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So what I want to do today is recap
a little bit what we talked about

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last time, reiterate some of the
important points,

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and then show you how we can learn
something about microorganisms in

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the environment by talking about
in-situ identification of

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microorganisms as well as genomics.
We'll first talk about genomics and

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general and then talk about some
applications of genomics to

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environmental microbiology because I
think there is some of the most

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exciting new developments are in the
area, actually.

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So last time we talked about
molecular evolution and ecology.

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And just to recap, some of the main
points were that we can actually use

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genes or gene sequences for a couple
of very important questions that we

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want to explore.
The first one was gene sequences act

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as evolutionary chronometers.

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Now, what do I mean by that?
Basically what we said last time

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was that each gene,
each sequence in the genome

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accumulates mutations with a certain
probability.  So what we mean is

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that all genes accumulate
mutations over time.

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Now, these of course are the
mutations that do not kill the

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organisms, so not the deleterious
mutations, but these are mutations

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that are either slightly deleterious,
or don't matter,

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or are beneficial mutations.
OK, and what this entails is that

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each gene accumulates mutation with
a certain probability over time.

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It basically means that two
organisms that come from species

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that are relatively closely related
to each other have gene sequences

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that will be much more similar to
each other than genes from an

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organism that comes from a species
that's much more distantly related.

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So, in practical terms what this
means is your genes are much,

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much more similar to those of a
monkey than they are to a crocodile,

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for example.  And we can take
advantage of that by applying some

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algorithms, some mathematical
modeling essentially,

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to constrain these relationships in
those phylogenetic trees that we

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talked about last time.
And I also mentioned that the

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ribosomal RNA genes are particularly
important for that process.

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In principle, you could do it with
any protein coding machine or any

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kind of gene in the genome,
but we use the ribosomal RNA genes

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in particular because all organisms
have them.  They're part of a

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handful of genes that are what we
called universally distributed

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last time.
And what this allows us to do is

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then construct phylogenetic
relationships for all living

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organisms.  And I just want to
remind you of the tree of life that

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I showed you last time where we can
really explore the relationships

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amongst all living organisms.
And some of the important points

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there that we made were,
for example, that the tree of life

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supports the endosymbiont theory,
that when you actually look on the

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tree where the mitochondria and the
chloroplasts tree, they fall

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into the bacteria.
Now, there is a question where

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somebody asked in the online survey,
can the mitochondria and

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chloroplasts still live outside of
the eukaryotic cell?

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And the answer is no,
they can't anymore because over

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evolutionary time the two organisms
have become so integrated that the

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mitochondria and chloroplasts both
lost their ability to live outside

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of the eukaryotic host cell.
Another important point that we made

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last time that I want to reiterate
here is that gene sequences,

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when we go into the environment,
and obtain them directly from the

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environment act as a proxy for
microbial diversity in

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the environment.
So, the number of genes recovered

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directly from the environment is a
measure of diversity.

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And we said that this actually plays
a very, very important role in the

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analysis of microbial communities,
and I showed you the example here

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where we went and took some ocean
water and basically apply this

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technique that outlined last time
where we can actually amplify

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ribosomal RNA genes from
environmental samples,

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clone them, determine the sequence,
and then constructs phylogenetic

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trees.
And what you see here is a tree

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where we summarize the major groups
that we found in the sample have

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been only for two of those groups
where we show the entire set of

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sequences that we actually obtained
because there were so many of them

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out there.  And what we basically
found was that over 1500 bacterial

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16S ribosomal RNA gene sequences
coexist in this environment.

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And what we said also last time was
that the analyses like these have

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really taught us that microorganisms
are the most diverse organisms

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on the planet.
So, most diversity is amongst the

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microorganisms,
and one of the big questions now is

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what are all those microorganisms
doing in the environment?

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And so, today what I want to do
with you is basically explore this

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question of how we can actually
figure out what those microorganisms

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are all doing in environmental
samples?

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So we can say we are exploring the

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function of microbes in the
environment.  At first,

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I want to cover how we can actually
identify them in the environment.

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And I want to show you one specific
example, and then I want to talk

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about genomics in general,
and then basically end with an

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application of genomics to
environmental questions.

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So, let's first talk about the
in-situ identification

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of microorganisms.

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And the basic problem that I alluded
to already before is that most

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microbes are only known
[SOUND OFF/THEN ON]

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-- from 16S ribosomal

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RNA clone libraries.

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And we basically want to search and
identify them in the environment.

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OK, and I'll show you a specific
example of that later on.

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Now last time, we said that the
ribosomal RNA sequences consist

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really, like all gene sequences,
in fact.  We identified several

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stretches of nucleotides,
types of stretches, that can be

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found.  We said the A type stretches
and B type stretches that are very

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important for construction of
phylogenetic relationships,

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because we can align them and look
for changes in the nucleotide

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sequences because they are the same
length and only differ in mutation

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and single nucleotide
base pair changes.

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But then there's also those C type
stretches, if you remember,

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and those we said vary at much
faster rates because they are not

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functionally constrained
in those genes.

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OK, so they can actually also
accumulate length changes.

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And, it's these C type stretches
that we can use sort of as

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diagnostic sequence stretches for
microorganisms.

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So, what we can say is we identify
organisms by the C type stretches,

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C type sequence stretches.
And we call those signature

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sequences.  OK,
and they allow the differentiation

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of closely related organisms,
-- because they vary at very fast

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rates between organisms.
And the way we do this is that we

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construct so-called phylogenetic
probes.  I should probably

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write this over here.
Now what are those phylogenetic

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probes?  They're basically short
pieces of DNA that have a

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fluorescent molecule
attached to them.

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-- DNA molecules that are roughly 20

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nucleotides in length,
and they carry a florescent molecule.

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Now what the short,
single-stranded stretches of DNA

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basically are is they are
complementary to those C type

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sequence stretches --

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-- in the ribosomal RNA.
And so basically what we can do is

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we can collect microbial cells from
the environment --

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-- make them permeable --

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-- and then basically mix them with

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those phylogenetic probes.

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And these probes will then permeate
into the cell and bind to their

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complementary sequences.

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Then we wash away the
unbound probe --

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-- and we can view it in a

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microscope under UV light.

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Let me show you an example of this.
What you see here is basically a

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light micrograph.
So this is what you see basically

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when you collect microbial cells
from the environment under the

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microscope.  Most bacteria look the
same, so you cannot actually

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differentiate them all by just
looking at them.

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But then these cells were fixed and
permeabilized and then basically

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mixed with two different
phylogenetic probes that identified

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two different types of organisms.
One was labeled with a red Fluor,

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the other one with a green Fluor.

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And what you see is that you can now
differentiate those two organisms.

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Now, why is this especially
interesting?  Well here's just a

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specific example where people were
looking for bacteria capable of

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nitrogen oxidation.
These are bacteria that are very

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important in, for example,
sewage treatment.  And it was known

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that there were two different types
out there, one that oxidizes

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ammonia to nitrite,
-- and that a second one that

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oxidizes nitrite to nitrate.
And by doing this type of analysis

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what people basically learned is
that those two organisms live in

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very, very close proximity
at all times.

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So the organisms that oxidized
ammonia to nitrite are really

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attached, and oftentimes even
surround by the organisms that take

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the nitrite to nitrate.
So, where you have is a very close

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cooperation between two different
types of microorganisms,

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and the transfer of one of the
substrates that's a product of the

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metabolism of one of the organisms
to another one: so extremely

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efficient process that really is
very important to take into

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consideration when you want to
understand processes like sewer

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treatment, but also nitrogen
biogeochemistry and

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the environment.
Any questions?

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OK, so for the remainder of the
lecture I want to talk

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about genomics,
-- and then in particular also its

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application to questions of
environmental microbiology and

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environmental science.
So first, what I want to do is give

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you a little bit of the definition
of genomics, and then cover how it

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is actually possible that we can
sequence entire genomes,

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and I want to give you some
highlights of what we have found by

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comparing different genomes
to each other.

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And then I want to talk about this
field about environmental genomics

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where we can use genomic techniques
to actually learn something about

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the function of different uncultured
microorganisms in the environment.

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So first, our definition, it's
basically to interpret

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or to sequence,
-- interpret, and compare whole

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genomes.  And as you will see the
comparison part actually plays an

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increasingly important role because
we have now actually genome

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sequences available from almost all,
or from at least some of the major

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groups of life.
So this, again,

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is a different kind of
representation of the tree of life.

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You have bacteria, archaea, and
eukarya again.

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And as you can see,
we have a lot of representatives.

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In fact, this doesn't even come
close to the diversity that we have

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now sequenced as well over a hundred
bacterial genome sequence now,

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several archeael genomes, and
increasingly also in

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eukaryotic genomes.
Now, genomes, so how is this done?

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How can we actually sequence
genomes?  Well,

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on the face of it we use very large
facilities where you have sequencing

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machines present.
There's one very important one at

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MIT, actually at the Broad Institute,
and here you see all those really

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industrial scale production
lines actually.

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But the basic problem is that
genomes are large.

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E. coli, for example,
has roughly 4.4 million base pairs,

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and the human genome is even much,
much larger.

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It has about 3 billion base pairs.
OK, so genomes are very, very large.

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But a single sequencing reaction--

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-- gives you only roughly 500-1,
00 nucleotides or base pairs.  So

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how is it that we can actually
sequence entire genomes?

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I'm going to walk you through this,
and there is some variation on the

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theme, but this is still a major
approach that's still used in some

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of the sequencing facilities.
Now, you start out by extracting

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genomic DNA from organisms,
and then you use restriction enzymes

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to cut the DNA into relatively large
pieces of DNA, so about 160

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kilobase pairs long.
On average, this is shown here.

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Kilo means a thousand, so 160,000
base pairs long.

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These pieces are then cloned into
specific cloning vectors that are

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called BAC vectors.

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So therefore, cloning large pieces
of DNA, and BAC stands for Bacterial

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Artificial Chromosome.
And what they basically are,

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are plasmids, very special plasmids
that can carry large pieces of

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genome, or large genome fragments.
So, by cloning into those BAC

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vectors, what you do is you
basically divide up the genome,

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and then the step number three is
mostly done for eukaryotic genomes

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because they are so much larger.
You can actually map and analyze

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the fragments,
and map them onto genome maps where

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you know the location of different
restriction fragments and different

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genes, actually.
For bacteria, this step is mostly

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skipped, actually.
What you do with each one of those

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BACs, is you cut them further up
into 1 kilobase per fragment,

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so much smaller fragments.  And
these are called,

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and these are cloned then into
normal plasmid vectors.

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And so you generate what are called
shotgun clones.

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So, these are then cloned into E.
coli, you go through the same type

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of steps that we discussed before
already with environmental clone

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libraries.  And you can actually
determine the sequence of each one

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of those pieces of DNA.
And what you will then get,

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is small fragments of overlapping
DNA sequences.

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That it shown here.
You'll find overlaps,

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basically, which piece together the
whole genome.  And so,

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first to assemble, you piece
together these genome fragments that

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are present in the BACs,
and then finally you piece together

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the entire genome propose large
sequence pieces,

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and you get a so-called draft genome
sequence.

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The next step in this analysis,
then, is that you do so-called

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genome annotation is.
And the first very important step

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is that you translate the gene
sequences into amino acids.

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So, the nucleotide sequences into
amino acids particularly in

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prokaryotes.  This step can
be done right away --

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-- and what this allows you to do,

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is you can look for what we call
open reading frames,

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or ORFs.  And what you look for is a
start codon and a stop codon that

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00:26:44,000 --> 00:26:50,000
basically branches or frames a
stretch of amino acids encoded by

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00:26:50,000 --> 00:26:57,000
the nucleotides. So
you look for ORFs.

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And these are your putative genes.

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The next step that you can do,

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then, is you can go to databases and
now you compare your ORFs to

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information that is present in the
databases.  So basically,

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00:27:39,000 --> 00:27:45,000
you inquire the database and ask,
is a gene sequence that is similar

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00:27:45,000 --> 00:27:52,000
to the one that I have statistically
significantly similar present that

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00:27:52,000 --> 00:27:58,000
allows me to say something about the
function of this particular gene?

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00:27:58,000 --> 00:28:05,000
So function, can then be identified
by comparison with databases.

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00:28:05,000 --> 00:28:29,000
Any questions?

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00:28:29,000 --> 00:28:34,000
OK, so that allows you,
then, to basically say something

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00:28:34,000 --> 00:28:39,000
about the different genes that you
have found in the genome,

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but to give you an impression of how
new this field really is and how

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00:28:44,000 --> 00:28:49,000
little we still know about the
diversity of genes and organisms,

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on average when we sequence a new
bacterial genome we find about 30%

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00:28:54,000 --> 00:28:59,000
of the genes, or a third of the
genes have no known functional

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00:28:59,000 --> 00:29:05,000
analog of the databases.
OK, so there's a lot to learn about

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the diversity of life and about the
functional diversity of life.

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00:29:11,000 --> 00:29:18,000
In eukaryotes, there are some
little twists,

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00:29:18,000 --> 00:29:24,000
as you all know.
And basically, that is that genes

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00:29:24,000 --> 00:29:31,000
of course consist of introns
and exons, right?

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00:29:31,000 --> 00:29:35,000
And so it's basically relatively
difficult to directly identify those

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00:29:35,000 --> 00:29:40,000
open reading frames.
And what you have to do is that you

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have to actually oftentimes,
so let's write this down.

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And what people oftentimes do,

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00:30:01,000 --> 00:30:12,000
then, is that they search for
matching sequences in so-called cDNA

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00:30:12,000 --> 00:30:24,000
libraries.  Now what are cDNA
libraries?  Let me just show you

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00:30:24,000 --> 00:30:32,000
this on the next slide.  Skip this.
Basically what you can do is you can

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00:30:32,000 --> 00:30:38,000
isolate messenger RNA from cells and
that translate the messenger RNA by

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a process called reverse
transcription that the viral enzyme

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00:30:43,000 --> 00:30:49,000
that translates RNA into DNA,
so you can translate it into DNA

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00:30:49,000 --> 00:30:54,000
fragments.  And you can then clone
those DNA fragments into plasmids,

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00:30:54,000 --> 00:31:00,000
sequence those, and then basically
see what are the pieces that are

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00:31:00,000 --> 00:31:06,000
actually, what are the
introns in the genes?

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00:31:06,000 --> 00:31:11,000
What are the pieces that are excised
when the messenger RNA is actually

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00:31:11,000 --> 00:31:29,000
created from the genome?

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00:31:29,000 --> 00:31:33,000
And so, let me just cover now a few
of the major insights that people

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have come up with.
Of course, it's a very growing

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00:31:37,000 --> 00:31:41,000
field and a lot of excitement
is coming out.

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00:31:41,000 --> 00:31:58,000
And I first want to talk about

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00:31:58,000 --> 00:32:09,000
bacteria and archaea --

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-- and then say a few words also
about eukaryotes or eukaryote.

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00:32:13,000 --> 00:32:17,000
First of all, what we learned about,
bacteria and archaea,

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is that their genomes are very
compact.

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00:32:21,000 --> 00:32:35,000
Whenever they have pieces of DNA

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00:32:35,000 --> 00:32:43,000
that are not frequently used,
they're actually lost from the

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00:32:43,000 --> 00:32:51,000
genome.  OK, so they lose genes,
I should say, relatively easily, and

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00:32:51,000 --> 00:32:59,000
we can see this that the genome size
is correlated to metabolic

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00:32:59,000 --> 00:33:12,000
diversity.

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00:33:12,000 --> 00:33:23,000
So, for example,
we have Mycoplasma genetalium and

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00:33:23,000 --> 00:33:37,000
Streptomyces --

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00:33:37,000 --> 00:33:42,000
coelicor are two very different
bacteria.  The first one is an

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00:33:42,000 --> 00:34:01,000
obligate intracellular parasite.

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00:34:01,000 --> 00:34:08,000
OK, so, which means it's actually
bathed in a nutrient solution in the

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00:34:08,000 --> 00:34:16,000
eukaryotic cells that it invades.
It doesn't have to make amino acids.

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00:34:16,000 --> 00:34:23,000
It gets it just from the host cell.
And it turns out it has a very

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00:34:23,000 --> 00:34:31,000
small genome, so only 0.
8-based mega-base pairs, so 580,

296
00:34:31,000 --> 00:34:37,000
00 base pairs, and only 517 genes.
And interestingly,

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00:34:37,000 --> 00:34:41,000
actually people are now using this
organism to try and ask,

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00:34:41,000 --> 00:34:46,000
well, what's the minimum number of
genes that organism can actually

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00:34:46,000 --> 00:34:50,000
will live with?
And so, they are deleting in a

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00:34:50,000 --> 00:34:55,000
stepwise fashion the different genes
in this organism,

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00:34:55,000 --> 00:34:59,000
and it turns out that you need about
two to 300 genes minimum in order to

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00:34:59,000 --> 00:35:03,000
make the things survive.
On the other hand,

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00:35:03,000 --> 00:35:15,000
streptomyces is a soil bacterium --

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00:35:15,000 --> 00:35:20,000
-- has a very complex lifestyle,
can degrade a lot of environmental

305
00:35:20,000 --> 00:35:26,000
substrates, and it has a very big
genome, one of the biggest bacterial

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00:35:26,000 --> 00:35:31,000
genomes.  And so,
those two organisms basically span

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00:35:31,000 --> 00:35:37,000
pretty much the range of
bacterial genome sizes.

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00:35:37,000 --> 00:35:41,000
And so, it's thought that it has
about 7,846 genes.

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00:35:41,000 --> 00:35:57,000
Now, we also have a very large

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00:35:57,000 --> 00:36:09,000
genetic diversity --

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00:36:09,000 --> 00:36:23,000
-- between species.
And typically what you find is that

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00:36:23,000 --> 00:36:38,000
roughly 15 to 30% of genes are
unique to a specific species.

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00:36:38,000 --> 00:36:44,000
And that's really because bacteria
and archaea have the capability to

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00:36:44,000 --> 00:36:50,000
affect a lot of chemical reactions
that eukaryotes,

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00:36:50,000 --> 00:36:56,000
for example, cannot.
There's about 20 million known

316
00:36:56,000 --> 00:37:02,000
organic substances,
organic chemicals, and almost all of

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00:37:02,000 --> 00:37:07,000
them are biodegradable by bacteria.
Even the minutest compounds if it

318
00:37:07,000 --> 00:37:12,000
were not biodegradable bacteria,
would build up in the environment,

319
00:37:12,000 --> 00:37:16,000
OK?  So, if it just where a cofactor
that some organism produces because

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00:37:16,000 --> 00:37:21,000
we have such a long period of time
of evolution on this planet and

321
00:37:21,000 --> 00:37:26,000
evolutionary history,
you probably would be able to dig it

322
00:37:26,000 --> 00:37:32,000
up in your backyard.
One of the other very important and

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00:37:32,000 --> 00:37:39,000
interesting insights that has come
out with comparing genomes for

324
00:37:39,000 --> 00:37:46,000
microorganisms is that lateral gene
transfer is a very important process

325
00:37:46,000 --> 00:38:07,000
amongst microorganisms.

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00:38:07,000 --> 00:38:11,000
Now what do I mean by lateral gene
transfer?  It basically means that

327
00:38:11,000 --> 00:38:16,000
we find evidence among bacterial
genomes that they have actually

328
00:38:16,000 --> 00:38:20,000
taken genes from completely
unrelated organisms.

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00:38:20,000 --> 00:38:25,000
And I just want to show you one
example here from that of

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00:38:25,000 --> 00:38:38,000
thermotoga maritima --

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00:38:38,000 --> 00:38:47,000
-- which lives in hot springs.
This is a very interesting

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00:38:47,000 --> 00:38:56,000
bacterium that lives in hot water of
around 80∞C and thrives only in

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00:38:56,000 --> 00:39:05,000
those kinds of environments.
And they coexist there with many

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00:39:05,000 --> 00:39:14,000
archaea.  And when people sequenced
the genome of thermotoga maritima

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00:39:14,000 --> 00:39:23,000
what they found was that about 25%
of the genes have their closest

336
00:39:23,000 --> 00:39:32,000
relatives in archaeal genomes.
So roughly 25% of genes in

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00:39:32,000 --> 00:39:39,000
thermotoga are of archaeal origin.
And how can we actually figure

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00:39:39,000 --> 00:39:44,000
something like that out?
Well, the most important technique

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00:39:44,000 --> 00:39:49,000
is, again, phylogenetic tree
construction.  And so when you have,

340
00:39:49,000 --> 00:39:54,000
for example, gene A, well let me
draw this, actually,

341
00:39:54,000 --> 00:40:10,000
on a new board.

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00:40:10,000 --> 00:40:15,000
So you're comparing,
say, three organisms,

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00:40:15,000 --> 00:40:21,000
organism A, B, and C and you compare
gene one with gene two.

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00:40:21,000 --> 00:40:27,000
And you notice that most genes
adhere to this pattern,

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00:40:27,000 --> 00:40:33,000
but that every now and then there's
a gene that gives you this

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00:40:33,000 --> 00:40:38,000
type of pattern.
What you can then conclude is that

347
00:40:38,000 --> 00:40:43,000
this gene, C, has not coevolved with
the other genes in the genome of

348
00:40:43,000 --> 00:40:48,000
these organisms but was actually
transferred into it from another

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00:40:48,000 --> 00:40:53,000
source.  And I don't have time to go
actually into the mechanisms.

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00:40:53,000 --> 00:40:58,000
If you're interested, I teach a
graduate class that undergraduates

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00:40:58,000 --> 00:41:03,000
actually take in our department,
environmental microbiology, where we

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00:41:03,000 --> 00:41:08,000
discussed a lot of the mechanisms.
It's basically a lot of viruses can

353
00:41:08,000 --> 00:41:13,000
affect gene transfer but also
plasmids and transposons.

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00:41:13,000 --> 00:41:18,000
But for bacteria, again, you should
remember that often new function is

355
00:41:18,000 --> 00:41:23,000
actually oftentimes arises by
lateral gene transfer.

356
00:41:23,000 --> 00:41:28,000
And one of the interesting things
is that lateral gene transfer is

357
00:41:28,000 --> 00:41:34,000
actually very important in the
evolution of pathogenic bacteria.

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00:41:34,000 --> 00:41:48,000
So, the so-called virulence genes,

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00:41:48,000 --> 00:41:57,000
which are the genes that basically
affect pathogenesis.  Do

360
00:41:57,000 --> 00:42:13,000
you have a question?
Among pathogenic bacteria,

361
00:42:13,000 --> 00:42:35,000
often arise by lateral gene transfer.
OK.  Any questions?

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00:42:35,000 --> 00:42:43,000
OK, now for eukarya,
I just want to make the point that

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00:42:43,000 --> 00:42:52,000
their genomes are generally orders
of magnitudes larger --

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00:42:52,000 --> 00:43:16,000
OK, and that the exons,

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00:43:16,000 --> 00:43:23,000
so the stretches that really encode
the protein that make up the

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00:43:23,000 --> 00:43:30,000
organism, the exons are only
typically a few percent

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00:43:30,000 --> 00:43:37,000
of the genome.
That's particularly in higher

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00:43:37,000 --> 00:43:44,000
eukaryotes.  Yeasts,
for example, have a much more

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00:43:44,000 --> 00:43:50,000
compact genome also.
We, for example, are full of DNA

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00:43:50,000 --> 00:43:57,000
that people still have a very hard
time figuring out what that actually

371
00:43:57,000 --> 00:44:04,000
does.  But it seems that the
majority of the genome,

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00:44:04,000 --> 00:44:10,000
so-called repeated sequences --
-- many of which seems to be ancient

373
00:44:10,000 --> 00:44:15,000
retroviruses that have inserted
themselves into the genome and have

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00:44:15,000 --> 00:44:21,000
since then lost actually their
function.  OK,

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00:44:21,000 --> 00:44:26,000
so the remaining time I want to just
give you an example of how we can

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00:44:26,000 --> 00:44:31,000
now use these techniques that I
outlined before to learn something

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00:44:31,000 --> 00:44:37,000
about microorganisms
in the environment.

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00:44:37,000 --> 00:44:47,000
It's called environmental.

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00:44:47,000 --> 00:45:04,000
Basically, the way this all started

380
00:45:04,000 --> 00:45:08,000
was by going into the environment
and extracting nuclear gases and

381
00:45:08,000 --> 00:45:13,000
treating them exactly the same way
as if you had a single genome.

382
00:45:13,000 --> 00:45:18,000
But, again, remember, we have a
very large mixture of microorganisms

383
00:45:18,000 --> 00:45:22,000
present in the environment.
And where this is mostly done was

384
00:45:22,000 --> 00:45:27,000
in the ocean, actually.
And what people did, was they

385
00:45:27,000 --> 00:45:32,000
constructed those BAC clones
directly from the environment and

386
00:45:32,000 --> 00:45:36,000
then looked amongst those BAC clones
for specific 16S ribosomal

387
00:45:36,000 --> 00:45:41,000
RNA genes.
Remember, this is the marker that we

388
00:45:41,000 --> 00:45:45,000
have for microorganisms in the
environment.  We know the diversity

389
00:45:45,000 --> 00:45:49,000
of microorganisms through those
types of genes,

390
00:45:49,000 --> 00:45:53,000
and we have a lot of the data
available.  And so,

391
00:45:53,000 --> 00:45:57,000
in order to link a specific function
of such an organism that we only

392
00:45:57,000 --> 00:46:01,000
know from the 16S ribosomal
RNA genes.

393
00:46:01,000 --> 00:46:06,000
So, to ask the question of what much
of this organism might be carrying

394
00:46:06,000 --> 00:46:12,000
out in the environment,
it's very useful to sequence BAC

395
00:46:12,000 --> 00:46:17,000
clones that have 16S ribosomal RNA
genes on them,

396
00:46:17,000 --> 00:46:23,000
and determine what kinds of protein
coding genes are on there that might

397
00:46:23,000 --> 00:46:28,000
reveal some of the function of the
organism in the environment.

398
00:46:28,000 --> 00:46:34,000
And one example that I want to show
you is that of the proteorhodopsin.

399
00:46:34,000 --> 00:46:45,000
So basically, the initial task was
to sequence BAC clones containing

400
00:46:45,000 --> 00:46:57,000
ribosomal RNA genes,
and look for other genes that might

401
00:46:57,000 --> 00:47:15,000
reveal some of the function.

402
00:47:15,000 --> 00:47:18,000
So, you don't want to look for all
the genes that encode proteins that

403
00:47:18,000 --> 00:47:22,000
are important to the cell cycle and
things like that,

404
00:47:22,000 --> 00:47:25,000
but really sort of metabolic genes
that might tell you something about

405
00:47:25,000 --> 00:47:29,000
the type of metabolism that this
organism carries out

406
00:47:29,000 --> 00:47:33,000
in the environment.
And so, what the first example that

407
00:47:33,000 --> 00:47:39,000
turned out to be really,
really important is that people

408
00:47:39,000 --> 00:47:44,000
found rhodopsin genes on one of
those BAC fragments,

409
00:47:44,000 --> 00:47:50,000
and it turns out this rhodopsin
catalyzes or these rhodopsin genes

410
00:47:50,000 --> 00:47:55,000
produce a protein that inserts
itself into the bacterial membrane,

411
00:47:55,000 --> 00:48:01,000
and it's a photoreceptor that when
it's hit by light,

412
00:48:01,000 --> 00:48:06,000
it actually becomes a proton pump.
So, it expels protons from the cell

413
00:48:06,000 --> 00:48:11,000
interior to the outside,
and you already know that this is

414
00:48:11,000 --> 00:48:16,000
important in energy generation in
all living cells.

415
00:48:16,000 --> 00:48:20,000
So proton gradient across membranes
basically give the cells sort of a

416
00:48:20,000 --> 00:48:25,000
battery status that can be exploited
by ATPase molecules or ATPase

417
00:48:25,000 --> 00:48:30,000
proteins that equalize the proton
gradient and affect ATP

418
00:48:30,000 --> 00:48:35,000
synthesis in doing so.
Now, why is this so important?

419
00:48:35,000 --> 00:48:40,000
Well, it turned out that this type
of protein is present in almost all

420
00:48:40,000 --> 00:48:45,000
microbial cells that were previously
thought to be heterotrophs alone in

421
00:48:45,000 --> 00:48:49,000
the ocean in the parts of the ocean
that receive enough life.

422
00:48:49,000 --> 00:48:54,000
And what this means is that our
estimates of the global carbon

423
00:48:54,000 --> 00:48:59,000
budget of the ocean were basically
wrong because most microorganisms in

424
00:48:59,000 --> 00:49:12,000
the ocean have this.

425
00:49:12,000 --> 00:49:31,000
So most prokaryotes in the ocean
have a light-driven proton pump

426
00:49:31,000 --> 00:49:52,000
which is called proteorhodopsin.
And it basically allows them to gain

427
00:49:52,000 --> 00:50:06,000
energy from sunlight.
And there's an increasing number of

428
00:50:06,000 --> 00:50:12,000
such examples now where we are
learning to interpret environmental

429
00:50:12,000 --> 00:50:18,000
communities, and the function of
environmental microbial communities

430
00:50:18,000 --> 00:50:23,000
through those genomic approaches.
And it reveals basically an

431
00:50:23,000 --> 00:50:29,000
enormous diversity of organisms out
there.  And what we also are

432
00:50:29,000 --> 00:50:35,000
learning to do now is to assemble
entire genomes from those samples by

433
00:50:35,000 --> 00:50:40,000
applying genomic techniques.
And this is an example here where

434
00:50:40,000 --> 00:50:44,000
you see, this was published last
year, where people went out and

435
00:50:44,000 --> 00:50:49,000
basically were able to piece
together from pieces of genes

436
00:50:49,000 --> 00:50:53,000
obtained from the environment,
entire genomes or fragments of

437
00:50:53,000 --> 00:50:58,000
entire genomes.
And that's shown here.

438
00:50:58,000 --> 00:51:02,000
Those are contiguous sequences.
OK, so if you have any questions

439
00:51:02,000 --> 00:51:07,000
let me know by e-mail,
or if you're interested in pursuing

440
00:51:07,000 --> 00:51:11,000
this further I also teach another
class in civil and environmental

441
00:51:11,000 --> 00:51:14,000
engineering.