:53:18,870
the ones that advised me in this project

1139
00:53:18,870 --> 00:53:20,130
were going to be there.

1140
00:53:20,130 --> 00:53:23,351
- Wait what?
(all laughs)

1141
00:53:23,351 --> 00:53:26,010
- So they report great things.

1142
00:53:26,010 --> 00:53:27,580
So I was very proud of her.

1143
00:53:27,580 --> 00:53:28,900
And it's exactly what she said.

1144
00:53:28,900 --> 00:53:31,520
I told her like, we will
never know everything

1145
00:53:31,520 --> 00:53:32,460
and that's okay.

1146
00:53:32,460 --> 00:53:34,630
And even now you might ask
a question that I don't know

1147
00:53:34,630 --> 00:53:35,900
the answer and that's okay.

1148
00:53:35,900 --> 00:53:38,510
But that exchange of ideas is,

1149
00:53:38,510 --> 00:53:40,360
might be the solution to a problem

1150
00:53:40,360 --> 00:53:42,180
that we hadn't even considered.

1151
00:53:42,180 --> 00:53:46,910
So if you are, if you wanna
present at a conference,

1152
00:53:46,910 --> 00:53:49,590
I'm gonna plug in the
research interest group again.

1153
00:53:49,590 --> 00:53:51,440
You can come and practice with us.

1154
00:53:51,440 --> 00:53:52,830
We can take a look at your poster

1155
00:53:52,830 --> 00:53:54,970
or we can look at your presentation.

1156
00:53:54,970 --> 00:53:57,560
These are the things,
we are here to help you.

1157
00:53:57,560 --> 00:53:58,393
And--

1158
00:53:58,393 --> 00:54:01,130
- Practice a talk with
them too. It's great.

1159
00:54:01,130 --> 00:54:02,440
- Yeah, so you can always,

1160
00:54:02,440 --> 00:54:04,810
you can always count on us to help.

1161
00:54:04,810 --> 00:54:06,550
Because think about it like this.

1162
00:54:06,550 --> 00:54:10,210
It's a lot of people with
PhD that if you go to a job

1163
00:54:10,210 --> 00:54:13,370
and you hire them as a
consultant, it's a lot of money.

1164
00:54:13,370 --> 00:54:15,540
And we all wanna do it
because we wanna help.

1165
00:54:15,540 --> 00:54:18,340
So you have all of this
free resource to you.

1166
00:54:18,340 --> 00:54:21,080
So if you wanna present,
go for it. It's okay.

1167
00:54:21,080 --> 00:54:22,560
You are going to get practice

1168
00:54:22,560 --> 00:54:25,700
and you're gonna make
mistakes and it's good too.

1169
00:54:25,700 --> 00:54:26,533
- Absolutely.

1170
00:54:26,533 --> 00:54:28,910
I feel so much stronger
in my presentation skills.

1171
00:54:28,910 --> 00:54:31,460
Now that I've done, you
know, four or five of them.

1172
00:54:34,860 --> 00:54:35,693
- Great.

1173
00:54:35,693 --> 00:54:38,040
I bet you do know the
answer to this question

1174
00:54:38,040 --> 00:54:40,120
since you sort of teed it up earlier.

1175
00:54:40,120 --> 00:54:43,930
But can you elaborate a little bit on the,

1176
00:54:43,930 --> 00:54:48,640
the strand bias mechanism
and what's going on there?

1177
00:54:48,640 --> 00:54:49,560
- Yeah, absolutely.

1178
00:54:49,560 --> 00:54:52,820
So we were looking into it and everybody

1179
00:54:52,820 --> 00:54:55,440
who was studying this
saw this exactly effect.

1180
00:54:55,440 --> 00:54:58,790
It was called the leading
strand bias effect.

1181
00:54:58,790 --> 00:55:00,830
So they say are there
more mutations happening

1182
00:55:00,830 --> 00:55:03,220
on the leading strand than
on the lagging strand.

1183
00:55:03,220 --> 00:55:07,480
So my work, this was my
first scientific publication.

1184
00:55:07,480 --> 00:55:11,630
What we did is that we looked
at things that were present

1185
00:55:11,630 --> 00:55:14,990
in the lagging strand and
actually preventing mutations

1186
00:55:14,990 --> 00:55:18,010
from happening or destroying them

1187
00:55:18,010 --> 00:55:20,570
before we were able to catch it.

1188
00:55:20,570 --> 00:55:23,710
So for example, there are exonucleases.

1189
00:55:23,710 --> 00:55:27,330
Exonucleases are able to
degrade that three prime end

1190
00:55:27,330 --> 00:55:30,030
of the DNA that I told
you that has to fold

1191
00:55:30,030 --> 00:55:32,350
in order to make the mutation.

1192
00:55:32,350 --> 00:55:35,220
And there are a lot more of exonucleases

1193
00:55:35,220 --> 00:55:38,040
in the lagging strand than
on the leading strength.

1194
00:55:38,040 --> 00:55:41,700
So if the exonucleases come in
the lagging and destroy that,

1195
00:55:41,700 --> 00:55:44,280
now the polymerase has a chance to go back

1196
00:55:44,280 --> 00:55:46,410
and go back to the original template.

1197
00:55:46,410 --> 00:55:49,660
The other things that besides
having more exonucleases

1198
00:55:49,660 --> 00:55:51,030
on the lagging strand.

1199
00:55:51,030 --> 00:55:54,150
The lagging strand has single
strand binding proteins,

1200
00:55:54,150 --> 00:55:57,400
which recruit more exonucleases.

1201
00:55:57,400 --> 00:55:59,260
So the leading strand, yeah.

1202
00:55:59,260 --> 00:56:04,210
Some exonucleases do go there
and do help by degrading

1203
00:56:04,210 --> 00:56:06,590
and not letting the mutation happen.

1204
00:56:06,590 --> 00:56:10,080
But on the lagging strand,
we have tons of exonucleases,

1205
00:56:10,080 --> 00:56:12,720
plus a strong wrong pull to the ones

1206
00:56:12,720 --> 00:56:14,400
that would go to the leading strand.

1207
00:56:14,400 --> 00:56:16,760
So it's just that the
lagging strand is better

1208
00:56:16,760 --> 00:56:18,323
at correcting the mutations.

1209
00:56:18,323 --> 00:56:21,280
It's not better at preventing them.

1210
00:56:21,280 --> 00:56:24,290
So that's when we
eliminated the exonucleases

1211
00:56:24,290 --> 00:56:27,250
and when we mutated the
single-strand binding protein,

1212
00:56:27,250 --> 00:56:29,760
we saw that the disease
trend bio was eliminated.

1213
00:56:29,760 --> 00:56:32,339
So if we do a playing fair field,

1214
00:56:32,339 --> 00:56:35,400
we have the same rate of mutation

1215
00:56:35,400 --> 00:56:36,800
in both leading and lagging.

1216
00:56:38,260 --> 00:56:39,307
- Cool. Thank you.

1217
00:56:40,810 --> 00:56:43,120
- Another sort of related questions

1218
00:56:43,120 --> 00:56:46,593
is it seems that the
formation of the palindrome,

1219
00:56:48,190 --> 00:56:50,590
is it causative or is it an effect of

1220
00:56:51,610 --> 00:56:56,150
the polymerases falling off?

1221
00:56:56,150 --> 00:56:59,373
Or not falling off, but
just stopping I guess.

1222
00:57:00,210 --> 00:57:05,210
But it may, there's single
stranded binding proteins,

1223
00:57:05,330 --> 00:57:07,930
aren't they supposed to prevent
the formation of all of this

1224
00:57:07,930 --> 00:57:10,650
and what's going on with palindromes?

1225
00:57:10,650 --> 00:57:12,960
Why are they forming in the first place?

1226
00:57:12,960 --> 00:57:14,080
- Yeah, that's a great question.

1227
00:57:14,080 --> 00:57:16,810
So they have physics experts,

1228
00:57:16,810 --> 00:57:19,290
they're actually calculating the pole

1229
00:57:19,290 --> 00:57:22,440
to make it a hairpin versus a cruciform.

1230
00:57:22,440 --> 00:57:25,530
And without going too much into a physics,

1231
00:57:25,530 --> 00:57:28,770
there is some force that
because of the region

1232
00:57:28,770 --> 00:57:30,650
is a quasi palindromic region,

1233
00:57:30,650 --> 00:57:32,750
because it's or a palindromic region,

1234
00:57:32,750 --> 00:57:35,210
there is some force to
make those structures.

1235
00:57:35,210 --> 00:57:37,920
So it is first, it's not
that these structures

1236
00:57:37,920 --> 00:57:40,230
make the palindrome,
is that the palindrome,

1237
00:57:40,230 --> 00:57:41,330
because of the sequence,

1238
00:57:41,330 --> 00:57:44,580
because of how the basis
are the order that they are,

1239
00:57:44,580 --> 00:57:48,690
it makes this specific force
that will cause this effect.

1240
00:57:48,690 --> 00:57:50,960
So single strand binding protein prevents

1241
00:57:50,960 --> 00:57:52,280
a lot of it from happening.

1242
00:57:52,280 --> 00:57:54,740
And that's why this
mutation is not so common

1243
00:57:54,740 --> 00:57:56,650
as other mutations.

1244
00:57:56,650 --> 00:57:59,461
And when we've removed the
single strand binding protein,

1245
00:57:59,461 --> 00:58:03,180
then we see very, very, very high rate.

1246
00:58:03,180 --> 00:58:04,340
So you are absolutely right.

1247
00:58:04,340 --> 00:58:06,080
So the palindrome is the,

1248
00:58:06,080 --> 00:58:07,970
is what cause, what forces,

1249
00:58:07,970 --> 00:58:09,680
but we have a bunch of stuff in the cell,

1250
00:58:09,680 --> 00:58:12,680
like single and binding
protein, trying to prevent that.

1251
00:58:12,680 --> 00:58:15,200
But at some, at some portion,

1252
00:58:15,200 --> 00:58:17,563
it's not able to prevent everything.

1253
00:58:21,690 --> 00:58:23,490
- Okay. I see it's three o'clock.

1254
00:58:23,490 --> 00:58:27,993
And so we're supposed to try
to stay on, on time here.

1255
00:58:29,120 --> 00:58:31,210
Thank you again Dr. Laranjo

1256
00:58:31,210 --> 00:58:35,100
and Sydney Addorisio. That was wonderful.

1257
00:58:35,100 --> 00:58:36,730
Really did a great job.

1258
00:58:36,730 --> 00:58:38,040
And I'm gonna pass it over to my,

1259
00:58:38,040 --> 00:58:42,123
our colleague, Dr. Ryan
Fisher, for some closing words.

1260
00:58:42,123 --> 00:58:43,867
- [Laura] Thank you.

1261
00:58:43,867 --> 00:58:44,700
- Thank you, Dr. Scottgale.

1262
00:58:44,700 --> 00:58:48,680
And thank you, especially
to Sydney and Dr. Laranjo

1263
00:58:48,680 --> 00:58:53,680
for such a interesting
talk about the role of DNA

1264
00:58:54,370 --> 00:58:56,610
in all of our lives.

1265
00:58:56,610 --> 00:59:01,610
Just a few announcements
before we close for,

1266
00:59:01,670 --> 00:59:06,350
close this 43rd festival,
and they include these.

1267
00:59:06,350 --> 00:59:09,120
The recordings of all these
webinars will be available

1268
00:59:09,120 --> 00:59:11,100
in about seven to 10 days.

1269
00:59:11,100 --> 00:59:14,790
So keep an eye on your emails
for where you might find them.

1270
00:59:14,790 --> 00:59:18,393
They will be digitally
lodged in our library.

1271
00:59:19,250 --> 00:59:20,870
In addition, if you're a student

1272
00:59:20,870 --> 00:59:23,160
and thinking about your
assignment that you might

1273
00:59:23,160 --> 00:59:25,593
be doing for this webinar today.

1274
00:59:26,710 --> 00:59:30,260
The attendance sheets will be passed

1275
00:59:30,260 --> 00:59:33,860
on to your professors sometime next week,

1276
00:59:33,860 --> 00:59:35,910
that provide evidence that you were here.

1277
00:59:37,240 --> 00:59:40,912
I'd like to thank all our
alumni that have been present.

1278
00:59:40,912 --> 00:59:43,570
So Sydney, who's a brand new alumni.

1279
00:59:43,570 --> 00:59:45,410
Thank you very much for joining us Sydney.

1280
00:59:45,410 --> 00:59:47,900
I can quite imagine
seeing you in 10 years,

1281
00:59:47,900 --> 00:59:50,100
just like Dr. Pelatio this morning,

1282
00:59:50,100 --> 00:59:52,963
coming to speak to us as Dr. Adrishio.

1283
00:59:54,280 --> 00:59:58,470
Also for Peter Shearstone
who dropped by and joined

1284
00:59:58,470 --> 01:00:01,010
and spent the day with
us today after graduating

1285
01:00:01,010 --> 01:00:02,383
all those years ago.

1286
01:00:04,060 --> 01:00:05,630
Also to thank the funders.

1287
01:00:05,630 --> 01:00:08,500
The two primary funders
of our annual event

1288
01:00:08,500 --> 01:00:11,270
is the Charles Albert Reid Trust,

1289
01:00:11,270 --> 01:00:14,780
which is administered by
the Salem City Council.

1290
01:00:14,780 --> 01:00:17,530
And for this year in the next four years,

1291
01:00:17,530 --> 01:00:19,640
Peter's company Thermo Fisher Scientific.

1292
01:00:19,640 --> 01:00:20,663
Thank you very much.

1293
01:00:21,780 --> 01:00:24,670
Before i forget we've had a
lot of fun behind the scenes

1294
01:00:24,670 --> 01:00:25,820
running these webinars,

1295
01:00:25,820 --> 01:00:28,200
and we are pretending
that these are flights,

1296
01:00:28,200 --> 01:00:29,370
air travel flights.

1297
01:00:29,370 --> 01:00:33,130
And so thank you to Gail,
who's our pilot today,

1298
01:00:33,130 --> 01:00:36,180
and to Derek Barr who
couldn't be with us today.

1299
01:00:36,180 --> 01:00:39,343
He flew all the other
webinars earlier in the week.

1300
01:00:41,250 --> 01:00:43,030
Finally, just to thank all my colleagues

1301
01:00:43,030 --> 01:00:44,520
in the Darwin festival team,

1302
01:00:44,520 --> 01:00:49,520
as well as all my fellow
biologists here at Salem State.

1303
01:00:50,160 --> 01:00:52,150
Thank you very much.

1304
01:00:52,150 --> 01:00:55,800
We will be back next year with
more Darwin festival talks

1305
01:00:55,800 --> 01:00:59,130
and I will quickly pass it
back to Sydney and Dr. Laranjo,

1306
01:00:59,130 --> 01:01:00,993
just to say goodbye to everybody.

1307
01:01:02,670 --> 01:01:04,280
- Bye everyone. Thank you so much.

1308
01:01:04,280 --> 01:01:05,980
- [Sydney] Bye, thank you so much.