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[Music]

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so the optimum temperature of the last

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Universal common ancestor has been hotly

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debated over time there's been lots of

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lots of papers sort of that say it's hot

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say it's cold but we you know have very

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little access to actual and it any real

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data about what the physical

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characteristics of the environment we're

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for early life if we look at a cartoon

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diagram here of Tree of Life and look at

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extant temperatures a data that we can

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actually measure we noticed that

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temperature Optima across the tree is

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really diverse so we see sicker files

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all the way through hyperthermophiles

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search spread throughout the tree on

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multiple different branches which means

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that temperature Optima have evolved

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multiple times independently and this

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makes us ask the question how do how did

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that temperate how do those temperature

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Optima evolve and where did we come from

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one thing to notice about the tree of

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life is that if we look where the sort

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of the inferred root would be we have a

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lot of deeply branching

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hyperthermophilic and thermophilic

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groups here we often infer that we have

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to use steeply branching taxa on trees

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to infer ancestral States we assume that

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they've retained some of the ancestral

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characteristics over early life and that

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so we can use them as a proxy for that

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so given that so given that clustering

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is Luca was Luca a hot organism or not

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well there's three computational metrics

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and I only do computational value

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there's three computational ways that we

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can sort of infer temperature of

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organisms and I'm just going to go

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through those three matches pretty

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quickly the first is just doing looking

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at quantitative values doing

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quantitative trait prediction so one

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example of that would just be looking at

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parsimony parsimony means looking at the

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existing data that we have

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trying to come up with a model or an

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explanation that requires the fewest

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number of changes to arrive at that data

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except for ever loose evolution doesn't

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really work like that it rarely takes

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the simplest path so another way we can

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do this is by doing Bayesian inferences

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and that requires taking phylogenetic

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trees which we can do pretty you know

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pretty nicely today even at large scales

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and mapping the extant data that we have

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on that tree so for example temperature

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I give it a tree enteric a program tree

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in temperature and it considers the

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rates of evolution are you the branch

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lengths and we can infer what the

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ancestral nodes are on that tree a

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second method the computational method

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would be to look at the ribosomal RNA GC

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content so this is a little diagram of

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episomal stem and loop structure RNA

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stem and loop structure and since these

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are single-stranded pieces the GC bonds

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which are three hydrogen bonds provide a

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little extra stability compared to the

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two bonds of an au pair and so what we

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see is enrichment in the stems for GC in

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ribosomal RNA this is shown been shown

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in multiple papers to correlate really

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well with optimal growth temperature the

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third computational metric would be to

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look at the amino acid bias of a genome

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so in this case this particular example

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we might explode we might expect that

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amino acids are might differ between

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thermophiles and meso files in the same

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way that GC content might differ based

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on just the physical and chemical

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characteristics of the environment and

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so in 2007 zelda biologists did a

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statistical correlation to try to find

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amino acid sets that seemed to be

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correlated with temperature in this set

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of seven amino acids the IBEW round set

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was correlated well across bacteria in

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archaea so these three metrics have been

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used in again multiple papers to make

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arguments for and against a hot

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universe hot Lucca or a cold Lucca so

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for example in 1996 that it all uses 16s

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ribosomal RNA tree map these

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quantitative traits that you put you

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both may be these branches pulled where

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there were hyperthermophiles and

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therefore insertive had this person Moni

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as explanation and that since all the

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deeply branching lineages were

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another example is in 1999 Gaultier used

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20 this 23 s ribosomal RNA tree and

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looked at the GC content of stems and

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inferred that the last Universal common

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ancestor was a meso file but even in

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this model even in this with this

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conclusion we're looking at deeply

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branching lineages that are thermophilic

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so one one to notice is the thermal toga

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which is a the deepest trench in the

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bacterial group he used and is labeled

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as a vial and the final example is from

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2008 boo so at all looked at again last

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Universal common ancestors temperature

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using both 16s ribosomal RNA GC content

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and an amino acid bias metric and found

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in mesophilic

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concluded that it was an as a file for

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low temperature organism but again even

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in this tree we have deeply branching

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hyperthermophilic lineages like the

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thermic alga showing up so this is a

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consistent so again we keep seeing this

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group the thermo chugga Therma toga

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which we the phylum is now called a

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thermo tokido represents it as an early

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branching a deeply branching lineage on

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the Tree of Life these are deep

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biosphere bacteria as well so that's

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another sort of tangential interest to

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astrobiologists so the family to go to

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phylum is no longer just

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hyperthermophilic early isolates

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just were simply from the genus thermo

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tota which a lot of those papers were

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just using thermo toget taxa or

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taxer from the server to galleys which

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is this broader group here which are all

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hyperthermophiles so it makes sense to

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label that should that branch as

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hyperthermophilic but we now have

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temperatures ranging from 80 degrees all

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the way to some Meza files at 37 degrees

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so that's pretty diverse so if we want

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to keep using this it is a deeply

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branching lineage that's true that's

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been established but if you want to keep

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sort of inferring it using the branch

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and categorizing it is one thing it

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might be best if we got a better

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estimate for the temperature of the

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ancestor of this phylum so that's what

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I'm going to talk today not about Luka

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necessarily but about the temperature of

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this phylum so in 2009 my adviser Olga's

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active a Ava took the data that she had

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available which again was mostly

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organisms from this clade and used

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genomic composition metrics to estimate

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an optimum growth temperature of 80

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degrees Celsius for the node with a red

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star here

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it makes again parsimonious sense that

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these are all hyperthermophiles so that

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this new hyperthermophiles that seemed

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great at the time in 2013 and a green at

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all had an updated set of data and used

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16s ribosomal RNA to estimate the

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temperature for this node here so it's

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sort of a new ancestral node of the

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phylum to be 76 degrees Celsius a little

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lower

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still firma file but it's a little bit

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more of a moderate thermal file but

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since Ana even did her paper in 2013

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which is not going on six years ago we

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have had it a whole bunch of new

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saratoga this list is not exhaustive it

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doesn't really matter what their names

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are it just matters that we keep adding

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temperature diversity we're not just

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adding a single temperature to this tree

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we're adding temperatures ranging from

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79 degrees Celsius to 45 degrees Celsius

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and one particular organism of interest

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that has been added since this last this

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laughs analysis with them is mezu

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acidity total Owens's

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so mezzo acidity oka is up here at the

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top of the tree and when I rotate the

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tree in a minute it's gonna be on the

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left so I'm just gonna rotate this tree

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I'm gonna keep using the same tree but

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rotate it kind of clockwise so mezzo

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saratoga is awesome it's it was found in

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a hydrothermal vent

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Wow spreading better you guys probably

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know more about that thank you so the

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deep-sea organism and it is deeply

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branching and that's true when we do

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ribosomal protein phylogeny x' and 16s

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ribosomal RNA phylogenies as well as a

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bunch of other genes so it seems to be

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pretty well established as deeply

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branching I can talk more about that

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later

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and since as people you branching we

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might assume again that it would retain

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some characteristics of early life in

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this violin um but it's actually a

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moderate thermophiles it's still

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thermofoil but just barely at 58 degrees

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Celsius so much lower than we would have

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anticipated given its deep blue

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branching placement so the question is

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does the addition of this mezzo city

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galleys change the predicted optimum

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temperature of the phylum of the violins

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last common ancestor and again I use the

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three methods that I've already gone

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over so what I did quantitative trait

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estimation I use a program called bass

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traits which I'm happy to talk to people

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about it interested later and again I

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provided it with a reliable phylogeny

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this is a ribosomal protein phylogeny hi

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bootstrap support and all the extant

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temperature data that we have

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experimental verified in the lab based

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rates infers the ancestral knows the

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temperatures of the necessarily knows

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and inferred that the temp the optimum

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temperature of the last common ancestor

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was around 65 degrees Celsius so this is

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lower than the past two estimates the

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previous estimates were for this node

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and this node so that was kind of

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interesting we also see that

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hyperthermophilic had to arise and

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cooling mezzo philly or just a general

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cooling down of certain branches also

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rose separately so we see sort of we

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one general temperature trend in this

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violin we definitely see variation when

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I use the ribosomal RNA GC content I

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first checked that the thermit ago today

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data still correlates with temperature

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so this is just thermo to go to 16s

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ribosomal RNA stems has a nice

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correlation temperature with temperature

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on the x-axis and the stem GC content on

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the y-axis and then I so then I so that

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was you get the sequences and I want to

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first infer the ancestral sequences for

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each node calculate the GC content and

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then use the GC content to estimate

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temperature when I did that I recessed

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a'mma to Greece Celsius for the last

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common ancestor of this violin and again

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hyperthermophilic had to arise at one

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point and cooling down of other branches

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had to occur separately but again this

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is pretty close to that base trait

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estimate 65 degrees

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the final metric that I the final

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computational method that I used was

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looking at the amino acid bias and I

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used that I've URL amino acid set and

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again I first checked that in the genes

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that I used in the from the thermo

276
00:12:16,419 --> 00:12:12,769
Takota specifically that there is a

277
00:12:17,799 --> 00:12:16,429
correlation with temperature and so

278
00:12:21,100 --> 00:12:17,809
temperatures on the x-axis and the

279
00:12:25,720 --> 00:12:21,110
fraction of IBL in medium fraction of

280
00:12:27,189 --> 00:12:25,730
IBL in the genes is on the y-axis I had

281
00:12:28,989 --> 00:12:27,199
to use a pretty small data set for this

282
00:12:31,679 --> 00:12:28,999
analysis because I'm still working on

283
00:12:34,629 --> 00:12:31,689
cleaning up gene treaties and so I used

284
00:12:38,079 --> 00:12:34,639
175 homologous genes that are shared

285
00:12:39,429 --> 00:12:38,089
across the across all taxa these

286
00:12:42,759 --> 00:12:39,439
organisms have genomes that are about

287
00:12:46,359 --> 00:12:42,769
2,000 genes long so 175 out of 2000s a

288
00:12:48,280 --> 00:12:46,369
pretty small subset but because they

289
00:12:49,539 --> 00:12:48,290
have a huge pan genome there's a there's

290
00:12:51,669 --> 00:12:49,549
a lot of genes that some of them have

291
00:12:54,879 --> 00:12:51,679
something to do I can talk more about

292
00:12:56,530 --> 00:12:54,889
that separately so I had to use a pretty

293
00:13:00,579 --> 00:12:56,540
small subset so I did check that the

294
00:13:02,859 --> 00:13:00,589
distribution of I've URL in the 175

295
00:13:04,869 --> 00:13:02,869
genes that I selected match the

296
00:13:08,470 --> 00:13:04,879
distribution of IBL in whole genome ax

297
00:13:09,900 --> 00:13:08,480
sets so for example in giotto go petraea

298
00:13:11,610 --> 00:13:09,910
which is one organism

299
00:13:16,080 --> 00:13:11,620
look at the IV rail fraction on the

300
00:13:18,690 --> 00:13:16,090
x-axis here and the blue line the gene

301
00:13:21,300 --> 00:13:18,700
subset matches the whole genome

302
00:13:23,400 --> 00:13:21,310
distribution pretty pretty pretty

303
00:13:25,440 --> 00:13:23,410
perfectly and that held true for all the

304
00:13:26,610 --> 00:13:25,450
data I was able to use so I felt the

305
00:13:30,800 --> 00:13:26,620
hundred and seventy-five genes were

306
00:13:33,000 --> 00:13:30,810
reliable proxy for whole genome analysis

307
00:13:35,270 --> 00:13:33,010
and the reconstruction of one hundred

308
00:13:37,920 --> 00:13:35,280
and seventy-five ancestral sequences

309
00:13:39,780 --> 00:13:37,930
provided a median IPL of about forty

310
00:13:41,460 --> 00:13:39,790
four point five percent and this value

311
00:13:48,540 --> 00:13:41,470
reflected a temperature of 73 degrees

312
00:13:50,970 --> 00:13:48,550
Celsius for the ancestor so finally in

313
00:13:53,010 --> 00:13:50,980
summary I have three different values

314
00:13:55,850 --> 00:13:53,020
that I was able to estimate 65 degrees

315
00:13:58,770 --> 00:13:55,860
63 degrees in a bit higher at 73 degrees

316
00:14:01,110 --> 00:13:58,780
these are all lower than the previous

317
00:14:04,800 --> 00:14:01,120
two estimates from Zack sadaiva and

318
00:14:06,600 --> 00:14:04,810
green at all for those earlier nodes so

319
00:14:09,090 --> 00:14:06,610
adding this data point as well as some

320
00:14:11,310 --> 00:14:09,100
other data in the tree seems to have

321
00:14:14,130 --> 00:14:11,320
lowered our estimates it's pasta it's

322
00:14:16,230 --> 00:14:14,140
still a thermophilic it still is ever

323
00:14:17,580 --> 00:14:16,240
feel like ancestor so we can still say

324
00:14:19,170 --> 00:14:17,590
it's a thermo file but it might actually

325
00:14:24,390 --> 00:14:19,180
be more of a moderate thermo file then

326
00:14:26,310 --> 00:14:24,400
we have previously assumed we'd keep

327
00:14:28,200 --> 00:14:26,320
seeing all these models the evolution of

328
00:14:30,480 --> 00:14:28,210
both hyperthermophilic and mesophilic

329
00:14:32,100 --> 00:14:30,490
were secondary so we actually see

330
00:14:34,650 --> 00:14:32,110
temperature fluctuations happening a lot

331
00:14:36,540 --> 00:14:34,660
over time and our findings are in

332
00:14:39,000 --> 00:14:36,550
concordance with several analyses that

333
00:14:40,590 --> 00:14:39,010
argue that possibly early life wasn't

334
00:14:46,020 --> 00:14:40,600
quite as hyperthermophilic as we

335
00:14:48,690 --> 00:14:46,030
initially wanted to to believe and with

336
00:14:52,050 --> 00:14:48,700
that i'd like to thank my amazing

337
00:14:57,020 --> 00:14:52,060
adviser Olga's Activia who's great the

338
00:14:58,830 --> 00:14:57,030
lab of oz all of our lab members and our

339
00:15:00,810 --> 00:14:58,840
collaborator camilla des Beaux who's

340
00:15:03,000 --> 00:15:00,820
worked a lot with Meza togas these

341
00:15:06,030 --> 00:15:03,010
moderate temperature organisms Simon's

342
00:15:07,770 --> 00:15:06,040
foundation for funding ecology evolution

343
00:15:09,870 --> 00:15:07,780
ecosystems and society program at

344
00:15:11,910 --> 00:15:09,880
Dartmouth which is a great program and

345
00:15:14,800 --> 00:15:11,920
well being there and Dartmouth for all

346
00:15:21,920 --> 00:15:14,810
our funding as well mm-hmm Thanks

347
00:15:21,930 --> 00:15:27,170
I'm happy okay questions yeah

348
00:15:33,269 --> 00:15:30,090
hi thank you uh for the talk I'm just

349
00:15:35,189 --> 00:15:33,279
curious are there any like geological

350
00:15:37,050 --> 00:15:35,199
timescales attached to these predictions

351
00:15:38,629 --> 00:15:37,060
so I have not done that I've gotten that

352
00:15:42,629 --> 00:15:38,639
question a few times and I have not done

353
00:15:44,429 --> 00:15:42,639
like um you know any kind of dating if

354
00:15:46,199 --> 00:15:44,439
someone here is an expert at bacterial

355
00:15:47,999 --> 00:15:46,209
microfossils and let's talk me through

356
00:15:50,670 --> 00:15:48,009
sort of what I could maybe do with this

357
00:15:53,100 --> 00:15:50,680
I'd be interested in that but that's not

358
00:15:54,269 --> 00:15:53,110
my area of expertise and doing something

359
00:15:55,800 --> 00:15:54,279
like applying a molecular clock

360
00:15:58,650 --> 00:15:55,810
assumption is probably gonna give a

361
00:15:59,850 --> 00:15:58,660
really crummy that's me anyways so I

362
00:16:04,939 --> 00:15:59,860
haven't that hasn't been worked my time

363
00:16:07,379 --> 00:16:04,949
or anything like that really love I

364
00:16:10,170 --> 00:16:07,389
really love this talk thank you thanks

365
00:16:13,470 --> 00:16:10,180
um I was really curious about the IV L

366
00:16:15,749 --> 00:16:13,480
amino acid bias I was wondering if you

367
00:16:20,699 --> 00:16:15,759
were aware of any driving logic behind

368
00:16:23,129 --> 00:16:20,709
that - yeah so I'm here L is definitely

369
00:16:24,629 --> 00:16:23,139
it was purely based on Cisco correlation

370
00:16:26,040 --> 00:16:24,639
like they went through all these sets of

371
00:16:31,259 --> 00:16:26,050
amino acids it were like which one's the

372
00:16:33,120 --> 00:16:31,269
best this is the best but it does

373
00:16:34,590 --> 00:16:33,130
contain a lot of a couple charged amino

374
00:16:36,120 --> 00:16:34,600
acids and we do know that actually

375
00:16:37,650 --> 00:16:36,130
charge versus polar amino acids or

376
00:16:39,900 --> 00:16:37,660
another really strong amino acid

377
00:16:43,620 --> 00:16:39,910
correlate in genome so there's some

378
00:16:45,329 --> 00:16:43,630
biological significance there um I've

379
00:16:49,290 --> 00:16:45,339
also heard that it's inexpensive amino

380
00:16:54,120 --> 00:16:49,300
acids set someone who's better and can

381
00:16:56,040 --> 00:16:54,130
probably verify that and even though

382
00:16:57,329 --> 00:16:56,050
it's an expensive amino acid set we

383
00:16:58,920 --> 00:16:57,339
think it's probably there's been some

384
00:17:01,620 --> 00:16:58,930
hypotheses that it's better to use that

385
00:17:02,759 --> 00:17:01,630
set rather than deal with like producing

386
00:17:05,159 --> 00:17:02,769
a lot of chaperones to deal with

387
00:17:17,610 --> 00:17:05,169
denatured and miss folded proteins so

388
00:17:22,620 --> 00:17:19,769
anyways I have the question so this deep

389
00:17:24,299 --> 00:17:22,630
branching organism you said that the

390
00:17:26,939 --> 00:17:24,309
phylogeny is that you have here a pretty

391
00:17:29,490 --> 00:17:26,949
high support is it consider percent

392
00:17:32,190 --> 00:17:29,500
consistent that this deep branch or ends

393
00:17:35,580 --> 00:17:32,200
up as the out group this will clean yeah

394
00:17:37,080 --> 00:17:35,590
so if I look at my ribosomal protein

395
00:17:38,850 --> 00:17:37,090
tree

396
00:17:40,560 --> 00:17:38,860
it's a wrapper so it's a concatenated

397
00:17:43,529 --> 00:17:40,570
ribosomal protein tree which often

398
00:17:45,330 --> 00:17:43,539
employed support values highly so the

399
00:17:47,880 --> 00:17:45,340
only two nodes that are pretty poor are

400
00:17:49,350 --> 00:17:47,890
below ninety five four hundred straps

401
00:17:51,510 --> 00:17:49,360
are these two nodes you can kind of see

402
00:17:52,830 --> 00:17:51,520
them in a few circles there so otherwise

403
00:17:54,840 --> 00:17:52,840
everything's above ninety five right

404
00:17:57,210 --> 00:17:54,850
it's really really well supported

405
00:17:58,769 --> 00:17:57,220
but that could possibly be an artifact

406
00:18:04,320 --> 00:17:58,779
so we definitely we're concerned about

407
00:18:06,269 --> 00:18:04,330
that if we look at the 16s tree so this

408
00:18:07,500 --> 00:18:06,279
is a similar tree I didn't draw your

409
00:18:10,260 --> 00:18:07,510
attention to it but actually instead of

410
00:18:13,560 --> 00:18:10,270
having one leaf here actually up to so

411
00:18:15,899 --> 00:18:13,570
we have at least one of the described

412
00:18:18,570 --> 00:18:15,909
organism with temperature with 16s data

413
00:18:21,440 --> 00:18:18,580
available which breaks that branch and

414
00:18:23,430 --> 00:18:21,450
it's still an out and long branch

415
00:18:25,769 --> 00:18:23,440
additionally when we break that branch

416
00:18:29,159 --> 00:18:25,779
further with more environmental clones

417
00:18:32,610 --> 00:18:29,169
it stays where it is we also have other

418
00:18:33,720 --> 00:18:32,620
genome like metagenomes data that helps

419
00:18:36,389 --> 00:18:33,730
break that branch and then I'm also

420
00:18:37,409 --> 00:18:36,399
doing an analysis of which genes I'm

421
00:18:39,389 --> 00:18:37,419
saying and where those genes are

422
00:18:41,190 --> 00:18:39,399
grouping with other organisms right now

423
00:18:43,710 --> 00:18:41,200
so I'm trying to establish that it's

424
00:18:48,360 --> 00:18:43,720
really belong that it's truly a deeply

425
00:18:50,789 --> 00:18:48,370
branching lineage all right I'm just

426
00:18:53,659 --> 00:18:50,799
went to culinary briefly on the on the

427
00:18:58,200 --> 00:18:53,669
question of dating yeah these lineages

428
00:19:01,260 --> 00:18:58,210
given that there is going to be I would

429
00:19:03,330 --> 00:19:01,270
imagine no micro fossils available from

430
00:19:06,060 --> 00:19:03,340
this particular Python this will have to

431
00:19:08,149 --> 00:19:06,070
depend almost entirely on finding

432
00:19:10,560 --> 00:19:08,159
horizontal gene transfer events from

433
00:19:12,600 --> 00:19:10,570
lineages where the fossil records such

434
00:19:14,190 --> 00:19:12,610
as cyanobacteria or some hearing that

435
00:19:15,600 --> 00:19:14,200
and I think that's something that's

436
00:19:18,899 --> 00:19:15,610
actually easy to look at have there been

437
00:19:21,180 --> 00:19:18,909
identified or Aldean transfer events

438
00:19:24,029 --> 00:19:21,190
into this phylum which would then allow

439
00:19:25,710 --> 00:19:24,039
to propagate the dating constraints from

440
00:19:26,480 --> 00:19:25,720
a lineage which does have fossil

441
00:19:29,690 --> 00:19:26,490
calibration

442
00:19:32,090 --> 00:19:29,700
okay so first of all I can say for sure

443
00:19:33,710 --> 00:19:32,100
that this phylum has undergone becomes a

444
00:19:34,850 --> 00:19:33,720
horizontal gene transfer for a while

445
00:19:36,290 --> 00:19:34,860
they thought maybe it was actually in

446
00:19:38,600 --> 00:19:36,300
archaea because that's so many a pale

447
00:19:39,620 --> 00:19:38,610
genes and then they thought maybe they

448
00:19:41,150 --> 00:19:39,630
weren't even a file open

449
00:19:42,710 --> 00:19:41,160
they were they were Firmicutes because

450
00:19:44,710 --> 00:19:42,720
there's so many from acute gain so lots

451
00:19:46,490 --> 00:19:44,720
of gene transfer terms of cyanobacteria

452
00:19:48,140 --> 00:19:46,500
specifically I'm not sure but I would

453
00:19:54,380 --> 00:19:48,150
love to talk to you more about that we

454
00:19:58,930 --> 00:19:54,390
should not we should be I'm gonna ask a

455
00:20:02,180 --> 00:19:58,940
quick question so because I'm more of

456
00:20:03,799 --> 00:20:02,190
ecologists I'm just kind of wondering so

457
00:20:05,570 --> 00:20:03,809
you know we kind of all have this idea

458
00:20:08,510 --> 00:20:05,580
of Luca being in vents and this really

459
00:20:11,270 --> 00:20:08,520
hot thing so if it's not an event could

460
00:20:13,160 --> 00:20:11,280
you speculate and you could go as wild

461
00:20:15,080 --> 00:20:13,170
as you want here but like on the type of

462
00:20:19,730 --> 00:20:15,090
environment that this Musa philic Luca

463
00:20:22,880 --> 00:20:19,740
would live in oh gosh I feel like I feel

464
00:20:26,210 --> 00:20:22,890
like it's way more rich in this cabin I