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good morning folks

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i hope everybody's having a wonderful

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time at absycon it's good to see a nice

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crowd here

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at 8 30 for our plenary session before

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we get going

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i would like to remind everyone of

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what promises to be a really fun event

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tonight

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entitled when worlds collide

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many of us are

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aware

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from personal experience how science and

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the arts grow from the same roots

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and reinforce one another and mutually

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inform one another

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and so this issue will be explored this

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evening

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and there will be a uh

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but a self-described

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geek musician uh mr griffin who will be

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uh performing and i think he'll all get

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a big kick out of that so please join us

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so it's my pleasure to start this

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morning's uh plenary session

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and give an introduction to detect a

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speaker

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professor

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batul

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so a long time ago in a galaxy far away

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called montana

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i received a a a message from michael

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new

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and specifically michael summoned me

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to ab grad khan which was taking place

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in the fly fishing capital of ennis

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montana

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specifically michael summoned me

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and so having just received uh an

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exobiology grant i immediately hopped

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into my old land cruiser and drove the

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three hours from missoula

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lucky for me the drive was as always

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spectacular

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but also the interaction with that young

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scientist was

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delightful and it was auspicious as well

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and so over the course of more than a

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decade

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it has been my pleasure as well as my

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privilege

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to witness the growth and maturation of

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today's plenary speaker

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dr khachar

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received her

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bs from marmara university in istanbul

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and then went on to earn her doctorate

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in biological chemistry

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at emory university

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she pursued postdoctoral studies at my

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home institution

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georgia tech where she began what i

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would call

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her scientific vision quest

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that she'll share with us today but what

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do i mean by that well i would direct

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your attention to a 2000

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uh 2011 paper in the journal artificial

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life

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entitled towards the recapitulation of

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ancient history in the laboratory

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this was what was on dr khachar's mind

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when we conversed in ennis

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now at that time ancestral state

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reconstruction was a well-established

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practice in phylogenetics

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what was daring about

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batul was that she aspired to carry on

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ancestral state

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reconstruction in the laboratory

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using

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integrated synthetic biology and

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experimental evolution

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so did she realize these aspirations

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i would direct your attention to

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last month's article in cell

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entitled resurrected rubisco suggests

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uniform carbon

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isotope signatures over geologic time a

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real

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masterpiece

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uh integrating

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all of these different fields uh with

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geology

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now i would like to emphasize to the

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young investigators gathered with us

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this morning that the path that led from

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istanbul to ennis montana

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to madison wisconsin where she's

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currently a professor was not an easy

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one

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but along that path baitou held fast to

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her vision

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and showed true grit

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taking on challenges and overcoming

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obstacles and adversity that would have

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defeated a lesser human being

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that to my mind more than anything else

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is what makes professor kachar's

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journey her story as a scientist and as

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a woman

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so compelling

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after georgia tech

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baitul continued postdoctoral work with

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dan anderson uppsala

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university and rich linsky at michigan

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state

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and then she spent three years at

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harvard

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working with scott edwards before

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accepting her first faculty appointment

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just a few years ago at the university

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of arizona

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this past year her burgeoning laboratory

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group moved from tucson to madison where

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she is a professor in the department of

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bacteriology

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dr gachar has been the recipient of

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numerous accolades most recently the

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stanley l miller award from the

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international society for the study of

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the origin of life

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a cylon fellowship from the simons

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foundation a nasa early career award

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her laboratory in madison is currently

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supported by a nasa templeton foundation

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grant the human frontier science program

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and

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the nasa icar grant that serves as a

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cornerstone for the new research

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coordination network

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life from early cells to

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multicellularity

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please join me in giving me a warm abs

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icon welcome to one of our own

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batul

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kachor wow

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thank you so much um frank

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i um it really feels like homecoming

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being here

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i'm i always define my job as one of the

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most

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special jobs one can ever have to be a

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biologist on the only planet that you

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can be one

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right you can be a physicist or chem or

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you can study physics or chemistry and

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geology anywhere else in the universe

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but this is the only place where we can

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be biologists that's to me amazing and

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of course this is the crowd that that's

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trying to change that so we can be

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biologists on different planets

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and we know

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that the challenge is big

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what if i told you that these two images

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well it kind of feels weird to not have

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slides here there

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all right one of these images is

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lifeless whereas the other one is living

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at the very superficial level life can

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trick us

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for his existence

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the image on the

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um

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left in fact is lifeless

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the fern

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that may be tricking you to be a fern is

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a silver dendrite

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this is a

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outcome of a malfunctioning engineering

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experiment

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and on the right you're looking at a

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forest

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a fern

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a lot of life

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so while at the superficial level life

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can trick us and i think this very

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aspect makes our jobs as a cerebiologist

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very difficult things start to fall

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apart the moment we go up a scale or

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down a scale and ask deeper questions

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about the image that we are

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we are examining

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what i mean by that is that if you

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travel up a level or down

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you have to

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experience or what you are looking needs

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to express a level of complexity or in

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increasing complexity or a decrease in

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complexity which is what we generally

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think a living organism or living state

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attributes

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so let's just start with

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an organism we have uh if you go up a

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level we have populations that form

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communities that form ecosystems they

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impact the ecosystem and ultimately

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shape the biosphere

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if you go down a level we have cell

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groups and cell at the very bottom we

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have molecules and you can also uh strip

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them apart

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we know that there are self similar

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patterns of interactions between each

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layers and i am i'm showing you b

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because i think they're fascinating is a

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very top-level example of what life

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organ or how it organizes itself there

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are rules between levels and there is

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different level of complexity in each

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layer

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so let's just drill down this image a

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little bit

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at the top we have biogeochemical cycles

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catalyzed or driven or moderated by

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populations and again if you go down a

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level if you have organisms and cells

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and subcellular micromolecules that

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compose cells and finally at the very

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bottom we have energy input in

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substrates and i know that this group is

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studying this aspect of life it made at

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various levels

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so let's consider a modern ecosystem and

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i'm showing an image of the dinosaurs

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and i know this is tricky because when i

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talk about early life at least when i

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teach at university most students think

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that i'm talking about dinosaurs because

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they're ancient right

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but of course this group knows that

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dinosaurs existed in a relatively modern

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ecosystem

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and what we care about when when an

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astrobiologist looks at an image like

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this

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what will really

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get their interest is that it's not

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going to be the individuals per se but

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the effect of the individual on the

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planet scale reservations

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export service

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atmosphere maybe temperature we are

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interested in understanding the impact

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of whatever the living state is in that

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very body with it is environment

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and of course the challenge might then

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be to detect these impacts

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and i'm not even talking about the

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detectability problem just yet but we

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know that direct observation is not easy

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of course if we find dinosaurs on a

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different planet in our solar system at

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so biologists are aware of these issues

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and and have amazing tools to study

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these interactions but what biologists

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are lacking especially molecular studies

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i think is how

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are the the concept of how environments

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and evolutionary innovations impacted

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our planets

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we lack an understanding of the

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underlying variation and the impact of

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this variation at the planetary scale

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over long time scales

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we also don't understand the

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circumstances and timing of events that

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made the early earth a much different

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place

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and we know these things through various

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studies that come from paleontology

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paleobiology geology geobiology

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geochemistry so on so forth

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so let's review what i'm talking about

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uh of course you know we're interested

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in the birth of life how this planet

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gave birth to this magnificent thing

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that we get to study right now and but

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in some cases if you think about our our

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00:11:44,230 --> 00:11:42,240
kind earth or the hideon earth it's

292
00:11:47,670 --> 00:11:44,240
really the best of times but also the

293
00:11:49,269 --> 00:11:47,680
worst of times to study

294
00:11:51,269 --> 00:11:49,279
it's the best of times because life

295
00:11:53,269 --> 00:11:51,279
emerged it's the worst of times because

296
00:11:55,350 --> 00:11:53,279
we have very little to work with when it

297
00:11:57,590 --> 00:11:55,360
comes to studying the past

298
00:11:59,990 --> 00:11:57,600
there is very limited record it comes

299
00:12:01,829 --> 00:12:00,000
from very limited data

300
00:12:03,829 --> 00:12:01,839
that represents a very limited

301
00:12:06,710 --> 00:12:03,839
subfraction of whatever the living

302
00:12:08,310 --> 00:12:06,720
organism or the population was reciting

303
00:12:09,350 --> 00:12:08,320
in that very locality that we are

304
00:12:11,829 --> 00:12:09,360
studying

305
00:12:13,030 --> 00:12:11,839
and the bigger the mystery the fewer the

306
00:12:15,750 --> 00:12:13,040
data

307
00:12:18,310 --> 00:12:15,760
so let's review what these major

308
00:12:20,230 --> 00:12:18,320
innovations are over time

309
00:12:24,310 --> 00:12:20,240
we have cellular life

310
00:12:26,150 --> 00:12:24,320
emerging around 3.8 say 4 billion i put

311
00:12:28,069 --> 00:12:26,160
these arrows as absolute numbers but of

312
00:12:29,509 --> 00:12:28,079
course they do not represent an absolute

313
00:12:32,470 --> 00:12:29,519
day

314
00:12:36,150 --> 00:12:32,480
emergence of cyanobacteria

315
00:12:38,150 --> 00:12:36,160
eukaryotes followed by green algae and

316
00:12:39,670 --> 00:12:38,160
of course these are underlined by in

317
00:12:42,230 --> 00:12:39,680
this case and the second in the

318
00:12:43,910 --> 00:12:42,240
symbiosis event that gave rise to these

319
00:12:46,389 --> 00:12:43,920
what i'm sharing here and then we have

320
00:12:48,150 --> 00:12:46,399
animals and land plants and surface

321
00:12:50,870 --> 00:12:48,160
ecosystems

322
00:12:53,590 --> 00:12:50,880
so when we look at the history of life

323
00:12:55,990 --> 00:12:53,600
on earth these are the top innovations i

324
00:12:57,750 --> 00:12:56,000
think that really impacted what we see

325
00:13:00,310 --> 00:12:57,760
around us today

326
00:13:02,230 --> 00:13:00,320
this is magnificent to me and if you

327
00:13:05,110 --> 00:13:02,240
think about it all of these events are

328
00:13:07,030 --> 00:13:05,120
interconnected it all comes back to the

329
00:13:08,790 --> 00:13:07,040
cellular life and some innovation that

330
00:13:09,990 --> 00:13:08,800
happened at that point that got

331
00:13:12,069 --> 00:13:10,000
transferred

332
00:13:12,790 --> 00:13:12,079
i'm using the word transfer with a lot

333
00:13:14,389 --> 00:13:12,800
of

334
00:13:16,629 --> 00:13:14,399
conservation here

335
00:13:18,870 --> 00:13:16,639
um maybe in some cases adapted or it was

336
00:13:21,509 --> 00:13:18,880
a fluke accident we don't know but they

337
00:13:23,829 --> 00:13:21,519
all rely on each other so if i am here

338
00:13:26,069 --> 00:13:23,839
and talking to you it is because some

339
00:13:27,030 --> 00:13:26,079
innovation at the molecular level

340
00:13:28,629 --> 00:13:27,040
happened

341
00:13:30,550 --> 00:13:28,639
billions of years ago the fact that we

342
00:13:33,670 --> 00:13:30,560
are having this very conversation relies

343
00:13:34,470 --> 00:13:33,680
on these interactions of the past

344
00:13:35,829 --> 00:13:34,480
and

345
00:13:38,949 --> 00:13:35,839
some of these

346
00:13:41,350 --> 00:13:38,959
in fact majority of them are seeing what

347
00:13:42,870 --> 00:13:41,360
we like to think of as singular events

348
00:13:44,790 --> 00:13:42,880
they happened once

349
00:13:47,189 --> 00:13:44,800
and nothing like them happened ever

350
00:13:49,269 --> 00:13:47,199
again and we cannot compare them to

351
00:13:51,030 --> 00:13:49,279
independent data sets we don't have any

352
00:13:53,189 --> 00:13:51,040
other planet with life so this is what

353
00:13:55,189 --> 00:13:53,199
we have even in this place we can we

354
00:13:58,550 --> 00:13:55,199
don't have an independent record yet at

355
00:14:01,430 --> 00:13:58,560
least to compare these events to

356
00:14:03,269 --> 00:14:01,440
and they happened billions of years ago

357
00:14:05,590 --> 00:14:03,279
you cannot have an ecosystem or

358
00:14:07,670 --> 00:14:05,600
biosphere without cells

359
00:14:09,750 --> 00:14:07,680
i just recently read a paper that

360
00:14:10,949 --> 00:14:09,760
animals and and the rise of animals may

361
00:14:13,110 --> 00:14:10,959
be due to

362
00:14:14,870 --> 00:14:13,120
evolution of nitrogen fixation and the

363
00:14:16,949 --> 00:14:14,880
impact of that with the rise of oxygen

364
00:14:19,509 --> 00:14:16,959
on this planet so these are all

365
00:14:22,470 --> 00:14:19,519
interconnected and it's quite remarkable

366
00:14:23,990 --> 00:14:22,480
that these events happened at singular

367
00:14:25,189 --> 00:14:24,000
themselves

368
00:14:26,710 --> 00:14:25,199
in early life

369
00:14:29,110 --> 00:14:26,720
it's complicated

370
00:14:31,430 --> 00:14:29,120
if you were a bacteria looking for

371
00:14:33,509 --> 00:14:31,440
a date with a really really old organism

372
00:14:35,030 --> 00:14:33,519
and go to your tinder profile or just

373
00:14:36,949 --> 00:14:35,040
check it out this is what you would see

374
00:14:39,430 --> 00:14:36,959
it's some sort of

375
00:14:41,430 --> 00:14:39,440
i mean as a biologist who's studying you

376
00:14:43,269 --> 00:14:41,440
know biochemical and chemical and

377
00:14:45,430 --> 00:14:43,279
molecular systems when i first saw this

378
00:14:47,350 --> 00:14:45,440
i thought this is it

379
00:14:49,670 --> 00:14:47,360
so when we look at past this this is

380
00:14:52,150 --> 00:14:49,680
what we see so the favorite food is

381
00:14:54,949 --> 00:14:52,160
complicated relationship status is very

382
00:14:57,509 --> 00:14:54,959
complicated age it's very complicated

383
00:14:59,030 --> 00:14:57,519
family lame let's not even go there

384
00:15:01,269 --> 00:14:59,040
so this is what we have is an early

385
00:15:04,069 --> 00:15:01,279
pre-cambrian microbe

386
00:15:06,470 --> 00:15:04,079
and the foundations of these jumps like

387
00:15:09,430 --> 00:15:06,480
call them jumps that i just showed you

388
00:15:10,949 --> 00:15:09,440
that define our planet are completely

389
00:15:12,949 --> 00:15:10,959
unknown

390
00:15:15,350 --> 00:15:12,959
and i will add another layer to this

391
00:15:18,949 --> 00:15:15,360
challenge it is not even clear whether

392
00:15:21,030 --> 00:15:18,959
these events themselves were inevitable

393
00:15:23,269 --> 00:15:21,040
right so that's another that's another

394
00:15:24,949 --> 00:15:23,279
challenge we got there so let's revisit

395
00:15:27,350 --> 00:15:24,959
the image i just showed you this time

396
00:15:29,990 --> 00:15:27,360
with some pretty pictures but what we

397
00:15:32,389 --> 00:15:30,000
know comes from maybe some chemical

398
00:15:34,310 --> 00:15:32,399
isotopic probing and and those if we are

399
00:15:36,790 --> 00:15:34,320
lucky we have some fossil data again as

400
00:15:39,509 --> 00:15:36,800
we go back in time the the we have

401
00:15:41,910 --> 00:15:39,519
scattered information and and the record

402
00:15:43,910 --> 00:15:41,920
gets spottier and spottier

403
00:15:45,910 --> 00:15:43,920
but i want you to look at the gaps

404
00:15:48,230 --> 00:15:45,920
between these major biological

405
00:15:51,910 --> 00:15:48,240
innovations in the history of life and

406
00:15:53,430 --> 00:15:51,920
by gaps i mean the timing right hundreds

407
00:15:57,189 --> 00:15:53,440
millions

408
00:16:01,910 --> 00:15:59,350
and i will tell you now why as a as a

409
00:16:04,069 --> 00:16:01,920
biologist i find that amazing

410
00:16:06,790 --> 00:16:04,079
because we know that at the molecular

411
00:16:08,310 --> 00:16:06,800
scale variations variation happens very

412
00:16:10,949 --> 00:16:08,320
rapidly

413
00:16:13,110 --> 00:16:10,959
okay we do not have

414
00:16:15,269 --> 00:16:13,120
an obvious way deterministic or

415
00:16:17,829 --> 00:16:15,279
mechanistic

416
00:16:19,430 --> 00:16:17,839
to link variability on the scale of

417
00:16:21,829 --> 00:16:19,440
minutes

418
00:16:24,069 --> 00:16:21,839
with micro evolutionary innovation that

419
00:16:26,870 --> 00:16:24,079
expresses themselves on the scale of

420
00:16:27,990 --> 00:16:26,880
billions of years

421
00:16:29,990 --> 00:16:28,000
think about

422
00:16:31,990 --> 00:16:30,000
translation machinery it's it's the

423
00:16:35,189 --> 00:16:32,000
computer of life right it processes

424
00:16:37,110 --> 00:16:35,199
everything that in every organism that

425
00:16:39,590 --> 00:16:37,120
ever existed on this planet has this

426
00:16:41,670 --> 00:16:39,600
computer in itself

427
00:16:44,069 --> 00:16:41,680
it is able to generate a peptide chain

428
00:16:46,710 --> 00:16:44,079
in some cases less than a second

429
00:16:48,470 --> 00:16:46,720
that is very fast we know when we study

430
00:16:50,470 --> 00:16:48,480
molecule and molecular life that the

431
00:16:53,030 --> 00:16:50,480
variation exists in that very rapid

432
00:16:54,870 --> 00:16:53,040
speed so how is it possible that a

433
00:16:56,470 --> 00:16:54,880
variation that happens at that really

434
00:16:58,470 --> 00:16:56,480
fast pace

435
00:17:03,350 --> 00:16:58,480
leads to these major innovations that

436
00:17:06,150 --> 00:17:04,630
i think this is

437
00:17:07,990 --> 00:17:06,160
a miracle

438
00:17:09,990 --> 00:17:08,000
but also a curse at the same time that's

439
00:17:12,230 --> 00:17:10,000
why i think studying early life is very

440
00:17:14,150 --> 00:17:12,240
fascinating but also very challenging

441
00:17:16,789 --> 00:17:14,160
because we don't have ways to link this

442
00:17:18,390 --> 00:17:16,799
thing these two events and and

443
00:17:19,909 --> 00:17:18,400
i think the fact that we are struggling

444
00:17:21,510 --> 00:17:19,919
to even comprehend these things or at

445
00:17:24,069 --> 00:17:21,520
least i do

446
00:17:26,150 --> 00:17:24,079
also perhaps indicates that we do not

447
00:17:28,150 --> 00:17:26,160
teach biology and biological concepts

448
00:17:30,870 --> 00:17:28,160
the way we should we don't learn them in

449
00:17:33,990 --> 00:17:30,880
the way we should we need to integrate

450
00:17:36,630 --> 00:17:34,000
life's history when we teach life

451
00:17:37,590 --> 00:17:36,640
and i don't think we do that

452
00:17:39,110 --> 00:17:37,600
at least

453
00:17:40,710 --> 00:17:39,120
comprehensively at the moment in in

454
00:17:41,909 --> 00:17:40,720
biology

455
00:17:43,990 --> 00:17:41,919
so

456
00:17:46,549 --> 00:17:44,000
as i mentioned just adding another layer

457
00:17:48,950 --> 00:17:46,559
to the challenge we have this issue of

458
00:17:51,430 --> 00:17:48,960
inevitability and i think

459
00:17:53,270 --> 00:17:51,440
in my mind contingency is sort of

460
00:17:55,270 --> 00:17:53,280
it's kind of a reasonable

461
00:17:57,029 --> 00:17:55,280
assumption to make here

462
00:18:00,310 --> 00:17:57,039
just because it happened that way

463
00:18:02,310 --> 00:18:00,320
doesn't mean it had to happen that way

464
00:18:04,230 --> 00:18:02,320
right so the case for this in especially

465
00:18:06,630 --> 00:18:04,240
in deep time evolution of these earliest

466
00:18:08,549 --> 00:18:06,640
organisms and biomolecules is a pretty

467
00:18:10,390 --> 00:18:08,559
reasonable one

468
00:18:12,830 --> 00:18:10,400
and we are aware of this because of the

469
00:18:16,549 --> 00:18:12,840
studies that come from chemistry and

470
00:18:18,789 --> 00:18:16,559
physics that they provide us this all of

471
00:18:21,190 --> 00:18:18,799
the chemical space that is possible in

472
00:18:23,510 --> 00:18:21,200
the universe and then when you think of

473
00:18:24,789 --> 00:18:23,520
it about the innovations on this planet

474
00:18:26,789 --> 00:18:24,799
they each

475
00:18:29,669 --> 00:18:26,799
are our sort of

476
00:18:32,470 --> 00:18:29,679
universe of possibilities themselves

477
00:18:35,190 --> 00:18:32,480
so we know this vast chemical space and

478
00:18:38,870 --> 00:18:35,200
we know how narrow life reduced it down

479
00:18:40,549 --> 00:18:38,880
to to some selective amino acids we know

480
00:18:42,789 --> 00:18:40,559
there's a handful of amino acids like

481
00:18:44,870 --> 00:18:42,799
life selected or certain backbones that

482
00:18:46,470 --> 00:18:44,880
life likes to use if you think about it

483
00:18:48,470 --> 00:18:46,480
considering all the possibilities it's a

484
00:18:52,549 --> 00:18:48,480
pretty selected

485
00:18:58,310 --> 00:18:54,549
so while physics and chemistry can give

486
00:19:01,270 --> 00:18:58,320
us this oven wonder of the general what

487
00:19:06,549 --> 00:19:01,280
we need is to understand the depths of

488
00:19:09,990 --> 00:19:07,909
in this regard

489
00:19:12,390 --> 00:19:10,000
there is no substitute for fundamental

490
00:19:14,710 --> 00:19:12,400
biology in the assessment of planetary

491
00:19:16,549 --> 00:19:14,720
scale life in the universe

492
00:19:17,510 --> 00:19:16,559
we need to understand these rules of

493
00:19:21,029 --> 00:19:17,520
life

494
00:19:22,789 --> 00:19:21,039
that enabled the selection the subset of

495
00:19:25,990 --> 00:19:22,799
the chemicals that it studies and

496
00:19:27,830 --> 00:19:26,000
utilizes frequently

497
00:19:29,190 --> 00:19:27,840
we need to understand these events on

498
00:19:30,870 --> 00:19:29,200
their own terms

499
00:19:32,789 --> 00:19:30,880
and with what little data we have

500
00:19:34,710 --> 00:19:32,799
available

501
00:19:36,710 --> 00:19:34,720
and this is also coming

502
00:19:38,710 --> 00:19:36,720
without the benefit and of course maybe

503
00:19:41,909 --> 00:19:38,720
in some cases the limitation of

504
00:19:43,830 --> 00:19:41,919
comparing them to any other evolutionary

505
00:19:46,390 --> 00:19:43,840
history

506
00:19:49,990 --> 00:19:46,400
this was really where

507
00:19:52,470 --> 00:19:50,000
my focus uh has has been drawn into

508
00:19:54,470 --> 00:19:52,480
and and and this is really the crowd

509
00:19:56,310 --> 00:19:54,480
that made me think about these things

510
00:19:58,710 --> 00:19:56,320
and and showed me these gaps in our

511
00:20:01,110 --> 00:19:58,720
understanding of life and and i've been

512
00:20:03,830 --> 00:20:01,120
thinking how do we do this how do how do

513
00:20:07,270 --> 00:20:03,840
we draw the benefit of all the tools and

514
00:20:10,470 --> 00:20:07,280
systems that we now develop

515
00:20:12,149 --> 00:20:10,480
to questions such as this

516
00:20:14,549 --> 00:20:12,159
and it was inevitable that we need to

517
00:20:17,029 --> 00:20:14,559
develop systems however limited that

518
00:20:19,350 --> 00:20:17,039
might be and we i'm i would love to talk

519
00:20:23,029 --> 00:20:19,360
to you about that more and we will be

520
00:20:25,830 --> 00:20:23,039
to travel backwards from present

521
00:20:28,549 --> 00:20:25,840
that let's develop systems where we can

522
00:20:29,830 --> 00:20:28,559
at least make an attempt to understand

523
00:20:31,430 --> 00:20:29,840
the

524
00:20:33,590 --> 00:20:31,440
house

525
00:20:35,669 --> 00:20:33,600
how can a molecular system give birth to

526
00:20:39,510 --> 00:20:35,679
such major innovation

527
00:20:42,230 --> 00:20:40,310
i

528
00:20:45,990 --> 00:20:42,240
narrowed my focus to

529
00:20:49,350 --> 00:20:46,000
not only organisms and cells but the

530
00:20:51,909 --> 00:20:49,360
proteins that enable majority of the bio

531
00:20:53,990 --> 00:20:51,919
geochemical cycles and metabolisms that

532
00:20:55,669 --> 00:20:54,000
whose outputs we get to study in the

533
00:20:57,669 --> 00:20:55,679
rock record

534
00:21:00,470 --> 00:20:57,679
and i spent quite a bit of time trying

535
00:21:03,029 --> 00:21:00,480
to link what sort of protein system can

536
00:21:06,390 --> 00:21:03,039
completely realize not only the history

537
00:21:08,630 --> 00:21:06,400
of life but but enable us to study the

538
00:21:10,630 --> 00:21:08,640
impact at the planetary scale

539
00:21:13,909 --> 00:21:10,640
it seems like a big stretch

540
00:21:16,710 --> 00:21:13,919
even though i showed you how a cell at

541
00:21:18,470 --> 00:21:16,720
the at the if we go up a layer is

542
00:21:21,909 --> 00:21:18,480
connected to what we study at the

543
00:21:27,350 --> 00:21:24,470
overall the approach in my mind at least

544
00:21:30,789 --> 00:21:27,360
was quite straightforward

545
00:21:32,630 --> 00:21:30,799
i nasa loves to use the term

546
00:21:34,950 --> 00:21:32,640
not a fruit salad but we're making a

547
00:21:36,549 --> 00:21:34,960
smoothie here and i always thought well

548
00:21:38,470 --> 00:21:36,559
that's great but you have to be very

549
00:21:39,990 --> 00:21:38,480
careful with what you put in the blender

550
00:21:41,990 --> 00:21:40,000
when you make a smoothie because if you

551
00:21:43,510 --> 00:21:42,000
use it wrong ingredient you can ruin the

552
00:21:44,789 --> 00:21:43,520
entire smoothie and there's no going

553
00:21:46,549 --> 00:21:44,799
back

554
00:21:49,350 --> 00:21:46,559
so uh

555
00:21:51,110 --> 00:21:49,360
with this dramatic pursue i i thought

556
00:21:53,110 --> 00:21:51,120
about all right so we have the genes and

557
00:21:55,510 --> 00:21:53,120
proteins and and yes we have

558
00:21:58,390 --> 00:21:55,520
phylogenetic approaches that allow us to

559
00:22:01,110 --> 00:21:58,400
study the revolution today we

560
00:22:03,029 --> 00:22:01,120
know so much by studying viral evolution

561
00:22:04,789 --> 00:22:03,039
predicting where mutations might be to

562
00:22:06,710 --> 00:22:04,799
some degree yes they have their

563
00:22:09,350 --> 00:22:06,720
restrictions but let's try to stretch

564
00:22:12,230 --> 00:22:09,360
this at the deep time scale

565
00:22:14,470 --> 00:22:12,240
and can we engineer systems with these

566
00:22:17,350 --> 00:22:14,480
phylogenetically reconstructed genes to

567
00:22:20,549 --> 00:22:17,360
reprogram the behavior of the cell which

568
00:22:25,669 --> 00:22:20,559
we can then study geochemically in the

569
00:22:29,270 --> 00:22:26,870
with that

570
00:22:31,510 --> 00:22:29,280
started this pursuit of evolutionary

571
00:22:33,510 --> 00:22:31,520
construction of ancient enzymes yes the

572
00:22:35,350 --> 00:22:33,520
ancestral state reconstruction was

573
00:22:37,270 --> 00:22:35,360
frequently applied to understand the

574
00:22:40,470 --> 00:22:37,280
evolutionary mechanisms and particularly

575
00:22:42,230 --> 00:22:40,480
how mutations drive adaptation but

576
00:22:44,390 --> 00:22:42,240
wasn't being stretched i think deep

577
00:22:45,590 --> 00:22:44,400
enough to understand the planetary scale

578
00:22:47,350 --> 00:22:45,600
phenomena

579
00:22:49,590 --> 00:22:47,360
and of course that was

580
00:22:52,149 --> 00:22:49,600
those applications i was exposed to

581
00:22:55,350 --> 00:22:52,159
those possibilities here

582
00:22:57,669 --> 00:22:55,360
and here is a paper by margaret day hall

583
00:22:59,669 --> 00:22:57,679
published in 1981

584
00:23:02,549 --> 00:22:59,679
this might be one of the papers that she

585
00:23:04,070 --> 00:23:02,559
has that have the fewest citations maybe

586
00:23:06,549 --> 00:23:04,080
20 30

587
00:23:08,630 --> 00:23:06,559
we might be half of those citations

588
00:23:10,549 --> 00:23:08,640
this is a remarkable paper evolution of

589
00:23:12,630 --> 00:23:10,559
major metabolic innovations in the

590
00:23:14,789 --> 00:23:12,640
pre-cambrian margaret dayhoff was a

591
00:23:16,789 --> 00:23:14,799
biophysicist

592
00:23:18,950 --> 00:23:16,799
she revolutionized how we study

593
00:23:22,630 --> 00:23:18,960
computate how we utilize computational

594
00:23:26,870 --> 00:23:22,640
power to study protein structures

595
00:23:28,470 --> 00:23:26,880
in this paper they examine pathways

596
00:23:30,710 --> 00:23:28,480
where there is information on the

597
00:23:32,870 --> 00:23:30,720
chemical structure of enzyme say so they

598
00:23:35,190 --> 00:23:32,880
focus on extracting some information

599
00:23:36,870 --> 00:23:35,200
from the structure of the proteins

600
00:23:38,789 --> 00:23:36,880
and they assume

601
00:23:40,789 --> 00:23:38,799
that the gene products

602
00:23:42,390 --> 00:23:40,799
whose structures have been conserved of

603
00:23:45,110 --> 00:23:42,400
these proteins

604
00:23:47,269 --> 00:23:45,120
have remains relatively unchanged in

605
00:23:48,310 --> 00:23:47,279
their basic functions

606
00:23:49,510 --> 00:23:48,320
over

607
00:23:51,430 --> 00:23:49,520
um

608
00:23:53,029 --> 00:23:51,440
throughout pre-cambrian

609
00:23:54,549 --> 00:23:53,039
so there is this assumption in their

610
00:23:58,470 --> 00:23:54,559
paper it's a very brief paper i highly

611
00:24:01,909 --> 00:24:00,870
we make this assumption very frequently

612
00:24:04,710 --> 00:24:01,919
especially when it comes to

613
00:24:07,590 --> 00:24:04,720
understanding your life we in some cases

614
00:24:09,110 --> 00:24:07,600
have to but we know that it's not that

615
00:24:11,190 --> 00:24:09,120
straightforward when it comes to

616
00:24:13,590 --> 00:24:11,200
understanding how mutations and

617
00:24:16,070 --> 00:24:13,600
variation impacts protein structure and

618
00:24:18,870 --> 00:24:16,080
how that consequently impacts protein

619
00:24:20,870 --> 00:24:18,880
function

620
00:24:22,789 --> 00:24:20,880
single insertions or deletions can

621
00:24:24,149 --> 00:24:22,799
change the behavior of a protein

622
00:24:25,830 --> 00:24:24,159
altogether

623
00:24:28,789 --> 00:24:25,840
we now know that it's not all about the

624
00:24:31,510 --> 00:24:28,799
active sites meaning the the heart of

625
00:24:33,830 --> 00:24:31,520
the protein where the reaction

626
00:24:36,149 --> 00:24:33,840
whatever the specific function happens

627
00:24:38,310 --> 00:24:36,159
we know they operate in a complex

628
00:24:39,190 --> 00:24:38,320
network inside the system inside the

629
00:24:41,909 --> 00:24:39,200
cell

630
00:24:44,070 --> 00:24:41,919
and there a single mutation that changes

631
00:24:46,310 --> 00:24:44,080
the structure of a protein can impact

632
00:24:49,190 --> 00:24:46,320
the entire cellular function because of

633
00:24:51,350 --> 00:24:49,200
this disturbance in the network

634
00:24:55,190 --> 00:24:51,360
a single insertion can be as dramatic as

635
00:24:55,200 --> 00:24:59,430
it's true

636
00:25:04,070 --> 00:25:01,190
with that in hindsight

637
00:25:06,149 --> 00:25:04,080
we work towards establishing a method

638
00:25:07,750 --> 00:25:06,159
the idea was to start with protein

639
00:25:09,750 --> 00:25:07,760
sequence alignments

640
00:25:11,269 --> 00:25:09,760
form phylogenies

641
00:25:12,390 --> 00:25:11,279
we spend a lot of time on this and i

642
00:25:15,269 --> 00:25:12,400
will show

643
00:25:17,269 --> 00:25:15,279
an example uh how we utilize our

644
00:25:19,350 --> 00:25:17,279
assessment of protein to uh tree

645
00:25:21,350 --> 00:25:19,360
topology and our alignment output so i

646
00:25:22,710 --> 00:25:21,360
will be drilling down a little bit there

647
00:25:25,269 --> 00:25:22,720
towards the end

648
00:25:27,029 --> 00:25:25,279
well we infer the ancestral sequence

649
00:25:29,029 --> 00:25:27,039
this is where the synthetic biology part

650
00:25:31,350 --> 00:25:29,039
kicks in we can synthesize these genes

651
00:25:33,830 --> 00:25:31,360
it's getting cheaper every year to

652
00:25:35,110 --> 00:25:33,840
create these things it's not cheap but

653
00:25:38,070 --> 00:25:35,120
it's cheaper

654
00:25:40,789 --> 00:25:38,080
and it and in one level we purify these

655
00:25:42,870 --> 00:25:40,799
proteins to look at their uh behavior in

656
00:25:44,470 --> 00:25:42,880
vitro outside of the cell environment

657
00:25:46,549 --> 00:25:44,480
but what i really was interested in is

658
00:25:47,750 --> 00:25:46,559
again change the behavior of the

659
00:25:50,310 --> 00:25:47,760
organism

660
00:25:51,110 --> 00:25:50,320
to the degree that we can then

661
00:25:54,149 --> 00:25:51,120
um

662
00:25:56,470 --> 00:25:54,159
implement biogeochemical applications to

663
00:25:58,310 --> 00:25:56,480
interpret or at least provide

664
00:26:00,390 --> 00:25:58,320
who knows an entirely independent data

665
00:26:02,950 --> 00:26:00,400
set using artificial

666
00:26:07,750 --> 00:26:02,960
biology in the lab to see what life can

667
00:26:11,669 --> 00:26:08,630
we

668
00:26:13,269 --> 00:26:11,679
started uh this is the translation was

669
00:26:16,390 --> 00:26:13,279
my first focus

670
00:26:19,510 --> 00:26:16,400
uh i started studying how this machinery

671
00:26:22,549 --> 00:26:19,520
that i just referred to you elongates

672
00:26:25,029 --> 00:26:22,559
peptides these are very uh not only they

673
00:26:27,510 --> 00:26:25,039
are singular in their own rights right

674
00:26:29,190 --> 00:26:27,520
there's only one translation machinery

675
00:26:31,110 --> 00:26:29,200
it may be different if you look at

676
00:26:33,190 --> 00:26:31,120
bacteria or archaea and eukaryotes there

677
00:26:34,149 --> 00:26:33,200
might be differences but there is only

678
00:26:37,110 --> 00:26:34,159
one

679
00:26:39,350 --> 00:26:37,120
computation center inside us inside all

680
00:26:41,750 --> 00:26:39,360
of us

681
00:26:44,310 --> 00:26:41,760
so i started by talking with the

682
00:26:47,190 --> 00:26:44,320
elongation step of this translation

683
00:26:49,350 --> 00:26:47,200
machinery inside bacteria i was curious

684
00:26:51,350 --> 00:26:49,360
to see what will it do

685
00:26:53,430 --> 00:26:51,360
to the entire cell

686
00:26:55,909 --> 00:26:53,440
well when you poke with something that

687
00:26:58,950 --> 00:26:55,919
is really essential turns out it breaks

688
00:27:00,549 --> 00:26:58,960
so this was a very childlike i think

689
00:27:02,149 --> 00:27:00,559
venture you know how kids like to break

690
00:27:03,190 --> 00:27:02,159
things so we were just breaking cells

691
00:27:05,029 --> 00:27:03,200
all the time

692
00:27:07,669 --> 00:27:05,039
but i was interested in understanding

693
00:27:09,669 --> 00:27:07,679
how far can the cell tolerate the

694
00:27:11,830 --> 00:27:09,679
perturbance that we are introducing by

695
00:27:14,230 --> 00:27:11,840
poking

696
00:27:16,149 --> 00:27:14,240
and by introducing ancient genes that

697
00:27:18,389 --> 00:27:16,159
are older and older and translation is

698
00:27:20,710 --> 00:27:18,399
one of the first innovations of life

699
00:27:22,630 --> 00:27:20,720
anyway that dates at about four billion

700
00:27:24,230 --> 00:27:22,640
year right so we're

701
00:27:27,110 --> 00:27:24,240
messing with a four billion year old

702
00:27:29,110 --> 00:27:27,120
machine by asking it to interact and

703
00:27:31,669 --> 00:27:29,120
recognize a three billion year old

704
00:27:37,750 --> 00:27:34,870
after spending some time working on this

705
00:27:39,510 --> 00:27:37,760
system and then subjecting these perdarb

706
00:27:41,430 --> 00:27:39,520
systems to evolution to see how they

707
00:27:43,110 --> 00:27:41,440
will repeat themselves and i was

708
00:27:45,430 --> 00:27:43,120
interested in this replaying tape of

709
00:27:47,830 --> 00:27:45,440
life phenomena introduced by gold at

710
00:27:51,590 --> 00:27:47,840
that time

711
00:27:53,909 --> 00:27:51,600
recognize that we cannot directly link

712
00:27:55,750 --> 00:27:53,919
such engineering to the environment

713
00:27:57,269 --> 00:27:55,760
you can break translation in very

714
00:27:59,350 --> 00:27:57,279
different ways that may have nothing to

715
00:28:01,350 --> 00:27:59,360
do with deep past

716
00:28:02,470 --> 00:28:01,360
so we need to focus on proteins that

717
00:28:04,389 --> 00:28:02,480
actually

718
00:28:07,190 --> 00:28:04,399
interact with the environment in a bit

719
00:28:09,029 --> 00:28:07,200
more direct way than the translation

720
00:28:12,710 --> 00:28:09,039
so while we are continuing that work for

721
00:28:14,470 --> 00:28:12,720
different purposes we moved on

722
00:28:16,149 --> 00:28:14,480
because i developed an experimental

723
00:28:17,669 --> 00:28:16,159
framework for reconstructing these

724
00:28:20,230 --> 00:28:17,679
metabolisms

725
00:28:21,909 --> 00:28:20,240
i thought okay here is what i learned

726
00:28:24,310 --> 00:28:21,919
we need to have

727
00:28:25,830 --> 00:28:24,320
a geologic history that we are tying the

728
00:28:27,909 --> 00:28:25,840
behavior of these

729
00:28:30,149 --> 00:28:27,919
engineered organisms to

730
00:28:33,430 --> 00:28:30,159
is there a fossil or a geochemical

731
00:28:35,430 --> 00:28:33,440
record related to this protein function

732
00:28:38,310 --> 00:28:35,440
there's vast amount of literature coming

733
00:28:40,310 --> 00:28:38,320
from biogeochemistry on these topics

734
00:28:43,350 --> 00:28:40,320
do we have a phenotype

735
00:28:45,350 --> 00:28:43,360
a characterizable and geologically

736
00:28:47,269 --> 00:28:45,360
relevant phenotype that we can generate

737
00:28:49,909 --> 00:28:47,279
in the lab

738
00:28:51,669 --> 00:28:49,919
what are we linking this to

739
00:28:54,470 --> 00:28:51,679
is there an ancestry

740
00:28:57,350 --> 00:28:54,480
are we dealing with a tractable system

741
00:28:59,029 --> 00:28:57,360
how far can we extend it to in deep time

742
00:29:01,669 --> 00:28:59,039
is there an appropriate modern host

743
00:29:03,190 --> 00:29:01,679
biological system yes we can engineer a

744
00:29:04,389 --> 00:29:03,200
spinach that's not a problem but that

745
00:29:07,510 --> 00:29:04,399
doesn't tell you much if you're

746
00:29:09,750 --> 00:29:07,520
interested in arcane innovations

747
00:29:13,669 --> 00:29:09,760
do we have those model systems that will

748
00:29:15,909 --> 00:29:13,679
allow us to study the past

749
00:29:16,870 --> 00:29:15,919
with that we turned into many different

750
00:29:18,630 --> 00:29:16,880
venues

751
00:29:19,669 --> 00:29:18,640
one of this that i'm going to focus on

752
00:29:22,950 --> 00:29:19,679
is

753
00:29:24,789 --> 00:29:22,960
our way connecting protein evolution to

754
00:29:27,430 --> 00:29:24,799
mental availabilities of earth over

755
00:29:28,389 --> 00:29:27,440
geologic time

756
00:29:30,149 --> 00:29:28,399
there is a

757
00:29:31,990 --> 00:29:30,159
there are some authors that contributed

758
00:29:34,789 --> 00:29:32,000
to these papers that are already in this

759
00:29:36,070 --> 00:29:34,799
room and and this is really remarkable i

760
00:29:39,510 --> 00:29:36,080
think generation of what astro

761
00:29:41,430 --> 00:29:39,520
biologists also created over decades

762
00:29:43,350 --> 00:29:41,440
that we understand

763
00:29:45,029 --> 00:29:43,360
to some degree that the metal

764
00:29:47,190 --> 00:29:45,039
concentration

765
00:29:48,549 --> 00:29:47,200
over geologic time did not remain

766
00:29:51,190 --> 00:29:48,559
constant

767
00:29:54,230 --> 00:29:51,200
for majority of these metals

768
00:29:56,549 --> 00:29:54,240
we know this for iron that has a higher

769
00:29:58,230 --> 00:29:56,559
concentration in the archaean then

770
00:30:01,029 --> 00:29:58,240
there's a drop

771
00:30:03,029 --> 00:30:01,039
and there's a lot of work into why such

772
00:30:05,750 --> 00:30:03,039
changes happened the links of these

773
00:30:07,190 --> 00:30:05,760
changes to great oxidation event or

774
00:30:10,710 --> 00:30:07,200
different

775
00:30:12,630 --> 00:30:10,720
earth surface related phenomena

776
00:30:13,510 --> 00:30:12,640
so this was very interesting

777
00:30:16,630 --> 00:30:13,520
uh

778
00:30:18,870 --> 00:30:16,640
to look at and i can tell you having

779
00:30:21,190 --> 00:30:18,880
interacting with a lot of metalloenzyme

780
00:30:22,549 --> 00:30:21,200
biochemists when i shove down this image

781
00:30:24,470 --> 00:30:22,559
they're mind blown

782
00:30:26,389 --> 00:30:24,480
they tell me they never thought about

783
00:30:28,549 --> 00:30:26,399
history of where these metals came from

784
00:30:30,630 --> 00:30:28,559
and i get it but that's what we do as

785
00:30:31,990 --> 00:30:30,640
astrobiologists right and that's amazing

786
00:30:33,110 --> 00:30:32,000
that we are able to connect these

787
00:30:36,870 --> 00:30:33,120
seemingly

788
00:30:38,870 --> 00:30:36,880
distant fields together

789
00:30:41,669 --> 00:30:38,880
nitrogenase is one of these

790
00:30:44,230 --> 00:30:41,679
enzymes a molybdenum dependent nitrogen

791
00:30:47,029 --> 00:30:44,240
fixing enzyme that also represents a

792
00:30:52,630 --> 00:30:50,149
it has a female code iron molybdenum

793
00:30:53,909 --> 00:30:52,640
co-factor there's different versions of

794
00:30:56,470 --> 00:30:53,919
this enzyme but this is the most

795
00:30:59,350 --> 00:30:56,480
prevalent version that exists today that

796
00:31:02,389 --> 00:30:59,360
fixes the advising inert nitrogen in the

797
00:31:07,669 --> 00:31:05,669
i think it is very amazing that life

798
00:31:08,950 --> 00:31:07,679
came up with this innovation once and

799
00:31:12,549 --> 00:31:08,960
has one

800
00:31:14,310 --> 00:31:12,559
protein to rely on to fix the nitrogen

801
00:31:16,710 --> 00:31:14,320
in the atmosphere at least so far that

802
00:31:18,389 --> 00:31:16,720
seems to be the case

803
00:31:19,669 --> 00:31:18,399
it's not very uh

804
00:31:21,509 --> 00:31:19,679
creative it is really putting all your

805
00:31:26,149 --> 00:31:21,519
eggs in one basket so we rely on this

806
00:31:29,509 --> 00:31:26,950
we

807
00:31:32,950 --> 00:31:29,519
looked at the literature to

808
00:31:35,110 --> 00:31:32,960
study the previous studies that tried to

809
00:31:36,470 --> 00:31:35,120
probe the evolution of nitrogenase over

810
00:31:38,549 --> 00:31:36,480
deep time

811
00:31:41,110 --> 00:31:38,559
and not surprisingly this came from

812
00:31:44,070 --> 00:31:41,120
astrobiologists here

813
00:31:46,470 --> 00:31:44,080
yet we don't quite know the exact timing

814
00:31:47,990 --> 00:31:46,480
of nitrogen fixation

815
00:31:50,389 --> 00:31:48,000
there's there's a big question mark

816
00:31:51,430 --> 00:31:50,399
there but in the origin of nitrogenases

817
00:31:53,669 --> 00:31:51,440
we think

818
00:31:55,750 --> 00:31:53,679
must have coincided with the emergence

819
00:31:57,590 --> 00:31:55,760
of nitrogen fixation because that's what

820
00:31:59,830 --> 00:31:57,600
we see now

821
00:32:03,350 --> 00:31:59,840
and that makes sense at the top level as

822
00:32:07,509 --> 00:32:05,830
our questions were as follows what was

823
00:32:08,870 --> 00:32:07,519
the metal dependence of ancestral

824
00:32:11,509 --> 00:32:08,880
enzymes

825
00:32:15,029 --> 00:32:11,519
where did this enzyme come from

826
00:32:17,750 --> 00:32:15,039
how did it evolve

827
00:32:20,630 --> 00:32:17,760
and big shout out to dr amanda garcia

828
00:32:22,549 --> 00:32:20,640
who joined my lab i was a first-year

829
00:32:25,029 --> 00:32:22,559
faculty member at the university of

830
00:32:27,029 --> 00:32:25,039
arizona 2018

831
00:32:28,950 --> 00:32:27,039
and that's when she joined my lab as an

832
00:32:31,590 --> 00:32:28,960
a postdoctoral fellow

833
00:32:33,669 --> 00:32:31,600
to take this journey having been trained

834
00:32:37,110 --> 00:32:33,679
in in paleobiology

835
00:32:40,830 --> 00:32:37,120
uh this was amazing for me to have

836
00:32:44,549 --> 00:32:40,840
someone coming directly from the fields

837
00:32:46,470 --> 00:32:44,559
in trained in paleontology at ucla

838
00:32:48,630 --> 00:32:46,480
to join our group to study molecular

839
00:32:50,710 --> 00:32:48,640
revolution system it was exactly the

840
00:32:53,669 --> 00:32:50,720
kind of scholars that i was hoping to

841
00:32:59,110 --> 00:32:57,269
amanda asked the question can we create

842
00:33:01,190 --> 00:32:59,120
phylogenies and now

843
00:33:02,950 --> 00:33:01,200
on top of building on the previous

844
00:33:04,549 --> 00:33:02,960
studies

845
00:33:05,430 --> 00:33:04,559
a lot of these work came from montana

846
00:33:06,389 --> 00:33:05,440
state

847
00:33:07,590 --> 00:33:06,399
can we

848
00:33:10,549 --> 00:33:07,600
use the

849
00:33:13,669 --> 00:33:10,559
upgraded updated sequence information in

850
00:33:16,789 --> 00:33:13,679
the database rebuilt the tree and it not

851
00:33:19,669 --> 00:33:16,799
only read the tree that we created but

852
00:33:21,669 --> 00:33:19,679
also reconstruct the ancestors using

853
00:33:22,870 --> 00:33:21,679
this tree to understand

854
00:33:28,070 --> 00:33:22,880
the

855
00:33:30,149 --> 00:33:28,080
preference of this enzyme

856
00:33:32,389 --> 00:33:30,159
she built machine learning models this

857
00:33:35,269 --> 00:33:32,399
was a computational study that were

858
00:33:39,830 --> 00:33:35,279
trained on the active site amino acids

859
00:33:41,430 --> 00:33:39,840
of old nitrogenases that exist today

860
00:33:42,630 --> 00:33:41,440
because not all of them prefer

861
00:33:44,870 --> 00:33:42,640
molybdenum

862
00:33:48,310 --> 00:33:44,880
we have others such as vanadium

863
00:33:51,029 --> 00:33:48,320
preferring nitrogenases to come up with

864
00:33:53,509 --> 00:33:51,039
a classification to determine whether

865
00:33:57,269 --> 00:33:53,519
the ancestral metal dependence focuses

866
00:33:59,909 --> 00:33:57,279
on the molybdenum or not

867
00:34:02,870 --> 00:33:59,919
we published this paper

868
00:34:04,870 --> 00:34:02,880
as our indicated what indicate that what

869
00:34:07,590 --> 00:34:04,880
the output of these analysis indicate

870
00:34:09,750 --> 00:34:07,600
that ancestral nitrogenases were in fact

871
00:34:11,109 --> 00:34:09,760
molybdenum periphery

872
00:34:12,470 --> 00:34:11,119
which wasn't

873
00:34:14,790 --> 00:34:12,480
in contradiction with what was

874
00:34:16,869 --> 00:34:14,800
previously thought

875
00:34:19,430 --> 00:34:16,879
we moved on to our second question where

876
00:34:22,310 --> 00:34:19,440
did this enzyme come from

877
00:34:24,310 --> 00:34:22,320
again we just because nitrogen fixation

878
00:34:26,389 --> 00:34:24,320
or something like it's

879
00:34:27,829 --> 00:34:26,399
existed and and that we read these

880
00:34:29,589 --> 00:34:27,839
signatures in the rug record doesn't

881
00:34:31,589 --> 00:34:29,599
mean and

882
00:34:33,589 --> 00:34:31,599
nitrogenase as we know today existed

883
00:34:35,190 --> 00:34:33,599
back then don't we love to talk about

884
00:34:37,510 --> 00:34:35,200
life is we don't know it

885
00:34:39,430 --> 00:34:37,520
this is an opportunity for us to explore

886
00:34:41,190 --> 00:34:39,440
a potential life as we don't know we

887
00:34:43,030 --> 00:34:41,200
should not make these assumptions so we

888
00:34:45,430 --> 00:34:43,040
asked the question where did this enzyme

889
00:34:48,790 --> 00:34:45,440
come from did early nitrogenase was in

890
00:34:50,710 --> 00:34:48,800
fact a nitrogenase

891
00:34:55,270 --> 00:34:50,720
for this we looked at the partnering

892
00:35:00,950 --> 00:34:58,470
the protein network that enables

893
00:35:03,270 --> 00:35:00,960
this enzyme to have a metal factor

894
00:35:05,109 --> 00:35:03,280
requires match raises as the name goes

895
00:35:07,030 --> 00:35:05,119
the mature they sort of

896
00:35:08,630 --> 00:35:07,040
you can imagine them as maturing the

897
00:35:10,069 --> 00:35:08,640
metal for the nitrogen they're sort of

898
00:35:11,030 --> 00:35:10,079
feeding them

899
00:35:15,270 --> 00:35:11,040
in

900
00:35:17,349 --> 00:35:15,280
so we looked at their partners sometimes

901
00:35:18,630 --> 00:35:17,359
what you're looking for is right

902
00:35:20,790 --> 00:35:18,640
under your nose

903
00:35:22,710 --> 00:35:20,800
and the person

904
00:35:24,310 --> 00:35:22,720
or in this case the protein that is get

905
00:35:27,190 --> 00:35:24,320
doing the most amount of the majority of

906
00:35:30,950 --> 00:35:27,200
the work may not get the credits

907
00:35:32,950 --> 00:35:30,960
so nitrogenases we find through

908
00:35:35,910 --> 00:35:32,960
creating a tree a joint tree of

909
00:35:38,390 --> 00:35:35,920
nitrogenases and match raises we we did

910
00:35:40,710 --> 00:35:38,400
a lot of tree reconstructions made

911
00:35:43,670 --> 00:35:40,720
different models we submitted this paper

912
00:35:45,829 --> 00:35:43,680
to the society of the of the molecular

913
00:35:48,950 --> 00:35:45,839
evolution we want to make sure that we

914
00:35:51,030 --> 00:35:48,960
get the the best reviewer feedback we

915
00:35:53,829 --> 00:35:51,040
can get and we did it was a great

916
00:35:57,829 --> 00:35:56,150
after we reconstructed the ancestor and

917
00:35:59,990 --> 00:35:57,839
studying it we found out that the

918
00:36:02,550 --> 00:36:00,000
ancestor was more like the match rays

919
00:36:07,510 --> 00:36:02,560
and not nitrogenase

920
00:36:10,069 --> 00:36:07,520
so they may have evolved from their body

921
00:36:12,550 --> 00:36:10,079
meaning the ancestor wasn't

922
00:36:15,270 --> 00:36:12,560
perhaps even fixing nitro nitrogen the

923
00:36:17,670 --> 00:36:15,280
way today's nitrogenases are doing these

924
00:36:20,230 --> 00:36:17,680
are all models so i cannot assert uh

925
00:36:23,030 --> 00:36:20,240
these outcomes just yet

926
00:36:25,430 --> 00:36:23,040
but if this is true

927
00:36:28,470 --> 00:36:25,440
what may might be the case then is that

928
00:36:31,750 --> 00:36:28,480
the molecular architecture

929
00:36:33,670 --> 00:36:31,760
required for the birth of this

930
00:36:36,310 --> 00:36:33,680
nitrogenase enzyme

931
00:36:38,710 --> 00:36:36,320
was unlikely shaped by the same

932
00:36:41,109 --> 00:36:38,720
environmental drivers that are

933
00:36:43,030 --> 00:36:41,119
implicated in the evolution of nitrogen

934
00:36:45,829 --> 00:36:43,040
fixation

935
00:36:48,150 --> 00:36:45,839
so this is not a

936
00:36:49,990 --> 00:36:48,160
judging the book by its cover situation

937
00:36:52,230 --> 00:36:50,000
just because you have

938
00:36:55,109 --> 00:36:52,240
certain elements or metal in the

939
00:36:56,630 --> 00:36:55,119
environment doesn't mean life will

940
00:36:58,950 --> 00:36:56,640
life adopted that just because it was in

941
00:37:01,589 --> 00:36:58,960
the environment there's definitely

942
00:37:03,190 --> 00:37:01,599
we have to consider evolutionary forces

943
00:37:04,710 --> 00:37:03,200
and the laws of biology then we

944
00:37:06,310 --> 00:37:04,720
interpret

945
00:37:08,710 --> 00:37:06,320
the timing and the origins of such

946
00:37:11,030 --> 00:37:08,720
events

947
00:37:12,870 --> 00:37:11,040
we moved on to our third question

948
00:37:14,790 --> 00:37:12,880
all right so how did this evolve it's

949
00:37:15,990 --> 00:37:14,800
great to do computational work but we

950
00:37:17,990 --> 00:37:16,000
have to

951
00:37:20,950 --> 00:37:18,000
get into the lab so we rolled up our

952
00:37:25,109 --> 00:37:20,960
sleeves and back back to our

953
00:37:27,270 --> 00:37:25,119
paleo phenotype assessment methodology

954
00:37:29,430 --> 00:37:27,280
we have the reconstructed ancestor let's

955
00:37:30,710 --> 00:37:29,440
engineer this inside a modern microbe

956
00:37:31,910 --> 00:37:30,720
and let's be

957
00:37:33,829 --> 00:37:31,920
calm here

958
00:37:35,190 --> 00:37:33,839
we have to work with an organism that is

959
00:37:37,109 --> 00:37:35,200
well studied because we don't want to

960
00:37:38,550 --> 00:37:37,119
reinvent an entire engineering pipeline

961
00:37:39,829 --> 00:37:38,560
for a new organism and we are very

962
00:37:42,150 --> 00:37:39,839
grateful that a lot of amazing

963
00:37:44,630 --> 00:37:42,160
scientists are doing that

964
00:37:47,910 --> 00:37:44,640
let's engineer this ancient dna inside a

965
00:37:53,030 --> 00:37:47,920
modern organism and then study what

966
00:37:58,310 --> 00:37:56,069
we've picked azodo bacteria this is the

967
00:38:01,430 --> 00:37:58,320
prime nitrogen-fixing bacteria it's a

968
00:38:03,030 --> 00:38:01,440
huge focus for a lot of studies that

969
00:38:04,630 --> 00:38:03,040
focus on

970
00:38:06,950 --> 00:38:04,640
it even goes to climate change

971
00:38:10,470 --> 00:38:06,960
implications and crop engineering and

972
00:38:13,349 --> 00:38:10,480
plants biologists love this enzyme

973
00:38:14,950 --> 00:38:13,359
but yet again uh and due to these

974
00:38:16,630 --> 00:38:14,960
there's really

975
00:38:19,030 --> 00:38:16,640
there was a lot of development in the

976
00:38:21,030 --> 00:38:19,040
engineering of this organism

977
00:38:23,190 --> 00:38:21,040
at the genomic level primarily developed

978
00:38:25,109 --> 00:38:23,200
by dennis steen at virginia tech

979
00:38:27,030 --> 00:38:25,119
so we visited his lab to learn these

980
00:38:29,750 --> 00:38:27,040
skills we brought them back to the lab

981
00:38:32,470 --> 00:38:29,760
we tweaked them for our own purposes

982
00:38:35,990 --> 00:38:32,480
so we did the following you are looking

983
00:38:38,230 --> 00:38:36,000
at the the purple and the the blue uh

984
00:38:40,790 --> 00:38:38,240
part of the screen just focus on that

985
00:38:42,870 --> 00:38:40,800
for now what we did is that we wanted

986
00:38:44,950 --> 00:38:42,880
nitrogenase is encoded by

987
00:38:46,310 --> 00:38:44,960
three different genes if hdk at least

988
00:38:48,710 --> 00:38:46,320
these are the ones that we focused for

989
00:38:51,270 --> 00:38:48,720
this study we wanted to know

990
00:38:52,710 --> 00:38:51,280
whether we can swap the entire

991
00:38:55,589 --> 00:38:52,720
genomic

992
00:38:57,190 --> 00:38:55,599
architecture with ancestral dna or we

993
00:38:59,750 --> 00:38:57,200
need whether we should follow

994
00:39:01,589 --> 00:38:59,760
a stepwise modularity so we have to do

995
00:39:03,349 --> 00:39:01,599
these things in order to set the system

996
00:39:05,349 --> 00:39:03,359
up because we have to understand the

997
00:39:06,710 --> 00:39:05,359
limits of what we're dealing with too

998
00:39:09,109 --> 00:39:06,720
and of course the majority of the

999
00:39:10,710 --> 00:39:09,119
question is that how ancient is this

1000
00:39:12,950 --> 00:39:10,720
it is going to be as ancient as we can

1001
00:39:16,710 --> 00:39:12,960
make it at this point

1002
00:39:18,310 --> 00:39:16,720
so on on one level amanda engineered the

1003
00:39:21,190 --> 00:39:18,320
nif d

1004
00:39:23,750 --> 00:39:21,200
with its ancestor that's one gene

1005
00:39:25,349 --> 00:39:23,760
out of the cluster and then in parallel

1006
00:39:28,150 --> 00:39:25,359
she engineered the entire cluster with

1007
00:39:30,069 --> 00:39:28,160
the entire ancient gene coming from the

1008
00:39:31,750 --> 00:39:30,079
tree and i'm pointing out where this is

1009
00:39:34,470 --> 00:39:31,760
coming from we are not yet at the

1010
00:39:36,550 --> 00:39:34,480
deepest part of the tree we are not yet

1011
00:39:37,910 --> 00:39:36,560
at the deep archaean or

1012
00:39:40,069 --> 00:39:37,920
not even there

1013
00:39:42,470 --> 00:39:40,079
we are still within uh

1014
00:39:45,109 --> 00:39:42,480
roughly a billion year maybe if i'm

1015
00:39:47,589 --> 00:39:45,119
generous so we are not traveling deep

1016
00:39:49,510 --> 00:39:47,599
time yet yet i mean a billion is pretty

1017
00:39:52,390 --> 00:39:49,520
long time

1018
00:39:55,990 --> 00:39:52,400
so we want to test the effects

1019
00:39:57,349 --> 00:39:56,000
of the ancestor h and ancestral subunits

1020
00:39:59,829 --> 00:39:57,359
and i'm very happy to share these

1021
00:40:01,990 --> 00:39:59,839
results i've actually just posted this

1022
00:40:03,589 --> 00:40:02,000
uh it will be online today so these are

1023
00:40:05,829 --> 00:40:03,599
the precious results that you're seeing

1024
00:40:08,230 --> 00:40:05,839
it's my first time showing them that we

1025
00:40:11,030 --> 00:40:08,240
resurrected these nitrogenases inside

1026
00:40:13,430 --> 00:40:11,040
modern bacteria on you're looking at the

1027
00:40:15,910 --> 00:40:13,440
growth curve the optical density plot on

1028
00:40:18,710 --> 00:40:15,920
the y-axis and towards time you're

1029
00:40:19,750 --> 00:40:18,720
basically assessing the growth of these

1030
00:40:21,670 --> 00:40:19,760
bugs

1031
00:40:23,270 --> 00:40:21,680
and i must say that

1032
00:40:25,750 --> 00:40:23,280
one thing we have in universities are

1033
00:40:28,069 --> 00:40:25,760
these amazing undergraduates big shout

1034
00:40:30,790 --> 00:40:28,079
out to arizona space program and brook

1035
00:40:33,510 --> 00:40:30,800
characters who spent her entire freshman

1036
00:40:36,470 --> 00:40:33,520
and sophomore year optimizing a growth

1037
00:40:38,470 --> 00:40:36,480
curve a plot she's just fantastic and

1038
00:40:40,390 --> 00:40:38,480
she did this we tried

1039
00:40:42,309 --> 00:40:40,400
batch cultures we tried test tubes we

1040
00:40:43,990 --> 00:40:42,319
tried um

1041
00:40:46,710 --> 00:40:44,000
multi-valve plates

1042
00:40:48,710 --> 00:40:46,720
uh and what worked was

1043
00:40:50,550 --> 00:40:48,720
the oldest most traditional method is

1044
00:40:53,030 --> 00:40:50,560
what ended up working

1045
00:40:55,030 --> 00:40:53,040
so we have we followed the growth of

1046
00:40:57,030 --> 00:40:55,040
these engineered strains

1047
00:40:58,470 --> 00:40:57,040
uh it it worked fine because that's how

1048
00:40:59,990 --> 00:40:58,480
long it took us to engineer these

1049
00:41:02,470 --> 00:41:00,000
strains anyway so by the time we

1050
00:41:04,390 --> 00:41:02,480
perfected the or at least optimized the

1051
00:41:06,390 --> 00:41:04,400
growth assessments our

1052
00:41:07,750 --> 00:41:06,400
engineered strains were ready

1053
00:41:10,230 --> 00:41:07,760
on the black line you're looking at the

1054
00:41:12,710 --> 00:41:10,240
wild type that's the sort of modern

1055
00:41:13,990 --> 00:41:12,720
native unengineered unperturbed

1056
00:41:16,550 --> 00:41:14,000
bacteria

1057
00:41:18,710 --> 00:41:16,560
delta represents deletion that we do we

1058
00:41:20,870 --> 00:41:18,720
delete these these one of these genes

1059
00:41:23,670 --> 00:41:20,880
inside out of the chromosome just to

1060
00:41:26,230 --> 00:41:23,680
make sure uh just as a control

1061
00:41:28,150 --> 00:41:26,240
and all the other blue and purple lines

1062
00:41:31,030 --> 00:41:28,160
represents the strains with the

1063
00:41:33,349 --> 00:41:31,040
ancestral nitrogenases

1064
00:41:36,069 --> 00:41:33,359
after we engineered these strains we've

1065
00:41:37,270 --> 00:41:36,079
also looked at the relative organismal

1066
00:41:39,990 --> 00:41:37,280
activity

1067
00:41:43,270 --> 00:41:40,000
uh that and by looking at the acetylene

1068
00:41:44,630 --> 00:41:43,280
ethylene reduction so it we are still

1069
00:41:48,069 --> 00:41:44,640
assessing the

1070
00:41:50,230 --> 00:41:48,079
behavior of the entire organism

1071
00:41:52,710 --> 00:41:50,240
and you're looking at the reduction rate

1072
00:41:56,309 --> 00:41:52,720
on the y-axis we normalize this to total

1073
00:41:59,430 --> 00:41:56,319
protein and different uh strains on the

1074
00:42:00,390 --> 00:41:59,440
x-axis and this was performed by alexa

1075
00:42:01,750 --> 00:42:00,400
va

1076
00:42:05,349 --> 00:42:01,760
and together with of course amanda

1077
00:42:07,750 --> 00:42:05,359
garcia and azul pinochet barros

1078
00:42:10,150 --> 00:42:07,760
so you might be seeing what i see

1079
00:42:12,550 --> 00:42:10,160
that these bacteria that contain the

1080
00:42:15,190 --> 00:42:12,560
ancient dna are active

1081
00:42:17,270 --> 00:42:15,200
and it's they're not

1082
00:42:18,950 --> 00:42:17,280
vast differences at least based on our

1083
00:42:20,550 --> 00:42:18,960
experience since we've done these with

1084
00:42:23,190 --> 00:42:20,560
other systems as well

1085
00:42:26,390 --> 00:42:23,200
fairly comparable reduction rates

1086
00:42:27,750 --> 00:42:26,400
compared to the modern

1087
00:42:29,750 --> 00:42:27,760
we didn't

1088
00:42:31,349 --> 00:42:29,760
stop there and just

1089
00:42:34,630 --> 00:42:31,359
quick shout out amanda will be talking

1090
00:42:37,430 --> 00:42:34,640
about this tomorrow in detail

1091
00:42:39,349 --> 00:42:37,440
as i said these were still inside the

1092
00:42:41,990 --> 00:42:39,359
organism right so we are still inside

1093
00:42:44,470 --> 00:42:42,000
the genome and and yet by the way we do

1094
00:42:46,069 --> 00:42:44,480
sequence the entire genome to make sure

1095
00:42:49,190 --> 00:42:46,079
we don't have any other

1096
00:42:51,349 --> 00:42:49,200
uh y type fragments left anywhere

1097
00:42:53,990 --> 00:42:51,359
that we ensure that there's no other

1098
00:42:56,309 --> 00:42:54,000
modern nitrogenase dna

1099
00:42:59,190 --> 00:42:56,319
that is remaining in this system to give

1100
00:43:01,910 --> 00:42:59,200
us any false positives

1101
00:43:03,750 --> 00:43:01,920
moving on we wanted to understand what

1102
00:43:06,470 --> 00:43:03,760
does this

1103
00:43:08,390 --> 00:43:06,480
enzyme what would it do outside of the

1104
00:43:10,550 --> 00:43:08,400
cell environment so we extracted this

1105
00:43:12,870 --> 00:43:10,560
protein from a zodobacter after

1106
00:43:14,790 --> 00:43:12,880
overexpressing it and i ran this through

1107
00:43:17,349 --> 00:43:14,800
proof by column and this was done by

1108
00:43:19,589 --> 00:43:17,359
derek harris also a remarkable postdoc

1109
00:43:23,270 --> 00:43:19,599
who spent a lot of time purifying these

1110
00:43:27,190 --> 00:43:24,950
followed by

1111
00:43:29,750 --> 00:43:27,200
enzymatic kinetics to understand the

1112
00:43:31,670 --> 00:43:29,760
catalytic function of these enzymes

1113
00:43:33,270 --> 00:43:31,680
on the um you're looking at the nitrogen

1114
00:43:36,150 --> 00:43:33,280
reduction rates

1115
00:43:39,190 --> 00:43:36,160
when different uh gas head spaces were

1116
00:43:41,349 --> 00:43:39,200
used and and derek has published a paper

1117
00:43:43,829 --> 00:43:41,359
previously to assess

1118
00:43:46,309 --> 00:43:43,839
a mechanism to understand the modern

1119
00:43:47,910 --> 00:43:46,319
nitrogenous mechanisms and and that

1120
00:43:50,230 --> 00:43:47,920
there's a reductive elimination

1121
00:43:53,030 --> 00:43:50,240
mechanism for the nitrogen binding being

1122
00:43:55,109 --> 00:43:53,040
the the core sort of characteristics for

1123
00:43:57,030 --> 00:43:55,119
modern nitrogenases

1124
00:43:58,950 --> 00:43:57,040
but what he is finding is that the

1125
00:44:02,150 --> 00:43:58,960
efficiency is similar at the catalytic

1126
00:44:04,710 --> 00:44:02,160
level to modern nitrogenases as well

1127
00:44:06,870 --> 00:44:04,720
and you're looking at the structures uh

1128
00:44:10,870 --> 00:44:06,880
these are the ratios

1129
00:44:13,109 --> 00:44:10,880
for the the mechanism output and and

1130
00:44:14,470 --> 00:44:13,119
that gives us a clue about the catalytic

1131
00:44:16,870 --> 00:44:14,480
efficiency

1132
00:44:19,109 --> 00:44:16,880
of the extent molybdenum and

1133
00:44:20,950 --> 00:44:19,119
nitrogenases uh compared to the

1134
00:44:23,109 --> 00:44:20,960
ancestral ones

1135
00:44:25,190 --> 00:44:23,119
so it's pretty good

1136
00:44:27,270 --> 00:44:25,200
and i've been working on nitrogenous

1137
00:44:29,510 --> 00:44:27,280
experts i am now in a department where

1138
00:44:31,270 --> 00:44:29,520
these enzymes were even discovered and

1139
00:44:34,470 --> 00:44:31,280
as other bacteria was discovered as an

1140
00:44:37,430 --> 00:44:34,480
organism and they tell me that it's very

1141
00:44:39,910 --> 00:44:37,440
easy to break these enzymes or at least

1142
00:44:42,390 --> 00:44:39,920
they're not as hard as i think

1143
00:44:45,270 --> 00:44:42,400
so the fact that we have in some cases

1144
00:44:46,950 --> 00:44:45,280
up to 20 25 percent difference in amino

1145
00:44:51,270 --> 00:44:46,960
acid and we're still able to recover

1146
00:44:55,910 --> 00:44:53,510
just to wrap up this part the ancient

1147
00:44:58,790 --> 00:44:55,920
environmental conditions do not fully

1148
00:45:01,030 --> 00:44:58,800
explain the timing

1149
00:45:03,349 --> 00:45:01,040
of critical and singular biogeochemical

1150
00:45:05,910 --> 00:45:03,359
innovations

1151
00:45:07,910 --> 00:45:05,920
alone at least and these experimental

1152
00:45:10,230 --> 00:45:07,920
studies i think will be very critical to

1153
00:45:12,790 --> 00:45:10,240
understand the molecular innovations

1154
00:45:16,150 --> 00:45:12,800
of life's earliest traces

1155
00:45:18,390 --> 00:45:16,160
and we have done some work as

1156
00:45:21,109 --> 00:45:18,400
frank mentioned where we resurrected

1157
00:45:22,630 --> 00:45:21,119
rubisco that is also to be thought to be

1158
00:45:23,430 --> 00:45:22,640
one of those major

1159
00:45:26,069 --> 00:45:23,440
uh

1160
00:45:27,910 --> 00:45:26,079
enzymes impacting carbon isotope a

1161
00:45:30,470 --> 00:45:27,920
fractionation record throughout earth's

1162
00:45:34,069 --> 00:45:30,480
history uh and because that work is

1163
00:45:36,470 --> 00:45:34,079
published a few a couple weeks ago

1164
00:45:39,670 --> 00:45:36,480
i opted to present the latest one so

1165
00:45:43,829 --> 00:45:41,990
but moving on where are we going with

1166
00:45:45,670 --> 00:45:43,839
this of course this is all laboratory

1167
00:45:48,309 --> 00:45:45,680
artificial life there's a lot of

1168
00:45:50,230 --> 00:45:48,319
molecular component here

1169
00:45:52,390 --> 00:45:50,240
and keep in mind that integration of

1170
00:45:53,750 --> 00:45:52,400
evolution with biochemistry itself is

1171
00:45:56,790 --> 00:45:53,760
still pretty

1172
00:45:59,349 --> 00:45:56,800
new grounds for for biochemists

1173
00:46:01,430 --> 00:45:59,359
that that we miss so much opportunity we

1174
00:46:03,670 --> 00:46:01,440
just when we don't understand

1175
00:46:05,670 --> 00:46:03,680
where life came from but not just for

1176
00:46:07,829 --> 00:46:05,680
astrobiologists but when we study what

1177
00:46:10,150 --> 00:46:07,839
we have it's true that we have to

1178
00:46:11,270 --> 00:46:10,160
understand the past to make sense of the

1179
00:46:13,510 --> 00:46:11,280
present

1180
00:46:15,910 --> 00:46:13,520
we we must do this and and we should not

1181
00:46:18,550 --> 00:46:15,920
miss this opportunity to harvest all the

1182
00:46:20,550 --> 00:46:18,560
sequin space and why life and understand

1183
00:46:22,230 --> 00:46:20,560
why life narrowed it down to a subset of

1184
00:46:24,470 --> 00:46:22,240
what we study today

1185
00:46:27,589 --> 00:46:24,480
but this is where muse comes in because

1186
00:46:29,109 --> 00:46:27,599
my desire was to connect these things to

1187
00:46:30,470 --> 00:46:29,119
actual earth

1188
00:46:31,670 --> 00:46:30,480
real life

1189
00:46:34,390 --> 00:46:31,680
to

1190
00:46:36,950 --> 00:46:34,400
sites where people go and study the

1191
00:46:38,630 --> 00:46:36,960
analogues for early earth environments

1192
00:46:41,589 --> 00:46:38,640
where they call the culture these

1193
00:46:43,670 --> 00:46:41,599
microbes if possible and study them so

1194
00:46:44,390 --> 00:46:43,680
that was my desire to be have to work

1195
00:46:48,710 --> 00:46:44,400
with

1196
00:46:51,670 --> 00:46:48,720
very important i think for astrobiology

1197
00:46:54,550 --> 00:46:51,680
because when we assess the habitability

1198
00:46:55,430 --> 00:46:54,560
or um or really when we analyze

1199
00:47:00,710 --> 00:46:55,440
different

1200
00:47:02,870 --> 00:47:00,720
or whatever we are visiting we are

1201
00:47:05,270 --> 00:47:02,880
focusing on a problem that is existing

1202
00:47:06,950 --> 00:47:05,280
largely in an organic chemistry space

1203
00:47:08,390 --> 00:47:06,960
but we need to integrate an

1204
00:47:10,230 --> 00:47:08,400
understanding of the elemental

1205
00:47:15,829 --> 00:47:10,240
composition

1206
00:47:17,670 --> 00:47:15,839
habitability as well so we are looking

1207
00:47:20,230 --> 00:47:17,680
and working towards finding ways to

1208
00:47:22,470 --> 00:47:20,240
transform this into a problem that can

1209
00:47:25,990 --> 00:47:22,480
be studied in inorganic chemistry space

1210
00:47:31,190 --> 00:47:28,790
and and perhaps uh

1211
00:47:32,630 --> 00:47:31,200
you might be wondering this is great and

1212
00:47:33,349 --> 00:47:32,640
what you're what you're studying is at

1213
00:47:35,750 --> 00:47:33,359
the

1214
00:47:37,670 --> 00:47:35,760
time scale that is relevant for earth

1215
00:47:38,630 --> 00:47:37,680
but how do we make this an observation

1216
00:47:40,870 --> 00:47:38,640
problem

1217
00:47:43,510 --> 00:47:40,880
how can i as an astronomer study this

1218
00:47:45,750 --> 00:47:43,520
what is the use sorry for exoplanets or

1219
00:47:47,750 --> 00:47:45,760
different observation

1220
00:47:49,589 --> 00:47:47,760
so in the remainder of time for

1221
00:47:50,950 --> 00:47:49,599
quickly i want to share one of our

1222
00:47:53,750 --> 00:47:50,960
latest papers

1223
00:47:55,349 --> 00:47:53,760
we thought about this we are largely at

1224
00:47:57,750 --> 00:47:55,359
the geochemistry and evolutionary

1225
00:48:00,150 --> 00:47:57,760
biochemistry space and the blue and the

1226
00:48:01,910 --> 00:48:00,160
green circles but what we need is a more

1227
00:48:03,349 --> 00:48:01,920
integration of the stellar atmospheric

1228
00:48:06,870 --> 00:48:03,359
chemistry

1229
00:48:09,670 --> 00:48:06,880
so the idea was then can we find

1230
00:48:12,069 --> 00:48:09,680
molecules proteins that not only impact

1231
00:48:15,910 --> 00:48:12,079
the earth and fill in all the criteria

1232
00:48:19,270 --> 00:48:15,920
that i just presented but also may be

1233
00:48:22,309 --> 00:48:19,280
maybe of use throughout integration of

1234
00:48:24,470 --> 00:48:22,319
say surface irradiance studies or

1235
00:48:27,030 --> 00:48:24,480
more generally

1236
00:48:28,870 --> 00:48:27,040
observation of gases by a signature

1237
00:48:29,829 --> 00:48:28,880
level assessment so with that we turned

1238
00:48:31,829 --> 00:48:29,839
into

1239
00:48:34,150 --> 00:48:31,839
pigments

1240
00:48:37,349 --> 00:48:34,160
our pursuit was to tie together

1241
00:48:39,670 --> 00:48:37,359
bioinformatic analysis geochemical and

1242
00:48:41,990 --> 00:48:39,680
solar spectral models

1243
00:48:44,309 --> 00:48:42,000
atmospheric attenuation in particular

1244
00:48:46,309 --> 00:48:44,319
here radiative transfer calculations and

1245
00:48:48,069 --> 00:48:46,319
enzymatic observation

1246
00:48:50,390 --> 00:48:48,079
simple

1247
00:48:52,309 --> 00:48:50,400
characterize the coupling between the

1248
00:48:55,190 --> 00:48:52,319
internal this could be as internal as

1249
00:48:58,150 --> 00:48:55,200
the inside in cellular

1250
00:49:00,630 --> 00:48:58,160
or population and external conditions

1251
00:49:03,829 --> 00:49:00,640
and infer biosignature characteristics

1252
00:49:05,430 --> 00:49:03,839
generated by enzymes that have changed

1253
00:49:07,430 --> 00:49:05,440
over time

1254
00:49:09,670 --> 00:49:07,440
one of the first

1255
00:49:11,270 --> 00:49:09,680
enzymes we focused is rhodopsin these

1256
00:49:12,710 --> 00:49:11,280
are interesting

1257
00:49:14,230 --> 00:49:12,720
they're pigment

1258
00:49:16,710 --> 00:49:14,240
they're really

1259
00:49:18,390 --> 00:49:16,720
actually fascinating they look simple

1260
00:49:20,470 --> 00:49:18,400
but they are not simpler

1261
00:49:22,950 --> 00:49:20,480
they are tuned to the peak photon

1262
00:49:24,549 --> 00:49:22,960
frequencies generated by the sun

1263
00:49:26,870 --> 00:49:24,559
and and this is really fascinating

1264
00:49:29,670 --> 00:49:26,880
especially for biologists i think that

1265
00:49:31,430 --> 00:49:29,680
when i'm from black sea so i was always

1266
00:49:33,750 --> 00:49:31,440
fascinated by the depths of black sea

1267
00:49:35,190 --> 00:49:33,760
and how organisms deep that and by the

1268
00:49:37,190 --> 00:49:35,200
way it really does look black if you

1269
00:49:39,589 --> 00:49:37,200
haven't visited it it's just remarkable

1270
00:49:42,630 --> 00:49:39,599
that these microbes what they can do to

1271
00:49:44,630 --> 00:49:42,640
harvest the limited of the environmental

1272
00:49:46,950 --> 00:49:44,640
energy that they got and they come up

1273
00:49:48,790 --> 00:49:46,960
with the most innovative ways to make

1274
00:49:51,030 --> 00:49:48,800
use of the harsh conditions that they

1275
00:49:53,030 --> 00:49:51,040
reside in a phenomena that i think most

1276
00:49:54,390 --> 00:49:53,040
of us in this room can relate to or some

1277
00:49:55,589 --> 00:49:54,400
of us

1278
00:49:57,430 --> 00:49:55,599
so we have to come up with these

1279
00:50:00,470 --> 00:49:57,440
innovative ways to survive life does

1280
00:50:03,109 --> 00:50:00,480
this so here is rhodopsin catherine

1281
00:50:04,630 --> 00:50:03,119
cephas this was a perfect student she

1282
00:50:07,510 --> 00:50:04,640
was an undergrad

1283
00:50:10,630 --> 00:50:07,520
at the astronomy department who also

1284
00:50:13,190 --> 00:50:10,640
loved biology so she came to me saying i

1285
00:50:16,549 --> 00:50:13,200
always want to be a biologist but i also

1286
00:50:17,349 --> 00:50:16,559
always want to be an astronomer i said

1287
00:50:19,750 --> 00:50:17,359
well

1288
00:50:21,190 --> 00:50:19,760
i have the perfect project for you

1289
00:50:24,630 --> 00:50:21,200
so

1290
00:50:26,870 --> 00:50:24,640
what she started with was to

1291
00:50:28,150 --> 00:50:26,880
create phylogeny of these rhodopsin

1292
00:50:30,630 --> 00:50:28,160
proteins

1293
00:50:32,230 --> 00:50:30,640
again we have this amazing amount of

1294
00:50:33,750 --> 00:50:32,240
information that is in literature this

1295
00:50:35,910 --> 00:50:33,760
vast amount of sequence data that

1296
00:50:38,470 --> 00:50:35,920
constantly keeps coming from this

1297
00:50:39,990 --> 00:50:38,480
wondrous metagenomic studies every year

1298
00:50:41,829 --> 00:50:40,000
it's really difficult to keep up with

1299
00:50:44,309 --> 00:50:41,839
which is very exciting

1300
00:50:45,349 --> 00:50:44,319
she updated a rhodopsin tree created it

1301
00:50:47,589 --> 00:50:45,359
here you're looking at different

1302
00:50:49,990 --> 00:50:47,599
topologies all these letters on the top

1303
00:50:52,950 --> 00:50:50,000
refer to a different evolutionary model

1304
00:50:55,109 --> 00:50:52,960
that we try to understand the best fits

1305
00:50:57,589 --> 00:50:55,119
of our topology so we go through this

1306
00:50:59,750 --> 00:50:57,599
iteration it takes us months we want to

1307
00:51:01,829 --> 00:50:59,760
understand by studying the sequence

1308
00:51:04,069 --> 00:51:01,839
again the ancestral functionality what

1309
00:51:06,230 --> 00:51:04,079
did this enzyme do

1310
00:51:08,630 --> 00:51:06,240
can we relate to any modern function to

1311
00:51:10,790 --> 00:51:08,640
the past we know that there is a proton

1312
00:51:13,109 --> 00:51:10,800
pump function or cholera uh pump

1313
00:51:15,190 --> 00:51:13,119
function for this enzyme today but what

1314
00:51:16,710 --> 00:51:15,200
did happen in the past so after going

1315
00:51:18,549 --> 00:51:16,720
through these inferences

1316
00:51:20,630 --> 00:51:18,559
we benefited from

1317
00:51:22,390 --> 00:51:20,640
these two studies that came from

1318
00:51:24,710 --> 00:51:22,400
university of tokyo

1319
00:51:26,790 --> 00:51:24,720
where they developed

1320
00:51:29,030 --> 00:51:26,800
back to back they published these papers

1321
00:51:33,190 --> 00:51:29,040
machine learning algorithms

1322
00:51:35,990 --> 00:51:33,200
to predict the valence spectra of the

1323
00:51:38,470 --> 00:51:36,000
rhodopsin proteins specifically and they

1324
00:51:40,630 --> 00:51:38,480
tested these the accuracies of these

1325
00:51:42,069 --> 00:51:40,640
training models in multiple ways not

1326
00:51:44,230 --> 00:51:42,079
only through

1327
00:51:46,309 --> 00:51:44,240
experiments that that they do in the

1328
00:51:48,309 --> 00:51:46,319
biochemical level but also they

1329
00:51:51,190 --> 00:51:48,319
generated multiple libraries and they

1330
00:51:52,630 --> 00:51:51,200
had a lot of experience in this studies

1331
00:51:55,910 --> 00:51:52,640
anyway and collaborated with marine

1332
00:51:57,510 --> 00:51:55,920
biologists to test the accuracy of the

1333
00:51:58,630 --> 00:51:57,520
predictions of their machine learning

1334
00:52:00,549 --> 00:51:58,640
algorithm

1335
00:52:02,549 --> 00:52:00,559
which was also great because due to

1336
00:52:05,670 --> 00:52:02,559
covet shutdown we had limited access to

1337
00:52:07,589 --> 00:52:05,680
lab anyway so really thankful for any

1338
00:52:08,870 --> 00:52:07,599
computational researcher who makes their

1339
00:52:11,349 --> 00:52:08,880
resources

1340
00:52:13,109 --> 00:52:11,359
available in open access and they were

1341
00:52:14,950 --> 00:52:13,119
able to answer all of our questions

1342
00:52:18,069 --> 00:52:14,960
throughout this process

1343
00:52:20,470 --> 00:52:18,079
doctoral student even fair she

1344
00:52:21,670 --> 00:52:20,480
took this method and and she wanted to

1345
00:52:23,670 --> 00:52:21,680
understand

1346
00:52:26,309 --> 00:52:23,680
what sort of different libraries we can

1347
00:52:28,630 --> 00:52:26,319
generate for our rhodopsins to implement

1348
00:52:30,549 --> 00:52:28,640
this machine learning methodology so we

1349
00:52:33,270 --> 00:52:30,559
weren't only interested in a handful of

1350
00:52:35,990 --> 00:52:33,280
ancestors what we can do computationally

1351
00:52:37,430 --> 00:52:36,000
that we can't do in the lab is to try

1352
00:52:40,230 --> 00:52:37,440
all kinds of variants we can do

1353
00:52:41,030 --> 00:52:40,240
thousands at once it's really remarkable

1354
00:52:43,270 --> 00:52:41,040
so

1355
00:52:45,430 --> 00:52:43,280
she created alternative sequences for

1356
00:52:47,750 --> 00:52:45,440
every ancestor that we generate we

1357
00:52:50,630 --> 00:52:47,760
assigned a threshold for what we define

1358
00:52:52,630 --> 00:52:50,640
as our confidence level and also the

1359
00:52:54,950 --> 00:52:52,640
threshold for the problems probabilistic

1360
00:52:57,829 --> 00:52:54,960
or most probabilistic ancestor that we

1361
00:52:59,109 --> 00:52:57,839
created and and we identified the sites

1362
00:53:01,190 --> 00:52:59,119
in the protein that are actually

1363
00:53:03,030 --> 00:53:01,200
functionally important and generated

1364
00:53:05,270 --> 00:53:03,040
these libraries and

1365
00:53:07,430 --> 00:53:05,280
performed wavelength prediction

1366
00:53:09,270 --> 00:53:07,440
and through the suggestion of an

1367
00:53:11,349 --> 00:53:09,280
excellent reviewer we also tested the

1368
00:53:15,030 --> 00:53:11,359
robustness of the machine learning

1369
00:53:18,390 --> 00:53:15,040
algorithm to our uh to the training data

1370
00:53:23,190 --> 00:53:20,950
and for that we created the wavelength

1371
00:53:25,750 --> 00:53:23,200
prediction values for all these

1372
00:53:27,589 --> 00:53:25,760
rhodopsin proteins in our tree that we

1373
00:53:28,790 --> 00:53:27,599
created using our kill and bacterial

1374
00:53:30,790 --> 00:53:28,800
sequences

1375
00:53:33,510 --> 00:53:30,800
what we find is that the spectral

1376
00:53:36,230 --> 00:53:33,520
properties of these ancestral adoptions

1377
00:53:39,270 --> 00:53:36,240
indicate a green ship

1378
00:53:42,950 --> 00:53:39,280
as we go further back in time that that

1379
00:53:46,390 --> 00:53:42,960
the lambda max is in a range between

1380
00:53:48,950 --> 00:53:46,400
480 and 585

1381
00:53:51,510 --> 00:53:48,960
so what does this mean now that we have

1382
00:53:53,829 --> 00:53:51,520
an ancestor and we can detect its

1383
00:53:56,710 --> 00:53:53,839
wavelength spectra we went to our

1384
00:53:58,309 --> 00:53:56,720
friends at nexus and kudos to these

1385
00:54:00,230 --> 00:53:58,319
research coordination networks and this

1386
00:54:03,510 --> 00:54:00,240
is really why they are there so that you

1387
00:54:07,109 --> 00:54:03,520
have your 1 800 astronomer that you can

1388
00:54:09,349 --> 00:54:07,119
go and talk to and and

1389
00:54:10,950 --> 00:54:09,359
perhaps collaborate with to make sense

1390
00:54:13,270 --> 00:54:10,960
of the biological data that you're

1391
00:54:15,430 --> 00:54:13,280
creating so eddie schwederman from uc

1392
00:54:17,750 --> 00:54:15,440
riverside came to the rescue having

1393
00:54:19,829 --> 00:54:17,760
worked on this with us sarma shilda

1394
00:54:22,549 --> 00:54:19,839
sarma for a long time

1395
00:54:25,109 --> 00:54:22,559
she he modeled the arcane

1396
00:54:27,589 --> 00:54:25,119
environment surface irradiance profile

1397
00:54:29,109 --> 00:54:27,599
independently and then we compared the

1398
00:54:31,190 --> 00:54:29,119
data that we generate with the

1399
00:54:32,390 --> 00:54:31,200
environmental the surface conditions of

1400
00:54:34,630 --> 00:54:32,400
early earth

1401
00:54:36,950 --> 00:54:34,640
what we think is happening is is that

1402
00:54:39,349 --> 00:54:36,960
the green wavelength absorption is

1403
00:54:42,150 --> 00:54:39,359
parsimonious with the spectral futures

1404
00:54:44,470 --> 00:54:42,160
of the early earth environment so we are

1405
00:54:46,549 --> 00:54:44,480
looking for a proton pumping protein

1406
00:54:48,630 --> 00:54:46,559
that may have residing in a microbe that

1407
00:54:52,150 --> 00:54:48,640
is swimming in the shallow waters

1408
00:54:53,270 --> 00:54:52,160
or perhaps was underneath some biofilm

1409
00:54:55,829 --> 00:54:53,280
and and

1410
00:54:57,430 --> 00:54:55,839
due to shallow coastal marine niches

1411
00:55:00,309 --> 00:54:57,440
which is maybe a great environment

1412
00:55:04,950 --> 00:55:00,319
abundant nutrients metabolically useful

1413
00:55:07,030 --> 00:55:04,960
light availability a tunated phototoxic

1414
00:55:09,190 --> 00:55:07,040
uv radiation so this may be a

1415
00:55:13,030 --> 00:55:09,200
nice little shelter for them

1416
00:55:15,670 --> 00:55:13,040
and we also just published this work

1417
00:55:18,309 --> 00:55:15,680
to summarize the histories of the

1418
00:55:21,190 --> 00:55:18,319
enzymes they can be aminable to

1419
00:55:24,309 --> 00:55:21,200
informatic and evolutionary construction

1420
00:55:25,109 --> 00:55:24,319
and such reconstructions in my opinion

1421
00:55:27,589 --> 00:55:25,119
are

1422
00:55:29,910 --> 00:55:27,599
opening entirely new possibilities for

1423
00:55:32,069 --> 00:55:29,920
the study of early life including the

1424
00:55:36,069 --> 00:55:32,079
fine resolution probing of geochemical

1425
00:55:38,230 --> 00:55:36,079
conditions and stellar properties

1426
00:55:40,470 --> 00:55:38,240
and let's go back to our major question

1427
00:55:42,789 --> 00:55:40,480
we want to know whether what we see here

1428
00:55:43,910 --> 00:55:42,799
is a particular case or a general

1429
00:55:45,990 --> 00:55:43,920
category

1430
00:55:47,510 --> 00:55:46,000
and as my friend and colleague sanjoy

1431
00:55:49,990 --> 00:55:47,520
likes to put it

1432
00:55:51,750 --> 00:55:50,000
exoplanets are one of the

1433
00:55:54,950 --> 00:55:51,760
early earth is one of the best exoplanet

1434
00:55:58,789 --> 00:55:57,270
but there's a lot of unknowns

1435
00:56:00,950 --> 00:55:58,799
and i know some of you are feeling this

1436
00:56:06,230 --> 00:56:00,960
bingo so i'll just help you out

1437
00:56:09,589 --> 00:56:08,870
and and study of early life

1438
00:56:11,510 --> 00:56:09,599
is

1439
00:56:13,349 --> 00:56:11,520
all hands on that problem we need

1440
00:56:15,109 --> 00:56:13,359
everybody in this endeavor because we

1441
00:56:17,270 --> 00:56:15,119
cannot find what we're looking for if we

1442
00:56:20,470 --> 00:56:17,280
don't understand it here and we owe it

1443
00:56:23,109 --> 00:56:20,480
to this life on this planet i think

1444
00:56:24,950 --> 00:56:23,119
and with that i will end uh first and

1445
00:56:27,349 --> 00:56:24,960
foremost my lab

1446
00:56:29,190 --> 00:56:27,359
i'm no longer by myself in this pursuit

1447
00:56:31,910 --> 00:56:29,200
it's amazing but

1448
00:56:33,829 --> 00:56:31,920
how many uh people are excited about

1449
00:56:35,670 --> 00:56:33,839
this in my group it's just one to bet i

1450
00:56:37,750 --> 00:56:35,680
created a lab that i wanted to be a part

1451
00:56:40,309 --> 00:56:37,760
of so this is just really makes me very

1452
00:56:42,150 --> 00:56:40,319
happy to work with them every day our

1453
00:56:44,470 --> 00:56:42,160
collaborators for different project

1454
00:56:46,309 --> 00:56:44,480
projects and funding

1455
00:56:49,589 --> 00:56:46,319
comes from nasa national science

1456
00:56:51,670 --> 00:56:49,599
foundation john templeton foundation nih

1457
00:56:53,910 --> 00:56:51,680
and also human frontiers and science

1458
00:56:55,910 --> 00:56:53,920
young investigator grants as well as

1459
00:56:57,510 --> 00:56:55,920
university of arizona and wisconsin

1460
00:57:00,390 --> 00:56:57,520
foundation

1461
00:57:01,750 --> 00:57:00,400
and also the spanish government that is

1462
00:57:04,470 --> 00:57:01,760
funding

1463
00:57:07,190 --> 00:57:04,480
one of our postdocs here and also our

1464
00:57:09,829 --> 00:57:07,200
cohort we just started our icard about

1465
00:57:11,910 --> 00:57:09,839
six months ago i'm already just

1466
00:57:13,430 --> 00:57:11,920
it's we've been doing a lot of cool work

1467
00:57:15,829 --> 00:57:13,440
i think we created a really great

1468
00:57:17,109 --> 00:57:15,839
community that is tackling this problem

1469
00:57:19,589 --> 00:57:17,119
of uh

1470
00:57:21,990 --> 00:57:19,599
understanding whether we can

1471
00:57:24,549 --> 00:57:22,000
integrate in organic thinking into

1472
00:57:27,510 --> 00:57:24,559
habitability from very different angles

1473
00:57:29,670 --> 00:57:27,520
i'm very grateful to work with them as

1474
00:57:31,829 --> 00:57:29,680
well and the names in yellow are

1475
00:57:34,309 --> 00:57:31,839
presenting here the asterix have talks

1476
00:57:36,069 --> 00:57:34,319
or posters

1477
00:57:38,150 --> 00:57:36,079
and finally we are launching a really

1478
00:57:39,990 --> 00:57:38,160
cool astrobiology program at university

1479
00:57:41,670 --> 00:57:40,000
of wisconsin-madison so i hope the

1480
00:57:44,390 --> 00:57:41,680
students interested in these problems

1481
00:57:59,190 --> 00:57:44,400
will check it out and join us

1482
00:58:03,829 --> 00:58:02,470
thank you very much bethel

1483
00:58:06,309 --> 00:58:03,839
um

1484
00:58:09,829 --> 00:58:06,319
we're close to the bottom of the hour oh

1485
00:58:11,190 --> 00:58:09,839
sorry but i would uh invite everyone to

1486
00:58:13,670 --> 00:58:11,200
stick around

1487
00:58:15,829 --> 00:58:13,680
who can do without coffee for a few

1488
00:58:18,230 --> 00:58:15,839
minutes so that we can enjoy

1489
00:58:20,069 --> 00:58:18,240
a question and answered period and i'm

1490
00:58:21,270 --> 00:58:20,079
sure there's going to be plenty of

1491
00:58:23,430 --> 00:58:21,280
questions

1492
00:58:26,549 --> 00:58:23,440
and i think we have somebody who can

1493
00:58:31,190 --> 00:58:28,390
first of all one of our guests last

1494
00:58:43,349 --> 00:58:31,200
night here is gideon ask the question

1495
00:58:47,270 --> 00:58:44,870
first off thanks

1496
00:58:48,630 --> 00:58:47,280
an amazing talk

1497
00:58:50,309 --> 00:58:48,640
so happy to have you

1498
00:58:54,309 --> 00:58:50,319
wesley swingley from northern illinois

1499
00:58:56,549 --> 00:58:54,319
university kind of a topical question um

1500
00:58:58,150 --> 00:58:56,559
sorry it's cutting it a philosophical

1501
00:58:59,670 --> 00:58:58,160
question you mentioned that these things

1502
00:59:02,950 --> 00:58:59,680
aren't

1503
00:59:04,950 --> 00:59:02,960
deterministic or inevitable

1504
00:59:06,789 --> 00:59:04,960
what is the what do you think that is

1505
00:59:09,109 --> 00:59:06,799
between these singular

1506
00:59:11,190 --> 00:59:09,119
systems like nitrogen or oxygenic

1507
00:59:13,030 --> 00:59:11,200
photosynthesis versus things that are

1508
00:59:14,870 --> 00:59:13,040
big buckets of stuff like carbon

1509
00:59:16,630 --> 00:59:14,880
fixation or pigments and things like

1510
00:59:19,349 --> 00:59:16,640
that

1511
00:59:20,950 --> 00:59:19,359
in terms of the like the molecular level

1512
00:59:23,510 --> 00:59:20,960
just likewise

1513
00:59:25,910 --> 00:59:23,520
why do we have one nitrogenase and

1514
00:59:27,990 --> 00:59:25,920
10 different carbon fixation pathways

1515
00:59:30,549 --> 00:59:28,000
that that's a very good question i think

1516
00:59:33,510 --> 00:59:30,559
maybe what determines the adaptation

1517
00:59:35,430 --> 00:59:33,520
isn't just the enzyme itself and i think

1518
00:59:37,670 --> 00:59:35,440
it goes back to

1519
00:59:39,829 --> 00:59:37,680
our lack of understanding

1520
00:59:42,069 --> 00:59:39,839
of what how earth the environment

1521
00:59:44,630 --> 00:59:42,079
impacted these molecular innovations so

1522
00:59:47,510 --> 00:59:44,640
i don't quite have a good answer

1523
00:59:49,670 --> 00:59:47,520
when it comes to evolutionary biology

1524
00:59:51,109 --> 00:59:49,680
because that's not even i think a

1525
00:59:53,589 --> 00:59:51,119
question that many evolutionary

1526
00:59:56,309 --> 00:59:53,599
biologists are tackling right now but

1527
00:59:58,630 --> 00:59:56,319
it's the uh so we have a big question

1528
01:00:00,789 --> 00:59:58,640
mark as to why there are certain

1529
01:00:03,109 --> 01:00:00,799
constraints

1530
01:00:08,549 --> 01:00:03,119
thanks

1531
01:00:12,030 --> 01:00:09,910
so

1532
01:00:15,109 --> 01:00:12,040
can you apply this technique to

1533
01:00:17,910 --> 01:00:15,119
chemosynthetic metabolisms and estimate

1534
01:00:20,390 --> 01:00:17,920
the likelihood in time for the origin of

1535
01:00:23,109 --> 01:00:20,400
them maybe methanogenesis

1536
01:00:25,589 --> 01:00:23,119
or iron reducing bacteria can we

1537
01:00:27,589 --> 01:00:25,599
translate this information to estimation

1538
01:00:30,150 --> 01:00:27,599
of timing of origin

1539
01:00:32,069 --> 01:00:30,160
for similar possible mechanisms on other

1540
01:00:34,710 --> 01:00:32,079
worlds

1541
01:00:35,670 --> 01:00:34,720
yeah i think if we if we have to be very

1542
01:00:39,750 --> 01:00:35,680
careful

1543
01:00:42,390 --> 01:00:39,760
um i think in when we set up the system

1544
01:00:43,270 --> 01:00:42,400
iv outlined the first criteria

1545
01:00:46,789 --> 01:00:43,280
and

1546
01:00:48,950 --> 01:00:46,799
i published that in 2017 i'm writing an

1547
01:00:51,190 --> 01:00:48,960
updated one as to what sort of criteria

1548
01:00:53,589 --> 01:00:51,200
i think is necessary for reconstruction

1549
01:00:56,630 --> 01:00:53,599
of ancient metabolisms when it comes to

1550
01:00:58,710 --> 01:00:56,640
the timing of origin i think we are now

1551
01:01:00,390 --> 01:00:58,720
able to not only reconstruct a single

1552
01:01:02,710 --> 01:01:00,400
gene but we can engineer entire

1553
01:01:04,870 --> 01:01:02,720
communities so uh there's nothing that

1554
01:01:06,789 --> 01:01:04,880
stops us from engineering a community

1555
01:01:08,870 --> 01:01:06,799
and and maybe that goes back to the

1556
01:01:10,710 --> 01:01:08,880
business question that if we understand

1557
01:01:12,230 --> 01:01:10,720
how these communities are interacting we

1558
01:01:13,750 --> 01:01:12,240
might be able to

1559
01:01:16,470 --> 01:01:13,760
understand whether

1560
01:01:18,230 --> 01:01:16,480
there is factors of determinism

1561
01:01:20,549 --> 01:01:18,240
so so yes i think it's definitely a

1562
01:01:22,710 --> 01:01:20,559
possibility but just be mindful is how

1563
01:01:25,510 --> 01:01:22,720
you pick your system

1564
01:01:27,349 --> 01:01:25,520
and be very careful with the design

1565
01:01:29,190 --> 01:01:27,359
happy to help thank you and those those

1566
01:01:32,710 --> 01:01:29,200
questions were from sean domicle goldman

1567
01:01:34,069 --> 01:01:32,720
and kendall lynch oh hi sean

1568
01:01:36,309 --> 01:01:34,079
next question

1569
01:01:39,190 --> 01:01:36,319
hypothetical beautiful talk um steven

1570
01:01:42,150 --> 01:01:39,200
freed from johns hopkins um this is a

1571
01:01:44,549 --> 01:01:42,160
question coming from a fellow biochemist

1572
01:01:46,710 --> 01:01:44,559
i was curious about so nitrogenases use

1573
01:01:48,630 --> 01:01:46,720
lots of chaperones to get their

1574
01:01:51,109 --> 01:01:48,640
biogenesis so i was curious if you were

1575
01:01:53,670 --> 01:01:51,119
surprised that the

1576
01:01:55,910 --> 01:01:53,680
nitrogenase was able to use the extant

1577
01:01:59,029 --> 01:01:55,920
as zodobacterous chaperones and did that

1578
01:02:00,789 --> 01:01:59,039
map with where the mutations were in the

1579
01:02:02,069 --> 01:02:00,799
ancestral node like

1580
01:02:04,069 --> 01:02:02,079
do you know where on the surface they

1581
01:02:07,109 --> 01:02:04,079
were and did that surprise you or not

1582
01:02:09,029 --> 01:02:07,119
surprise you yeah so uh well

1583
01:02:10,870 --> 01:02:09,039
so i i'm in this point where everything

1584
01:02:12,470 --> 01:02:10,880
is surprising me and nothing is

1585
01:02:14,230 --> 01:02:12,480
surprising me at the same time it's a

1586
01:02:17,029 --> 01:02:14,240
very quantum state

1587
01:02:19,829 --> 01:02:17,039
of existence but uh it's we have not

1588
01:02:21,430 --> 01:02:19,839
done an in-depth network analysis uh i

1589
01:02:23,029 --> 01:02:21,440
think our postdoc bruno here he's

1590
01:02:24,230 --> 01:02:23,039
interested in that exact question we

1591
01:02:26,390 --> 01:02:24,240
actually just talked about that in one

1592
01:02:28,230 --> 01:02:26,400
of our group meetings uh so yes we're

1593
01:02:30,549 --> 01:02:28,240
definitely interested in understanding

1594
01:02:33,510 --> 01:02:30,559
at the more uh larger level systems

1595
01:02:34,950 --> 01:02:33,520
level what what might be happening uh i

1596
01:02:38,309 --> 01:02:34,960
think it's fascinating that we're even

1597
01:02:39,670 --> 01:02:38,319
able to swap these highly constrained

1598
01:02:43,990 --> 01:02:39,680
systems

1599
01:02:49,910 --> 01:02:45,190
i had a

1600
01:02:53,430 --> 01:02:49,920
regarding the dobson you indicated that

1601
01:02:56,390 --> 01:02:53,440
the change in uh frequency optimization

1602
01:02:59,430 --> 01:02:56,400
of the rhodopsin suggested

1603
01:03:00,950 --> 01:02:59,440
of that that this particular adoption

1604
01:03:03,349 --> 01:03:00,960
involved in

1605
01:03:06,150 --> 01:03:03,359
could it also indicate a change in the

1606
01:03:07,109 --> 01:03:06,160
peak output of the sun a billion years

1607
01:03:08,870 --> 01:03:07,119
ago and

1608
01:03:10,470 --> 01:03:08,880
so it's actually reflecting physical

1609
01:03:12,309 --> 01:03:10,480
something happening at the sun by the

1610
01:03:14,230 --> 01:03:12,319
surface of the earth

1611
01:03:16,870 --> 01:03:14,240
uh yeah so the what

1612
01:03:19,349 --> 01:03:16,880
eddie has done was independent than from

1613
01:03:21,990 --> 01:03:19,359
our calculations so he did look at the

1614
01:03:24,230 --> 01:03:22,000
irradiance profile of the sun itself

1615
01:03:27,589 --> 01:03:24,240
um independent of uh

1616
01:03:29,910 --> 01:03:27,599
before we integrated and interpreted the

1617
01:03:32,390 --> 01:03:29,920
phenotype that we've predicted that

1618
01:03:34,870 --> 01:03:32,400
belongs to these ancestral proteins and

1619
01:03:36,309 --> 01:03:34,880
then we sort of mapped them as to this

1620
01:03:39,349 --> 01:03:36,319
is the environment

1621
01:03:41,990 --> 01:03:39,359
uh and it matched uh it in the water

1622
01:03:43,990 --> 01:03:42,000
column where the the host organism might

1623
01:03:45,430 --> 01:03:44,000
be residing but we haven't done any

1624
01:03:47,670 --> 01:03:45,440
direct uh

1625
01:03:50,150 --> 01:03:47,680
tests that focuses on the sun or the

1626
01:03:51,910 --> 01:03:50,160
star itself although

1627
01:03:53,510 --> 01:03:51,920
i think eddie is definitely interested

1628
01:04:01,349 --> 01:03:53,520
in

1629
01:04:04,710 --> 01:04:01,359
was stronger the local environment or

1630
01:04:07,349 --> 01:04:04,720
the or the solar radiance profile

1631
01:04:09,670 --> 01:04:07,359
that's so i'm i am very cautious when it

1632
01:04:11,670 --> 01:04:09,680
comes to interpreting the early

1633
01:04:14,230 --> 01:04:11,680
environments and and what sort of

1634
01:04:16,789 --> 01:04:14,240
possibilities there might be if we sort

1635
01:04:19,109 --> 01:04:16,799
of really conservatively propose that

1636
01:04:19,990 --> 01:04:19,119
perhaps they were either deeper that

1637
01:04:21,750 --> 01:04:20,000
they were

1638
01:04:25,750 --> 01:04:21,760
where they could be located in the water

1639
01:04:28,230 --> 01:04:25,760
column or maybe there was a biofilm um

1640
01:04:30,230 --> 01:04:28,240
that was covering these organisms but i

1641
01:04:31,029 --> 01:04:30,240
i want to be very careful since we don't

1642
01:04:33,349 --> 01:04:31,039
know

1643
01:04:34,789 --> 01:04:33,359
um anything really about those

1644
01:04:36,470 --> 01:04:34,799
environments so

1645
01:04:38,309 --> 01:04:36,480
i won't speculate

1646
01:04:40,549 --> 01:04:38,319
therefore it may be more likely i think

1647
01:04:41,270 --> 01:04:40,559
there's more information about the from

1648
01:04:43,349 --> 01:04:41,280
the

1649
01:04:45,270 --> 01:04:43,359
astronomy and anyway when it comes to

1650
01:04:47,190 --> 01:04:45,280
understanding these early models

1651
01:04:49,990 --> 01:04:47,200
thank you

1652
01:04:51,670 --> 01:04:50,000
thanks next question please um yeah your

1653
01:04:53,670 --> 01:04:51,680
story about nitrogenase was quite

1654
01:04:54,950 --> 01:04:53,680
inspiring as an outsider to the field i

1655
01:04:56,150 --> 01:04:54,960
was really impressed with what you could

1656
01:04:57,910 --> 01:04:56,160
do with it now i'm going to ask you

1657
01:05:00,789 --> 01:04:57,920
something really unfair

1658
01:05:02,390 --> 01:05:00,799
what about rubisco how far back do you

1659
01:05:04,309 --> 01:05:02,400
think that goes

1660
01:05:06,630 --> 01:05:04,319
and why

1661
01:05:09,190 --> 01:05:06,640
so uh rubisco is this a very fair

1662
01:05:10,870 --> 01:05:09,200
question first of all so rubisco is a

1663
01:05:12,230 --> 01:05:10,880
bit more obviously a different type of

1664
01:05:15,270 --> 01:05:12,240
beast

1665
01:05:17,430 --> 01:05:15,280
if you attribute the presence of early

1666
01:05:18,710 --> 01:05:17,440
carbon fixation

1667
01:05:20,549 --> 01:05:18,720
records to

1668
01:05:23,109 --> 01:05:20,559
rubisco or something like rubisco then

1669
01:05:23,910 --> 01:05:23,119
you can stretch its presence as early as

1670
01:05:24,710 --> 01:05:23,920
the

1671
01:05:27,029 --> 01:05:24,720
first

1672
01:05:30,309 --> 01:05:27,039
reliable data of the carbon isotope

1673
01:05:32,150 --> 01:05:30,319
biosignature right but

1674
01:05:34,150 --> 01:05:32,160
the simple answer to your question is

1675
01:05:35,910 --> 01:05:34,160
that nobody knows how old rubisco is and

1676
01:05:38,069 --> 01:05:35,920
we make this assumption that something

1677
01:05:41,510 --> 01:05:38,079
like it or itself was there

1678
01:05:43,829 --> 01:05:41,520
uh when the carbon fixation um

1679
01:05:45,109 --> 01:05:43,839
the biological fixation was

1680
01:05:47,910 --> 01:05:45,119
emerged

1681
01:05:50,710 --> 01:05:47,920
what we've done uh is to after we

1682
01:05:52,870 --> 01:05:50,720
created the phylogeny we included all

1683
01:05:54,710 --> 01:05:52,880
forms of rubisco from today

1684
01:05:56,950 --> 01:05:54,720
and we simply wanted to know whether we

1685
01:05:59,990 --> 01:05:56,960
can use the sequence information of this

1686
01:06:01,349 --> 01:06:00,000
enzyme to understand first whether we

1687
01:06:03,349 --> 01:06:01,359
can

1688
01:06:06,950 --> 01:06:03,359
find signatures of

1689
01:06:08,710 --> 01:06:06,960
rise of oxygen on this enzyme

1690
01:06:10,789 --> 01:06:08,720
everything that i know about molecular

1691
01:06:11,750 --> 01:06:10,799
evolution tells me that this enzyme

1692
01:06:13,430 --> 01:06:11,760
really

1693
01:06:14,710 --> 01:06:13,440
there will be variations

1694
01:06:16,950 --> 01:06:14,720
right but the assumption is that there's

1695
01:06:19,029 --> 01:06:16,960
some sort of uniformity and and we

1696
01:06:22,230 --> 01:06:19,039
looked at where the enzyme is binding to

1697
01:06:25,109 --> 01:06:22,240
oxygen to carbon dioxide uh and in fact

1698
01:06:26,950 --> 01:06:25,119
found that some parts of the enzyme was

1699
01:06:29,270 --> 01:06:26,960
evolving more rapidly than the others

1700
01:06:32,950 --> 01:06:29,280
that corresponded to the oxygen carbon

1701
01:06:34,309 --> 01:06:32,960
dioxide binding that also coincidentally

1702
01:06:35,910 --> 01:06:34,319
belonged to the ancestors of

1703
01:06:38,549 --> 01:06:35,920
cyanobacteria

1704
01:06:41,270 --> 01:06:38,559
so we published this in geobiology

1705
01:06:43,510 --> 01:06:41,280
saying we can constrain goe

1706
01:06:44,549 --> 01:06:43,520
using the rubisco sequence

1707
01:06:47,029 --> 01:06:44,559
perhaps

1708
01:06:48,549 --> 01:06:47,039
by analyzing the sequences itself we are

1709
01:06:50,150 --> 01:06:48,559
not timing anything but we're just

1710
01:06:51,510 --> 01:06:50,160
saying that there is a signature on this

1711
01:06:53,510 --> 01:06:51,520
enzyme that is telling us something

1712
01:06:55,430 --> 01:06:53,520
about what happened on earth

1713
01:06:59,270 --> 01:06:55,440
after we engineered this inside

1714
01:07:01,990 --> 01:06:59,280
cyanobacteria we did the isotope

1715
01:07:04,150 --> 01:07:02,000
studies and we we didn't go as far we

1716
01:07:05,109 --> 01:07:04,160
didn't go pre-goe yet that's in the

1717
01:07:06,150 --> 01:07:05,119
works

1718
01:07:08,069 --> 01:07:06,160
we think we have a bit more

1719
01:07:09,829 --> 01:07:08,079
understanding the two about under

1720
01:07:11,670 --> 01:07:09,839
evolution of this enzyme before we go to

1721
01:07:13,270 --> 01:07:11,680
that engineering but at the first

1722
01:07:15,190 --> 01:07:13,280
attempt our

1723
01:07:17,430 --> 01:07:15,200
biosignature reconstruction

1724
01:07:19,349 --> 01:07:17,440
was in agreement with what the

1725
01:07:21,190 --> 01:07:19,359
paleobiologists have been thinking about

1726
01:07:22,630 --> 01:07:21,200
or measuring

1727
01:07:24,309 --> 01:07:22,640
fantastic i'm going to have to go to the

1728
01:07:26,470 --> 01:07:24,319
library but um i have a follow-up

1729
01:07:29,029 --> 01:07:26,480
question carbonic anhydrase is similar

1730
01:07:30,789 --> 01:07:29,039
to rubisco and its fractionation ability

1731
01:07:32,950 --> 01:07:30,799
in your phylogeny did you find any

1732
01:07:34,390 --> 01:07:32,960
relationship between carbonic anhydrase

1733
01:07:35,990 --> 01:07:34,400
and rubisco yes so i actually

1734
01:07:37,990 --> 01:07:36,000
reconstructed the

1735
01:07:39,750 --> 01:07:38,000
history of carbonic anhydrase as well

1736
01:07:41,589 --> 01:07:39,760
and in the field transit paper we

1737
01:07:43,349 --> 01:07:41,599
published it there

1738
01:07:45,750 --> 01:07:43,359
actually funny you asked that so

1739
01:07:48,789 --> 01:07:45,760
carbonic anhydrase is uh sort of the

1740
01:07:50,789 --> 01:07:48,799
worst example so if anyone's wondering

1741
01:07:52,710 --> 01:07:50,799
that's how not to do it because it's a

1742
01:07:55,270 --> 01:07:52,720
very uh as you know we even have it in

1743
01:07:56,710 --> 01:07:55,280
our blood right so this enzyme is

1744
01:07:59,990 --> 01:07:56,720
everywhere

1745
01:08:01,430 --> 01:08:00,000
and uh in some form and uh it is very

1746
01:08:03,109 --> 01:08:01,440
hard to track it because of the high

1747
01:08:05,270 --> 01:08:03,119
horizontal gene transfer rate of this

1748
01:08:06,950 --> 01:08:05,280
enzyme so we reconstructed it and i

1749
01:08:09,109 --> 01:08:06,960
thought actually you know what we really

1750
01:08:10,549 --> 01:08:09,119
should not use this as a probe because

1751
01:08:12,710 --> 01:08:10,559
it is all over the place and i cannot

1752
01:08:14,230 --> 01:08:12,720
constrain anything so i included that as

1753
01:08:16,309 --> 01:08:14,240
an example of what

1754
01:08:18,630 --> 01:08:16,319
kind of protein does not fit the build

1755
01:08:21,430 --> 01:08:18,640
for deep time reconstruction

1756
01:08:23,669 --> 01:08:21,440
fantastic i'm off to the library

1757
01:08:26,070 --> 01:08:23,679
thank you very much i believe that we're

1758
01:08:28,229 --> 01:08:26,080
out of time now we've been out of time

1759
01:08:30,390 --> 01:08:28,239
but uh please uh

1760
01:08:33,590 --> 01:08:30,400
discuss your your questions with batul

1761
01:08:39,669 --> 01:08:36,390
and with that i will thank our speaker