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- And I'd like to introduce my colleague,

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Dr. Thea Popolizio who will
introduce our speaker today.

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- Good morning, everyone.

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It's my pleasure to
welcome Dr. Andrea Bodnar

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to speak at our 2022 Darwin Festival.

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Dr. Bodnar received her
bachelor's degree and PhD

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from McMaster University in Canada,

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followed by postdoctoral
work in neurological sciences

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at the University of London.

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From there, she held
senior scientist positions

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in the Oncology Department
of Hoffman LaRoche,

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the Department of Cell
Biology and Pharmacology

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at Geron Corporation

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and the Bioprocessing Technology Institute

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at the National University of Singapore.

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From 2003 to 2017, Dr.
Bodnar was a senior scientist

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in the Molecular Discovery Lab

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at the Bermuda Institute
of Ocean Sciences,

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where her research focused
on using sea urchins

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to understand the cellular
and molecular mechanisms

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underlying aging and cancer.

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In her present position,
as Science Director

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at the Gloucester Marine
Genomics Institute,

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Dr. Bodnar brings all this
(interference drowns out Thea)

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academic and industry experience,

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bridging marine biology, human
health, and biotechnology.

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Dr. Bodnar, I wanna extend
a warm welcome to you

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from the Salem State Community.

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We are so happy to have you
and look forward to learning

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about the work being done at GMGI.

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- Thank you Thea for
that nice introduction

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and good morning, everybody.

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Thanks to the organizers for
the opportunity to speak today

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as part of the Darwin Festival.

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I'm honored and humbled by the invitation.

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As you just heard from Thea,

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I have a very unusual
scientific background

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or unconventional scientific background.

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The early part of my
career was really focused

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on studying human aging
and cancer cell biology,

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but that all changed with a move

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to the beautiful island
of Bermuda, where I joined

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the Bermuda Institute of
Ocean Sciences in 2003.

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And that was a turning point in my career.

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I became incredibly excited and captivated

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by the amazing biodiversity that exists

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in the world's marine environment,

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surprised by how little we know

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about the world's marine environments

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and really inspired by
all the new discoveries

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that we could make by applying
new technologies to the sea.

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So today I'm gonna talk to you

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a little bit about the
work that we're doing

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at the Gloucester Marine
Genomics Institute, GMGI.

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I joined GMGI in the summer
of 2017 as a Science Director.

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And really our focus here is bringing

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new genetic technologies to the ocean

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to really promote sustainable
and healthy oceans

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and uncover new discoveries

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that impact fisheries and human health.

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And so I think I can share
my screen and get going here.

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All righty, I'm gonna start

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with this beautiful image from NASA,

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which I think beautifully
illustrates that the majority

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of the surface of our planet
is actually covered by water.

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The oceans constitute more than 71%

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of the surface of our planet,

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but when you take into
account that on average,

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the oceans are about two
and a half miles deep,

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the oceans actually
constitute greater than 90%

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of the biosphere or the
living space on this planet.

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Now, the oceans are incredibly important

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in sustaining life on the planet.

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More than 50% of the oxygen
in the atmosphere is created

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by tiny ocean plants called phytoplankton.

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The oceans are incredibly
important in governing

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climate and weather
patterns on our planet.

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The oceans of course, are
important in providing food

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to billions of inhabitants of our planet.

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And also there's a tremendous
array of biodiversity

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that exists in the world's
marine environments,

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and it's this biodiversity

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that really excites me about the oceans.

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It's estimated that there are more

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than a million different species

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of plants and animals
that live in the ocean.

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And today, we probably only
know about a quarter of those.

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When you think about the microscopic life

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that lives in the ocean,

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the tiny bacteria and
single-cell organisms,

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we know a really tiny fraction.

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It's estimated that could
be between a hundred million

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to a billion different species
of bacteria in the ocean

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of which we know only a tiny fraction.

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So this line captures some of my

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most favorite creatures in the ocean.

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I'm just gotta pick up a pointer.

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And starting up at the top
corner here, this is the smallest

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and most plentiful bacteria
on the planet, this is SAR11.

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And the combined weight of
SAR11 in the oceans is greater

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than the combined weight
of all the fish in the sea,

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and this tiny organism plays a vital role

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in scaffolding elements on our planet.

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And we go from this tiny,
tiny bacteria to the oceans,

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being home to the largest
animals on the planet,

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the largest animal being the blue whale.

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The blue whale can attain
length of over a hundred feet

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and weigh more than 300,000
pounds, which is the equivalent

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of more than 30 full grown elephants.

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The oceans are also home

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to the longest-lived
animals on our planet.

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And the longevity record
goes to this little clam,

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with the oldest individual so far

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identified being 507 years.

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Now, there are animals that are

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perfectly adapted to extreme environments,

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like the frigid polar
regions of our planet,

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and also adapted to extreme
temperatures and pressures

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at deep sea hydrothermal
vents in the ocean.

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And it's these adaptations
that these organisms have

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that really fascinate me and
we can learn so much from.

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And I think that most people
appreciate the importance

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of the oceans for the provision of food,

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but I'm not sure that
people really appreciate

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the impact that ocean organisms have had

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on science and biomedicine as well.

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Historically, marine animals have been

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really valuable models
for scientific research,

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and they've helped us to understand

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really fundamental biological processes,

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including many that are
relevant to human health.

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So a good way to look at this
is to look at the Nobel prizes

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that have been given out
to work that has been done

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using marine animals as models,

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and there are seven of those today.

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The sea star helped us to understand

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the fundamentals of cellular immunology.

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The giant axon of the squid helped us

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to understand how nerve conduction works,

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the eyes of the horseshoe crab were vital

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to understand how sensory perception

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and how our eyes perceive
light and color works.

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The the sea hares, little guy up here,

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perhaps one of the cutest
little animals in the ocean,

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but it's also very smart and can exhibit

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the three basic forms of learning.

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And by coupling the ability to learn

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with a very simple nervous system,

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it allowed us to elucidate
the neural circuitry

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associated with learning and memory.

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The sea urchin is another
vitally important research model,

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which attained two Nobel prizes,

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one for understanding
mitochondrial function

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in the respiratory burst

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and the other for
understanding the proteins

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that govern cell cycle and cell division,

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which is critically
important to understand

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if we're gonna understand what happens

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when we get cancer and we
lose control of cell cycle.

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So again, these wonderful
biological adaptations

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of these animals absolutely facilitated

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all these seminal discoveries.

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In addition to these
biological adaptations,

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marine organisms have
amazing chemical adaptations.

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They use a really unique
chemicals for communication,

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for defense against predators,

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for defense against infection and disease,

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and many of these molecules are becoming

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very useful therapeutics in human disease.

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There are now nine FDA-approved,

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marine-derived drugs on the market.

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Four of those are anti-cancer drugs.

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Three are for lowering
serum triglycerides.

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One's an anti-infective and
one is a medication for pain.

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There are more than two
dozen molecules from the sea

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in clinical trials for
various disease indication

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and thousands of molecules
in preclinical development.

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So we're really finding a rich
source of chemical diversity

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that can have a tremendous
impact on human health.

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Now, of course, we're all for familiar

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with the important role of
the ocean in provision of food

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and the United Nations Food
and Agriculture Organization

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estimates that anywhere between
one to three billion people

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on this planet depend on the ocean

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as their primary source of food.

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So the oceans are critically important,

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but this is also a really
critical time for our oceans.

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So the United Nations monitors
about 600 different species

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of fish and seafood that
are consumed by humans

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and they estimate that
more than 50% of them

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are considered fully exploited

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and another 24% are over
exploited or completely depleted.

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So overfishing is taking
its toll on our oceans.

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That, combined with the
impacts of climate change,

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where we have increasing temperature

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and acidification of our oceans

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are having a devastating
effect on marine organisms.

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In addition, we have the impacts

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of pollution and plastics in particular.

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So it's a really critical time for us

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to start to think about
building new tools,

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to better understand the
diversity that exists

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in the world's marine environments

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and tools to really help to
mitigate some of the impacts

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that we're having on
the marine environment.

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And so this is part of what
we're trying to do at GMGI is

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to bring new genetic and genomic
technologies to the ocean

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to better understand this
amazing biodiversity,

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to promote sustainable and healthy oceans

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and to uncover new discoveries

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that impact fisheries and human health

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and we're doing this really
using genomics as a foundation.

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So genomics is really the study

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of the genetic material
or the DNA of any organism

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and DNA is made up of these four

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basic building blocks, G, A, T and C.

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And it's really the order in
which these building blocks

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are arranged that determine all

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the characteristics of an organism.

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And so by sequencing the
genome or determining the order

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of these building blocks, really,

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we can start to understand
all of the capabilities,

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all of the characteristics

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and all of the adaptations
of any organism.

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Now, over the last several
decades, we've really been going

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through something of
a genomics revolution.

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And this is because the efficiency

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at which we can read the
sequence of the genome

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is really increasing dramatically,

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as well as the cost of doing
so is decreasing dramatically.

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So to give you an example,

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the very first human
genome that was sequenced

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was a project that was started in 1990.

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It took 13 years to accomplish.

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Hundreds of scientists around the world

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participated in this project.

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It costs nearly three billion to complete.

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00:11:01,580 --> 00:11:03,900
Now with the technology
that we have available,

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we can sequence that same human genome

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in about 48 hours for less than $1,000.

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And so this has really opened
up the field of genomics,

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such that the genomes of every
organism are available to us.

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And we can use this technology
to really understand

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the full scope of biodiversity
in any environment,

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and really understand the characteristics

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of the animals that live there,

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and from that we can learn a
lot that can benefit humankind.

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And so today I'm speaking
to you from GMGI's facility,

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our research facility in Gloucester

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on the Gloucester Harbor.

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I'm sure many of you are familiar

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that Gloucester is the oldest
seaport in the country,

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coming up to its 400th
anniversary in year 2023.

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And really the culture and
the economy of the region has

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really been inextricably
linked to the sea.

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Principally dependent on
capture fisheries industry

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and with the decline in capture fisheries

258
00:12:01,240 --> 00:12:02,410
over recent decades,

259
00:12:02,410 --> 00:12:04,810
it's really had a negative
impact on the region.

260
00:12:05,660 --> 00:12:09,263
And so the founders of GMGI
really wanted to do something,

261
00:12:10,650 --> 00:12:13,040
to build on this 400
years of maritime history,

262
00:12:13,040 --> 00:12:15,220
to bring new technologies to the sea

263
00:12:15,220 --> 00:12:19,000
and really promote sustainable
use of ocean resources

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00:12:19,000 --> 00:12:21,730
and spark a new era of marine innovation

265
00:12:21,730 --> 00:12:24,090
and marine commerce for the region.

266
00:12:24,090 --> 00:12:26,520
And so the established
GMGI and our mission

267
00:12:26,520 --> 00:12:28,330
is to address critical challenges

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00:12:28,330 --> 00:12:31,110
facing our oceans, human
health and the environment

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00:12:31,110 --> 00:12:34,550
through innovative scientific
research in education.

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00:12:34,550 --> 00:12:37,500
And in order to achieve this mission,

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00:12:37,500 --> 00:12:39,563
we have a strategy that has three parts.

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00:12:41,040 --> 00:12:43,500
The first part is to
establish a world class

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00:12:43,500 --> 00:12:46,670
marine research institute
powered by genomics.

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00:12:46,670 --> 00:12:47,980
The second part is to create

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00:12:47,980 --> 00:12:50,970
a vibrant science community
here in Gloucester.

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00:12:50,970 --> 00:12:52,930
By that, I mean, we'd like to promote

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00:12:52,930 --> 00:12:55,210
doing highly collaborative research,

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00:12:55,210 --> 00:12:56,620
attract scientists to come out

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00:12:56,620 --> 00:12:58,810
to Gloucester to attend conferences

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00:12:58,810 --> 00:13:01,780
and really to attract
other scientific entities

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00:13:01,780 --> 00:13:05,390
to consider Gloucester as a
great place to do science.

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00:13:05,390 --> 00:13:07,650
The third part of the
strategy is to create

283
00:13:07,650 --> 00:13:09,800
a vibrant science learning environment

284
00:13:09,800 --> 00:13:12,330
to provide new STEM career opportunities

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00:13:12,330 --> 00:13:14,620
for the young people of our community.

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00:13:14,620 --> 00:13:16,580
So the flagship program there is called

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00:13:16,580 --> 00:13:19,020
the Gloucester Biotechnology Academy.

288
00:13:19,020 --> 00:13:22,250
This is a 10-month intensive program.

289
00:13:22,250 --> 00:13:25,120
It's a lab immersion, hands-on program

290
00:13:25,120 --> 00:13:28,010
in which the students
learn all of the techniques

291
00:13:29,820 --> 00:13:33,390
to work in a modern genomics
molecular biology lab,

292
00:13:33,390 --> 00:13:36,780
either in biotech or in academic research.

293
00:13:36,780 --> 00:13:38,670
It's been incredibly successful program

294
00:13:38,670 --> 00:13:41,010
with 85% of the graduates going on

295
00:13:41,010 --> 00:13:44,040
to full-time employment
in the biotech industry.

296
00:13:44,040 --> 00:13:45,740
And some of the graduates
have actually gone

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00:13:45,740 --> 00:13:48,400
on to higher education as well,

298
00:13:48,400 --> 00:13:51,880
including several students
at Salem State University.

299
00:13:51,880 --> 00:13:55,520
So it's been a wonderful program
to provide new opportunity,

300
00:13:55,520 --> 00:13:58,220
and it's been terrific
to see these young people

301
00:13:58,220 --> 00:14:01,770
get excited about science
and to find themself able

302
00:14:01,770 --> 00:14:04,450
to participate in the
wonderful STEM economy

303
00:14:04,450 --> 00:14:07,213
and STEM community in Massachusetts.

304
00:14:08,190 --> 00:14:10,920
Now, my focus has really been
on the Research Institute

305
00:14:10,920 --> 00:14:13,160
and over the last 40 years at GMGI,

306
00:14:13,160 --> 00:14:15,340
my role has been to devise
our science strategy

307
00:14:15,340 --> 00:14:16,630
to hire our science staff

308
00:14:16,630 --> 00:14:19,793
and to oversee the building
of our research facility.

309
00:14:20,910 --> 00:14:22,750
So our science strategy is really based

310
00:14:22,750 --> 00:14:25,140
on a platform of oceans and human health

311
00:14:25,140 --> 00:14:27,550
and we have three focus areas:

312
00:14:27,550 --> 00:14:29,650
biomedicine and biotechnology,

313
00:14:29,650 --> 00:14:31,340
ecosystem function and health,

314
00:14:31,340 --> 00:14:33,680
and fisheries and aquaculture.

315
00:14:33,680 --> 00:14:36,350
So in the fisheries and
aquaculture program, really there,

316
00:14:36,350 --> 00:14:38,690
we're using genetic and genomic approaches

317
00:14:38,690 --> 00:14:41,330
to promote sustainable
fisheries in aquaculture

318
00:14:41,330 --> 00:14:43,980
to ensure global food security.

319
00:14:43,980 --> 00:14:46,190
In the ecosystem function
and health program,

320
00:14:46,190 --> 00:14:48,660
we're using genetic and
genomic technologies

321
00:14:48,660 --> 00:14:50,490
to really understand the full scope

322
00:14:50,490 --> 00:14:52,690
of biodiversity of marine ecosystems,

323
00:14:52,690 --> 00:14:55,100
how they function, how resilient they are,

324
00:14:55,100 --> 00:14:58,280
and their ability to adapt
to environmental change,

325
00:14:58,280 --> 00:15:01,910
which is particularly important
in light of climate change.

326
00:15:01,910 --> 00:15:04,340
In our biomedicine and
biotechnology program,

327
00:15:04,340 --> 00:15:05,810
we have a number of different programs,

328
00:15:05,810 --> 00:15:08,080
but we're principally
interested in studying

329
00:15:08,080 --> 00:15:10,030
the long-lived animals in the ocean

330
00:15:10,030 --> 00:15:11,700
to gain a better understanding

331
00:15:11,700 --> 00:15:15,003
of healthy aging, longevity
and resistance to cancer.

332
00:15:16,330 --> 00:15:18,920
So this slide captures
some of the projects

333
00:15:18,920 --> 00:15:20,190
that we have ongoing,

334
00:15:20,190 --> 00:15:21,297
and they range from studying

335
00:15:21,297 --> 00:15:23,410
the health of individual animals

336
00:15:23,410 --> 00:15:25,430
to the health of whole ecosystems.

337
00:15:25,430 --> 00:15:27,660
And they range from studying
the smallest creatures

338
00:15:27,660 --> 00:15:30,320
in the ocean, tiny little marine bacteria,

339
00:15:30,320 --> 00:15:33,703
to some of the largest creatures
in the ocean, like whales.

340
00:15:34,640 --> 00:15:37,240
And so today I thought
I'd just talk about a few

341
00:15:37,240 --> 00:15:39,200
of these programs just
to give you a flavor

342
00:15:39,200 --> 00:15:41,460
of the work we're doing at GMGI

343
00:15:41,460 --> 00:15:43,510
and how genomics really can help us

344
00:15:43,510 --> 00:15:45,593
to better understand the world's oceans.

345
00:15:46,480 --> 00:15:48,960
And I'll start right in our own backyard

346
00:15:48,960 --> 00:15:50,570
in the Gulf of Maine.

347
00:15:50,570 --> 00:15:53,370
And so the Gulf of Maine
historically has been one

348
00:15:53,370 --> 00:15:56,080
of the world's most
productive fishing grounds.

349
00:15:56,080 --> 00:15:57,980
It's an important whale feeding ground,

350
00:15:57,980 --> 00:16:01,360
and it's home to a great
array of biodiversity,

351
00:16:01,360 --> 00:16:03,274
as shown over here.

352
00:16:03,274 --> 00:16:05,450
In 2015, a paper was published,

353
00:16:05,450 --> 00:16:08,070
and this is a figure from that paper

354
00:16:08,070 --> 00:16:09,740
in which they measured sea surface

355
00:16:09,740 --> 00:16:12,180
temperatures in decadal time scales

356
00:16:12,180 --> 00:16:14,530
and this is a figure
showing the sea surface

357
00:16:14,530 --> 00:16:16,900
temperature change over the last decade

358
00:16:16,900 --> 00:16:18,630
with the red color indicating

359
00:16:19,540 --> 00:16:22,290
increasing warming of the ocean.

360
00:16:22,290 --> 00:16:23,680
And you can see from this,

361
00:16:23,680 --> 00:16:26,750
the Gulf of Maine is outlined
in this little box here,

362
00:16:26,750 --> 00:16:29,570
that it's an intensely
red part of the ocean

363
00:16:29,570 --> 00:16:32,420
indicating that the Gulf of
Maine is actually warming

364
00:16:32,420 --> 00:16:35,960
faster than 99% of the world's oceans.

365
00:16:35,960 --> 00:16:38,530
So it's a pretty critical
time for us to understand

366
00:16:38,530 --> 00:16:40,160
what's going on in the Gulf of Maine

367
00:16:40,160 --> 00:16:43,380
and how this changing conditions are going

368
00:16:43,380 --> 00:16:45,463
to impact the organisms that live there.

369
00:16:46,402 --> 00:16:47,780
We're really already seeing

370
00:16:47,780 --> 00:16:49,970
some changes in distribution patterns

371
00:16:49,970 --> 00:16:52,260
of some of the animals that live there.

372
00:16:52,260 --> 00:16:55,810
And certainly, there'll
be other effects as well

373
00:16:55,810 --> 00:16:58,280
in terms of not just distribution,

374
00:16:58,280 --> 00:17:00,610
but also timing of reproduction

375
00:17:00,610 --> 00:17:03,040
and great impacts on this community.

376
00:17:03,040 --> 00:17:05,120
So we really wanna start to understand

377
00:17:05,120 --> 00:17:09,030
this part of the ocean to
provide baseline of what's there

378
00:17:09,030 --> 00:17:11,850
so that we can understand
the impacts of climate change

379
00:17:11,850 --> 00:17:14,930
and mitigate some of those
impacts going forward.

380
00:17:14,930 --> 00:17:16,960
So at GMGI, we focused our attention

381
00:17:16,960 --> 00:17:18,230
on one part of the Gulf of Maine,

382
00:17:18,230 --> 00:17:20,540
the Stellwagen Bank
National Marine Sanctuary,

383
00:17:20,540 --> 00:17:25,540
which is shown outlined by
the red on this map over here.

384
00:17:26,810 --> 00:17:28,740
To orientate you to where we are,

385
00:17:28,740 --> 00:17:30,860
we're setting up here on the north shore,

386
00:17:30,860 --> 00:17:32,463
right where this star is.

387
00:17:33,720 --> 00:17:36,030
So Stellwagen Bank National
Marine Sanctuary is part

388
00:17:36,030 --> 00:17:39,050
of the national sanctuary
network throughout the U.S.

389
00:17:39,050 --> 00:17:42,973
It's one of 14 sanctuaries,
and it was established in 1992.

390
00:17:43,840 --> 00:17:47,860
NOAA oversees the sanctuary
and they're responsible for it

391
00:17:47,860 --> 00:17:50,600
for managing everything that
happens in the sanctuary.

392
00:17:50,600 --> 00:17:51,760
Now, if you look at this map,

393
00:17:51,760 --> 00:17:53,870
this is the topological map showing you

394
00:17:53,870 --> 00:17:55,160
the bottom of the sea floor,

395
00:17:55,160 --> 00:17:57,860
you see that it's very
unique part of the ocean,

396
00:17:57,860 --> 00:17:59,930
where you have a lot of deep valleys,

397
00:17:59,930 --> 00:18:01,850
as well as these plateaus.

398
00:18:01,850 --> 00:18:03,010
And this is what creates

399
00:18:03,010 --> 00:18:04,690
this really unique environment here,

400
00:18:04,690 --> 00:18:08,270
'cause what happens is
very nutrient-rich waters

401
00:18:08,270 --> 00:18:10,620
from the Atlantic are really forced up

402
00:18:10,620 --> 00:18:12,790
to the surface, the sunlit surface,

403
00:18:12,790 --> 00:18:16,400
where you really get a
flourish of plankton,

404
00:18:16,400 --> 00:18:19,560
which then attracts and
supports a lot of other animals,

405
00:18:19,560 --> 00:18:21,270
and this is historically why this is

406
00:18:21,270 --> 00:18:23,183
such a productive part of the ocean.

407
00:18:24,080 --> 00:18:26,440
Now I mentioned NOAA
oversees the sanctuary

408
00:18:26,440 --> 00:18:28,330
and biodiversity conservation is one

409
00:18:28,330 --> 00:18:30,900
of their key management objectives.

410
00:18:30,900 --> 00:18:34,220
NOAA has recorded the presence
of 72 different species

411
00:18:34,220 --> 00:18:37,560
of fish in the sanctuary
and more than 17 species

412
00:18:37,560 --> 00:18:39,830
of cetaceans, whales and dolphins,

413
00:18:39,830 --> 00:18:42,720
and a vast array of invertebrates,

414
00:18:42,720 --> 00:18:45,550
but the technology NOAA uses
to assess the biodiversity

415
00:18:45,550 --> 00:18:47,350
are really traditional technologies

416
00:18:47,350 --> 00:18:49,410
using cameras or (indistinct)

417
00:18:49,410 --> 00:18:51,663
to see what's present in these waters.

418
00:18:52,770 --> 00:18:54,520
Now, although this gives you a great view

419
00:18:54,520 --> 00:18:57,260
of some of the macro flora and fauna,

420
00:18:57,260 --> 00:18:59,700
it's missing a lot of
the smaller organisms,

421
00:18:59,700 --> 00:19:01,560
the microscopic organisms.

422
00:19:01,560 --> 00:19:04,460
And really we wanted
to see if we could use

423
00:19:04,460 --> 00:19:07,230
a genetic approach to try
and enhance our understanding

424
00:19:07,230 --> 00:19:09,410
of the full scale of biodiversity

425
00:19:09,410 --> 00:19:11,800
in this unique marine environment.

426
00:19:11,800 --> 00:19:14,150
And so we did this using a technique

427
00:19:14,150 --> 00:19:16,210
called environmental DNA

428
00:19:16,210 --> 00:19:19,550
and environmental DNA is
really based on the premise

429
00:19:19,550 --> 00:19:23,130
that anytime any organism
interacts with its environment,

430
00:19:23,130 --> 00:19:26,380
it's always leaving a trail of DNA behind.

431
00:19:26,380 --> 00:19:28,530
So as you're sitting
in your chair right now

432
00:19:28,530 --> 00:19:29,770
or standing where you are,

433
00:19:29,770 --> 00:19:31,370
you will drop some skin cells

434
00:19:31,370 --> 00:19:32,900
and maybe a few hair follicles.

435
00:19:32,900 --> 00:19:36,050
You will leave a signature that
you have been in that space

436
00:19:36,050 --> 00:19:38,080
and that occurs in the ocean as well.

437
00:19:38,080 --> 00:19:39,840
As the fish is swimming through the water,

438
00:19:39,840 --> 00:19:43,020
it's shedding cells, it's
excreting into the seawater,

439
00:19:43,020 --> 00:19:45,670
it's leaving a trail of DNA behind it.

440
00:19:45,670 --> 00:19:48,040
And so you don't actually
have to catch the fish

441
00:19:48,040 --> 00:19:50,400
or see the whale to know it's been there.

442
00:19:50,400 --> 00:19:52,390
You can just take a sample of seawater,

443
00:19:52,390 --> 00:19:55,700
filter out all the cells
and DNA, sequence that,

444
00:19:55,700 --> 00:19:59,660
and then you know what's been
present in that body of water.

445
00:19:59,660 --> 00:20:01,360
And we can look at both the water

446
00:20:01,360 --> 00:20:03,610
as well as the sediment to get an idea

447
00:20:03,610 --> 00:20:06,880
of the diversity of
organisms, both living there,

448
00:20:06,880 --> 00:20:09,050
the bacteria and other things
that are present living there,

449
00:20:09,050 --> 00:20:11,993
or the animals and plants
that have passed by.

450
00:20:12,890 --> 00:20:15,050
So sediment is collected with a device

451
00:20:15,050 --> 00:20:19,240
like this one called a
grab, which has a jaw open

452
00:20:19,240 --> 00:20:21,700
when it's lowered to
the bottom of the ocean,

453
00:20:21,700 --> 00:20:24,760
and then the jaw snaps
shut to collect sediment

454
00:20:24,760 --> 00:20:26,900
and water is collected through this device

455
00:20:26,900 --> 00:20:29,970
called a Niskin bottle, which
is really just an open tube

456
00:20:29,970 --> 00:20:32,610
that has a trap door
on the top and bottom.

457
00:20:32,610 --> 00:20:35,570
And it's lowered to the depth
that you wanna collect water,

458
00:20:35,570 --> 00:20:37,220
and then you send a trigger down,

459
00:20:37,220 --> 00:20:39,970
which snaps the top and the bottom shut

460
00:20:39,970 --> 00:20:42,160
and captures a parcel of water.

461
00:20:42,160 --> 00:20:45,690
And you can bring the sediment
and the water into the lab.

462
00:20:45,690 --> 00:20:47,130
For the water, to process it,

463
00:20:47,130 --> 00:20:49,910
you put it through a filter to
collect all the cells and DNA

464
00:20:49,910 --> 00:20:52,700
on the filter so that
you can extract them.

465
00:20:52,700 --> 00:20:55,790
And for this sediment, we just
simply take a little scoop

466
00:20:55,790 --> 00:20:58,550
full of sediment and extract DNA from it.

467
00:20:58,550 --> 00:21:02,470
So we can extract the
DNA, amplify gene regions

468
00:21:02,470 --> 00:21:05,000
that we can use to
identify different species,

469
00:21:05,000 --> 00:21:07,603
we can then sequence those
regions of the genome,

470
00:21:08,580 --> 00:21:10,430
we can then analyze the sequences,

471
00:21:10,430 --> 00:21:13,650
and then that tells us a
picture of the biodiversity

472
00:21:13,650 --> 00:21:15,660
that is present in those samples.

473
00:21:15,660 --> 00:21:18,970
Now the power of eDNA
is that you can answer

474
00:21:18,970 --> 00:21:20,670
a lot of different questions.

475
00:21:20,670 --> 00:21:23,450
You can actually look at the
full scale of biodiversity,

476
00:21:23,450 --> 00:21:25,460
so you can see all of the organisms

477
00:21:25,460 --> 00:21:27,780
that are present in those samples.

478
00:21:27,780 --> 00:21:30,600
You can just focus on
certain taxonomic groups.

479
00:21:30,600 --> 00:21:32,790
So you may only wanna
know all of the fishes

480
00:21:32,790 --> 00:21:34,970
that are there, or all of the bacteria,

481
00:21:34,970 --> 00:21:37,250
or you can just ask the question,

482
00:21:37,250 --> 00:21:39,380
is there a particular species present?

483
00:21:39,380 --> 00:21:41,000
So for example, you may wanna know,

484
00:21:41,000 --> 00:21:42,810
is there a signal from a white whale

485
00:21:42,810 --> 00:21:46,270
that has passed through this
water in recent few days?

486
00:21:46,270 --> 00:21:48,880
And so you can just target that species.

487
00:21:48,880 --> 00:21:51,440
So very, very powerful way to look

488
00:21:51,440 --> 00:21:53,020
at what's present in the water,

489
00:21:53,020 --> 00:21:56,240
which provides an alternative
to the traditional approaches

490
00:21:56,240 --> 00:21:58,830
and augments the traditional approaches

491
00:21:58,830 --> 00:22:01,360
that we use like (indistinct) and cameras.

492
00:22:01,360 --> 00:22:03,280
So in our first studies at GMGI,

493
00:22:03,280 --> 00:22:05,840
we took some sediment samples
from the northwest corner

494
00:22:05,840 --> 00:22:08,670
of the Stellwagen Bank
National Marine Sanctuary.

495
00:22:08,670 --> 00:22:10,920
These are the three sites
we collected samples from.

496
00:22:10,920 --> 00:22:13,100
We collected them over
a couple of seasons.

497
00:22:13,100 --> 00:22:16,020
We brought those back to
the lab, extracted DNA,

498
00:22:16,020 --> 00:22:18,853
and then sequenced the
DNA that was present.

499
00:22:20,010 --> 00:22:22,770
And we looked basically at two groups.

500
00:22:22,770 --> 00:22:27,740
We amplified the 16S ribosomal
RNA section of the genome

501
00:22:27,740 --> 00:22:29,180
to give an indication of all

502
00:22:29,180 --> 00:22:31,050
the prokaryotes that are present

503
00:22:31,050 --> 00:22:35,890
and we amplified the 18S ribosomal RNA

504
00:22:35,890 --> 00:22:38,680
sequence of the genome
to give us an indication

505
00:22:38,680 --> 00:22:40,920
of all the eukaryotes that are present.

506
00:22:40,920 --> 00:22:43,440
So these pie charts are just
showing you a distribution

507
00:22:43,440 --> 00:22:45,750
of all the different
bacteria and archaea phyla

508
00:22:45,750 --> 00:22:48,170
that were unearthed in the study

509
00:22:48,170 --> 00:22:50,950
and all the different of eukaryotes.

510
00:22:50,950 --> 00:22:53,610
So what was remarkable about the study was

511
00:22:53,610 --> 00:22:57,400
that (Andrea clears throat)
just by looking at the sediment

512
00:22:57,400 --> 00:22:59,770
samples from this tiny
part of the sanctuary,

513
00:22:59,770 --> 00:23:03,890
we identified 127
different phyla organisms.

514
00:23:03,890 --> 00:23:07,030
This was remarkable because
using traditional approaches,

515
00:23:07,030 --> 00:23:11,890
the standard way that NOAA has
been assessing the sanctuary,

516
00:23:11,890 --> 00:23:15,130
the biodiversity assessment
before this genetic assessment

517
00:23:15,130 --> 00:23:17,140
only contained 12 phyla.

518
00:23:17,140 --> 00:23:19,410
So by doing this small genetic study,

519
00:23:19,410 --> 00:23:22,890
we added 115 new phyla to
the biodiversity assessment

520
00:23:22,890 --> 00:23:25,430
of Stellwagen, and this included organisms

521
00:23:25,430 --> 00:23:27,730
across many different areas of life,

522
00:23:27,730 --> 00:23:31,530
bacteria, archaea, animals, chromista,

523
00:23:31,530 --> 00:23:33,850
protozoa, plantae and fungi.

524
00:23:33,850 --> 00:23:37,290
So it was really showing the
power of genomics and genetics

525
00:23:37,290 --> 00:23:40,090
to really understand the
full scale of diversity

526
00:23:40,090 --> 00:23:42,750
of what was present in the sanctuary.

527
00:23:42,750 --> 00:23:45,270
So NOAA was excited by
these initial results

528
00:23:45,270 --> 00:23:48,230
and invited us back this
summer to accompany them

529
00:23:48,230 --> 00:23:52,080
on their annual survey, in which
we collected surface water,

530
00:23:52,080 --> 00:23:54,550
bottom water and sediment in 30 sites

531
00:23:54,550 --> 00:23:56,880
in the southern part of the sanctuary.

532
00:23:56,880 --> 00:23:59,260
So we're just analyzing those now,

533
00:23:59,260 --> 00:24:02,160
but the hope is that we can
really build a framework

534
00:24:02,160 --> 00:24:04,210
for integrating genetic information

535
00:24:04,210 --> 00:24:06,260
into their biodiversity assessment

536
00:24:06,260 --> 00:24:08,110
to better understand the full scale

537
00:24:08,110 --> 00:24:10,400
of biodiversity that's in the sanctuary,

538
00:24:10,400 --> 00:24:13,610
provide a baseline so that
we can understand and monitor

539
00:24:13,610 --> 00:24:17,520
changes that are occurring
with the changing climate

540
00:24:17,520 --> 00:24:20,013
and the changing conditions
with climate change.

541
00:24:21,330 --> 00:24:24,220
Now at GMGI, we're not limited
to studying our own backyard.

542
00:24:24,220 --> 00:24:25,990
Through collaborations, we can get out

543
00:24:25,990 --> 00:24:28,700
into the broader ocean and ask questions

544
00:24:28,700 --> 00:24:30,300
throughout the global oceans.

545
00:24:30,300 --> 00:24:33,831
We have a wonderful collaboration
with a group called OceanX

546
00:24:33,831 --> 00:24:36,420
and OceanX have this wonderful ship,

547
00:24:36,420 --> 00:24:38,880
which is designed for ocean exploration,

548
00:24:38,880 --> 00:24:41,910
ocean resource and documentary filmmaking.

549
00:24:41,910 --> 00:24:45,440
So GMGI had the great pleasure
of joining an expedition

550
00:24:45,440 --> 00:24:47,780
with OceanX that started in the Azores,

551
00:24:47,780 --> 00:24:51,040
summer in July, and ended up in Norway.

552
00:24:51,040 --> 00:24:54,540
And there were two components
to this cruise that GMGI led.

553
00:24:54,540 --> 00:24:57,683
One was an education component
and one was research.

554
00:24:58,650 --> 00:25:01,630
The education component was
really providing an opportunity

555
00:25:01,630 --> 00:25:04,780
for undergraduate students
from underrepresented groups

556
00:25:04,780 --> 00:25:08,630
to experience life at sea
and to really experience

557
00:25:08,630 --> 00:25:10,720
what it would be like
to be an oceanography,

558
00:25:10,720 --> 00:25:14,753
for a marine biology, or
participating in the marine trades.

559
00:25:15,610 --> 00:25:17,270
The research part of this component was

560
00:25:17,270 --> 00:25:20,210
a really unique opportunity to
collect some genetic samples

561
00:25:20,210 --> 00:25:22,453
from an extreme environment in the ocean.

562
00:25:23,470 --> 00:25:26,220
The Moytirra hydrothermal vent lies north

563
00:25:26,220 --> 00:25:28,880
of the Azores on the North Atlantic Ridge.

564
00:25:28,880 --> 00:25:30,070
These hydrothermal vents are

565
00:25:30,070 --> 00:25:32,900
really unique parts of the ocean.

566
00:25:32,900 --> 00:25:34,480
They typically occur in areas

567
00:25:34,480 --> 00:25:36,220
where tectonic plates come together,

568
00:25:36,220 --> 00:25:38,850
and there are cracks
within the Earth's crust

569
00:25:38,850 --> 00:25:43,850
where seawater can seep below
the crust, mix with magma,

570
00:25:44,650 --> 00:25:48,000
and then you see this plume
of seawater that's carrying

571
00:25:48,000 --> 00:25:51,600
a lot of minerals and other
things into the ocean.

572
00:25:51,600 --> 00:25:53,480
This is a really extreme environment.

573
00:25:53,480 --> 00:25:55,870
The temperature of the water
that comes out of the deep sea

574
00:25:55,870 --> 00:26:00,870
hydrothermal bed can reach as
hot as 400 degrees Celsius.

575
00:26:00,870 --> 00:26:04,290
It's very, very rich in
minerals and heavy metals.

576
00:26:04,290 --> 00:26:08,480
The pH is about two, and
there's enormous pressure.

577
00:26:08,480 --> 00:26:12,290
This particular hydrothermal
is at 2,900 meters

578
00:26:12,290 --> 00:26:14,210
below the surface of the sea.

579
00:26:14,210 --> 00:26:15,500
So under these conditions,

580
00:26:15,500 --> 00:26:17,340
and there are many biology students

581
00:26:17,340 --> 00:26:19,640
in the audience today, you're thinking,

582
00:26:19,640 --> 00:26:22,740
how can anything survive
under these conditions?

583
00:26:22,740 --> 00:26:25,890
We know that proteins and
nucleic acids will denature

584
00:26:25,890 --> 00:26:29,080
if you expose them to acidic
conditions or temperature.

585
00:26:29,080 --> 00:26:31,510
So this is a part of the ocean
where you wouldn't expect

586
00:26:31,510 --> 00:26:34,470
any life to occur, and yet life thrives

587
00:26:34,470 --> 00:26:36,790
at these deep sea hydrothermal vents.

588
00:26:36,790 --> 00:26:40,120
And so really it's a really
cool opportunity to understand

589
00:26:40,120 --> 00:26:41,570
what are the organisms that live there

590
00:26:41,570 --> 00:26:43,220
and how are they adapted to living

591
00:26:43,220 --> 00:26:45,210
under these extreme conditions.

592
00:26:45,210 --> 00:26:48,290
And so we were able to
collect water samples

593
00:26:48,290 --> 00:26:50,500
around the deep sea hydrothermal vent,

594
00:26:50,500 --> 00:26:52,340
bringing them back to
the ship and filter them.

595
00:26:52,340 --> 00:26:54,830
And we've now have them in
the lab where we've extracted

596
00:26:54,830 --> 00:26:57,560
all the DNA and RNA in these samples

597
00:26:57,560 --> 00:27:00,280
so that we can see not only what's there,

598
00:27:00,280 --> 00:27:03,080
what are the organisms present
in this extreme environment,

599
00:27:03,080 --> 00:27:05,290
but by looking at gene expression in RNA,

600
00:27:05,290 --> 00:27:07,200
we can look at what they're doing there.

601
00:27:07,200 --> 00:27:09,810
And so we have this tremendous
opportunity to apply

602
00:27:09,810 --> 00:27:12,973
our genetic skills to
questions in the global oceans.

603
00:27:14,220 --> 00:27:16,460
Now, in addition to looking
at health of ecosystems,

604
00:27:16,460 --> 00:27:18,930
we can also apply our genetic
and genomic approaches

605
00:27:18,930 --> 00:27:21,310
to look at the health
of individual animals.

606
00:27:21,310 --> 00:27:22,940
And we have this wonderful collaboration

607
00:27:22,940 --> 00:27:24,762
with a group called Ocean Alliance,

608
00:27:24,762 --> 00:27:27,010
in which we're looking at the microbiome

609
00:27:27,010 --> 00:27:28,890
of the whale respiratory track

610
00:27:28,890 --> 00:27:32,920
as a way of judging the
health of individual whales.

611
00:27:32,920 --> 00:27:34,810
Now, Ocean Alliance is an organization

612
00:27:34,810 --> 00:27:36,900
that's been around since 1971,

613
00:27:36,900 --> 00:27:38,820
and they're committed to preserving

614
00:27:38,820 --> 00:27:42,340
and conserving whales in the ocean.

615
00:27:42,340 --> 00:27:45,230
And in 2014, they using drones

616
00:27:45,230 --> 00:27:49,210
as a noninvasive technology
to monitor whale populations.

617
00:27:49,210 --> 00:27:51,970
So they can use the
drones to capture images

618
00:27:51,970 --> 00:27:53,317
and videos of the whales,

619
00:27:53,317 --> 00:27:57,210
but they can also collect
biological samples with the drones

620
00:27:57,210 --> 00:27:59,340
by attaching Petri dishes to the drones,

621
00:27:59,340 --> 00:28:03,630
and then flying it through the
blow when the whale surfaces.

622
00:28:03,630 --> 00:28:06,240
And so this is a project
that we're collaborating

623
00:28:06,240 --> 00:28:08,830
with Ocean Alliance to
look at the microbes,

624
00:28:08,830 --> 00:28:12,500
the bacteria that live
within the respiratory tract

625
00:28:12,500 --> 00:28:15,110
of the whale to see if
we can get an indication

626
00:28:15,110 --> 00:28:17,310
of what would be a healthy microbiome

627
00:28:17,310 --> 00:28:19,683
or when a whale's health
may be compromised.

628
00:28:20,860 --> 00:28:25,270
So this drone with the Petri
dishes attached has been named

629
00:28:25,270 --> 00:28:28,060
the SnotBot for collecting whale snot.

630
00:28:28,060 --> 00:28:31,570
And the way this works from
a genetic perspective is

631
00:28:31,570 --> 00:28:34,480
the snot is collected
onto the Petri dishes,

632
00:28:34,480 --> 00:28:35,860
and then we can collect the snot

633
00:28:35,860 --> 00:28:40,200
with a sterile swab, a Q-Tip,

634
00:28:40,200 --> 00:28:41,900
and then put that into a preservative,

635
00:28:41,900 --> 00:28:44,800
bring it back to the lab,
extract DNA from that,

636
00:28:44,800 --> 00:28:46,470
amplify regions of the DNA,

637
00:28:46,470 --> 00:28:47,920
in this case, we're interested in all

638
00:28:47,920 --> 00:28:50,580
the microbes present in the samples,

639
00:28:50,580 --> 00:28:52,840
and then we can sequence
them to look the diversity

640
00:28:52,840 --> 00:28:56,860
of the microbes that
exist in the whale blow.

641
00:28:56,860 --> 00:28:59,948
And so this is a chart that's showing,

642
00:28:59,948 --> 00:29:00,830
(Andrea clears throat) excuse me,

643
00:29:00,830 --> 00:29:03,680
the relative abundance of
various classes of bacteria

644
00:29:04,670 --> 00:29:07,400
and this is in a number
of humpback whale samples

645
00:29:07,400 --> 00:29:09,200
that we collected the first summer

646
00:29:09,200 --> 00:29:10,920
we worked with Ocean Alliance

647
00:29:10,920 --> 00:29:13,140
and it was compared to the first two bars,

648
00:29:13,140 --> 00:29:16,430
which are the microbes that
are present just in seawater.

649
00:29:16,430 --> 00:29:18,770
So you can see many of the
microbes present in seawater,

650
00:29:18,770 --> 00:29:21,250
of course, are also present
in the blow samples,

651
00:29:21,250 --> 00:29:24,300
but there are some unique
bacteria that are present

652
00:29:24,300 --> 00:29:26,580
in the blow samples
that are characteristics

653
00:29:26,580 --> 00:29:29,440
of mammalian respiratory tract.

654
00:29:29,440 --> 00:29:30,400
So for example,

655
00:29:30,400 --> 00:29:32,800
these light orange bars
here are actinobacteria,

656
00:29:33,946 --> 00:29:37,200
The bright pink bars are fusobacteria,

657
00:29:37,200 --> 00:29:40,050
and the bright blue bars here are bacilli.

658
00:29:40,050 --> 00:29:43,010
And that represents organisms that are

659
00:29:43,010 --> 00:29:45,380
very common to us in
our respiratory tracts,

660
00:29:45,380 --> 00:29:48,470
such as staphylococcus and streptococcus.

661
00:29:48,470 --> 00:29:52,190
So this is representing a
pattern from humpback whales.

662
00:29:52,190 --> 00:29:55,374
We've now collected or Ocean
Alliance has now collected

663
00:29:55,374 --> 00:29:57,440
(indistinct) with
samples from blue whales.

664
00:29:57,440 --> 00:30:00,720
This is a beautiful image of
a blue whale mother and calf.

665
00:30:00,720 --> 00:30:03,710
They've also brought us in
some samples of sperm whales

666
00:30:03,710 --> 00:30:05,165
that they've collected from the Azores.

667
00:30:05,165 --> 00:30:06,720
(Andrea clears throat)

668
00:30:06,720 --> 00:30:10,240
And so the idea here is to
look at the profile of microbes

669
00:30:10,240 --> 00:30:12,860
that are present in the
blow of individual whales,

670
00:30:12,860 --> 00:30:15,380
look at how they change
over time in individuals,

671
00:30:15,380 --> 00:30:17,170
so from season to season,

672
00:30:17,170 --> 00:30:19,640
and how it differs from different species.

673
00:30:19,640 --> 00:30:21,840
And then can we look at
these data in determining

674
00:30:21,840 --> 00:30:23,670
what a healthy microbiome looks like

675
00:30:23,670 --> 00:30:25,580
and what a microbiome looks like

676
00:30:25,580 --> 00:30:28,370
in a whale whose health is compromised

677
00:30:28,370 --> 00:30:31,560
by comparing the genetic
data with the other data

678
00:30:31,560 --> 00:30:33,720
that's collected with the drones.

679
00:30:33,720 --> 00:30:36,220
And so we hope to continue
to expand this project,

680
00:30:36,220 --> 00:30:38,170
to create this noninvasive tool,

681
00:30:38,170 --> 00:30:40,703
to provide an indication of whale health.

682
00:30:41,960 --> 00:30:43,320
Now, switching over to fisheries,

683
00:30:43,320 --> 00:30:44,710
I wanted to talk a little bit about some

684
00:30:44,710 --> 00:30:46,520
of the iconic species of New England

685
00:30:46,520 --> 00:30:48,580
and one of those is Atlantic cod.

686
00:30:48,580 --> 00:30:50,500
Cod has a pretty wide range,

687
00:30:50,500 --> 00:30:53,740
ranging from North Carolina up
through the Sea of Labrador,

688
00:30:53,740 --> 00:30:55,790
around the coast of Greenland and Iceland

689
00:30:55,790 --> 00:30:58,710
throughout the North Sea and
into the Arctic Ocean here.

690
00:30:58,710 --> 00:31:00,940
And of course it's been
fished for thousands of years.

691
00:31:00,940 --> 00:31:02,650
In fact, Northern European fishers

692
00:31:02,650 --> 00:31:05,523
have followed cod over to North America.

693
00:31:06,720 --> 00:31:09,660
Now because it's been so intensely fished,

694
00:31:09,660 --> 00:31:13,560
there are tremendous records
dating back to the 1800s.

695
00:31:13,560 --> 00:31:18,560
And this is a graph showing the
amount of cod landed in tons

696
00:31:18,560 --> 00:31:21,723
from 1850 all the way to year 2000.

697
00:31:23,080 --> 00:31:24,940
What you can see from
this is when you look

698
00:31:24,940 --> 00:31:28,230
from the year 1850 to year 1960,

699
00:31:28,230 --> 00:31:30,820
you can see that the amount
of cod landed oscillates

700
00:31:30,820 --> 00:31:34,283
between about 100,000 to 300,000 tons.

701
00:31:35,380 --> 00:31:37,580
And really there's sort of a slow increase

702
00:31:37,580 --> 00:31:39,970
in the amount of cod
that's caught over time.

703
00:31:39,970 --> 00:31:42,630
But something quite
remarkable happens in 1960,

704
00:31:42,630 --> 00:31:44,530
where you'd see this tremendous increase

705
00:31:44,530 --> 00:31:48,190
in the number of cods landed
in a very short period of time.

706
00:31:48,190 --> 00:31:51,080
Now, this is a result of technology boom

707
00:31:51,080 --> 00:31:52,530
in the fishing industry.

708
00:31:52,530 --> 00:31:56,250
This is where we started
using sonar and radar.

709
00:31:56,250 --> 00:31:59,410
There was the introduction
of factory trollers,

710
00:31:59,410 --> 00:32:01,130
which had the ability to process

711
00:32:01,130 --> 00:32:03,210
and freeze fish while they were at sea

712
00:32:03,210 --> 00:32:04,970
so they could stay out at sea longer

713
00:32:04,970 --> 00:32:06,487
and catch larger amounts of fish.

714
00:32:06,487 --> 00:32:09,350
And so it really had a tremendous ability

715
00:32:09,350 --> 00:32:13,120
to bring in more fish than
historically was possible.

716
00:32:13,120 --> 00:32:17,290
Unfortunately for cod, this
resulted in a tragic collapse

717
00:32:17,290 --> 00:32:19,040
of the population from overfishing.

718
00:32:20,350 --> 00:32:23,380
And because of this, the
government stepped in

719
00:32:23,380 --> 00:32:25,580
to pass the Magnuson-Stevens Act,

720
00:32:25,580 --> 00:32:29,970
which really was a law to really introduce

721
00:32:29,970 --> 00:32:32,110
new fishery management strategies,

722
00:32:32,110 --> 00:32:33,910
to try and prevent overfishing,

723
00:32:33,910 --> 00:32:37,820
and also to try and rebuild
these collapsed fishery stocks.

724
00:32:37,820 --> 00:32:40,510
A couple of the things that
Magnuson and Stevens Act did

725
00:32:40,510 --> 00:32:44,140
were one, was to extend the
fishing boundaries in the U.S.

726
00:32:44,140 --> 00:32:47,110
from 12 nautical miles
to 200 nautical miles,

727
00:32:47,110 --> 00:32:49,820
meaning that foreign fishing
vessels could not fish

728
00:32:49,820 --> 00:32:52,483
within 200 miles of the U.S. coast.

729
00:32:53,380 --> 00:32:56,880
The other thing was to introduce
science-based catch limits

730
00:32:56,880 --> 00:32:59,540
to try and prevent overfishing.

731
00:32:59,540 --> 00:33:02,027
Unfortunately, it was a
little bit too late for cod

732
00:33:02,027 --> 00:33:04,600
and the population continued to decrease

733
00:33:04,600 --> 00:33:07,580
and collapse to an all-time low in 1992,

734
00:33:07,580 --> 00:33:09,369
and still has not recovered

735
00:33:09,369 --> 00:33:12,310
(Andrea clears throat) from this collapse.

736
00:33:12,310 --> 00:33:15,157
And so there's intense effort by fishers

737
00:33:15,157 --> 00:33:17,450
and fisheries managers
to really understand

738
00:33:17,450 --> 00:33:19,460
the current populations of cod

739
00:33:19,460 --> 00:33:21,550
and anything that can be done to sustain

740
00:33:21,550 --> 00:33:23,840
and help to rebuild the population.

741
00:33:23,840 --> 00:33:26,410
So recently there was a large group called

742
00:33:26,410 --> 00:33:31,410
the Atlantic Cod Stock
Structure Working Group

743
00:33:32,020 --> 00:33:35,660
who got together and created
a large collection of data

744
00:33:35,660 --> 00:33:39,050
with all of the data they have
on cod in the Gulf of Maine

745
00:33:39,050 --> 00:33:41,110
and from the data they
established that there were

746
00:33:41,110 --> 00:33:44,580
about five different
subpopulations of cod.

747
00:33:44,580 --> 00:33:47,550
Three of these populations
were geographically isolated,

748
00:33:47,550 --> 00:33:49,750
so they could follow the progress of them,

749
00:33:49,750 --> 00:33:52,730
but two of these populations
were a little more problematic,

750
00:33:52,730 --> 00:33:55,520
'cause they share the same
area of the Gulf of Maine.

751
00:33:55,520 --> 00:33:58,350
In fact, they spawn in the
same area of Gulf of Maine.

752
00:33:58,350 --> 00:34:00,160
The only thing that separates them

753
00:34:00,160 --> 00:34:02,620
is that they spawn at
different times of year.

754
00:34:02,620 --> 00:34:05,780
So the winter spawners spawn
between November and December

755
00:34:05,780 --> 00:34:08,930
and the spring spawners
spawn between May and June.

756
00:34:08,930 --> 00:34:12,000
So these are genetically
distinct populations of fish,

757
00:34:12,000 --> 00:34:14,710
but they're possible to tell
apart by looking at them

758
00:34:14,710 --> 00:34:16,040
and they share the same area.

759
00:34:16,040 --> 00:34:18,060
So there's no way to know how these two

760
00:34:18,060 --> 00:34:20,320
individual populations are doing.

761
00:34:20,320 --> 00:34:22,740
So together with the
Division of Marine Fisheries,

762
00:34:22,740 --> 00:34:25,670
our fisheries team decided
to take a genetics approach

763
00:34:25,670 --> 00:34:29,430
to try and devise a small
panel of genetic markers

764
00:34:29,430 --> 00:34:31,090
that would allow the fisheries manager

765
00:34:31,090 --> 00:34:33,740
to tell the difference
between these two populations.

766
00:34:34,760 --> 00:34:36,610
So Division of Marine Fisheries went out

767
00:34:36,610 --> 00:34:40,880
to collect samples,
hundreds of samples of cod,

768
00:34:40,880 --> 00:34:43,130
from these two different spawning grounds.

769
00:34:43,130 --> 00:34:45,480
So they'd go out in the
spring spawning season

770
00:34:45,480 --> 00:34:47,980
and collect fish that
were in spawning condition

771
00:34:47,980 --> 00:34:49,550
from the spring spawning group

772
00:34:49,550 --> 00:34:51,500
and they went out in the
winter spawning season

773
00:34:51,500 --> 00:34:55,190
and collect fish in spawning
condition in the winter group.

774
00:34:55,190 --> 00:34:56,510
And they provided fin clips

775
00:34:56,510 --> 00:35:00,220
of these hundreds of animals to GMGI.

776
00:35:00,220 --> 00:35:02,260
So our fisheries team extracted the DNA

777
00:35:02,260 --> 00:35:05,920
from these fin clips and
sequenced the full genome

778
00:35:05,920 --> 00:35:09,060
of more than 200 individual cod.

779
00:35:09,060 --> 00:35:10,830
Then they took these genome sequences

780
00:35:10,830 --> 00:35:12,470
and aligned them together

781
00:35:12,470 --> 00:35:15,080
and looked for variations
across the genome.

782
00:35:15,080 --> 00:35:17,630
They found there were
more than 3,000 areas

783
00:35:17,630 --> 00:35:20,620
of the genome that showed variation.

784
00:35:20,620 --> 00:35:23,610
These are called single
nucleotide polymorphisms.

785
00:35:23,610 --> 00:35:27,770
And then with some mathematical
computational work,

786
00:35:27,770 --> 00:35:31,170
they could reduce these
three million variations

787
00:35:31,170 --> 00:35:34,650
down to a panel of only
just 25 sites in the genome.

788
00:35:34,650 --> 00:35:38,780
So 25 variations which would
allow them to distinguish

789
00:35:38,780 --> 00:35:43,160
spring from winter spawners,
with an 88.5% assignment rate.

790
00:35:43,160 --> 00:35:47,370
So by looking at just 25
variants in the genome,

791
00:35:47,370 --> 00:35:49,390
the fisheries managers
can tell the difference

792
00:35:49,390 --> 00:35:51,980
between these two populations of fish.

793
00:35:51,980 --> 00:35:54,230
And at the same time, they
found that it was quite easy

794
00:35:54,230 --> 00:35:57,630
to devise a panel that could
distinguish males from females.

795
00:35:57,630 --> 00:36:00,013
And so they've now created
these two wonderful tools

796
00:36:00,013 --> 00:36:01,930
that fisheries managers can use

797
00:36:01,930 --> 00:36:04,880
to take any fish cod anytime, anywhere,

798
00:36:04,880 --> 00:36:07,550
and assign it back to
these spawning grounds

799
00:36:07,550 --> 00:36:09,890
so that they can really
understand the contributions

800
00:36:09,890 --> 00:36:13,010
of these two spawning grounds
to the overall population.

801
00:36:13,010 --> 00:36:15,070
And this is actually
pretty critically important

802
00:36:15,070 --> 00:36:16,650
because what's been realized is

803
00:36:16,650 --> 00:36:19,280
that spring spawning group is in decline

804
00:36:19,280 --> 00:36:20,250
and they're contributing

805
00:36:20,250 --> 00:36:22,670
very little fish to
the overall population.

806
00:36:22,670 --> 00:36:24,460
So understanding why that's happening

807
00:36:24,460 --> 00:36:26,320
and protecting the spring spawners is

808
00:36:26,320 --> 00:36:28,563
really a priority for
fisheries management.

809
00:36:29,960 --> 00:36:32,150
Another iconic species that we're

810
00:36:32,150 --> 00:36:34,670
interested in is American Lobster.

811
00:36:34,670 --> 00:36:38,490
The lobster is the most
valuable single species

812
00:36:38,490 --> 00:36:40,270
fisheries in the United States.

813
00:36:40,270 --> 00:36:44,600
In 2019, 126 million pounds
of lobster were landed,

814
00:36:44,600 --> 00:36:48,130
representing $630 million in value.

815
00:36:48,130 --> 00:36:49,810
The lobster lives for a long time.

816
00:36:49,810 --> 00:36:52,430
This is one of the things
I'm really fascinated about.

817
00:36:52,430 --> 00:36:56,070
Of course, the lobster grows
by stepwise process of molting.

818
00:36:56,070 --> 00:36:57,640
So it sheds its hard parts,

819
00:36:57,640 --> 00:37:00,460
which makes it very high,
hard to age a lobster,

820
00:37:00,460 --> 00:37:02,350
but it's estimated that lobsters live

821
00:37:02,350 --> 00:37:05,720
for greater than 50 years,
perhaps as long as 100 years.

822
00:37:05,720 --> 00:37:06,810
And even though they're long lived,

823
00:37:06,810 --> 00:37:09,027
there are very few
reported cases of cancer.

824
00:37:09,027 --> 00:37:12,040
It's a very interesting animal
to understand how it lives

825
00:37:12,040 --> 00:37:15,160
so long and avoids getting
cancer in its long life.

826
00:37:15,160 --> 00:37:16,680
Historically, the lobster has been

827
00:37:16,680 --> 00:37:19,380
a classic model for neuroscience.

828
00:37:19,380 --> 00:37:21,400
And so it's been really
important for understanding

829
00:37:21,400 --> 00:37:24,030
processes like repetitive motion,

830
00:37:24,030 --> 00:37:26,740
like the beating of the
heart, walking, chewing,

831
00:37:26,740 --> 00:37:29,150
and also the sensory nervous system.

832
00:37:29,150 --> 00:37:31,260
And also the lobster is susceptible

833
00:37:31,260 --> 00:37:32,870
to the impacts of climate change.

834
00:37:32,870 --> 00:37:35,260
So it's a pretty important time
to start understanding more

835
00:37:35,260 --> 00:37:37,063
about the biology of this animal.

836
00:37:38,080 --> 00:37:43,080
When you look at lobster landings
since 1982 to present day,

837
00:37:43,610 --> 00:37:46,570
there are two stocks, the
Southern New England stock,

838
00:37:46,570 --> 00:37:48,210
which is shown with the red line,

839
00:37:48,210 --> 00:37:50,240
and the Gulf of Maine
or Georges Bank stock,

840
00:37:50,240 --> 00:37:52,250
which is shown by the blue line.

841
00:37:52,250 --> 00:37:55,540
And what you see over the
last decade or couple decades

842
00:37:55,540 --> 00:37:57,240
is that the Southern New England stock

843
00:37:57,240 --> 00:37:58,930
has reached an all-time low.

844
00:37:58,930 --> 00:38:01,340
Whereas the Gulf of Maine
and Georges Bank stock

845
00:38:01,340 --> 00:38:03,200
has reached an all-time high.

846
00:38:03,200 --> 00:38:05,930
And this is thought to be because
of the changing conditions

847
00:38:05,930 --> 00:38:09,160
in the Gulf of Maine and the
lobster migrating northward,

848
00:38:09,160 --> 00:38:10,993
to stay in the cooler waters.

849
00:38:12,100 --> 00:38:16,030
In addition to this northward
migration of lobsters

850
00:38:16,030 --> 00:38:17,910
with warming waters in
Southern New England,

851
00:38:17,910 --> 00:38:19,750
we're also seeing an increase incidence

852
00:38:19,750 --> 00:38:21,610
of shell disease in lobster.

853
00:38:21,610 --> 00:38:24,690
And this is a devastating
bacterial infection,

854
00:38:24,690 --> 00:38:26,300
degrades the shell of the lobster,

855
00:38:26,300 --> 00:38:29,720
ultimately causing its mortality
because it fails to molt.

856
00:38:29,720 --> 00:38:32,730
And so this is a critical
time to start understanding

857
00:38:32,730 --> 00:38:35,260
and providing tools that
we can better understand

858
00:38:35,260 --> 00:38:37,340
the interesting biology of these animals

859
00:38:37,340 --> 00:38:40,200
and create tools for the
fisheries to better manage it

860
00:38:40,200 --> 00:38:43,483
as it is undergoing changes
with respect to climate change.

861
00:38:44,520 --> 00:38:45,740
So we undertook the project

862
00:38:45,740 --> 00:38:48,060
to sequence a genome of
the American Lobster.

863
00:38:48,060 --> 00:38:51,070
This was a collaborative
project that took place

864
00:38:51,070 --> 00:38:54,280
between GMGI and scientists
from Tufts University,

865
00:38:54,280 --> 00:38:56,960
Harvard, Johns Hopkins,
University of Florida,

866
00:38:56,960 --> 00:38:59,060
Dalhousie University in Nova Scotia

867
00:38:59,060 --> 00:39:00,840
and the University of Prince Edward Island

868
00:39:00,840 --> 00:39:02,740
in Canada as well.

869
00:39:02,740 --> 00:39:05,810
It was a project that took
us years to accomplish

870
00:39:05,810 --> 00:39:07,810
and that's because this
lobster genome turned out

871
00:39:07,810 --> 00:39:10,670
to be much more challenging
than we anticipated.

872
00:39:10,670 --> 00:39:12,290
The size of the lobster genome is

873
00:39:12,290 --> 00:39:14,350
about the same size as the human genome.

874
00:39:14,350 --> 00:39:17,010
That means it has about
three billion building blocks

875
00:39:17,010 --> 00:39:19,100
or nucleotides or base pairs.

876
00:39:19,100 --> 00:39:22,310
And the problem isn't
the size of the genome.

877
00:39:22,310 --> 00:39:23,870
The problem is the lobster genome

878
00:39:23,870 --> 00:39:26,420
is full of repetitive sequences.

879
00:39:26,420 --> 00:39:29,330
So there are certain sequences
of nucleotides that are

880
00:39:29,330 --> 00:39:32,330
repeated over and over and
over again in the genome.

881
00:39:32,330 --> 00:39:34,540
In fact, 80% of the genome is

882
00:39:34,540 --> 00:39:36,750
made up of repetitive sequences.

883
00:39:36,750 --> 00:39:39,600
So this posed a lot of technical
problems for sequencing

884
00:39:39,600 --> 00:39:41,190
through these repetitive regions,

885
00:39:41,190 --> 00:39:43,120
but it also caused tremendous challenges

886
00:39:43,120 --> 00:39:44,550
for assembling the genome

887
00:39:44,550 --> 00:39:48,510
and putting the genome
pieces back together again.

888
00:39:48,510 --> 00:39:50,870
You can imagine it was like doing a puzzle

889
00:39:50,870 --> 00:39:54,340
with three billion pieces
where 80% of the pieces

890
00:39:54,340 --> 00:39:57,600
were exactly the same color
and exactly the same shape,

891
00:39:57,600 --> 00:40:00,430
really hard to know how to
fit those pieces together.

892
00:40:00,430 --> 00:40:03,240
So after years of effort,
we finally assembled

893
00:40:03,240 --> 00:40:06,200
a reasonably good genome of the lobster.

894
00:40:06,200 --> 00:40:08,480
And this now provides a great resource

895
00:40:08,480 --> 00:40:11,450
for the scientific community,
the research community,

896
00:40:11,450 --> 00:40:14,370
the fisheries community to build upon.

897
00:40:14,370 --> 00:40:16,680
From my perspective, I'm
particularly interested

898
00:40:16,680 --> 00:40:18,500
in the longevity of this animal.

899
00:40:18,500 --> 00:40:20,710
And so I wanted to see if
we could look at the genes

900
00:40:20,710 --> 00:40:23,080
and get a hint as to why the lobster

901
00:40:23,080 --> 00:40:24,803
has such a long and healthy life.

902
00:40:25,840 --> 00:40:27,490
In its genome we could predict

903
00:40:27,490 --> 00:40:30,370
more than 25,000 individual genes.

904
00:40:30,370 --> 00:40:31,990
What was interesting about that is

905
00:40:31,990 --> 00:40:35,970
when we look at those genes
and infer their function

906
00:40:35,970 --> 00:40:38,410
by comparison to known genes,

907
00:40:38,410 --> 00:40:40,760
we find that more than
a third of the genomes

908
00:40:40,760 --> 00:40:43,360
in the genome are
completely uncharacterized.

909
00:40:43,360 --> 00:40:44,580
They have novel functions.

910
00:40:44,580 --> 00:40:46,340
They don't match anything that exists

911
00:40:46,340 --> 00:40:48,010
in a characterized database.

912
00:40:48,010 --> 00:40:50,440
So there's a lot of
biology of these animals

913
00:40:50,440 --> 00:40:52,940
that we just don't understand
and so much more to learn.

914
00:40:52,940 --> 00:40:56,620
But when we looked at
the genes of this animal,

915
00:40:56,620 --> 00:40:59,510
what we did was to look at what
gene families were expanded

916
00:40:59,510 --> 00:41:03,400
and contracted compared to
closely related crustaceans,

917
00:41:03,400 --> 00:41:05,662
crayfish, shrimp, and daphnia

918
00:41:05,662 --> 00:41:08,370
(Andrea clears throat) as example.

919
00:41:08,370 --> 00:41:10,670
Now all these other crustaceans
are very short lived.

920
00:41:10,670 --> 00:41:13,540
They live for anywhere
from 54 days to 3 years

921
00:41:13,540 --> 00:41:16,160
and the lobster of course
lives for more than 50 years.

922
00:41:16,160 --> 00:41:19,070
So a nice comparison to see
what parts of the genome

923
00:41:19,070 --> 00:41:21,150
are responsible for this longevity,

924
00:41:21,150 --> 00:41:23,570
and there were some tremendous clues here.

925
00:41:23,570 --> 00:41:26,850
So these numbers on this
biogenetic tree here

926
00:41:26,850 --> 00:41:30,190
are showing that 821 gene
families were expanded

927
00:41:30,190 --> 00:41:32,940
in the lobster relative
to its closest neighbor

928
00:41:32,940 --> 00:41:36,330
and 2,445 gene families were contracted

929
00:41:36,330 --> 00:41:39,060
in the lobster genome
relative to its neighbors.

930
00:41:39,060 --> 00:41:40,450
Now, we didn't get much information

931
00:41:40,450 --> 00:41:42,160
about the genes that were contracted

932
00:41:42,160 --> 00:41:44,340
because many of them had unknown function,

933
00:41:44,340 --> 00:41:46,290
but within the genes that were expanded,

934
00:41:46,290 --> 00:41:49,110
there was a tremendous amount
of interesting opportunity,

935
00:41:49,110 --> 00:41:52,170
which indicated the lobster
has really invested a lot

936
00:41:52,170 --> 00:41:56,910
in physical, biological and
chemical defense mechanisms.

937
00:41:56,910 --> 00:41:58,930
There was a tremendous expansion of genes

938
00:41:58,930 --> 00:42:01,090
involved in its shell formation,

939
00:42:01,090 --> 00:42:03,890
so giving it a tremendous
physical defense,

940
00:42:03,890 --> 00:42:06,570
an expansion of genes
involved in innate immunity,

941
00:42:06,570 --> 00:42:08,520
allowing the lobster to defend itself

942
00:42:08,520 --> 00:42:10,740
against bacterial infections,

943
00:42:10,740 --> 00:42:12,533
and also an expansion of genes

944
00:42:12,533 --> 00:42:15,760
that involved in chemical defense

945
00:42:15,760 --> 00:42:19,200
to protect this animal from chemicals

946
00:42:19,200 --> 00:42:21,130
that it might encounter
in the environment.

947
00:42:21,130 --> 00:42:22,480
There was an increase in genes

948
00:42:22,480 --> 00:42:24,570
associated with genome stability.

949
00:42:24,570 --> 00:42:26,960
And this may be part of the reason

950
00:42:26,960 --> 00:42:30,110
we don't see cancer in lobsters

951
00:42:30,110 --> 00:42:32,160
and also some very interesting expansion

952
00:42:32,160 --> 00:42:34,160
of its nervous system,

953
00:42:34,160 --> 00:42:37,100
including expansion of its
sensory nervous system.

954
00:42:37,100 --> 00:42:39,900
So really all of these
characteristics are part and parcel

955
00:42:39,900 --> 00:42:43,660
why this animal lives so long
and its ecological success

956
00:42:43,660 --> 00:42:45,593
in the benthic marine environment.

957
00:42:47,260 --> 00:42:49,760
So the lobster is not the
only long-lived animal

958
00:42:49,760 --> 00:42:51,570
that I'm interested in.

959
00:42:51,570 --> 00:42:52,840
In fact, the oceans are home to all

960
00:42:52,840 --> 00:42:55,740
of the longest living
animals on the planet.

961
00:42:55,740 --> 00:42:59,120
The longest living mammal on
the planet is the bowhead whale

962
00:42:59,120 --> 00:43:01,680
estimated to live for more than 200 years.

963
00:43:01,680 --> 00:43:03,940
The Greenland shark is the
longest lived vertebrate

964
00:43:03,940 --> 00:43:06,460
on the planet living for nearly 400 years

965
00:43:06,460 --> 00:43:09,280
and the quahog clam is the
longest lived invertebrate

966
00:43:09,280 --> 00:43:12,610
on the planet living
for more than 500 years.

967
00:43:12,610 --> 00:43:15,350
This is only outdone by this
little jellyfish up here,

968
00:43:15,350 --> 00:43:16,400
which is reported to be

969
00:43:16,400 --> 00:43:18,583
the only immortal animal on the planet,

970
00:43:19,540 --> 00:43:22,100
going from this fully formed adult stage

971
00:43:22,100 --> 00:43:24,220
to reverting to an embryonic stage

972
00:43:24,220 --> 00:43:26,700
and back to an adult continuously.

973
00:43:26,700 --> 00:43:29,170
And so it's the only organism we know

974
00:43:29,170 --> 00:43:31,500
of that's achieved immortality.

975
00:43:31,500 --> 00:43:33,650
Now these in animals
are really interesting

976
00:43:33,650 --> 00:43:35,150
and I'd love to look at their genomes

977
00:43:35,150 --> 00:43:37,900
to understand the secrets they hold,

978
00:43:37,900 --> 00:43:39,010
but these animals are really

979
00:43:39,010 --> 00:43:41,660
also very challenging to study in the lab,

980
00:43:41,660 --> 00:43:44,140
very difficult to study big marine animals

981
00:43:44,140 --> 00:43:49,140
or a colony of shark or clams
burying in the sea bottom.

982
00:43:49,310 --> 00:43:52,120
So I've turned my attention to sea urchins

983
00:43:52,120 --> 00:43:53,780
and the red sea urchin is among one

984
00:43:53,780 --> 00:43:55,220
of the Earth's longest living animals

985
00:43:55,220 --> 00:43:57,390
living for more than 200 years.

986
00:43:57,390 --> 00:43:59,060
And sea urchins are an interesting model

987
00:43:59,060 --> 00:44:00,040
because they've been a model

988
00:44:00,040 --> 00:44:02,180
for scientific research for centuries.

989
00:44:02,180 --> 00:44:03,810
There's lots of labs that study them,

990
00:44:03,810 --> 00:44:06,260
there are a lot of tools
available to study them

991
00:44:06,260 --> 00:44:09,100
and they're really easy to
keep and handle in the lab.

992
00:44:09,100 --> 00:44:11,740
And so I'm gonna, in the interest of time,

993
00:44:11,740 --> 00:44:13,440
I'm just gonna go through the last slides

994
00:44:13,440 --> 00:44:15,410
a little bit more quickly

995
00:44:15,410 --> 00:44:17,590
so that there's time for questions,

996
00:44:17,590 --> 00:44:20,050
but what I focused in on the sea urchins

997
00:44:20,050 --> 00:44:22,010
is a very closely related species

998
00:44:22,010 --> 00:44:23,900
at very different natural life spans.

999
00:44:23,900 --> 00:44:25,660
Where some species of sea urchins

1000
00:44:25,660 --> 00:44:29,610
only live for a short time,
4 to 10 years in the wild,

1001
00:44:29,610 --> 00:44:32,840
other species can live for more
than 200 years in the wild.

1002
00:44:32,840 --> 00:44:35,090
So nature's provided a lovely model system

1003
00:44:35,090 --> 00:44:37,570
to do comparative genomics to understand

1004
00:44:37,570 --> 00:44:40,390
why this animal is living
only a short life span

1005
00:44:40,390 --> 00:44:42,070
where this closely related cousin

1006
00:44:42,070 --> 00:44:44,700
can live for more than 200 years.

1007
00:44:44,700 --> 00:44:46,680
The other thing that's
interesting about these animals is

1008
00:44:46,680 --> 00:44:48,280
not just their difference in longevity,

1009
00:44:48,280 --> 00:44:51,030
but they appear to show no signs of aging,

1010
00:44:51,030 --> 00:44:52,710
and what I mean by that is they grow

1011
00:44:52,710 --> 00:44:54,730
and reproduce throughout their lifespan.

1012
00:44:54,730 --> 00:44:56,830
There's no increase in mortality rate

1013
00:44:56,830 --> 00:44:58,400
associated with getting older,

1014
00:44:58,400 --> 00:45:00,210
no increase incidence of disease

1015
00:45:00,210 --> 00:45:02,440
associated with getting older,

1016
00:45:02,440 --> 00:45:05,480
and that includes no
reported cases of cancer.

1017
00:45:05,480 --> 00:45:08,570
And so we're doing comparative
studies to try and understand

1018
00:45:08,570 --> 00:45:10,710
what's the difference
between these animals

1019
00:45:10,710 --> 00:45:13,210
that defines their different lifespans

1020
00:45:13,210 --> 00:45:15,920
and how do they remain healthy
throughout their lives?

1021
00:45:15,920 --> 00:45:18,670
How do they avoid cancer
throughout their lives?

1022
00:45:18,670 --> 00:45:20,300
And so we've been working on the genome

1023
00:45:20,300 --> 00:45:21,340
for the red sea urchin,

1024
00:45:21,340 --> 00:45:25,410
which is proving to be much
more easy than the lobster.

1025
00:45:25,410 --> 00:45:29,410
We now have the genome
assembled into 24 pieces,

1026
00:45:29,410 --> 00:45:31,220
which equal to 24 chromosomes.

1027
00:45:31,220 --> 00:45:34,020
So we have chromosome length
assembly of this animal,

1028
00:45:34,020 --> 00:45:35,540
which allows us to now compare it

1029
00:45:35,540 --> 00:45:37,430
to other sea urchin species.

1030
00:45:37,430 --> 00:45:40,330
And we also wanna do
comparison between animals

1031
00:45:40,330 --> 00:45:42,210
that are resistant to cancer,

1032
00:45:42,210 --> 00:45:45,590
like the long-lived sea urchin,
lobsters and bowhead whales,

1033
00:45:45,590 --> 00:45:48,110
and compare their
genomics to marine animals

1034
00:45:48,110 --> 00:45:50,230
that are highly susceptible to cancer,

1035
00:45:50,230 --> 00:45:54,820
like some species of bivalve,
like the soft-shell clam,

1036
00:45:54,820 --> 00:45:57,083
some fish and some marine mammals.

1037
00:45:58,460 --> 00:46:02,060
In addition to (Andrea clears
throat) neoplastic disease,

1038
00:46:02,060 --> 00:46:03,820
we're also interested in building tools

1039
00:46:03,820 --> 00:46:07,090
to understand infectious disease
in the marine environment.

1040
00:46:07,090 --> 00:46:09,810
Infectious disease is
increasing with warming waters,

1041
00:46:09,810 --> 00:46:12,020
as I mentioned already for the lobster,

1042
00:46:12,020 --> 00:46:15,060
but infectious disease is
also a problem in agriculture

1043
00:46:15,060 --> 00:46:16,870
where animals are grown in hot density

1044
00:46:16,870 --> 00:46:18,930
and diseases spread rapidly.

1045
00:46:18,930 --> 00:46:20,300
And so just very quickly,

1046
00:46:20,300 --> 00:46:22,450
we're building new next-generation tools

1047
00:46:22,450 --> 00:46:26,010
together with collaborators at
MIT and University of Arizona

1048
00:46:26,010 --> 00:46:30,450
using CRISPR technology to
create diagnostic tools.

1049
00:46:30,450 --> 00:46:33,090
CRISPR, it's something that I'm sure

1050
00:46:33,090 --> 00:46:34,430
most people have heard about now.

1051
00:46:34,430 --> 00:46:35,915
It's a ribonucleoprotein,

1052
00:46:35,915 --> 00:46:39,140
it's part of a bacterial defense
mechanism against viruses,

1053
00:46:39,140 --> 00:46:42,940
and it has the ability to
hone in on specific sequences

1054
00:46:42,940 --> 00:46:45,730
of nucleic acids and then cleave them.

1055
00:46:45,730 --> 00:46:47,940
And so we can create diagnostic tools

1056
00:46:47,940 --> 00:46:50,260
that when a certain sequence is detected,

1057
00:46:50,260 --> 00:46:54,233
it can cleave a reporter and
give a very sensitive output.

1058
00:46:55,140 --> 00:46:56,610
So we've been focusing our attention

1059
00:46:56,610 --> 00:46:58,660
on diseases that impact aquaculture

1060
00:46:58,660 --> 00:47:00,360
to viral diseases in shrimp,

1061
00:47:00,360 --> 00:47:02,560
white spot syndrome
virus being one of them,

1062
00:47:02,560 --> 00:47:03,770
and have designed an assay

1063
00:47:03,770 --> 00:47:06,760
that can detect down to one viral copy

1064
00:47:06,760 --> 00:47:08,783
and is very specific to detecting this.

1065
00:47:08,783 --> 00:47:12,000
But the beautiful thing about
these CRISPR-based diagnostics

1066
00:47:12,000 --> 00:47:14,490
is not just their
sensitivity and specificity,

1067
00:47:14,490 --> 00:47:17,100
but they can be adopted
to very simple assays,

1068
00:47:17,100 --> 00:47:18,880
like the lateral flow assays.

1069
00:47:18,880 --> 00:47:21,000
I think people are becoming
very familiar with these

1070
00:47:21,000 --> 00:47:22,450
'cause these are similar type of assays

1071
00:47:22,450 --> 00:47:24,490
you do with the at-home COVID test.

1072
00:47:24,490 --> 00:47:28,200
And this actually is very
powerful because it would allow

1073
00:47:28,200 --> 00:47:30,580
resource managers and fisheries managers

1074
00:47:30,580 --> 00:47:33,220
to have a very simple
test in which they can

1075
00:47:33,220 --> 00:47:35,690
quickly detect the presence of pathogens

1076
00:47:35,690 --> 00:47:39,060
and quickly respond to
mitigate the impacts

1077
00:47:39,900 --> 00:47:43,833
on their ecosystems and
in aquaculture scenarios.

1078
00:47:44,700 --> 00:47:47,660
So with that, I'm gonna just
acknowledge all of the people

1079
00:47:47,660 --> 00:47:50,560
at GMGI who contributed to
the work that we're doing.

1080
00:47:50,560 --> 00:47:53,080
There are 11 members of our
research team at the moment.

1081
00:47:53,080 --> 00:47:56,400
So we're a small group, a very
talented group of scientists.

1082
00:47:56,400 --> 00:47:59,800
And so I wanted to thank
all of our researchers

1083
00:47:59,800 --> 00:48:02,643
for all of the work that
that we're doing at GMGI.

1084
00:48:03,760 --> 00:48:05,970
In addition, I wanna thank
all of our collaborators.

1085
00:48:05,970 --> 00:48:07,700
We have wonderful collaborators

1086
00:48:07,700 --> 00:48:09,510
and all of our supporters who provide us

1087
00:48:09,510 --> 00:48:11,060
with the financial support to do

1088
00:48:11,060 --> 00:48:13,270
the work that we're doing at GMGI.

1089
00:48:13,270 --> 00:48:16,360
And with that, I'd be
happy to take any questions

1090
00:48:16,360 --> 00:48:19,840
from the audience and welcome
you to visit our facility

1091
00:48:19,840 --> 00:48:22,293
in Gloucester at sometime in the future.

1092
00:48:25,510 --> 00:48:28,340
Sorry, I realize I covered
a lot of ground there,

1093
00:48:28,340 --> 00:48:30,740
but hopefully everyone
found something interesting

1094
00:48:30,740 --> 00:48:33,020
and there's some questions
from the audience.

1095
00:48:33,020 --> 00:48:35,440
- Thank you so much, Dr.
Bodnar. That was fascinating.

1096
00:48:35,440 --> 00:48:37,140
And there are lots of questions

1097
00:48:38,150 --> 00:48:43,150
and I'm gonna start off with
a question from a student.

1098
00:48:43,757 --> 00:48:46,580
"What is the length of time
in which eDNA measures?

1099
00:48:46,580 --> 00:48:49,660
Is it hours, days, et cetera?"

1100
00:48:49,660 --> 00:48:50,660
- That's a wonderful question

1101
00:48:50,660 --> 00:48:52,980
and it's actually quite variable.

1102
00:48:52,980 --> 00:48:54,960
It depends on the conditions of the ocean.

1103
00:48:54,960 --> 00:48:57,810
So because the oceans are so dynamic,

1104
00:48:57,810 --> 00:49:01,080
the signal can last from
anywhere from hours,

1105
00:49:01,080 --> 00:49:04,900
or hours to days, but
typically not longer than days.

1106
00:49:04,900 --> 00:49:07,360
So it does give you a very recent snapshot

1107
00:49:07,360 --> 00:49:08,770
of what's been present.

1108
00:49:08,770 --> 00:49:11,240
The things that we have to
take into account are currents,

1109
00:49:11,240 --> 00:49:14,710
which will carry the eDNA away,

1110
00:49:14,710 --> 00:49:16,770
also the depth at which
we're collecting water.

1111
00:49:16,770 --> 00:49:18,040
So in the surface waters,

1112
00:49:18,040 --> 00:49:22,340
UV radiation will help
to degrade DNA quicker.

1113
00:49:22,340 --> 00:49:26,170
And if in deeper waters, the
cold will help to preserve DNA.

1114
00:49:26,170 --> 00:49:28,820
So we really have to take in
the environmental conditions

1115
00:49:28,820 --> 00:49:31,300
into account when we're interpreting data,

1116
00:49:31,300 --> 00:49:34,210
but generally speaking, it's
generally in the order of days.

1117
00:49:34,210 --> 00:49:36,340
And so we're really
seeing a recent snapshot

1118
00:49:36,340 --> 00:49:38,853
of what's present in
the marine environment.

1119
00:49:41,780 --> 00:49:43,750
- Thank you, Dr. Bodnar.

1120
00:49:43,750 --> 00:49:45,280
This is Ethel Gordon,

1121
00:49:45,280 --> 00:49:47,910
also from the Darwin Festival committee.

1122
00:49:47,910 --> 00:49:51,287
I'm gonna ask a question
from one of our colleagues.

1123
00:49:51,287 --> 00:49:54,400
"Do you look for any viruses in whale blow

1124
00:49:55,360 --> 00:50:00,287
or other metagenomic analyses
and what gene is targeted?"

1125
00:50:01,390 --> 00:50:02,900
- Yeah, so so far all we've looked

1126
00:50:02,900 --> 00:50:05,980
at are the bacterial profile.

1127
00:50:05,980 --> 00:50:10,670
So we've been targeting the 16S
ribosomal RNA gene for that.

1128
00:50:10,670 --> 00:50:12,980
We are really interested
in looking at viruses

1129
00:50:12,980 --> 00:50:14,370
and I think everybody's thinking

1130
00:50:14,370 --> 00:50:17,900
about respiratory viruses
over the last few years,

1131
00:50:17,900 --> 00:50:21,410
but we haven't yet
designed our primers yet

1132
00:50:21,410 --> 00:50:22,880
to look at viral sequences,

1133
00:50:22,880 --> 00:50:24,930
but we've connected with
a lab that does a lot

1134
00:50:24,930 --> 00:50:27,660
of looking at viruses throughout the ocean

1135
00:50:27,660 --> 00:50:30,680
and we hope to add that to that analysis

1136
00:50:30,680 --> 00:50:32,450
so we can start to look
at the viral profiles

1137
00:50:32,450 --> 00:50:35,820
that are present in the
respiratory tract of whales.

1138
00:50:35,820 --> 00:50:36,750
- [Ethel] Thank you.

1139
00:50:36,750 --> 00:50:38,357
- There's another question from a student.

1140
00:50:38,357 --> 00:50:42,020
"Have you done similar work
in fresh water areas in Maine?

1141
00:50:42,020 --> 00:50:44,240
There are some pretty
remote bodies of water

1142
00:50:44,240 --> 00:50:46,510
in northern Maine, and I'd love
to know if there is any sort

1143
00:50:46,510 --> 00:50:48,937
of genetic variability
due to their seclusion."

1144
00:50:49,840 --> 00:50:51,300
- Yeah, that's an excellent question

1145
00:50:51,300 --> 00:50:53,700
and eDNA technologies are being used

1146
00:50:53,700 --> 00:50:56,030
in both marine and
freshwater environments.

1147
00:50:56,030 --> 00:50:58,910
We haven't done much work
in freshwater environments,

1148
00:50:58,910 --> 00:51:00,700
but there are groups
that are looking at that.

1149
00:51:00,700 --> 00:51:03,570
And it's a wonderful way to
look at the full diversity

1150
00:51:03,570 --> 00:51:07,290
of those unique ecosystems
and really to understand

1151
00:51:07,290 --> 00:51:09,390
what's there and what they're doing there.

1152
00:51:11,450 --> 00:51:14,055
It's a great way to look at that question.

1153
00:51:14,055 --> 00:51:16,230
(Andrea clears throat)

1154
00:51:16,230 --> 00:51:18,763
- I have another question from a student.

1155
00:51:19,687 --> 00:51:22,750
"How does finding these
organisms from sediments

1156
00:51:22,750 --> 00:51:25,537
in water samples connect to evolution?"

1157
00:51:26,800 --> 00:51:28,950
- Oh, that's another great question.

1158
00:51:28,950 --> 00:51:32,540
And so again, we can, by
looking at the genetic sequence,

1159
00:51:32,540 --> 00:51:36,280
we can infer the relationship
between the organisms

1160
00:51:36,280 --> 00:51:38,270
and draft those wonderful trees,

1161
00:51:38,270 --> 00:51:40,220
which help us look back in time.

1162
00:51:40,220 --> 00:51:43,310
And so it is possible to get an idea

1163
00:51:43,310 --> 00:51:46,790
of sort of the rate of
evolution of different organisms

1164
00:51:46,790 --> 00:51:48,080
and the scope of evolution

1165
00:51:48,080 --> 00:51:49,794
by looking at the genetic patterns.

1166
00:51:49,794 --> 00:51:53,870
So really looking at it
through the variation

1167
00:51:53,870 --> 00:51:55,530
in the various genetic sequences

1168
00:51:56,650 --> 00:51:59,480
that you can unearth
from these technologies

1169
00:51:59,480 --> 00:52:00,880
and looking them from a more

1170
00:52:00,880 --> 00:52:02,340
from a phylogenetic point of view.

1171
00:52:02,340 --> 00:52:05,730
So putting them in
relationship, one to the other.

1172
00:52:05,730 --> 00:52:07,500
So you can certainly can infer

1173
00:52:07,500 --> 00:52:11,973
evolutionary information
from the analysis.

1174
00:52:13,970 --> 00:52:14,803
- Here's another question.

1175
00:52:14,803 --> 00:52:16,960
"How are animals like Greenland sharks

1176
00:52:16,960 --> 00:52:18,610
and urchins able to live so long?

1177
00:52:18,610 --> 00:52:20,760
Is it because they have
a slower metabolism

1178
00:52:20,760 --> 00:52:22,460
that they burn energy at a slower rate

1179
00:52:22,460 --> 00:52:26,040
than less stationary animals?"

1180
00:52:26,040 --> 00:52:27,510
- Yeah, that's another great question.

1181
00:52:27,510 --> 00:52:30,710
And certainly metabolism is a
very important part of aging

1182
00:52:30,710 --> 00:52:32,930
and things that do slow
down their metabolism,

1183
00:52:32,930 --> 00:52:34,660
typically slow down the accumulation

1184
00:52:34,660 --> 00:52:35,830
of damaging their tissue.

1185
00:52:35,830 --> 00:52:38,550
So that's part of the
secret of their success,

1186
00:52:38,550 --> 00:52:40,317
but I think it's much
more complicated than that

1187
00:52:40,317 --> 00:52:45,317
and there are many parts (Andrea
chuckles) to that question.

1188
00:52:46,410 --> 00:52:50,400
We find that they can maintain
stability of their genome,

1189
00:52:50,400 --> 00:52:53,210
which is a really important part of aging.

1190
00:52:53,210 --> 00:52:55,810
So by maintaining, for example,

1191
00:52:55,810 --> 00:52:58,320
the caps at the end of the
chromosomes' telomeres,

1192
00:52:58,320 --> 00:53:00,040
these animals maintain their telomeres.

1193
00:53:00,040 --> 00:53:02,880
Whereas aging animals
shorten their telomeres.

1194
00:53:02,880 --> 00:53:05,000
Some of these animals,
sea urchins in particular

1195
00:53:05,000 --> 00:53:07,330
have wonderful regenerative properties.

1196
00:53:07,330 --> 00:53:10,560
So they can continually regenerate tissues

1197
00:53:10,560 --> 00:53:12,840
presumably from stem cell compartments.

1198
00:53:12,840 --> 00:53:15,470
So they're constantly
removing damaged tissue

1199
00:53:15,470 --> 00:53:17,470
and replacing it with healthy new tissue

1200
00:53:17,470 --> 00:53:19,870
from this regenerative process.

1201
00:53:19,870 --> 00:53:22,000
So there are a lot of different mechanisms

1202
00:53:22,000 --> 00:53:24,400
by which these animals
are living a long time.

1203
00:53:24,400 --> 00:53:26,870
One of the most remarkable
studies we did recently

1204
00:53:26,870 --> 00:53:28,260
with the red sea urchin was to look

1205
00:53:28,260 --> 00:53:30,720
at the gene expression
in its nervous system.

1206
00:53:30,720 --> 00:53:33,670
And we see that the gene
expression in its nervous system

1207
00:53:33,670 --> 00:53:36,200
is so completely different
from the gene expression

1208
00:53:36,200 --> 00:53:39,710
in the aging nervous system
of humans and aging animals.

1209
00:53:39,710 --> 00:53:42,500
They're really preserving
their nervous system

1210
00:53:42,500 --> 00:53:43,940
in a very unique way.

1211
00:53:43,940 --> 00:53:47,230
And so preservation of the
nervous system may also be key

1212
00:53:47,230 --> 00:53:48,710
because the nervous system, of course,

1213
00:53:48,710 --> 00:53:50,270
innervates the whole body

1214
00:53:50,270 --> 00:53:53,820
and preserving the nervous
system may systemically preserve

1215
00:53:53,820 --> 00:53:56,283
the health of the animal for centuries.

1216
00:53:58,890 --> 00:54:01,407
- I'd like to ask another
question from the audience.

1217
00:54:01,407 --> 00:54:04,190
"Do we see the shell-eating bacteria

1218
00:54:04,190 --> 00:54:05,840
in only the American Lobster

1219
00:54:05,840 --> 00:54:09,740
or do you see it affecting
other crustaceans?

1220
00:54:09,740 --> 00:54:12,497
Is it only present in salt water?"

1221
00:54:13,800 --> 00:54:15,150
- Yeah, unfortunately I don't know

1222
00:54:15,150 --> 00:54:16,183
the answer to that question.

1223
00:54:16,183 --> 00:54:17,141
That's a great question.

1224
00:54:17,141 --> 00:54:18,760
It's something I'll
have to look into more.

1225
00:54:18,760 --> 00:54:21,960
Certainly it is in lobsters,

1226
00:54:21,960 --> 00:54:24,710
but I don't know if that also transfers

1227
00:54:24,710 --> 00:54:26,460
to other crustaceans, for example.

1228
00:54:26,460 --> 00:54:28,307
So that's sounds thing
I'm afraid I can't answer,

1229
00:54:28,307 --> 00:54:30,092
but great question.

1230
00:54:30,092 --> 00:54:30,925
- [Ethel] Thank you.

1231
00:54:30,925 --> 00:54:33,870
- Another question, "any
opportunities for internships,

1232
00:54:33,870 --> 00:54:35,137
undergraduate research?"

1233
00:54:36,430 --> 00:54:38,000
- Yeah, we're very small still.

1234
00:54:38,000 --> 00:54:40,640
We're hoping to establish
an internship program

1235
00:54:40,640 --> 00:54:43,950
in the summer and in the
summer months going forward.

1236
00:54:43,950 --> 00:54:45,830
At the moment, it's been a little bit

1237
00:54:45,830 --> 00:54:48,180
of an ad hoc (Andrea chuckles) process,

1238
00:54:48,180 --> 00:54:50,730
but we're certainly interested in,

1239
00:54:50,730 --> 00:54:52,930
when we can and when we have funding,

1240
00:54:52,930 --> 00:54:55,080
to have students and
to have the opportunity

1241
00:54:55,080 --> 00:54:56,350
to train students in the lab.

1242
00:54:56,350 --> 00:54:57,640
So if you're interested,

1243
00:54:57,640 --> 00:55:01,320
please feel free to send your CV onto us

1244
00:55:01,320 --> 00:55:04,730
with a cover letter, expressing
your interest in the ocean.

1245
00:55:04,730 --> 00:55:06,630
And we'd be happy to consider that

1246
00:55:06,630 --> 00:55:09,323
for when opportunities
do come up in the future.

1247
00:55:14,770 --> 00:55:16,527
- Another question from the audience.

1248
00:55:16,527 --> 00:55:21,107
"How is the Turritopsis
dohrnii proven immortal?"

1249
00:55:22,590 --> 00:55:25,680
- Yeah, I think because it
has such a unique mechanism

1250
00:55:25,680 --> 00:55:28,820
that when it encounters
stress in the environment,

1251
00:55:28,820 --> 00:55:32,650
it can revert and
essentially de-differentiate

1252
00:55:32,650 --> 00:55:34,740
into an embryonic state,

1253
00:55:34,740 --> 00:55:36,990
which is a very unique
part of its biology.

1254
00:55:36,990 --> 00:55:38,610
And then when conditions improve,

1255
00:55:38,610 --> 00:55:43,430
it will then progress through
the embryonic development

1256
00:55:43,430 --> 00:55:45,010
and reform an adult.

1257
00:55:45,010 --> 00:55:48,070
And so it's through going
through this sort of process

1258
00:55:48,070 --> 00:55:51,390
of a fully-formed adult,
reverting back to an embryo,

1259
00:55:51,390 --> 00:55:52,720
and then going back to an embryo,

1260
00:55:52,720 --> 00:55:56,020
this continual process is how
it's achieved immortality.

1261
00:55:56,020 --> 00:55:58,850
Although I think one caveat to that is

1262
00:56:00,295 --> 00:56:02,930
I think we all know it's
impossible to achieve immortality

1263
00:56:02,930 --> 00:56:05,130
because we can't account for accidents

1264
00:56:05,130 --> 00:56:07,640
and eventually these things are eaten

1265
00:56:07,640 --> 00:56:10,480
by other things and meet
their demise. (Andrea laughs)

1266
00:56:10,480 --> 00:56:13,870
So unfortunately, theoretically immortal,

1267
00:56:13,870 --> 00:56:16,883
but practically speaking,
immortality is quite elusive.

1268
00:56:19,150 --> 00:56:21,097
- We have another question.

1269
00:56:21,097 --> 00:56:23,050
"Do you have any intention
in the future to do

1270
00:56:23,050 --> 00:56:25,720
the same research at other
countries, such as in Africa,

1271
00:56:25,720 --> 00:56:29,490
since a lot of people like to
eat seafood in those areas?"

1272
00:56:29,490 --> 00:56:31,120
- Yeah. That's another great question.

1273
00:56:31,120 --> 00:56:33,578
And we'd love to bring
these genetic technologies

1274
00:56:33,578 --> 00:56:37,850
to promote sustainable fisheries
in aquaculture globally.

1275
00:56:37,850 --> 00:56:42,440
And I think we'd love to bring
the skills and the talents

1276
00:56:42,440 --> 00:56:45,000
that we have to address
questions throughout the globe

1277
00:56:45,000 --> 00:56:47,960
and I think Africa is a
particularly interesting scenario

1278
00:56:47,960 --> 00:56:51,330
as the growth of aquaculture
is tremendous right now.

1279
00:56:51,330 --> 00:56:53,290
And if we can bring tools and techniques

1280
00:56:53,290 --> 00:56:55,940
to make that more sustainable,
I think that would be

1281
00:56:55,940 --> 00:56:59,650
a tremendous contribution to
global aquaculture practices.

1282
00:56:59,650 --> 00:57:02,530
So that's something that
definitely is on our radar.

1283
00:57:02,530 --> 00:57:06,220
We'd love to find a way to
make an impact globally.

1284
00:57:06,220 --> 00:57:07,053
- [Nelson] Cool.

1285
00:57:08,800 --> 00:57:10,857
- Another question from the audience is,

1286
00:57:10,857 --> 00:57:12,760
"What do you mean by the three forms

1287
00:57:12,760 --> 00:57:14,677
of learning in the seahorse?"

1288
00:57:15,690 --> 00:57:17,100
- Oh, okay. That's in the sea hare.

1289
00:57:17,100 --> 00:57:19,980
And the three basic forms of learning are

1290
00:57:19,980 --> 00:57:24,840
classical conditioning,
habituation and sensitization.

1291
00:57:24,840 --> 00:57:28,180
And so basically the
different types of reflexes,

1292
00:57:28,180 --> 00:57:31,890
some that are learned (Andrea chuckles)

1293
00:57:31,890 --> 00:57:33,850
and some that are more of a reflex.

1294
00:57:33,850 --> 00:57:36,000
And so each of them have a different

1295
00:57:36,000 --> 00:57:39,250
neurocircuitry associated with them.

1296
00:57:39,250 --> 00:57:42,310
And so you can think about
sort of classical conditioning.

1297
00:57:42,310 --> 00:57:43,960
So studies that they did with the dogs,

1298
00:57:43,960 --> 00:57:46,080
where they ring the bell and feed the dogs

1299
00:57:46,080 --> 00:57:48,830
so the dog would only
have to hear the bell

1300
00:57:48,830 --> 00:57:49,930
and then start salivating

1301
00:57:49,930 --> 00:57:51,780
'cause they know the food is coming.

1302
00:57:51,780 --> 00:57:54,740
The sea hare is capable
of that level of learning

1303
00:57:54,740 --> 00:57:58,180
and having some kind of stimulus

1304
00:57:58,180 --> 00:58:00,660
that actually invokes a response.

1305
00:58:00,660 --> 00:58:04,020
And so those sort of
sensitization, habituation,

1306
00:58:04,020 --> 00:58:06,283
and classical conditioning are
the three forms of learning

1307
00:58:06,283 --> 00:58:09,260
that this relatively primitive animal

1308
00:58:09,260 --> 00:58:11,613
can exhibit all three forms of learning.

1309
00:58:13,300 --> 00:58:15,963
- And then I think we'll
end with one last question.

1310
00:58:16,977 --> 00:58:19,250
"How is the age of a lobster determined?

1311
00:58:19,250 --> 00:58:21,640
Just size, since there's no tree rings

1312
00:58:21,640 --> 00:58:23,550
when it sheds its skeleton?"

1313
00:58:23,550 --> 00:58:25,190
- Yeah, another great question,

1314
00:58:25,190 --> 00:58:27,330
'cause it's very, very
challenging to do so.

1315
00:58:27,330 --> 00:58:29,530
Typically we age these
animals through looking

1316
00:58:29,530 --> 00:58:30,880
at the hard parts of the animal,

1317
00:58:30,880 --> 00:58:33,420
which we do with sea urchins
and bivalves and other

1318
00:58:33,420 --> 00:58:35,870
because when they grow,
they lay down shell

1319
00:58:35,870 --> 00:58:37,100
in a seasonal growth pattern.

1320
00:58:37,100 --> 00:58:38,280
So you can count growth rings,

1321
00:58:38,280 --> 00:58:39,677
similar to counting
growth rings on a tree.

1322
00:58:39,677 --> 00:58:42,030
The lobster creates a serious problem

1323
00:58:42,030 --> 00:58:44,840
because it sheds its
hard parts when it molts.

1324
00:58:44,840 --> 00:58:46,310
So through growth kinetics,

1325
00:58:46,310 --> 00:58:48,430
so looking at the growth characteristics

1326
00:58:48,430 --> 00:58:50,440
of these animals over time

1327
00:58:50,440 --> 00:58:52,400
and also by looking at the accumulation

1328
00:58:52,400 --> 00:58:56,230
of something called
lipofuscin in the tissues

1329
00:58:56,230 --> 00:58:57,820
are two methods by which people

1330
00:58:57,820 --> 00:58:59,610
have assigned age to lobster,

1331
00:58:59,610 --> 00:59:02,750
but there's a tremendous
error in those estimates.

1332
00:59:02,750 --> 00:59:05,810
And so we really don't know for
sure how long lobsters live.

1333
00:59:05,810 --> 00:59:08,010
And so that's another interesting problem.

1334
00:59:08,010 --> 00:59:10,830
If we could find a biological
indicator of aging,

1335
00:59:10,830 --> 00:59:13,200
it would be a wonderful
tool for these animals

1336
00:59:13,200 --> 00:59:14,853
where they're challenging to age.

1337
00:59:16,000 --> 00:59:19,075
- Wonderful, thank you
so much for of your time

1338
00:59:19,075 --> 00:59:21,523
and your wonderful talk and presentation.