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(gentle instrumental music)

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- Oh my gosh, I'm totally embarrassed.

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Thank you so much Dr. Fisher.

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Really great to be here.

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So my name's Peter Shearstone.

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I'm the Vice President of Global Quality

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and Regulatory for
Thermo Fisher Scientific.

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We're in Waltham, Mass,
and all over the world,

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actually 314 different plants.

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So I handed my card out to a
bunch of folks earlier today,

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but if I didn't get to see you,

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please stop and say hi afterwards.

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We're looking for good
employees and interns,

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depending upon your
year in the university.

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I'm really pleased today
to introduce Jason Brown,

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Dr. Jason Brown at the Darwin Festival.

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I was here, where you're
sitting, back in 1985.

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And the big topic then was AIDS of course,

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and now we know that we can treat AIDS

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and I think about web
year presentation today

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and how fantastic it would be
that maybe not 35 years later,

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maybe it's five years from now,

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we'll be talking about
a cure for ALS, right?

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Amazing presentation earlier.

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Along with another amazing
presentation is Dr. Jason Brown.

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I'm gonna introduce Dr. Brown.

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Dr. Brown earned his BS in genetics

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and a PhD in cellular biology

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from the University of
Georgia, go Bulldogs, go Dogs.

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Taught biology at Young Harris
College for seven years.

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He was a post-doc associate

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at the University of
Massachusetts Medical School.

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He's been a part-time lecturer

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in the Department of
Biology at Tufts, Jumbos?

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So you've had Bulldogs, Jumbos,

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UMass is the...

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What is UMass's mascot?

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The Vikings?

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Oh, Salem State's Vikings.

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UMass.

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Minute Men, I think it's the Minute Men.

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Minute Men, yes.

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Dr. Brown's been at Salem State since 2014

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and during the fall of 2020 he was tenured

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and promoted to Associate Professor.

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Dr. Brown's research applies
genetics, biochemistry,

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and cell biology to study the assembly

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and function of cilia.

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These hairlike organelles,

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and Anne Barker's gonna
get an education here

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in what hairlike organelles are,

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extend from the surface
of almost every cell

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in the human body,

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and are required for processes

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to drive diverse processes
such as respiration,

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reproduction, vision, and
embryonic development.

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Dr. Brown will discuss cilia assembly,

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the ciliopathies,

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and the work he and his
students have been doing

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to understand the mechanism
of cilia gene regulation

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in his talk.

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And his talk is sponsored
by the biology department

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in Thermo Fisher.

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So I'll pass it over to Laura
for an additional introduction

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for Dr. Brown.
- Thank you.

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Hi.

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Hello everyone, and hello to
our online audience as well.

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So you already heard great
things about Dr. Jason Brown

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in this introduction

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and you might be impressed
even before he starts speaking.

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Well, he has over 22 years
of teaching experience,

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and when I was talking to him about that,

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he said that it made him feel really old.

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With that being said most of our students

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are younger than 22, so,

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so his mentoring experience
goes back all the way to 1999,

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and his honors and awards started in 1993,

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when he was a presidential scholar at UGA.

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That's a fun fact for you guys.

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He has been investing in his teaching

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by attending multiple
meetings and workshops,

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and his scholarly success is demonstrated

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with over 16 publications.

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With all this success,

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you who might not know him in person,

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might feel a little intimidated,

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and I'm here to share with
you a few words about him

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in addition to the
accomplishments on his CV.

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Dr. Brown is a great colleague.

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He cares for his peers and students,

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and he is always trying to
help everyone around him.

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He comes in early, leaves late,

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and always has time to lend a hand.

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He's respectful and respected,
sympathizes with others,

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and many times has worked above

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and beyond his required job
description to help students

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and faculty who are still
learning the ropes, like myself.

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In the few years I have known him,

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I have seen firsthand how
well he collaborates, teaches,

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learns, and most importantly,
focus on his goal,

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which is improving learning
in the classroom and lab.

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It is a great honor to
introduce you to my colleague,

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Dr. Jason Brown.

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- Let me get these things outta the way.

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Thank you so much for
the introduction. Wow.

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So,

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thanks to the biology department

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and to the Darwin Festival committee

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for inviting me to speak.

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Thanks to Thermo Fisher Scientific

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and the biology department
for sponsoring the talk,

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and thanks to you all for being here

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and the online audience as well.

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So when I was thinking
about preparing this talk,

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I thought a lot about what
it means to do research

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at a primarily teaching
oriented institution.

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And for me, it's really
about the students.

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And so in addition to, I'm
talking to you about cilia today,

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I want to talk to you about experiences

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that I've had over the years

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with a number of

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really outstanding
undergraduate researchers.

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And so we'll start out
talking about the significance

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and structure of cilia.

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I'll talk to you about some,

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a couple of really
important research questions

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in this field,

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namely how are cilia assembled?

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And how is the motility of
cilia generated and regulated?

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And then really the bulk
of the talk is gonna be

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three kind of vignettes about
undergraduate researchers.

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Sort of snapshots of experiences

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that I've had with undergraduate
researchers over the years,

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including web, in the
back of the room, so.

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So cilia were first
described about 345 years ago

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when Leeuwenhoek using really
high quality microscopes

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that he had made himself,

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saw single celled eukaryotic
organisms under the microscope,

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and he described those as animalcules,

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and the cilia that he
saw in those organisms

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as these incredibly thin little feet

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or little legs.

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The cilia that you're seeing

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at the bottom of the screen here

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are actually at ependymal cilia,

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which extend out from glial cells

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in a mouse brain in this case.

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And this is work from Karl Lechtreck

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working in George Witman's lab

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at University of
Massachusetts Medical School,

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when he was there.

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He's now at the University
of Georgia, Go Dogs.

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And then it was actually
almost exactly 300 years later

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when the first disease
caused by immotile cilia

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was described by Afzelius.

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And the patients with this
immotile cilia syndrome,

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have chronic sinus and
respiratory infections.

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They're infertile, and kind of amazingly,

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about half of them have something called

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Situs inversus totalis.

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And this is a rare condition
where the abdominal

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and thoracic organs are
in a mirror image position

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relative to the normal
positions of the organs.

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So in 2000,

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the field or the connection
between cilia and diseases

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really started to take off as
an interesting research area

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when this paper was published

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looking at a mouse model of
polycystic kidney disease.

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So PKD effects somewhere
between one in 500

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and one in a 1000 live births.

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And people who have
polycystic kidney disease

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typically need to go on dialysis

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or end up needing a kidney transplant.

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So on the right, you're looking
at a normal human kidney

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in the center,

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and these are kidneys of people

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with polycystic kidney disease.

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So you can see it's a really
dramatic effect on the kidneys.

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In this same study,

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they found that the gene that was mutated

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in their mouse model of
polycystic kidney disease

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was this gene Tg737.

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And the other thing that they
found was that a mutation

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in the homologous gene to that one

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in this single cell organism,

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Chlamydomonas reinhardtii,

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caused this organism,

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which is a single cell green
alga, to lack its cilia.

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And so of course they immediately went

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and looked in the kidneys in their mice

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to see if they saw any cilia defects.

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And what they found was,

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inside the kidney collecting duct tubules,

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there were cilia extending
out from the surfaces

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of the cells in that tubule.

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But in the mutant mouse,

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the cilia were either completely missing

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or very much reduced.

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So at this point,

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researchers got really excited

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about the possibility that
there might be other diseases

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that are caused by problems with cilia,

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other than just immotile cilia syndrome.

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And one of the reasons

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that they got so excited about this,

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is that cilia are found
on virtually every cell

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in the human body.

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So they're not found on red blood cells,

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but basically just about
every other cell in the body.

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So the cilia in humans
come in two varieties,

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the motile cilia and the primary cilia

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or the immotile cilia.

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So for the motile cilia,

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they include the ependymal cilia

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that I just showed you a minute ago,

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as well as oviduct cilia,

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nodal cilia, and respiratory cilia,

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which are all moving fluids
across the surfaces of cells

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and sperm flagella, which
are moving whole cells.

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The primary cilia, which are
the ones that are not beating,

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they actually just extend out
from the surfaces of cells

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and they act like a little antenna

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to receive signals from other cells.

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Those include olfactory cilia in our nose,

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which are packed with odorant receptors

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that allow the sense of smell,

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and the photoreceptor
outer segments in the eye,

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which are packed with visual
pigments that allow vision

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and those kidney collecting duct cilia

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that I mentioned before,
among many others.

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I'm just showing you a few examples here.

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So it turns out that now we know that

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there are a variety of human diseases

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that are caused by problems with cilia.

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In fact, this whole class of diseases

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is now called the ciliopathies.

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And ciliopathies include diseases

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that are caused by
problems with motile cilia.

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In those cases people have infertility

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and that's due to the
inability of the oviduct cilia

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to move egg cells and the
inability of sperm cells to swim.

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Also chronic sinus and
respiratory infections.

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And sometimes those can
actually be so severe

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that it causes scarring of the lungs,

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which is called bronchiectasis.

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And then that left right asymmetry defect

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that I mentioned before,

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where the organs are in
this mirror image position,

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that's actually caused
by problems with cilia

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on the embryonic node,

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which is a structure on
the surface of the embryo

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that's involved in establishing
the left-right asymmetry

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during embryonic development.

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Hydrocephalus is an accumulation
of fluid in the brain

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that's caused by a problem
with those ependymal cilia

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that I showed you before,

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which is involved in moving
cerebrospinal fluid around.

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The disorders that are caused by problems

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with the primary cilia include
cystic kidneys, blindness,

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obesity, and skeletal and
nervous system abnormalities,

261
00:13:30,360 --> 00:13:33,152
which are all the common theme,

262
00:13:33,152 --> 00:13:36,240
they are caused by problems with signaling

263
00:13:36,240 --> 00:13:38,523
that those cilia are involved in.

264
00:13:41,640 --> 00:13:43,410
So one of the other things

265
00:13:43,410 --> 00:13:47,790
that explains why cilia can
have so many different effects

266
00:13:47,790 --> 00:13:52,500
on our health is that cilia

267
00:13:52,500 --> 00:13:54,630
are really complicated structures.

268
00:13:54,630 --> 00:13:57,460
So they're made out of
hundreds of different proteins

269
00:13:58,470 --> 00:14:03,470
and they're organized though
all around microtubules,

270
00:14:03,570 --> 00:14:07,800
which are composed of alpha
and beta tubulin subunits

271
00:14:07,800 --> 00:14:11,100
that are organized into
these helical filaments

272
00:14:11,100 --> 00:14:14,193
that are part of the
cytoskeleton in the cell.

273
00:14:15,570 --> 00:14:20,220
The core of cilia is called the axoneme

274
00:14:20,220 --> 00:14:24,660
and it's composed in both
the non motile cilia,

275
00:14:24,660 --> 00:14:26,130
which are like the ones on the top,

276
00:14:26,130 --> 00:14:30,390
and the motile cilia on the bottom here,

277
00:14:30,390 --> 00:14:34,320
they have nine microtubule doublets

278
00:14:34,320 --> 00:14:38,790
that are arranged into a
cylinder around the outside.

279
00:14:38,790 --> 00:14:40,440
And in the motile cilia,

280
00:14:40,440 --> 00:14:43,170
there's also this structure
called the central pair,

281
00:14:43,170 --> 00:14:46,380
and a number of other
structures like inner

282
00:14:46,380 --> 00:14:48,270
and outer dynein arms,

283
00:14:48,270 --> 00:14:53,270
and radial spokes that
are involved in generating

284
00:14:53,280 --> 00:14:56,430
and regulating the motility of the cilia.

285
00:14:56,430 --> 00:14:58,260
So on the left side here,

286
00:14:58,260 --> 00:15:00,600
you're gonna see an animation

287
00:15:00,600 --> 00:15:03,720
that is from cryo
electrons tomography work

288
00:15:03,720 --> 00:15:07,980
that was collaboration
between Win Sale's lab

289
00:15:07,980 --> 00:15:09,180
at Emory University

290
00:15:09,180 --> 00:15:14,180
and Daniela Nicastro's lab
when she was at Brandeis.

291
00:15:14,490 --> 00:15:17,160
And what we're looking at
is a single one of these

292
00:15:17,160 --> 00:15:19,593
microtubule doublets over here,

293
00:15:20,610 --> 00:15:24,840
on this side, the outer dynein arms,

294
00:15:24,840 --> 00:15:25,950
and on this side over here,

295
00:15:25,950 --> 00:15:28,110
the inner dynein arms,

296
00:15:28,110 --> 00:15:31,320
and then these structures
sticking out on this side

297
00:15:31,320 --> 00:15:33,390
are these radial spoke structures

298
00:15:33,390 --> 00:15:34,830
that you're looking at over here.

299
00:15:34,830 --> 00:15:37,890
So we're looking at just one
of these doublet microtubules

300
00:15:37,890 --> 00:15:39,480
and just a very short part

301
00:15:39,480 --> 00:15:41,730
of one of those doublet microtubules.

302
00:15:41,730 --> 00:15:43,920
So that hopefully gives
you kind of a sense

303
00:15:43,920 --> 00:15:46,083
of the complexity of these organelles.

304
00:15:48,450 --> 00:15:52,440
The axoneme is covered
by the ciliary membrane,

305
00:15:52,440 --> 00:15:54,570
which has a distinct lipid

306
00:15:54,570 --> 00:15:57,630
and protein composition
from the plasma membrane,

307
00:15:57,630 --> 00:15:58,950
but it's connected to

308
00:15:58,950 --> 00:16:01,650
and is continuous with
the plasma membrane.

309
00:16:01,650 --> 00:16:03,960
So there's this region
at the base of the cilia

310
00:16:03,960 --> 00:16:06,120
that keeps them sort of separate

311
00:16:06,120 --> 00:16:09,900
and determines that distinction
between the plasma membrane

312
00:16:09,900 --> 00:16:11,493
and the ciliary membrane.

313
00:16:12,420 --> 00:16:15,360
So really importantly that super structure

314
00:16:15,360 --> 00:16:16,560
that I'm showing in there,

315
00:16:16,560 --> 00:16:18,690
as well as the proteins that are found

316
00:16:18,690 --> 00:16:21,120
in the cilia are really highly conserved

317
00:16:21,120 --> 00:16:23,850
between different eukaryotic organisms.

318
00:16:23,850 --> 00:16:27,510
And so what you're looking at
here are electron micrographs

319
00:16:27,510 --> 00:16:32,510
of cross-sections of cilia
from human and chlamydomonas,

320
00:16:32,880 --> 00:16:34,440
that single celled green algae

321
00:16:34,440 --> 00:16:36,720
that I just showed you a few minutes ago.

322
00:16:36,720 --> 00:16:38,730
So I hope you would agree
it's kind of difficult

323
00:16:38,730 --> 00:16:40,590
to tell them apart from each other, right?

324
00:16:40,590 --> 00:16:43,860
So it turns out on the
left is the human cilium

325
00:16:43,860 --> 00:16:47,083
and on the right is the
chlamydomonas cilium.

326
00:16:49,470 --> 00:16:51,120
In fact,

327
00:16:51,120 --> 00:16:53,610
it turns out that cilia are found

328
00:16:53,610 --> 00:16:58,560
in every major lineage
of eukaryotic organisms.

329
00:16:58,560 --> 00:17:02,190
And what that tells us is that

330
00:17:02,190 --> 00:17:07,190
the last common ancestor of all eukaryotes

331
00:17:07,200 --> 00:17:10,680
was almost certainly a ciliated organism.

332
00:17:10,680 --> 00:17:11,970
The other reason that's important

333
00:17:11,970 --> 00:17:14,790
is because it means that we can use

334
00:17:14,790 --> 00:17:17,040
a variety of different model organisms

335
00:17:17,040 --> 00:17:21,450
to study cilia function and assembly.

336
00:17:21,450 --> 00:17:22,740
So for the rest of this talk

337
00:17:22,740 --> 00:17:27,360
I'm gonna be focusing on
organisms in two clays here,

338
00:17:27,360 --> 00:17:28,560
the alveolates,

339
00:17:28,560 --> 00:17:30,900
which includes ciliates,

340
00:17:30,900 --> 00:17:34,773
and chloroplastida, which
includes the green algae.

341
00:17:37,050 --> 00:17:40,470
So these are movies of these two organisms

342
00:17:40,470 --> 00:17:42,720
slowed down dramatically,

343
00:17:42,720 --> 00:17:45,153
so that you can actually see the cilia.

344
00:17:46,140 --> 00:17:50,490
On the left is a ciliate,
tetrahymena thermophila,

345
00:17:50,490 --> 00:17:53,310
which is covered with hundreds of cilia.

346
00:17:53,310 --> 00:17:56,760
You can see a few of them in focus

347
00:17:56,760 --> 00:17:58,530
around the outside edge of the cell,

348
00:17:58,530 --> 00:18:02,130
but the whole cell is covered
with hundreds of cilia.

349
00:18:02,130 --> 00:18:05,190
And on the right is
chlamydomonas reinhardtii,

350
00:18:05,190 --> 00:18:09,600
which is that green alga that
I introduced you to before,

351
00:18:09,600 --> 00:18:12,090
that swims through its media

352
00:18:12,090 --> 00:18:14,370
by pulling itself forward

353
00:18:14,370 --> 00:18:17,310
with this kind of
breaststroke like motion.

354
00:18:17,310 --> 00:18:18,510
And just to give you a sense

355
00:18:18,510 --> 00:18:21,060
of how much we're slowing
these movies down,

356
00:18:21,060 --> 00:18:23,823
that's chlamydomonas
swimming in real time.

357
00:18:24,870 --> 00:18:28,080
So we slow them down a lot,
so that you can see them.

358
00:18:28,080 --> 00:18:31,530
So the two questions that
I'm gonna kind of focus on

359
00:18:31,530 --> 00:18:34,080
for the rest of the talk here are,

360
00:18:34,080 --> 00:18:36,000
how are cilia assembled?

361
00:18:36,000 --> 00:18:40,410
And how is that motility
of the cilia generated

362
00:18:40,410 --> 00:18:41,763
and regulated?

363
00:18:43,260 --> 00:18:45,930
And both of those organisms
that I just showed you

364
00:18:45,930 --> 00:18:50,790
have been used to study these
questions over the years.

365
00:18:50,790 --> 00:18:52,860
So I'd like to start with
the first of those questions,

366
00:18:52,860 --> 00:18:55,440
talking about cilia assembly.

367
00:18:55,440 --> 00:18:59,700
And during the process
of building a cilium,

368
00:18:59,700 --> 00:19:04,700
a cell needs to transcribe and
translate hundreds of genes.

369
00:19:05,640 --> 00:19:08,010
The proteins that are made are assembled

370
00:19:08,010 --> 00:19:11,520
into many different protein complexes

371
00:19:11,520 --> 00:19:14,790
and they get transported to the cilium,

372
00:19:14,790 --> 00:19:16,950
and then transported into the cilium

373
00:19:16,950 --> 00:19:18,450
to their sites of assembly

374
00:19:18,450 --> 00:19:22,233
so that the cilia can then grow.

375
00:19:25,410 --> 00:19:28,710
What we now know is that
many of those proteins

376
00:19:28,710 --> 00:19:31,950
are actually transported into the cilia.

377
00:19:31,950 --> 00:19:34,083
Oh, let me back up and say one thing.

378
00:19:36,270 --> 00:19:39,150
Cells need to have a mechanism
to get these proteins

379
00:19:39,150 --> 00:19:42,300
into cilia and transport it in the cilia,

380
00:19:42,300 --> 00:19:45,180
because proteins are not made in cilia.

381
00:19:45,180 --> 00:19:48,420
There's no ribosomes or
protein making machinery

382
00:19:48,420 --> 00:19:50,100
inside the cilia.

383
00:19:50,100 --> 00:19:52,590
So all of those hundreds
of different proteins

384
00:19:52,590 --> 00:19:55,140
have to get made in the cell body

385
00:19:55,140 --> 00:19:59,550
and then transported into
the cilia to where they go.

386
00:19:59,550 --> 00:20:01,260
So over on the right side,

387
00:20:01,260 --> 00:20:05,310
you're looking at an immobilized
chlamydomonas cilium,

388
00:20:05,310 --> 00:20:09,360
and this process called
intra flagellar transport,

389
00:20:09,360 --> 00:20:12,030
which is this bidirectional movement

390
00:20:12,030 --> 00:20:15,060
of protein particles from
the base of the cilia,

391
00:20:15,060 --> 00:20:16,443
out toward the tip.

392
00:20:22,140 --> 00:20:25,080
We're gonna talk about
intracellular transport more

393
00:20:25,080 --> 00:20:25,950
in a couple of minutes.

394
00:20:25,950 --> 00:20:27,030
This is gonna bring us

395
00:20:27,030 --> 00:20:31,323
to our three undergraduate
research vignettes.

396
00:20:33,270 --> 00:20:34,890
So in the first of these,

397
00:20:34,890 --> 00:20:39,890
I'm gonna take you all the way
back to my PhD training days.

398
00:20:40,680 --> 00:20:45,680
And during my PhD I studied
kinesin motor proteins.

399
00:20:46,620 --> 00:20:50,940
So motor proteins use the energy from ATP

400
00:20:50,940 --> 00:20:55,940
to walk along cytoskeletal
filaments in the cell

401
00:20:56,760 --> 00:20:58,710
and when they do,

402
00:20:58,710 --> 00:21:02,490
they carry cargo with them.

403
00:21:02,490 --> 00:21:06,450
So in this picture from Ron Vale's lab

404
00:21:06,450 --> 00:21:09,000
that they put together to
illustrate this process,

405
00:21:09,000 --> 00:21:12,780
this car is carrying the
cargo along filament, right?

406
00:21:12,780 --> 00:21:15,180
So the motor proteins do that

407
00:21:15,180 --> 00:21:18,090
and they have lots of different
really important functions.

408
00:21:18,090 --> 00:21:22,290
So they're carrying secretory
vesicles in the cell,

409
00:21:22,290 --> 00:21:26,760
they're moving chromosomes
around during mitosis,

410
00:21:26,760 --> 00:21:30,240
and actually in the cilium,

411
00:21:30,240 --> 00:21:34,110
they're causing filaments
to slide against each other,

412
00:21:34,110 --> 00:21:35,130
within the axoneme,

413
00:21:35,130 --> 00:21:38,523
which is the mechanism
for ciliary beating.

414
00:21:40,740 --> 00:21:43,920
So most of the projects that
I do start with mutants.

415
00:21:43,920 --> 00:21:48,920
And, just to kind of orient you

416
00:21:49,110 --> 00:21:52,320
to the complexity of
this problem of making

417
00:21:52,320 --> 00:21:54,930
and sort of isolating these mutants,

418
00:21:54,930 --> 00:21:56,610
the organisms that I'm talking about,

419
00:21:56,610 --> 00:22:01,610
range from having about
17,000 genes in chlamydomonas,

420
00:22:01,710 --> 00:22:06,270
to in tetrahymena cells,
having about 26,000 genes,

421
00:22:06,270 --> 00:22:09,510
and humans kind of fall right
about in the middle of that

422
00:22:09,510 --> 00:22:11,790
with around 20,000 genes.

423
00:22:11,790 --> 00:22:16,790
So you all probably know that
a variety of different genes

424
00:22:18,510 --> 00:22:20,490
will code for proteins

425
00:22:20,490 --> 00:22:22,470
with different important
functions in the cell,

426
00:22:22,470 --> 00:22:25,200
like analyzing chemical reactions,

427
00:22:25,200 --> 00:22:30,200
or regulating the
expression of other genes,

428
00:22:30,540 --> 00:22:33,210
or signaling from cell to cell.

429
00:22:33,210 --> 00:22:35,370
And in both of those organisms,

430
00:22:35,370 --> 00:22:37,140
tetrahymena and chlamydomonas,

431
00:22:37,140 --> 00:22:38,550
there are mechanisms

432
00:22:38,550 --> 00:22:42,993
or methods available to
generate mutations in genes.

433
00:22:43,830 --> 00:22:45,450
When we do that,

434
00:22:45,450 --> 00:22:48,570
very often the function of
proteins will be disrupted

435
00:22:48,570 --> 00:22:52,890
and then we can look to see
what the effect on the cell is,

436
00:22:52,890 --> 00:22:55,830
to try to build a
biological story basically,

437
00:22:55,830 --> 00:23:00,063
and understand what the
function of that protein is.

438
00:23:01,920 --> 00:23:06,180
So when I was a PhD
student with Jacek Gaertig

439
00:23:06,180 --> 00:23:08,370
at the University of Georgia,

440
00:23:08,370 --> 00:23:10,950
we created a mutant

441
00:23:10,950 --> 00:23:13,620
that was totally lacking
this motor protein

442
00:23:13,620 --> 00:23:15,900
called Kinesin two.

443
00:23:15,900 --> 00:23:19,530
And so this is in tetrahymena,

444
00:23:19,530 --> 00:23:24,530
and when we did
immunofluorescence microscopy,

445
00:23:24,600 --> 00:23:27,900
thank you Webb for that
wonderful introduction earlier,

446
00:23:27,900 --> 00:23:30,150
any of you that were here for Webb's talk,

447
00:23:30,150 --> 00:23:33,363
you did a great intro to
immunofluorescence microscopy.

448
00:23:34,320 --> 00:23:37,620
When we did immunofluorescence
microscopy on these cells,

449
00:23:37,620 --> 00:23:42,240
we could stain the tubulin
inside the cilia with one color,

450
00:23:42,240 --> 00:23:44,310
the green that you're seeing here,

451
00:23:44,310 --> 00:23:48,453
and the DNA inside the nucleus
in this other color, in blue.

452
00:23:49,590 --> 00:23:52,620
And what we found is that
wild-type cells, as I said,

453
00:23:52,620 --> 00:23:55,740
are covered with hundreds
of different cilia,

454
00:23:55,740 --> 00:23:59,790
and our mutant was unable
to assemble new cilia

455
00:23:59,790 --> 00:24:00,623
during division,

456
00:24:00,623 --> 00:24:04,563
but it's also unable to
maintain its existing cilia.

457
00:24:06,000 --> 00:24:07,590
So that was interesting and exciting,

458
00:24:07,590 --> 00:24:10,590
but we got really excited and interested

459
00:24:10,590 --> 00:24:14,580
when we realized that the
mutant also had a defect

460
00:24:14,580 --> 00:24:17,340
in cell division.

461
00:24:17,340 --> 00:24:20,820
So on the left side is
a normal cell dividing ,

462
00:24:20,820 --> 00:24:24,750
and you can see that during division

463
00:24:24,750 --> 00:24:27,750
the cell builds a new oral apparatus,

464
00:24:27,750 --> 00:24:31,920
which is the feeding
structure of these cells.

465
00:24:31,920 --> 00:24:34,740
And what that does for us
is it helps us very easily

466
00:24:34,740 --> 00:24:38,433
get oriented to what the front
cell and the back cell are.

467
00:24:39,450 --> 00:24:41,370
So what you can see on the right side

468
00:24:41,370 --> 00:24:43,230
is that mutant cells

469
00:24:43,230 --> 00:24:46,440
had divided their nuclei normally, right?

470
00:24:46,440 --> 00:24:49,020
So the nuclei divided as they should,

471
00:24:49,020 --> 00:24:53,823
but the cell bodies
weren't dividing normally.

472
00:24:56,100 --> 00:24:59,550
In fact, if we let these
cells grow for longer,

473
00:24:59,550 --> 00:25:02,700
they would make these
massive monster cells

474
00:25:02,700 --> 00:25:04,920
that had multiple nuclei in them.

475
00:25:04,920 --> 00:25:06,390
So they had clearly failed

476
00:25:06,390 --> 00:25:10,533
to divide multiple times
during that process.

477
00:25:12,960 --> 00:25:16,920
So the interesting
question, at this point,

478
00:25:16,920 --> 00:25:21,750
we wanted to dig into the problem
with cell division further

479
00:25:21,750 --> 00:25:24,480
and try to understand what
was happening with that.

480
00:25:24,480 --> 00:25:27,810
So we used live cell imaging

481
00:25:27,810 --> 00:25:30,270
to take pictures of cells
during cell division.

482
00:25:30,270 --> 00:25:32,520
And if you look on the left side here,

483
00:25:32,520 --> 00:25:34,920
you see that from the
time that wild-type cells

484
00:25:34,920 --> 00:25:37,440
started making a cleavage furrow,

485
00:25:37,440 --> 00:25:39,330
until they completely divided,

486
00:25:39,330 --> 00:25:41,640
was only about 15 or 20 minutes, right?

487
00:25:41,640 --> 00:25:43,413
We could watch this process.

488
00:25:44,460 --> 00:25:46,590
But if we looked at the mutant cells,

489
00:25:46,590 --> 00:25:47,880
the thing that was interesting

490
00:25:47,880 --> 00:25:51,120
is that they went almost
to the end, right?

491
00:25:51,120 --> 00:25:56,120
The cleavage furrow almost
completed, but around 25 minutes,

492
00:25:56,550 --> 00:25:57,510
most of the cells,

493
00:25:57,510 --> 00:25:59,910
even though they had almost
finished their cleavage furrow,

494
00:25:59,910 --> 00:26:02,580
hadn't completely divided yet.

495
00:26:02,580 --> 00:26:04,230
If we looked at later time points,

496
00:26:04,230 --> 00:26:06,840
we could actually start
to see those mutant cells

497
00:26:06,840 --> 00:26:09,750
fusing back together
to make those monsters

498
00:26:09,750 --> 00:26:11,703
that we had observed.

499
00:26:12,570 --> 00:26:14,220
So the question at this point

500
00:26:14,220 --> 00:26:16,770
that we thought was interesting is,

501
00:26:16,770 --> 00:26:20,670
is that kinesin two motor
protein that we had disrupted

502
00:26:20,670 --> 00:26:24,030
directly involved in
cell division somehow,

503
00:26:24,030 --> 00:26:27,570
or is it a more indirect
effect that we're seeing?

504
00:26:27,570 --> 00:26:32,570
And so, in steps our first undergraduate,

505
00:26:32,580 --> 00:26:34,680
so this is Frank Hardin,

506
00:26:34,680 --> 00:26:37,830
who was an undergraduate
student at UGA at the time,

507
00:26:37,830 --> 00:26:41,310
who was doing a project,
an independent project,

508
00:26:41,310 --> 00:26:44,700
as part of his cell biology lab course.

509
00:26:44,700 --> 00:26:48,960
And he decided to tackle this problem

510
00:26:48,960 --> 00:26:52,050
about cell division with
me, as part of that project.

511
00:26:52,050 --> 00:26:54,990
And what we did is use video microscopy

512
00:26:54,990 --> 00:26:57,930
of live dividing cells.

513
00:26:57,930 --> 00:26:59,820
And if you see on the left side,

514
00:26:59,820 --> 00:27:02,280
this is the anterior daughter cell,

515
00:27:02,280 --> 00:27:06,030
almost at the end of
division, it's not moving.

516
00:27:06,030 --> 00:27:08,970
That posterior daughter
cell that you just saw,

517
00:27:08,970 --> 00:27:11,580
is spinning dramatically.

518
00:27:11,580 --> 00:27:13,860
So let me go back and
show that to you again.

519
00:27:13,860 --> 00:27:17,280
So that cell on the left
is the anterior daughter,

520
00:27:17,280 --> 00:27:19,830
it's stuck in place,

521
00:27:19,830 --> 00:27:22,410
the daughter cell on the
right is the posterior

522
00:27:22,410 --> 00:27:24,873
and it's spinning like this.

523
00:27:26,670 --> 00:27:30,180
And notice that's right
before the cells split.

524
00:27:30,180 --> 00:27:34,200
So we hypothesized at this point

525
00:27:34,200 --> 00:27:35,940
that this process that we are observing,

526
00:27:35,940 --> 00:27:37,743
which we call rotokinesis,

527
00:27:39,030 --> 00:27:40,800
actually helps to weaken the bridge

528
00:27:40,800 --> 00:27:41,910
between the daughter cells

529
00:27:41,910 --> 00:27:44,100
at the very end of cell division,

530
00:27:44,100 --> 00:27:48,690
and that the problem with these cells,

531
00:27:48,690 --> 00:27:50,250
with the mutant cells,

532
00:27:50,250 --> 00:27:51,990
was actually that they couldn't divide

533
00:27:51,990 --> 00:27:54,780
because they couldn't do that process.

534
00:27:54,780 --> 00:27:56,430
So that was just a hypothesis,

535
00:27:56,430 --> 00:27:58,740
but we needed to test
that hypothesis, right?

536
00:27:58,740 --> 00:28:03,330
So the first thing we did
was to take wild type cells

537
00:28:03,330 --> 00:28:05,220
and actually inhibit their motility

538
00:28:05,220 --> 00:28:07,830
by putting them into a viscous,

539
00:28:07,830 --> 00:28:11,760
like really thick media that
they couldn't swim through.

540
00:28:11,760 --> 00:28:14,850
Under those conditions, the
cleavage furrow was normal,

541
00:28:14,850 --> 00:28:17,910
but if you notice here,
at later time points,

542
00:28:17,910 --> 00:28:21,060
there was often this long membrane bridge

543
00:28:21,060 --> 00:28:23,370
that was connecting the cells.

544
00:28:23,370 --> 00:28:26,370
So it really did look like
motility was important

545
00:28:26,370 --> 00:28:28,830
for these cells to be able to divide.

546
00:28:28,830 --> 00:28:32,227
But that wasn't enough,
because we also said,

547
00:28:32,227 --> 00:28:33,570
"Well if that's true,

548
00:28:33,570 --> 00:28:36,420
then what if we just add
motility to our mutants?

549
00:28:36,420 --> 00:28:38,490
Can we get them to divide normally?"

550
00:28:38,490 --> 00:28:40,980
So we did that,

551
00:28:40,980 --> 00:28:44,700
we just put the mutant
cells in a shaking culture,

552
00:28:44,700 --> 00:28:46,380
and it turns out just by doing that,

553
00:28:46,380 --> 00:28:48,480
we could get them to
divide almost normally.

554
00:28:48,480 --> 00:28:51,030
So this is the mutant with shaking,

555
00:28:51,030 --> 00:28:52,920
and this is the mutant without shaking,

556
00:28:52,920 --> 00:28:55,803
making those huge monster cells.

557
00:28:57,240 --> 00:29:00,330
So that was, we felt
like, pretty good support

558
00:29:00,330 --> 00:29:03,600
for this idea that motility is important

559
00:29:03,600 --> 00:29:05,100
for that organism to divide.

560
00:29:05,100 --> 00:29:07,673
So just to conclude this part,

561
00:29:07,673 --> 00:29:11,910
kinesin two was required
for cilia assembly

562
00:29:11,910 --> 00:29:14,880
and maintenance in tetrahymena.

563
00:29:14,880 --> 00:29:19,140
Tetrahymena uses this highly
coordinated kind of dance,

564
00:29:19,140 --> 00:29:23,490
this rotokinesis process
to complete cell division

565
00:29:23,490 --> 00:29:27,630
and the kinesin two mutants
fail to divide normally,

566
00:29:27,630 --> 00:29:28,463
we think,

567
00:29:28,463 --> 00:29:30,773
because they lack the ability to do that

568
00:29:30,773 --> 00:29:32,673
rotokinesis process.

569
00:29:33,540 --> 00:29:34,950
It turns out since then,

570
00:29:34,950 --> 00:29:37,110
there have been a number
of different papers

571
00:29:37,110 --> 00:29:39,330
that have been published on

572
00:29:39,330 --> 00:29:42,840
motility assisted cytokinesis processes

573
00:29:42,840 --> 00:29:45,690
in other types of organisms as well.

574
00:29:45,690 --> 00:29:50,370
So this is not a one-off, this
happens in other organisms.

575
00:29:50,370 --> 00:29:52,747
So for these vignettes,
I'm gonna do a little,

576
00:29:52,747 --> 00:29:53,880
"Where are they now?"

577
00:29:53,880 --> 00:29:55,620
For these people.

578
00:29:55,620 --> 00:29:59,490
So Frank completed his
bachelor's, master's,

579
00:29:59,490 --> 00:30:01,980
and PhD degrees in cellular biology

580
00:30:01,980 --> 00:30:03,720
at the University of Georgia,

581
00:30:03,720 --> 00:30:05,550
and later went on to found

582
00:30:05,550 --> 00:30:07,710
and run for several years,

583
00:30:07,710 --> 00:30:09,810
a youth education program

584
00:30:09,810 --> 00:30:14,160
at the Noble Research
Institute in Oklahoma,

585
00:30:14,160 --> 00:30:17,850
and he's now a licensing
associate at Washington University

586
00:30:17,850 --> 00:30:19,683
in St. Louis, Missouri.

587
00:30:20,700 --> 00:30:22,530
So thanks Frank.

588
00:30:22,530 --> 00:30:27,420
So for our second vignette,
we're gonna switch organisms,

589
00:30:27,420 --> 00:30:29,280
we're gonna switch research questions,

590
00:30:29,280 --> 00:30:31,593
and we're gonna switch institutions.

591
00:30:32,550 --> 00:30:34,950
So I'm gonna talk about a question

592
00:30:34,950 --> 00:30:37,620
about the regulation of motility

593
00:30:37,620 --> 00:30:42,620
in chlamydomonas reinhardtii
that started as a project

594
00:30:44,190 --> 00:30:45,990
that I was working on when I was a postdoc

595
00:30:45,990 --> 00:30:48,450
at UMass Medical School.

596
00:30:48,450 --> 00:30:52,020
But then that project basically
transitioned to Salem State

597
00:30:52,020 --> 00:30:53,730
when I came here,

598
00:30:53,730 --> 00:30:57,060
and I've had a number
of students work on that

599
00:30:57,060 --> 00:30:58,113
over the years.

600
00:30:59,430 --> 00:31:03,210
So at this point it's
important to understand that

601
00:31:03,210 --> 00:31:06,570
in chlamydomonas, we can generate mutants

602
00:31:06,570 --> 00:31:10,680
by introducing a DNA fragment that goes in

603
00:31:10,680 --> 00:31:14,610
and it lands in a random
place in the genome.

604
00:31:14,610 --> 00:31:17,370
So we're making random mutations by this.

605
00:31:17,370 --> 00:31:19,470
And if we happen to hit a gene

606
00:31:19,470 --> 00:31:21,360
that's important for
the function of cilia,

607
00:31:21,360 --> 00:31:25,710
we can screen out those
mutants by looking for defects

608
00:31:25,710 --> 00:31:30,393
in swimming or the inability
to assemble cilia at all.

609
00:31:32,280 --> 00:31:35,430
And when I was a postdoc at UMass,

610
00:31:35,430 --> 00:31:37,560
I did a lot of this mutant screening

611
00:31:37,560 --> 00:31:40,263
and I generated quite
a few of these mutants.

612
00:31:42,390 --> 00:31:43,737
I generated so many of those mutants

613
00:31:43,737 --> 00:31:45,270
and I couldn't handle them all.

614
00:31:45,270 --> 00:31:47,670
So I sent out a number of these mutants

615
00:31:47,670 --> 00:31:51,960
to other labs that I thought
might be interested in those.

616
00:31:51,960 --> 00:31:56,960
And so one of those mutants
that I sent to a colleague,

617
00:31:57,090 --> 00:31:58,340
Maureen Wirschell,

618
00:31:58,340 --> 00:32:00,813
at the University of
Mississippi Medical Center,

619
00:32:02,880 --> 00:32:04,650
was a chlamydomonas mutant

620
00:32:04,650 --> 00:32:06,930
that had a slow swimming phenotype

621
00:32:06,930 --> 00:32:09,690
that suggested that it might have a defect

622
00:32:09,690 --> 00:32:13,683
in those outer dynein arms
that I mentioned to you before.

623
00:32:14,670 --> 00:32:17,880
So just to remind you
about outer dynein arms,

624
00:32:17,880 --> 00:32:21,570
they're attached to those
outer microtubule doublets

625
00:32:21,570 --> 00:32:23,220
in the axoneme.

626
00:32:23,220 --> 00:32:28,140
And what they're doing is
what this is showing you

627
00:32:28,140 --> 00:32:30,030
on the left here,

628
00:32:30,030 --> 00:32:34,470
they are on one end of the dynein,

629
00:32:34,470 --> 00:32:37,420
they're attached to one of the
microtubules in the axoneme,

630
00:32:38,790 --> 00:32:41,760
and on the other end they're walking along

631
00:32:41,760 --> 00:32:45,330
another microtubule
that's adjacent to them.

632
00:32:45,330 --> 00:32:46,950
So what the effect of that is,

633
00:32:46,950 --> 00:32:50,610
is that the microtubules are
sliding against each other

634
00:32:50,610 --> 00:32:52,380
like this.

635
00:32:52,380 --> 00:32:56,430
That's the mechanism
behind the ciliary beating

636
00:32:56,430 --> 00:32:57,420
that I showed you before.

637
00:32:57,420 --> 00:33:01,200
So that movement of cilia is
about sliding of microtubules

638
00:33:01,200 --> 00:33:03,060
against each other.

639
00:33:03,060 --> 00:33:05,610
So on the right you're seeing a schematic

640
00:33:05,610 --> 00:33:07,290
of the outer dynein arm.

641
00:33:07,290 --> 00:33:10,440
It's a super complicated
structure just by itself.

642
00:33:10,440 --> 00:33:13,710
It's made up of multiple
different proteins.

643
00:33:13,710 --> 00:33:15,480
It's anchored at the base

644
00:33:15,480 --> 00:33:19,140
by a structure called the outer
dynein arm docking complex,

645
00:33:19,140 --> 00:33:22,929
which is made of three
different proteins called DC1

646
00:33:22,929 --> 00:33:24,930
DC2, and DC3.

647
00:33:24,930 --> 00:33:26,700
And before I jump to the next slide,

648
00:33:26,700 --> 00:33:29,940
I just wanna kind of
orient you very quickly

649
00:33:29,940 --> 00:33:33,930
to the nomenclature, 'cause it
could get a little confusing.

650
00:33:33,930 --> 00:33:36,870
The protein here is called DC1.

651
00:33:36,870 --> 00:33:38,400
So that's what I'm showing you there.

652
00:33:38,400 --> 00:33:41,010
The gene is called DCC1,

653
00:33:41,010 --> 00:33:43,710
and our mutant allele that we generated

654
00:33:43,710 --> 00:33:46,080
is called oda3-6.

655
00:33:46,080 --> 00:33:48,240
That's just like a nomenclature thing

656
00:33:48,240 --> 00:33:49,770
that we didn't have anything to do with.

657
00:33:49,770 --> 00:33:53,133
We're following the
guidelines for nomenclature.

658
00:33:54,600 --> 00:33:59,310
So in Maureen Wirschell's lab,

659
00:33:59,310 --> 00:34:02,100
our collaborator, they
used western blotting.

660
00:34:02,100 --> 00:34:05,580
Thanks Webb, for the
introduction on that earlier too.

661
00:34:05,580 --> 00:34:06,960
Basically all you need to understand

662
00:34:06,960 --> 00:34:08,340
about western blotting here,

663
00:34:08,340 --> 00:34:10,530
is that it's a way to
look for the presence

664
00:34:10,530 --> 00:34:13,020
of specific proteins.

665
00:34:13,020 --> 00:34:18,020
And so if you look in the wild type cells

666
00:34:18,120 --> 00:34:21,480
where we're looking for this DC1 protein,

667
00:34:21,480 --> 00:34:24,960
you see that the wild type
cells have DC1, right?

668
00:34:24,960 --> 00:34:28,200
The mutant, which is our
oda3-6 mutant over here,

669
00:34:28,200 --> 00:34:30,780
is lacking that DC1 protein,

670
00:34:30,780 --> 00:34:34,410
and also lacking the DC2 protein,

671
00:34:34,410 --> 00:34:36,843
which is the other one here.

672
00:34:38,160 --> 00:34:42,420
And that is actually very similar

673
00:34:42,420 --> 00:34:45,820
to a previously identified
oda mutant, oda3-1.

674
00:34:49,578 --> 00:34:51,810
So that suggested that
we might have a mutation

675
00:34:51,810 --> 00:34:55,440
in one of those genes,
maybe in DC1 or DC2.

676
00:34:55,440 --> 00:34:59,970
And so what Maureen's
lab did at that point

677
00:34:59,970 --> 00:35:04,380
was to use the polymerase
chain reaction or PCR,

678
00:35:04,380 --> 00:35:08,610
to basically look at the
structure of the DCC2 gene

679
00:35:08,610 --> 00:35:11,580
and the DCC1 gene in this mutant,

680
00:35:11,580 --> 00:35:13,473
to see if they could find the defect.

681
00:35:14,400 --> 00:35:18,690
If you're not used to looking
at polymerase chain reaction

682
00:35:18,690 --> 00:35:19,650
or thinking about that,

683
00:35:19,650 --> 00:35:21,810
all you really need to
understand at this point,

684
00:35:21,810 --> 00:35:23,790
is that it's a method that allows us

685
00:35:23,790 --> 00:35:26,610
to look for segments of DNA

686
00:35:26,610 --> 00:35:29,010
and determine whether they're there.

687
00:35:29,010 --> 00:35:32,340
And so like here we're
looking for this segment

688
00:35:32,340 --> 00:35:34,800
that's between these two arrowheads,

689
00:35:34,800 --> 00:35:37,200
which, if you are familiar with PCR,

690
00:35:37,200 --> 00:35:39,423
those are the PCR primer errors.

691
00:35:40,770 --> 00:35:43,920
And so what they found was, oh yeah,

692
00:35:43,920 --> 00:35:47,190
and I was gonna say also
this is basically like

693
00:35:47,190 --> 00:35:49,110
PCR-based COVID testing, right?

694
00:35:49,110 --> 00:35:51,810
Where you're looking for a sequence

695
00:35:51,810 --> 00:35:55,440
of the viral nucleic acid,

696
00:35:55,440 --> 00:35:58,080
by PCR in this case.

697
00:35:58,080 --> 00:35:59,970
So what they found was that

698
00:35:59,970 --> 00:36:01,860
when they looked at the DCC2 gene,

699
00:36:01,860 --> 00:36:02,790
it was totally normal.

700
00:36:02,790 --> 00:36:04,560
They didn't see any problems with that.

701
00:36:04,560 --> 00:36:08,160
The DCC1 gene was almost normal

702
00:36:08,160 --> 00:36:09,300
throughout the whole gene.

703
00:36:09,300 --> 00:36:13,080
But at the very beginning of the gene,

704
00:36:13,080 --> 00:36:16,500
there were some problems
that we could see on a gel.

705
00:36:16,500 --> 00:36:20,670
So in this diagram,

706
00:36:20,670 --> 00:36:24,540
this is looking at the
products of this PCR reaction.

707
00:36:24,540 --> 00:36:26,280
So really all you need to notice

708
00:36:26,280 --> 00:36:30,243
is that in this region where
these dotted lines are,

709
00:36:31,350 --> 00:36:34,890
there is a difference
between wild type and mutant.

710
00:36:34,890 --> 00:36:38,010
So here, wild type and look different,

711
00:36:38,010 --> 00:36:40,170
here wild type and mutant look different.

712
00:36:40,170 --> 00:36:41,850
Wild type mutant look different.

713
00:36:41,850 --> 00:36:43,680
Here they look pretty similar.

714
00:36:43,680 --> 00:36:46,290
There is a band about that same size,

715
00:36:46,290 --> 00:36:49,740
but Wirschell's lab,
they sequenced that band,

716
00:36:49,740 --> 00:36:54,540
and actually found that
it wasn't the DCC1 gene

717
00:36:54,540 --> 00:36:55,373
in that region,

718
00:36:55,373 --> 00:36:57,843
it was a non-specific PCR product.

719
00:36:59,280 --> 00:37:03,000
So this is when Daniela Montes Berrueta

720
00:37:03,000 --> 00:37:05,430
joined my research group,

721
00:37:05,430 --> 00:37:10,430
and Daniela, her idea
was to basically analyze

722
00:37:13,260 --> 00:37:14,700
that mutant further

723
00:37:14,700 --> 00:37:17,580
and try to get down to
the molecular details

724
00:37:17,580 --> 00:37:18,413
of that mutant,

725
00:37:18,413 --> 00:37:21,420
and understand what's going
on at the molecular level.

726
00:37:21,420 --> 00:37:23,850
And her idea was to use PCR primers

727
00:37:23,850 --> 00:37:26,520
that are inside that inserted DNA,

728
00:37:26,520 --> 00:37:30,000
in combination with the primers

729
00:37:30,000 --> 00:37:34,323
that were in the DCC1 gene
flanking that insertion site,

730
00:37:35,520 --> 00:37:39,600
do PCR to generate the PCR products here,

731
00:37:39,600 --> 00:37:44,280
and then sequence that,
determine the order of the bases

732
00:37:44,280 --> 00:37:47,790
in the PCR products that she generated,

733
00:37:47,790 --> 00:37:50,430
and then analyze those sequences

734
00:37:50,430 --> 00:37:53,973
to try to figure out the
molecular details there.

735
00:37:54,990 --> 00:37:56,430
So when she did that,

736
00:37:56,430 --> 00:38:00,270
she figured out that there
was a 12 base paired deletion

737
00:38:00,270 --> 00:38:05,270
from the five prime untranslated
region of the DCC1 gene,

738
00:38:05,970 --> 00:38:08,283
which is on chromosome 17.

739
00:38:09,360 --> 00:38:12,150
And at that site,

740
00:38:12,150 --> 00:38:16,170
the selectable marker DNA that
we expected to find there,

741
00:38:16,170 --> 00:38:17,003
was there.

742
00:38:17,003 --> 00:38:17,850
So that was kind of good.

743
00:38:17,850 --> 00:38:21,993
It told us that that probably
was the site of our mutation.

744
00:38:22,920 --> 00:38:24,030
But interestingly,

745
00:38:24,030 --> 00:38:27,840
there's also a piece of
chlamydomonas chromosome six

746
00:38:27,840 --> 00:38:30,690
that was inserted in that same site.

747
00:38:30,690 --> 00:38:33,510
And maybe even more interestingly,

748
00:38:33,510 --> 00:38:36,000
there was a 500 base pair region

749
00:38:36,000 --> 00:38:40,860
of kind of poor sequence quality.

750
00:38:40,860 --> 00:38:43,020
It didn't give us great
sequence information,

751
00:38:43,020 --> 00:38:44,280
but what we got out of it

752
00:38:44,280 --> 00:38:48,060
was that it was about 88% identical

753
00:38:48,060 --> 00:38:50,970
to the human RB1 gene.

754
00:38:50,970 --> 00:38:52,620
So what we think is going on there,

755
00:38:52,620 --> 00:38:55,980
is that during the process
of generating that mutant,

756
00:38:55,980 --> 00:38:59,040
some human DNA actually got introduced

757
00:38:59,040 --> 00:39:03,060
as a contaminating sequence

758
00:39:03,060 --> 00:39:05,883
that got introduced at that location.

759
00:39:07,110 --> 00:39:08,670
So this was important

760
00:39:08,670 --> 00:39:10,650
because this is actually the first

761
00:39:10,650 --> 00:39:15,650
of these oda3 mutants that
had ever been characterized

762
00:39:16,470 --> 00:39:19,680
at that level of molecular detail.

763
00:39:19,680 --> 00:39:24,680
And Daniela ended up as
a co-author on this paper

764
00:39:26,610 --> 00:39:28,470
that we published on this mutant.

765
00:39:28,470 --> 00:39:30,090
So that was exciting.

766
00:39:30,090 --> 00:39:32,280
So in the where are they now segment,

767
00:39:32,280 --> 00:39:35,010
so Daniella is now a
senior research associate

768
00:39:35,010 --> 00:39:38,940
in virology and infectious
disease at Moderna.

769
00:39:38,940 --> 00:39:42,120
And the couple of publications
that you can see here

770
00:39:42,120 --> 00:39:45,930
show you that she's really
been kind of at the forefront

771
00:39:45,930 --> 00:39:48,600
of helping to develop some of the vaccines

772
00:39:48,600 --> 00:39:51,783
against COVID variants of late.

773
00:39:53,940 --> 00:39:56,880
So this is gonna take us to
our last of these vignettes.

774
00:39:56,880 --> 00:40:00,280
And in this we're gonna talk about

775
00:40:01,590 --> 00:40:04,290
some genetic techniques

776
00:40:04,290 --> 00:40:06,180
that we're trying to implement,

777
00:40:06,180 --> 00:40:11,180
to think about this process
of cilia assembly again.

778
00:40:11,640 --> 00:40:13,353
So just to remind you,

779
00:40:14,220 --> 00:40:16,530
during cilia assembly
there are hundreds of genes

780
00:40:16,530 --> 00:40:18,120
that get turned on.

781
00:40:18,120 --> 00:40:20,400
And what we're trying to answer here

782
00:40:20,400 --> 00:40:22,593
is what flips the switch?

783
00:40:24,390 --> 00:40:28,440
How do these genes get
regulated all at the same time

784
00:40:28,440 --> 00:40:30,030
so that they can build cilium?

785
00:40:30,030 --> 00:40:33,030
So it turns out that
chlamydomonas is a great organism

786
00:40:33,030 --> 00:40:35,100
for answering this question,

787
00:40:35,100 --> 00:40:37,200
because we can get these cells

788
00:40:37,200 --> 00:40:42,200
to express these genes in
the lab simultaneously,

789
00:40:43,080 --> 00:40:44,790
in a whole population of cells,

790
00:40:44,790 --> 00:40:48,120
by getting the cells to lose their cilia

791
00:40:48,120 --> 00:40:50,250
and then regrow those cilia.

792
00:40:50,250 --> 00:40:52,530
And we can do this really easily

793
00:40:52,530 --> 00:40:56,400
with just a brief exposure
to acidic conditions.

794
00:40:56,400 --> 00:40:58,230
Cells drop their cilia,

795
00:40:58,230 --> 00:41:00,510
and then we return the cells to neutral

796
00:41:00,510 --> 00:41:02,730
and they regrow at that point,

797
00:41:02,730 --> 00:41:05,283
and those genes are
expressed during that time.

798
00:41:06,960 --> 00:41:08,430
So you can see here

799
00:41:08,430 --> 00:41:12,000
in this graph of cilia length versus time,

800
00:41:12,000 --> 00:41:14,730
that these cells are able to regrow

801
00:41:14,730 --> 00:41:18,450
almost full length cilia
within about 90 minutes.

802
00:41:18,450 --> 00:41:21,450
So it makes it really
practical to do this in the lab

803
00:41:21,450 --> 00:41:23,370
with undergraduate students, right?

804
00:41:23,370 --> 00:41:26,403
Between classes, we can do
these kind of experiments.

805
00:41:27,630 --> 00:41:31,980
So during my time as a postdoc

806
00:41:31,980 --> 00:41:34,440
in George Witman's lab
at UMass Medical School,

807
00:41:34,440 --> 00:41:37,680
I had developed a system
for us to actually be able

808
00:41:37,680 --> 00:41:41,610
to follow a gene expression in these cells

809
00:41:41,610 --> 00:41:43,950
during the time that they're regrowing.

810
00:41:43,950 --> 00:41:46,680
And so the system is based in part

811
00:41:46,680 --> 00:41:50,130
on the coding region from a gene,

812
00:41:50,130 --> 00:41:54,100
from a marine crustacean
called Gaussia princeps

813
00:41:55,260 --> 00:41:58,500
that's involved in the
bioluminescence of this organism.

814
00:41:58,500 --> 00:42:02,703
So it allows these cells to
glow basically in the water.

815
00:42:03,570 --> 00:42:08,460
And when the GLuc protein
is expressed from that,

816
00:42:08,460 --> 00:42:10,710
which is this Gaussia luciferase protein,

817
00:42:10,710 --> 00:42:13,500
when that's expressed
from that coding region,

818
00:42:13,500 --> 00:42:17,130
and then you expose that
enzyme to its substrate,

819
00:42:17,130 --> 00:42:19,830
coelenterazine, it releases light.

820
00:42:19,830 --> 00:42:21,543
And so we can do this in the lab,

821
00:42:22,410 --> 00:42:26,250
and the idea was to connect up a promoter,

822
00:42:26,250 --> 00:42:30,240
which is kind of a regulatory
region for the gene

823
00:42:30,240 --> 00:42:34,830
to that coding region for
that Gaussia luciferase,

824
00:42:34,830 --> 00:42:38,910
and then put that whole thing
into chlamydomonas cells,

825
00:42:38,910 --> 00:42:42,420
get the cells to regrow their
cilia like I just showed you,

826
00:42:42,420 --> 00:42:45,000
and during cilia regrowth,

827
00:42:45,000 --> 00:42:47,370
those cells would produce the luciferase,

828
00:42:47,370 --> 00:42:50,100
and we'd be able to detect it

829
00:42:50,100 --> 00:42:53,880
as a way to look at the
expression of these genes

830
00:42:53,880 --> 00:42:55,380
in those cells.

831
00:42:55,380 --> 00:42:58,590
So it turned out that
system worked really nicely.

832
00:42:58,590 --> 00:43:00,060
So if you look on the bottom here,

833
00:43:00,060 --> 00:43:02,680
this is a graph of a population of cells

834
00:43:03,810 --> 00:43:07,770
regrowing their cilia after de-ciliation.

835
00:43:07,770 --> 00:43:10,950
And in that same population of cells,

836
00:43:10,950 --> 00:43:13,980
we're looking at the luciferase activity

837
00:43:13,980 --> 00:43:17,340
in cells expressing that
luciferase construct.

838
00:43:17,340 --> 00:43:20,070
And in green here are
the mock treated cells,

839
00:43:20,070 --> 00:43:22,860
so you can see they
didn't lose their cilia,

840
00:43:22,860 --> 00:43:25,590
and they also don't
up-regulate the luciferase.

841
00:43:25,590 --> 00:43:29,010
So around 60 minutes after de-ciliation,

842
00:43:29,010 --> 00:43:33,753
we get this really nice peak
of that luciferase activity.

843
00:43:34,650 --> 00:43:38,910
So now that we had a way to
look at the gene expression,

844
00:43:38,910 --> 00:43:42,840
we wanted to try to use a
couple of different approaches

845
00:43:42,840 --> 00:43:45,000
to tackle this problem.

846
00:43:45,000 --> 00:43:48,180
So the first of these is forward genetics.

847
00:43:48,180 --> 00:43:49,680
And in the forward genetics approach,

848
00:43:49,680 --> 00:43:51,210
it's like what I showed you before,

849
00:43:51,210 --> 00:43:54,240
we randomly mutate the cell,

850
00:43:54,240 --> 00:43:57,300
and then the idea was to look for mutants

851
00:43:57,300 --> 00:44:01,560
that didn't up-regulate that
luciferase expression anymore.

852
00:44:01,560 --> 00:44:04,383
So we had disrupted that pathway somehow.

853
00:44:05,310 --> 00:44:08,130
And kind of importantly,
and interestingly,

854
00:44:08,130 --> 00:44:11,460
those cells very often regrew
their cilia really slowly.

855
00:44:11,460 --> 00:44:14,280
So sometimes we could also just screen for

856
00:44:14,280 --> 00:44:19,280
slow regrowth of cilia, as
another thing to look for.

857
00:44:19,320 --> 00:44:23,190
And because these are random
mutations in the cells,

858
00:44:23,190 --> 00:44:27,123
we then need to go try to
figure out where the genes are.

859
00:44:28,320 --> 00:44:31,620
And a couple of students that
worked really hard on this,

860
00:44:31,620 --> 00:44:32,730
among others,

861
00:44:32,730 --> 00:44:36,390
but a couple of the students
that worked really hard on this

862
00:44:36,390 --> 00:44:40,830
were Ellen Acheampong and Webb Camille.

863
00:44:40,830 --> 00:44:42,810
And Ellen,

864
00:44:42,810 --> 00:44:44,880
during her honors thesis work

865
00:44:44,880 --> 00:44:48,750
screened about 3000
randomly generated mutants.

866
00:44:48,750 --> 00:44:49,740
And among those,

867
00:44:49,740 --> 00:44:53,100
14 of them had this
delayed regrowth phenotype

868
00:44:53,100 --> 00:44:54,660
that she was looking for,

869
00:44:54,660 --> 00:44:57,180
and two of them appeared
to be really interesting

870
00:44:57,180 --> 00:44:59,850
'cause they looked like
they were signaling proteins

871
00:44:59,850 --> 00:45:02,160
which could be in this pathway somehow

872
00:45:02,160 --> 00:45:04,590
of regulating these genes.

873
00:45:04,590 --> 00:45:08,010
But when we dug into
those mutants further,

874
00:45:08,010 --> 00:45:11,970
it turns out that those genes
were not actually mutated

875
00:45:11,970 --> 00:45:14,040
in those mutant strains.

876
00:45:14,040 --> 00:45:14,880
I showed you before,

877
00:45:14,880 --> 00:45:17,910
we get these complicated
inserts in chlamydomonas

878
00:45:17,910 --> 00:45:19,020
in these mutants,

879
00:45:19,020 --> 00:45:21,570
and it turns out that that sometimes

880
00:45:21,570 --> 00:45:22,860
makes it kind of difficult

881
00:45:22,860 --> 00:45:25,323
to identify these insertion
sites a little bit.

882
00:45:26,400 --> 00:45:29,250
And Webb also did some work on this

883
00:45:29,250 --> 00:45:31,140
and screened about a thousand mutants,

884
00:45:31,140 --> 00:45:35,460
and when he did the PCR
and sequencing on these,

885
00:45:35,460 --> 00:45:39,630
basically they were in
known cilia proteins,

886
00:45:39,630 --> 00:45:40,590
which were interesting,

887
00:45:40,590 --> 00:45:44,220
but we were pushing hard to
try to identify new pathways

888
00:45:44,220 --> 00:45:46,320
that hadn't been identified before,

889
00:45:46,320 --> 00:45:49,680
so we didn't follow up
on those mutants further.

890
00:45:49,680 --> 00:45:51,960
So we don't have any
like major breakthroughs

891
00:45:51,960 --> 00:45:54,570
to report from the forward
genetics approach at this point,

892
00:45:54,570 --> 00:45:57,360
but it's still a really promising approach

893
00:45:57,360 --> 00:45:59,730
and I think it could be really important.

894
00:45:59,730 --> 00:46:04,730
So Ellen is now a third year
PhD candidate in microbiology

895
00:46:05,160 --> 00:46:08,670
and physiological systems at
UMass Chan Medical School.

896
00:46:08,670 --> 00:46:12,060
And those of you that
saw Webb's talk earlier,

897
00:46:12,060 --> 00:46:15,150
know that he's currently a
fourth year MD PhD candidate

898
00:46:15,150 --> 00:46:18,540
in biochemistry and
molecular biotechnology

899
00:46:18,540 --> 00:46:21,513
at UMass Chan Medical School as well.

900
00:46:22,350 --> 00:46:25,170
All right, so we're nearly there.

901
00:46:25,170 --> 00:46:27,360
In the reverse genetics approach,

902
00:46:27,360 --> 00:46:32,360
the idea is we identify genes
that we pick our interest in

903
00:46:32,640 --> 00:46:36,270
and then we go in and
make targeted mutations

904
00:46:36,270 --> 00:46:37,140
in those genes.

905
00:46:37,140 --> 00:46:41,370
So not random mutations,
but targeted mutations.

906
00:46:41,370 --> 00:46:44,100
And the idea here was that

907
00:46:44,100 --> 00:46:46,110
we were gonna use either biochemical

908
00:46:46,110 --> 00:46:48,900
or bioinformatics methods

909
00:46:48,900 --> 00:46:52,380
to try to identify the candidate genes,

910
00:46:52,380 --> 00:46:54,570
and then use a CRISPR based approach

911
00:46:54,570 --> 00:46:57,480
to try to disrupt those genes.

912
00:46:57,480 --> 00:47:00,180
And so it turns out that we decided

913
00:47:00,180 --> 00:47:02,400
to go with the CRISPR first,

914
00:47:02,400 --> 00:47:05,910
just to show that we could
do it here in our lab,

915
00:47:05,910 --> 00:47:07,530
at Salem State,

916
00:47:07,530 --> 00:47:10,983
before we dug in, to try to
identify the candidate genes.

917
00:47:11,846 --> 00:47:16,846
And so a paper was published
in 2020 by George Witman's lab

918
00:47:17,820 --> 00:47:19,350
and some of his collaborators,

919
00:47:19,350 --> 00:47:23,310
basically showing that you
could do CRISPR efficiently

920
00:47:23,310 --> 00:47:25,080
in chlamydomonas,

921
00:47:26,730 --> 00:47:29,400
which had not been the case prior to that.

922
00:47:29,400 --> 00:47:31,290
And that Gleidia Sauli,

923
00:47:31,290 --> 00:47:34,560
when she joined the research group,

924
00:47:34,560 --> 00:47:38,730
was gonna basically
disrupt this FAP70 gene

925
00:47:38,730 --> 00:47:43,650
by inserting a hygromycin
resistance cassette,

926
00:47:43,650 --> 00:47:46,890
or a piece of DNA that confers
resistance to hygromycin

927
00:47:46,890 --> 00:47:48,190
in the middle of the gene.

928
00:47:49,410 --> 00:47:53,280
For any of you that
don't know about CRISPR,

929
00:47:53,280 --> 00:47:54,840
basically there are three parts.

930
00:47:54,840 --> 00:47:57,690
There's a nuclease that
makes a cut in the gene,

931
00:47:57,690 --> 00:48:00,060
and then there are these two RNAs

932
00:48:00,060 --> 00:48:05,060
that are involved in getting
the nuclease to the DNA.

933
00:48:05,280 --> 00:48:09,960
So the nuclease enzyme binds
to the DNA, opens it up,

934
00:48:09,960 --> 00:48:13,023
and makes this double
stranded cut at that site.

935
00:48:14,790 --> 00:48:17,100
So the idea in Gleidia's project was

936
00:48:17,100 --> 00:48:20,370
she was gonna start with the FAP70 gene,

937
00:48:20,370 --> 00:48:23,790
target the Cas9 enzyme to the FAP70 gene

938
00:48:23,790 --> 00:48:25,620
to make that cut,

939
00:48:25,620 --> 00:48:29,970
and then take that hygromycin
resistance cassette

940
00:48:29,970 --> 00:48:34,560
and add some FAP70 sequences
onto the end of it,

941
00:48:34,560 --> 00:48:38,580
and then get it to insert in
the middle of the FAP70 gene.

942
00:48:38,580 --> 00:48:40,893
We decided to do FAP70,

943
00:48:41,730 --> 00:48:45,390
it's a gene that encodes
part of the central pair

944
00:48:45,390 --> 00:48:47,700
in chlamydomonas axoneme,

945
00:48:47,700 --> 00:48:51,360
but mainly we did it because
that's one of the genes

946
00:48:51,360 --> 00:48:54,570
that the Whitman lab had
targeted in their paper.

947
00:48:54,570 --> 00:48:56,040
So she was just trying to repeat

948
00:48:56,040 --> 00:49:00,420
what they had done
essentially, and it worked.

949
00:49:00,420 --> 00:49:01,860
That's the short story here.

950
00:49:01,860 --> 00:49:05,220
Basically she tested
out some of the strains

951
00:49:05,220 --> 00:49:08,820
that she generated with the
polymerase chain reaction,

952
00:49:08,820 --> 00:49:10,140
and again,

953
00:49:10,140 --> 00:49:13,110
by amplifying with these pairs of primers

954
00:49:13,110 --> 00:49:14,280
in wild type cells,

955
00:49:14,280 --> 00:49:18,630
she expected to get something
that was about 317 base pairs,

956
00:49:18,630 --> 00:49:21,690
which she saw very
nicely in the wild type.

957
00:49:21,690 --> 00:49:25,200
So these are wild type cells
here, but in the mutant,

958
00:49:25,200 --> 00:49:29,220
she expected to see either
no band between those primers

959
00:49:29,220 --> 00:49:31,920
or a larger fragment,

960
00:49:31,920 --> 00:49:34,830
because that hygromycin piece got inserted

961
00:49:34,830 --> 00:49:36,130
in the middle of the gene.

962
00:49:37,890 --> 00:49:39,900
So what did she see?

963
00:49:39,900 --> 00:49:41,850
With her FAP70 primers,

964
00:49:41,850 --> 00:49:46,850
only four out of the 17
strains that she tested

965
00:49:46,980 --> 00:49:50,460
had the normal wild type sequence.

966
00:49:50,460 --> 00:49:53,670
So that meant that 13
out of the 17 strains

967
00:49:53,670 --> 00:49:56,220
were either completely
missing that sequence,

968
00:49:56,220 --> 00:50:00,780
or had that two kilobase
insert, approximately,

969
00:50:00,780 --> 00:50:03,540
that she was expecting
to see in the mutants.

970
00:50:03,540 --> 00:50:07,050
So it really looks like
that she was successful

971
00:50:07,050 --> 00:50:10,443
in getting CRISPR to work at Salem State.

972
00:50:12,390 --> 00:50:15,780
The mutation rate that
she got was about 76%,

973
00:50:15,780 --> 00:50:19,380
which is comparable to what the
Witman lab had shown before.

974
00:50:19,380 --> 00:50:22,680
So that was really exciting.

975
00:50:22,680 --> 00:50:25,350
Gleidia is now a research technician

976
00:50:25,350 --> 00:50:28,320
in the Fanning Lab in the
Department of Neurology

977
00:50:28,320 --> 00:50:30,900
at Brigham and Women's Hospital,

978
00:50:30,900 --> 00:50:35,583
and she's working on Parkinson's disease.

979
00:50:37,260 --> 00:50:40,650
And so just next steps really quickly,

980
00:50:40,650 --> 00:50:43,353
a larger genetic screen potentially,

981
00:50:44,220 --> 00:50:47,790
and for that reverse genetics method,

982
00:50:47,790 --> 00:50:52,790
identifying genes to try to
target with the CRISPR system

983
00:50:52,800 --> 00:50:54,270
that we've got working now.

984
00:50:54,270 --> 00:50:56,130
So either a biochemical approach

985
00:50:56,130 --> 00:50:57,810
or a bioinformatics approach.

986
00:50:57,810 --> 00:51:02,670
And Campbell Boisvert has been
working with me this year,

987
00:51:02,670 --> 00:51:03,720
since the beginning of the year,

988
00:51:03,720 --> 00:51:05,760
trying to do the bioinformatics approach

989
00:51:05,760 --> 00:51:09,270
to identify those candidate genes.

990
00:51:09,270 --> 00:51:13,410
So I just wanted to
acknowledge the long list

991
00:51:13,410 --> 00:51:15,540
of other students,

992
00:51:15,540 --> 00:51:18,210
and a colleague who has worked

993
00:51:18,210 --> 00:51:20,373
on this project a little bit with me,

994
00:51:21,540 --> 00:51:24,780
and other mentors and students,

995
00:51:24,780 --> 00:51:27,810
and all of the funding for the
Salem State part of this work

996
00:51:27,810 --> 00:51:32,810
has come for from the MSCA Fund
for Continuing Scholarship,

997
00:51:33,240 --> 00:51:36,450
as well as to an SSU Minigrant

998
00:51:36,450 --> 00:51:40,023
and a Flash Grant that I
was awarded for this work.

999
00:51:40,980 --> 00:51:44,130
I couldn't do a talk about
undergraduate research

1000
00:51:44,130 --> 00:51:46,650
without honoring my mentors

1001
00:51:46,650 --> 00:51:49,890
when I was an undergraduate researcher.

1002
00:51:49,890 --> 00:51:54,300
Mary Case gave me my first
start in a research lab

1003
00:51:54,300 --> 00:51:59,300
and got me going, doing
research on fungal genetics.

1004
00:52:00,240 --> 00:52:03,630
And Michael Bender, who's now at the NIH,

1005
00:52:03,630 --> 00:52:05,670
introduced me to fruit fly genetics.

1006
00:52:05,670 --> 00:52:08,580
So I did drosophila work with him.

1007
00:52:08,580 --> 00:52:10,110
So thanks so much to them,

1008
00:52:10,110 --> 00:52:13,020
and then I'm gonna end with this slide.

1009
00:52:13,020 --> 00:52:17,250
I dug around looking for this,
to see if this were the case.

1010
00:52:17,250 --> 00:52:20,040
So when Charles Darwin

1011
00:52:20,040 --> 00:52:24,093
was at the University
of Edinburgh studying,

1012
00:52:25,320 --> 00:52:30,320
he did work with Robert Grant,
who was a marine biologist.

1013
00:52:30,570 --> 00:52:34,080
And the work was to
basically take a microscope

1014
00:52:34,080 --> 00:52:38,010
and go look at marine
life in the local area.

1015
00:52:38,010 --> 00:52:43,010
And in March of 1827, when
Darwin was 18 years old,

1016
00:52:43,110 --> 00:52:45,337
he wrote this in his lab notebook.

1017
00:52:45,337 --> 00:52:48,480
"In this species I believe
I was the first to observe

1018
00:52:48,480 --> 00:52:51,180
both the animal and it's ciliae."

1019
00:52:51,180 --> 00:52:53,497
Yes.

1020
00:52:53,497 --> 00:52:55,890
"In most rapid movement.

1021
00:52:55,890 --> 00:52:57,410
By the aid of these ciliae

1022
00:52:57,410 --> 00:52:59,340
it could revolve in its capsule

1023
00:52:59,340 --> 00:53:02,250
and when freed from it move so quickly,

1024
00:53:02,250 --> 00:53:05,220
as to be discernible to the naked eye

1025
00:53:05,220 --> 00:53:06,600
at some distance.

1026
00:53:06,600 --> 00:53:08,580
To what animal these ova belong.

1027
00:53:08,580 --> 00:53:10,260
I am ignorant?"

1028
00:53:10,260 --> 00:53:14,743
So if you happen to decide
to do undergraduate research,

1029
00:53:14,743 --> 00:53:17,820
if you happen to do research on cilia,

1030
00:53:17,820 --> 00:53:19,413
you're in really good company.

1031
00:53:21,240 --> 00:53:22,443
Thank you very much.

1032
00:53:23,993 --> 00:53:27,613
(host faintly speaking)

1033
00:53:27,613 --> 00:53:30,360
- [Host] A 130 people on the webinar,

1034
00:53:30,360 --> 00:53:33,360
and I'll read the first question,

1035
00:53:33,360 --> 00:53:36,713
you don't have to repeat it,
'cause I'm unmuted at this end.

1036
00:53:36,713 --> 00:53:39,390
And the question is basically,

1037
00:53:39,390 --> 00:53:42,990
do you find that being an
educator and teaching classes

1038
00:53:42,990 --> 00:53:45,000
helps you come up with new ideas

1039
00:53:45,000 --> 00:53:49,200
and questions to conduct
research in the lab?

1040
00:53:49,200 --> 00:53:51,153
- Yeah, absolutely.

1041
00:53:53,400 --> 00:53:56,040
Teaching genetics and
teaching cell biology,

1042
00:53:56,040 --> 00:53:59,850
I'm all the time having to
learn new things, right?

1043
00:53:59,850 --> 00:54:04,850
And so the types of
new cutting edge things

1044
00:54:05,130 --> 00:54:07,860
I'm having to learn in
teaching genetics all the time,

1045
00:54:07,860 --> 00:54:12,390
are always giving me ideas
about new experiments to do.

1046
00:54:12,390 --> 00:54:17,390
So just very recently I've
had this flood of ideas

1047
00:54:18,720 --> 00:54:20,850
as I was starting to work on this talk,

1048
00:54:20,850 --> 00:54:25,260
a lot of which have come
out of genetics courses

1049
00:54:25,260 --> 00:54:26,550
and cell biology courses.

1050
00:54:26,550 --> 00:54:27,693
So definitely.

1051
00:54:36,270 --> 00:54:37,966
- [Audience Member] That
was a great talk by the way.

1052
00:54:37,966 --> 00:54:38,949
- Thanks.
- So,

1053
00:54:38,949 --> 00:54:42,150
I have a question about Boolean genetics

1054
00:54:42,150 --> 00:54:47,100
on mutations and genes that are known

1055
00:54:47,100 --> 00:54:48,393
to cause kidney diseases,

1056
00:54:50,035 --> 00:54:51,641
and I'm wondering what's the best avenue

1057
00:54:51,641 --> 00:54:54,063
(audience member faintly speaking)

1058
00:54:54,063 --> 00:54:55,063
in your lab.

1059
00:54:56,160 --> 00:54:58,950
- Tackling disease-causing genes?

1060
00:54:58,950 --> 00:55:02,760
Yeah, so there's the potential for that.

1061
00:55:02,760 --> 00:55:06,000
In the area that we're
kind of focusing on now,

1062
00:55:06,000 --> 00:55:08,793
with looking at gene regulation,

1063
00:55:10,530 --> 00:55:15,120
it turns out that gene regulation pathways

1064
00:55:15,120 --> 00:55:18,930
are not super well
conserved evolutionarily,

1065
00:55:18,930 --> 00:55:20,460
there's quite a bit of divergence

1066
00:55:20,460 --> 00:55:24,780
between different evolutionary
lineages and that.

1067
00:55:24,780 --> 00:55:28,260
So we don't know what
we'll find in that area.

1068
00:55:28,260 --> 00:55:30,570
Certainly I always have my eyes open

1069
00:55:30,570 --> 00:55:31,590
for interesting things.

1070
00:55:31,590 --> 00:55:35,010
Signaling pathways maybe
are a little more likely

1071
00:55:35,010 --> 00:55:37,650
than the direct transcriptional regulators

1072
00:55:37,650 --> 00:55:39,723
to be evolutionarily conserved.

1073
00:55:41,520 --> 00:55:45,573
But yeah, I mean I always
have my eyes open for that.

1074
00:55:47,160 --> 00:55:50,310
One of the things that I
always kind of have in my mind

1075
00:55:50,310 --> 00:55:52,950
with the transcriptional
regulation part of it,

1076
00:55:52,950 --> 00:55:55,740
is that even if you find things

1077
00:55:55,740 --> 00:55:58,920
that are only conserved
in ciliates for instance,

1078
00:55:58,920 --> 00:56:02,490
or single-celled organisms,

1079
00:56:02,490 --> 00:56:03,970
there are a number of those

1080
00:56:05,586 --> 00:56:08,490
that are pathogens in humans, right?

1081
00:56:08,490 --> 00:56:10,410
That cause infectious disease.

1082
00:56:10,410 --> 00:56:13,260
And so finding a novel pathway

1083
00:56:13,260 --> 00:56:16,440
that shuts down the ability
of a single-celled organism

1084
00:56:16,440 --> 00:56:19,020
to make its cilia, for instance,

1085
00:56:19,020 --> 00:56:21,300
could be a really interesting
therapeutic target

1086
00:56:21,300 --> 00:56:24,663
for infectious eukaryotic diseases.

1087
00:56:28,410 --> 00:56:31,073
- [Host] We have another question
actually, this is from me.

1088
00:56:31,920 --> 00:56:34,650
So if you could repeat it
for our online audience,

1089
00:56:34,650 --> 00:56:37,980
knowing that we have
130, possibly, hopefully,

1090
00:56:37,980 --> 00:56:39,960
students online,

1091
00:56:39,960 --> 00:56:42,660
what do you look for in the ideal student

1092
00:56:42,660 --> 00:56:43,893
to work in lab with you?

1093
00:56:45,480 --> 00:56:46,503
- Yeah,

1094
00:56:47,730 --> 00:56:49,170
so I mentioned this a little bit

1095
00:56:49,170 --> 00:56:52,083
in my introduction to Webb earlier.

1096
00:56:53,250 --> 00:56:58,020
I look for students who
just have sort of a gleam

1097
00:56:58,020 --> 00:56:59,580
in their eye when they're thinking about

1098
00:56:59,580 --> 00:57:01,470
and talking about science.

1099
00:57:01,470 --> 00:57:02,640
I can just see that.

1100
00:57:02,640 --> 00:57:03,603
I just know.

1101
00:57:05,760 --> 00:57:08,040
They have that kind of level of excitement

1102
00:57:08,040 --> 00:57:10,470
that I felt where I woke up

1103
00:57:10,470 --> 00:57:11,940
in the middle of the night sometimes

1104
00:57:11,940 --> 00:57:14,760
when I was in grad school and
even when I was a postdoc,

1105
00:57:14,760 --> 00:57:16,860
I would sometimes just wake
up in the middle of the night

1106
00:57:16,860 --> 00:57:19,020
going like, "Oh, what I'm gonna..."

1107
00:57:19,020 --> 00:57:22,110
Like having an idea, an
epiphany about my experiment

1108
00:57:22,110 --> 00:57:23,910
in the middle of the night, right?

1109
00:57:23,910 --> 00:57:25,350
It happens, yes.

1110
00:57:25,350 --> 00:57:26,373
You dream about it.

1111
00:57:28,080 --> 00:57:30,150
And I can just sort of like...

1112
00:57:30,150 --> 00:57:31,500
When a student comes to talk to me,

1113
00:57:31,500 --> 00:57:32,700
I kind of get a sense of,

1114
00:57:32,700 --> 00:57:35,073
if you have that kind
of level of excitement,

1115
00:57:35,970 --> 00:57:36,870
I'm looking for that,

1116
00:57:36,870 --> 00:57:41,593
I'm looking a little bit of
sort of confidence on your part.

1117
00:57:45,540 --> 00:57:47,730
I don't mean you have
to be super confident,

1118
00:57:47,730 --> 00:57:50,910
but you have to be confident enough

1119
00:57:50,910 --> 00:57:55,500
to just go to a faculty
member and ask, right?

1120
00:57:55,500 --> 00:57:59,130
To say like, "I don't really
know what research is about.

1121
00:57:59,130 --> 00:58:01,140
So I'm not super confident about that,

1122
00:58:01,140 --> 00:58:02,340
but I'm at least confident enough

1123
00:58:02,340 --> 00:58:06,270
to come talk to you about
it and have a conversation."

1124
00:58:06,270 --> 00:58:08,506
I see those two things, (fingers click)

1125
00:58:08,506 --> 00:58:09,450
I'm good.

1126
00:58:09,450 --> 00:58:10,732
That's about all I need,

1127
00:58:10,732 --> 00:58:14,553
and we'll sit and talk for a while and so.