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[Music]

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[Applause]

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hi everybody i'm ben from the university

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of southern california at the center for

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dark energy buys for your investigations

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so i have two pieces of bad news one I

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woke up with a really bad head cold

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today so I'm being fueled mostly by

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dayquil and caffeine and two I don't

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have any phase diagrams in my talk I

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didn't realize that was a requirement

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for apps icon is my first meeting so I I

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apologize for that in advance too just

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to start I'm gonna make the talk is

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mostly gonna be about phage host

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dynamics and I'm gonna make some soft

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pitches about why evolution is important

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for thinking about astrobiology as well

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as the system that we're working in as

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an analog for majority of ocean world so

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even though it's in the title it will be

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just a fairly small part of the talk at

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the end of the day and we start off by

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thanking the people who are part of this

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project most of this work is being done

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with my graduate student Lena Graham I

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have a lot of conversations with Olivia

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and I grow about phages

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and then there's this big group of

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people who have all worked on some

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aspect of North Pond at some point the

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site that we work at and I like to

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really thank Julie Huber who brought me

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on on this project a bunch of years ago

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now and and has kept me in the

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subsurface thinking about these types of

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processes even though life moves on and

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we go different places so I'm gonna

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introduce North Pond which is a site

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that's off axis at the mid-atlantic

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ridge an 8 million year old crust and

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this is we're gonna make my pitch about

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it being an analog for a majority of

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ocean worlds so the ridge flanks that we

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see off axis occupy about 70 percent of

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the area of the ocean basins and so

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while hydrothermal vents are very fun

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and active and have a lot of energy it

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means that a lot of the water rock

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interactions that we're thinking about

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on a global scale are actually happening

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in colder waters interacting with rocks

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in these Ridge flank areas North might

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might be might not be the best example

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for in solar system water worlds because

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it actually has a quite a bit of oxygen

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in it but if we start thinking about

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other systems and other solar systems it

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might be good to be thinking about these

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low energy cool water rock interactions

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that might lead to evidence for life

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when

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there and so we can observe go randomly

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we can observe microorganisms in the

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crust using these Cork observatories

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short for circulation obvi a shin

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retrofit kits and so we can hang some

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equipment off the bottom with this

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platform here and we can measure life

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and look for elements that might be

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interesting to us as microbiologists in

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the subsurface a North Pond we have two

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set of corks set up right here I'm only

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going to talk about this first one what

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we were able to do is we were able to

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leave these genome microbes sleds the

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top of the well here and sample fluids

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over a two-year time course represent

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right here where were able to collect

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DNA and RNA while we weren't there and

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then come back and see what that looked

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like and in in the while we weren't

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there and so the water comes and flows

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this way so we're getting fairly fresh

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recharged water so this is looks a lot

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like bottom water but not quite and then

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we'll travel north and discharge on the

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northeast flank here and so in the

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bottom water we can expect about 10 to

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the fourth cells per meal while in the

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subsurface we actually get too much

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lower level something about but some

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occasionally a mostly ten to the third

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cells per mil and this is where I want

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to make the pitch for evolution right so

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hypothetically we send something

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somewhere to an ocean world and maybe we

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make a really big mistake and we

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contaminate where we get there but it's

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low abundance and so we're measuring

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some sample and we see numbers go up and

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we go ha we got growth it's not

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contamination it's something that was

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there from the beginning but when you're

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talking about 10 to the third cells per

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mil what you're talking about is you

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know if you're a microbiologist you

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think 1% is like okay one percent of

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cells that's that's pretty good that's

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ten cells so if you go from 1% to 2%

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you're going from 10 to 20 cells you're

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really not sure if that's growth if

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that's something else that's happening

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something that you've contaminated with

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so if you add evolution into that you'd

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actually would be able to say to

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checkmarks we have growth we have

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evolution this might actually be

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something that's a sign of life and and

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something that we can confirm more

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accurately so we've answered a number of

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force first-order questions at North

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Pond such as which microorganisms are

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there what are they doing and can they

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shape the chemistry of the marine

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aquifer the answer being there are

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some endemic microbes so these are

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microbes that phylogenetically look like

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they're unique to North Pond

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there are also some microbes coming in

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from the surface ocean so that would be

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interesting that would be allow us to

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ground truth some of our evolution

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questions do we have microbes that

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aren't really designed to be in the

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subsurface occurring there what are they

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doing we have evidence that's just both

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heterotroph II and autotroph II and can

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they shape the aquifer the answer is yes

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through changes in the nitrogen and

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carbon cycle if you're interested in

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this work there are two papers here from

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Julie's lab that really cover this

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pretty well the other thing that's

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interesting about all this is that when

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these microbes entered the aquifer

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normally through this recharge site they

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actually are down there for a really

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long time and so this work shows that we

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can see organic carbon being removed

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slowly it the longer it's down there for

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but then it's actually also aging so by

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the time you reach our second site it's

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about 2,000 years of time so less here

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so maybe like 200 so we're looking in

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our two-year time series about 1% of

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maybe what could happen over I love

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evolutionary timescale for the

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consumption of this organic carbon which

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allows us to do something because we

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have time to look at evolution and

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adaptation and so we do this by sampling

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the bulk cutoff here a little bit

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sampling the bulk DNA like this we can

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use this to then regenerate microbial

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genomes which will tell us what they're

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doing and how many there are of them and

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then sometimes if we're lucky we can

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reconstruct phage and firewall genome

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viral genomes so phage are specifically

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viruses that infect microbes and so one

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of the things that happens here is we we

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don't know the difference or we can't

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tell who these organisms belong to right

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so the phage are just entities that we

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have the microbes were just energy so we

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have we don't actually know how this

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interacts because what normally happens

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or one of the classical models of

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viruses is that they act as predators

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and so here you have a microbe and a

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phage the phage infects kills the host

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and then spreads to other organisms when

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we look at population dynamics it looks

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something like this where we have the

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prey our microbe here being consumed and

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then the virus is peaking in abundance

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afterwards and then collapsing and this

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continues as the prey keeps coming up

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the viruses then can eat them down and

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then it goes on so on and so forth

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but if your virus and your full predator

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that's one option for staying alive the

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other option is you know I made a DNA

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you're made of DNA what if I just hitch

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a ride with you and so this in this case

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the virus infects the host incorporates

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itself into the genome and then spreads

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by every time the host divides it

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becomes part of the host genome and then

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there's a mixture of this called a

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chronic infection where you can have an

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infected host that's also producing

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viruses and also passing it along

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through its daughter cells and so I was

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saying we don't actually know most of

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time how viruses and/or phages and their

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hosts are interacting the microbes but

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sometimes we get to do that we can't

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actually see that using this mechanism

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called CRISPR which is actually an

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innate way for for the microbes to

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prevent infection going forward and the

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way this works is when a phage infects a

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cell the host will cut it up if it's

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lucky and doesn't die and will acquire

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what is known as a protostar that

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matches the virus exactly and

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incorporates it into its DNA and what

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happens then is then this spacer is

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expressed and any time it sees this

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virus again the host it can actually

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degrade the DNA before the virus has a

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chance to infect it fully so it's a it's

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a way of preventing infection going

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forward and so once we do this we can

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actually say this spacer matches this

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virus so this host must be infected by

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this virus and so we can do that in

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North Pond which is pretty exciting so

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we can get examples like this where we

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have a CRISPR array or these spacers in

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green this one here matches one phage

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right here this one has seven spacers

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that match one phage and this one is a

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fairly small CRISPR that then matches

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this phage here and so we know that

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CRISPR targets are non-random so this is

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a recent paper just came out this year

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that shows here in yellow and and some

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of the lighter blue colors that the

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CRISPR spacers actually target genes in

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the virus that potentially have some

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essential part of viral replication so

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this

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new element to understanding CRISPR

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phage and microbe dynamics because at

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some point we all thought it didn't

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matter if you can just degrade a virus

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you can just do create a virus why does

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it matter what gene you target but it

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turns out that some of these genes

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actually are more important than others

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and there's some type of selection

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potentially happening between hosts and

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phage which are selecting for spacers at

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target certain and proteins so

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ultimately can we observe phage

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evolution that would eventually lead to

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CRISPR avoidance in our data set so this

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means if your phage and you're trying to

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infect a cell and they have your spacer

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you're you're done you're not getting

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anywhere evolutionarily but if you have

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the ability to change in your space or

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if you can change your DNA just one base

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pair all of a sudden you now avoid that

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CRISPR target and you can go back and be

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awesome and infect cells as much as you

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want so we can track phage abundance and

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correlate it to hosts abundance here so

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and I have been blue in both of these we

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have the hosts and in orange we have the

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00:09:42,600 --> 00:09:40,510
phage and this axis we have just

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abundance phage and host are on the same

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scale here over here phage are much more

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abundant relative to the hosts over time

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and then the time are 24 months of

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sampling that we have so in this

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instance a lot of times the phage

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correlate exactly directly with the

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hosts which is interesting because if

283
00:09:58,920 --> 00:09:57,580
you remember the first abundance graph I

284
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showed you there's might be some

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expected to be some offset between where

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we see the phage and where we see the

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host but that could just be the

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resolution that we're sampling at and

289
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then in some instances we actually have

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examples where the host is abundant and

291
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we don't see the phage so this is this

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is the meat and potatoes of this top

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right here and so I'm going to walk

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through this pretty slowly and then I'll

295
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show you two more examples of it so here

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we have the phage genome all along the

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x-axis with the genes that represent the

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phage here these light gray lines that

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you can see shifting just represent the

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coverage how much of the phage that we

301
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can measure in our samples in this case

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time points 1 3 & 4 so we're having this

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phage appear and then disappear and then

304
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appear and disappear in our samples and

305
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then these large black lines you can see

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represent changes in at the DNA level

307
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and so these phage is here and here and

308
00:10:51,630 --> 00:10:49,570
here look different at the pot as you

309
00:10:52,700 --> 00:10:51,640
look at the population in these time

310
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points than they did at the

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so if you have this nice little cluster

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here or this one right here this one

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wasn't present here it's not present

314
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here but it is present there and then I

315
00:11:03,860 --> 00:11:01,980
can overlay where the crisper spacer

316
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matches and look to see if there's a

317
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change in the DNA level of the virus so

318
00:11:09,769 --> 00:11:07,800
in this example there are two CRISPR

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spacers that hit one particular gene and

320
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in this case even though we have six

321
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months of data the virus doesn't change

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at all it's DNA so the fate of the host

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keeps killing the phage and at this

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point this phage would not be able to

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infect the one host that we're looking

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at that we see this match for it might

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be able to infect other hosts which

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might mean it can still hang around for

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a little while but for right now that's

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what we show it's the second example we

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have the CRISPR here and so we have one

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sample where this is probably the origin

333
00:11:42,199 --> 00:11:40,110
of it because there's no evidence of DNA

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00:11:44,210 --> 00:11:42,209
changes here but we have one example

335
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here where the genus targeting is

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essential for viral replication and at

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the same time there's lots of changes

338
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happening on that particular gene but

339
00:11:57,440 --> 00:11:55,080
the CRISPR spacer still hits a region

340
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that doesn't change there's an element

341
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of this which I don't have enough data

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for that might suggest that the phage is

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able to freely allow some mutations to

344
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occur on this part of the gene of its

345
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gene in an attempt of quote-unquote

346
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attempt there's no actual it's just

347
00:12:17,150 --> 00:12:14,730
evolution here's know not directed it

348
00:12:19,550 --> 00:12:17,160
might actually allow the the phage to

349
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try and avoid detectives but it can so

350
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maybe in the same way that we're

351
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selecting certain spacers that hit

352
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certain genes for the viruses the hosts

353
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are also selecting certain regions that

354
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can't change in the virus and the last

355
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one the real big daddy of them all

356
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so we have multiple time points on this

357
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again spacers hitting certain genes

358
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again with annotations that are

359
00:12:42,079 --> 00:12:40,079
important to the virus for replication

360
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you have to think my word on it but for

361
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every one of these pink lines again it

362
00:12:48,560 --> 00:12:45,839
hits a region where there aren't any D

363
00:12:51,199 --> 00:12:48,570
changes in the DNA except for this last

364
00:12:53,750 --> 00:12:51,209
one right here in this last one we

365
00:12:56,630 --> 00:12:53,760
actually see changes so if in this

366
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example this organism only had one

367
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CRISPR spacer and it was only hitting

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00:13:03,490 --> 00:13:01,260
this one spot after the first time the

369
00:13:04,750 --> 00:13:03,500
phage and host interacted

370
00:13:06,700 --> 00:13:04,760
you would actually have this ability for

371
00:13:08,290 --> 00:13:06,710
the phage to keep infecting the hosts

372
00:13:11,140 --> 00:13:08,300
unfortunately for it that actually

373
00:13:12,940 --> 00:13:11,150
there's six other spacers hitting it so

374
00:13:15,550 --> 00:13:12,950
it's gonna keep getting degraded and and

375
00:13:17,260 --> 00:13:15,560
not be able to infect anymore so with

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00:13:19,660 --> 00:13:17,270
that I'll wrap up so we're able to

377
00:13:21,730 --> 00:13:19,670
change watch look at the changes at the

378
00:13:24,520 --> 00:13:21,740
phage population at the DNA level over

379
00:13:26,260 --> 00:13:24,530
time that these CRISPR spacers target

380
00:13:28,150 --> 00:13:26,270
regions on the phage that seem to not be

381
00:13:30,190 --> 00:13:28,160
able to undergo change if that's

382
00:13:31,420 --> 00:13:30,200
intentional or not intentional we don't

383
00:13:33,760 --> 00:13:31,430
know yet we don't have enough data and

384
00:13:36,520 --> 00:13:33,770
then except for in all instances except

385
00:13:39,700 --> 00:13:36,530
for one the CRISPR is keep working and

386
00:13:41,050 --> 00:13:39,710
and the phage and the phage dies so the

387
00:13:42,220 --> 00:13:41,060
next step is actually to go back to the

388
00:13:45,280 --> 00:13:42,230
host and just look at the host without

389
00:13:46,870 --> 00:13:45,290
any elements of phage and part of it so

390
00:13:48,460 --> 00:13:46,880
here's an example where we have an

391
00:13:50,850 --> 00:13:48,470
organism that's abundant and then not

392
00:13:53,230 --> 00:13:50,860
abundant over time in the green lines

393
00:13:55,300 --> 00:13:53,240
our reference point actually ends up

394
00:13:57,460 --> 00:13:55,310
being our last time point but as we go

395
00:13:59,740 --> 00:13:57,470
back in time the population of organisms

396
00:14:02,110 --> 00:13:59,750
that make up this or host actually

397
00:14:04,180 --> 00:14:02,120
changes its proteome changes it has

398
00:14:05,890 --> 00:14:04,190
changes at the amino acid level in its

399
00:14:08,700 --> 00:14:05,900
proteins that might suggest that it's

400
00:14:11,410 --> 00:14:08,710
undergoing some type of evolution either

401
00:14:13,540 --> 00:14:11,420
randomly or through some positive

402
00:14:15,130 --> 00:14:13,550
selection on there and if that's the

403
00:14:17,350 --> 00:14:15,140
case that we'd have this change over

404
00:14:18,579 --> 00:14:17,360
time which we could call evolution and

405
00:14:20,950 --> 00:14:18,589
that would give us two check marks in

406
00:14:22,600 --> 00:14:20,960
terms of both growth and evolution in

407
00:14:25,420 --> 00:14:22,610
our samples that were collecting

408
00:14:28,030 --> 00:14:25,430
somewhere else far far from Earth and

409
00:14:30,030 --> 00:14:28,040
that's it I'll take some questions

410
00:14:37,560 --> 00:14:30,040
[Applause]

411
00:14:40,410 --> 00:14:37,570
it's time for one question remember we

412
00:15:04,680 --> 00:14:40,420
come up to the microphone can you or

413
00:15:06,540 --> 00:15:04,690
just yeah sure yeah so the questions

414
00:15:09,270 --> 00:15:06,550
about the methodologies you use to

415
00:15:11,220 --> 00:15:09,280
sterilize a system going out into space

416
00:15:13,200 --> 00:15:11,230
that's a great question and I don't have