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hi so I'm Lawrence Tyler I am just

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beginning my second year as a postdoc at

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Michigan State University working with

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Matt shrink and I'd like to talk to you

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today about using metabolomics to look

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at metabolic processes and surprising

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environments I'm very excited to be

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kicking off the rocks session and also

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to be introducing you to surprising

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environment a couple of people in this

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session are going to be talking about

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them so it's pretty cool that I get to

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introduce the topic to you if you're not

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very familiar with it so serpenton

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ization is the process by which this

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really pretty green rock turns into this

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really ugly black rock basically what

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that means is that the mineral olivene

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reacts with water and produces

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serpentine minerals as well as a high

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amounts of hydrogen so it creates a very

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high ph environment energy in the form

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of methane and also these small organics

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which can be food for microorganisms so

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it creates this really incredible

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environment where it's very extreme

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there's very low amount of dissolved

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inorganic carbon available because it

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all precipitates as calcite and very

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high p age but there's all these

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organics and there's all this energy

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available for microbes to harness so

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it's a very extreme environment but with

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a lot of opportunities for microbes also

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it is interesting because not only was

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it very prevalent on early Earth but

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it's also very likely that surprising

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environments exist in the subsurface of

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Mars and fuel hydrothermal environments

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on icy worlds like Europa and Enceladus

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and there are a number of origin of life

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theories that implicates or penalizing

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environments because they create these

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high-energy environments with gradients

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that can be harnessed by early life so

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there are a number of different

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serpenton izing environments all over

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the globe that our lab studies but I'm

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going to focus on the Coast Range

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ophiolite microbial observatory it's a

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nice place to work because it's just

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north of napa so you know drunk science

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but anyway

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there's two sites that we work at here

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at chromel the quarry valley and the

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core shed well and I'm going to be

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talking about some microbes isolated

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from quarry valley we have six wells dug

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at quarry valley and another six tug at

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the core shed well and they are highly

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alkaline and that's the drill rig that

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was used to drill these wells with just

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a few years ago so we have a ton of 16s

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data from these wells we have a pretty

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good idea of what kind of microbes are

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living in these highly alkaline

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environments and as you might suspect

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they're not very diverse because it is a

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very extreme environment um but we do

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see some trends associated with pH

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certain groups show up in the higher pH

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wells and versus the lower pH 12 I'm not

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going to focus on any of that today at

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all I'm not interested in who's there

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I'm interested in what they're doing and

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in an environment like this it's very

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difficult to distinguish between biotic

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and abiotic processes these reactions

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occurring between minerals and water

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producing organics producing methane

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microbes produce organics and methane so

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it's difficult for us to figure out

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what's going on at the geological level

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and what's going on at the biological

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level and it's very likely that microbes

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are feeding off of these geologic

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processes so there's a very gray area

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here so that's where a technique like

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metabolomics comes into play

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metabolomics basically is a snapshot of

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the metabolic activity of a microbe it

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takes all of the small organics that

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microbes are producing and looks at them

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at the chemical level to try to figure

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out what metabolic processes are

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occurring in the cell and this technique

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can be used for a number of different

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applications we can figure out whoops

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got ahead of myself we can figure out

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what chemicals are being produced by the

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cells obviously um we can annotate

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enzymes based on what is being produced

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we can look at pathways we can figure

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out what metabolic processes are

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occurring we can even phenotype

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different cells that we can't different

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organisms that we can't necessarily

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culture in the lab so this is all very

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useful right

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but there are some caveats of course

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just like with every technique first of

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all no extraction method is ideal so

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especially if you're doing a nun

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targeted approach and you want to look

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at all of the metabolites being produced

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by microbes now no extraction method is

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going to get every single metabolite

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there's always going to be some bias

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involved there's a bunch of different

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ways to analyze the metabolites once

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you've extracted them we use a liquid

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chromatography mass spec but you can

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also use GC you can use NMR and each one

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of those methods has its own perks and

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drawbacks which I'm not going to get

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into right now so the way that we

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characterize each of these compounds

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once we've extracted them is looking at

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the mass-to-charge ratio and the

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retention time in the column but that

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can vary depending on your methodology

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right so if you're trying to

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characterize a compound based on these

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things and it varies from method to

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method then one person will get

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completely different results from yours

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in another lab it only provides

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potential chemical formula and is this

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is especially a problem if you're

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looking at really big molecules because

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you get these results where you have a

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code cake and have so many carbon atoms

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and it's so many nitrogen atoms and it's

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so many oxygen atoms the bigger molecule

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is the more potential possibilities

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there are for that molecules identity up

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to thousands of potential identities

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right stable isotope waving labeling can

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help constrain that so if you feed

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microbes with saying 14c that will help

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you constrain it at least how many

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carbon atoms are in the molecule which

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narrows things down quite a bit and

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obviously we haven't identified every

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single metabolites that exists there's a

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lot of them um and there's a lot of what

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we call unknown unknowns we don't know

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that it's there and we don't know what

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it is so that's also an issue but

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databases are currently being developed

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and they're constantly being added to

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and work like this helps enhance the

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development of these databases so a

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technique like this is a lot more

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powerful when you combine it with other

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molecular techniques like genomics or

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transcriptomics if you know what genes

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microbes have or what genes that

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currently have turned on then you can

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kind of guess it at least what metabolic

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processes are occurring and then use

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that to inform your identification of

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all the metabolites and vice versa if

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you see certain metabolites that are

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being produced you can say okay well I

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know certain metabolic processes are

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currently occurring so metabolomics can

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also help you annotate a genome or a

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transcript on so this is what the data

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looks like coming out the other side of

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the LCMS we use tandem liquid

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chromatography mass spec can bind with

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quadrupole time-of-flight if you don't

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know what that is don't worry about it

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but this is what the data looks like and

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each one of these Peaks represents an

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individual metabolites that we can pull

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out and try to identify um it's not very

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informative to look at this though right

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so this is a tree of a number of

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isolates that we have from chrome oh and

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I know every time I see a phylogenetic

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tree and a talk my eyes start to glaze

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over because this is a lot of

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information and it doesn't really mean

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much but I want you to focus on these

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two isolates over here but we're

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isolated from quarry valley 11 well

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which has a pH of about 11.5 and the

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reason why it's important is because

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it's they're both very closely related

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to each other but they're also closely

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related to bacillus su de formas which

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is a very well-known alkyl o file that's

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pretty well characterized so we decided

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to focus on these two isolates for now

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because it they're so closely related to

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a microbe that's genome has been a

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sequenced that we know quite a bit about

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and what its preferences are when we're

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trying to grow it in the lab so I grew

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these two isolates and as you can see by

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the growth curves it's a pretty standard

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growth curve we have your exponential

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phase here where the cells are very

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actively growing and dividing and then

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they just kind of plateau off here at

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the stationary phase where you have the

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same number of cells being produced as

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are dying so I what's interesting here

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is that the number one isolate takes off

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pretty rapidly but then number two even

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though it's very simple costly related

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to number one has kind of a different

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growth pattern here so it has this

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little dip in the beginning and then it

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goes in an exponential phase so we

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decided to look at this

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or if our the exponential phase on the

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stationary phase of both of these

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isolates and compare the metabolites

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being produced by both of these isolates

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to see if they're using different

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strategies to adapt to this highly

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alkaline environment and what we found

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is that this is a PCA plot of the total

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metabolites of each one of these

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cultures and you can see that the

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stationary phase of both of these

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isolates clumps pretty closely together

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but they are distinct from each other I

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likewise for the exponential phase and

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then our blank is all the way over here

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which is always nice so we know I have

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good clean controls here so the cool

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thing is that we can look at individual

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peaks and look at their relative

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abundance between all of our different

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samples and you can see that there's

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certain compounds that are present only

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in the exponential phase of both of

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these isolates or only in the stationary

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phase and then there's ones that show up

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only for example in the number two

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culture but not in number one at either

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phase of growth so that's kind of neat

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and then there's also compounds that

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only show up during one phase of growth

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in one isolate so all of these compounds

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are distinguishing between these

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different isolates at their different

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phases of growth we can use them to look

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at what metabolic strategies are being

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used in these isolates we haven't

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identified these compounds yet and

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that's going to be an interesting

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exercise because again they're very

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likely unknown unknowns if they were

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very well known metabolites they'd

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probably be seen in all of the cells

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because they're extremely common and

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very readily identified so we're going

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to start searching the databases and see

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what we can come up with but we also

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have transcriptomic data that we need to

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analyze from these cultures and so we

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can combine that with the metabolomic

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data to try to get it exactly what

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strategy these isolates are using to

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survive in this extremely alkyl and

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environment the thing about these

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bacillus cultures though is that they're

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not really going to tell us a whole lot

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about what goes on in a serpentine izing

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environment because they're arif illic

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and because bacillus is a pretty

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an organism and its found basically all

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over the place so we can start to

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understand things about like alkyl alkyl

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0 tolerance tolerance of high pH but

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understanding some of the other

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processes that occur in surprising

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environments looking at the bacillus is

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not going to be particularly useful so

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we have this isolate mehsana bacterium

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subterranea which is not from a

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surprising environment but it's very

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closely related to organisms that we

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have identified in surprising

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environments including chroma this was

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actually isolated from a subterranean

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aquifer I think in Sweden in 1998 it is

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alkyl Oh Phillip just like the bacillus

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cultures and halo halo tolerant it will

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grow at a wide range of temperatures but

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it's also a Miss Hannigan and it might

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convert formate co2 or both into methane

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so we started growing this in the lab

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anaerobically and there's a couple of

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different carbon sources available in

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the medium I focus on for me and

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bicarbonate because they're implicated

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in the production of methane and i added

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a 13 c label bicarbonate and formate so

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I set up cultures where either the

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bicarbonate or the formate was labeled

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with see 13 in the hopes of tracking

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that into the metabolites and these were

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harvested during the exponential phase I

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put a question mark here whoops because

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I'm not really sure if this was the

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exponential phase we haven't established

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a very good growth curve for these

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because they clump a lot and it makes it

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difficult to sell counts unfortunately

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we didn't get as meat of a distribution

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here so the but the media looks an awful

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lot like blank media which means that if

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these cells are producing extracellular

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metabolites they're not as easily

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distinguished from the rest of the media

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and also we couldn't really tell much of

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a difference between the cultures that

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were fed 13 c-labeled for meat and the

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cultures that were fed 13 c-labeled

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acetate and this makes sense if you look

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at the piece because if 13c label was

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being incorporated into the metabolites

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you would see a distribution of various

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Peaks here like a spread where there's

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car compounds that kick that

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incorporated some of the 13 c and

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there's ones that still have 12 see it

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doesn't look like any label has been

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incorporated here so all of the 13 c is

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probably going into the methane and not

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into the metabolites or at least most of

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it but again we can still see there's

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certain compounds that occur only in say

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the palate or only in the media so our

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next steps are to grow the bacillus in

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more defined media sequence the genome

353
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and also look at the transcriptome to

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try to tease apart what exactly is going

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on metabolically in those isolates but

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also to take the Madonna bacterium and

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try feeding at 13 C acetate to see if

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that is incorporated into the

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metabolites trace the 13 c into the

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headspace gases to see if it's going

361
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into the methane from the older cultures

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it should be it's got to be going

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somewhere and also look at the

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transcriptome and hopefully develop a

365
00:14:07,530 --> 00:14:05,380
better growth curve we've been looking

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at protein assays as a substitute for

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doing direct cell counts and we're also

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working on doing field metabolomics on

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environmental samples from chroma so

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with that I'm going to thank you for

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your attention and I'll take your

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questions for Lauren yep had a feeling

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you'd have one so I thought you have a

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abundant khammam honest and recycle Asia

375
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at the beginning when you sampled and

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look for the 16s mm-hmm did you also get

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to isolate those are in what type of

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00:14:50,310 --> 00:14:48,400
media um in which in the some of the

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earlier the 60s Taylor slice you showed

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we're all the 16s of abundance they're

381
00:15:08,850 --> 00:15:03,160
up there okay you have so that pretty

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00:15:11,760 --> 00:15:08,860
much the Red Army yeah so um I believe

383
00:15:14,130 --> 00:15:11,770
some of these have been isolated I don't

384
00:15:15,660 --> 00:15:14,140
know what media we're currently growing

385
00:15:21,410 --> 00:15:15,670
them in so I've been focusing on the

386
00:15:24,360 --> 00:15:21,420
bacillus but um if you look at the tree

387
00:15:26,400 --> 00:15:24,370
we have a ton of isolates right now that

388
00:15:29,790 --> 00:15:26,410
we just haven't actively culture because

389
00:15:36,510 --> 00:15:29,800
there's just so many of them but that's