The Climate Biotech Podcast
Are you fascinated by the power and potential of biotechnology? Do you want to learn about cutting-edge innovations that can address climate change?
The Climate Biotech Podcast explores the most pressing problems at the intersection of climate and biology, and most importantly, how to solve them. Hosted by Dan Goodwin, a neuroscientist turned biotech enthusiast, the podcast features interviews with leading experts diving deep into topics like plant synthetic biology, mitochondrial engineering, gene editing, and more.
This podcast is powered by Homeworld Collective, a non-profit whose mission is to ignite the field of climate biotechnology.
The Climate Biotech Podcast
Optical biosensors for neural circuits and methane-eating enzymes with Loren Looger
When Loren Looger walks into a room, he doesn't want recognition, he wants to make things that work. The creator of revolutionary, open-source tools that transformed how we visualize brain activity is increasingly turning his protein engineering expertise to formidable challenges in climate, including methane degradation. .
Methane sits at the heart of our climate crisis as a greenhouse gas 80 times more potent than carbon dioxide. Yet nature has evolved only a few enzyme scapable of breaking it down. Methane monooxygenase (MMO) is on eof these remarkable proteins existing in methanotrophs, specialized microbes that have evolved unique cellular structures specifically to process methane. Despite its discovery decades ago, MMO remains stubbornly mysterious, with scientists still uncertain about its basic biochemical requirements.
In this fascinating conversation, Looger describes how he's applying the same methodical approach that revolutionized neuroscience to this critical climate challenge. His project aims to create fluorescent biosensors that can reveal MMO's secrets—how it interacts with membranes, what metals it requires, and why it struggles to function when expressed in other organisms. The ultimate vision? Engineering plants that can express functional MMO, potentially transforming forests into methane-capturing systems.
What makes this story particularly compelling is Looger's journey—from a math-obsessed kid in Alabama who worked at NASA after school, to a biochemist who stumbled into neuroscience, to a climate biotechnologist driven by urgency. "We've got one last chance to save a planet where we can study neuroscience," he notes, explaining his pivot to climate work.
Throughout his career, Looger has championed a culture of scientific openness, freely sharing tools before publication—a philosophy he believes is essential for climate innovation. His approach reminds us that sometimes the most meaningful scientific contributions come not from flashy breakthroughs but from methodical improvements that make complex systems accessible to all researchers.
Ready to bring your expertise to climate challenges? Email Lauren directly—he welcomes collaborations from scientists willing to apply their skills to our planet's most pressing problems.
methane degradation is so hard that this is one of the few things in nature that have been done, as far as we can tell exactly once. There's only a handful of these things. There is one enzyme in nature that breaks down methane, and it is MO.
Speaker 2:Welcome to the Climate Biotech Podcast, where we explore the most important problems at the intersection of climate and biology and, most importantly, how we can solve them. I'm Dan Goodwin, a technologist who spent years transitioning from software and neuroscience to a career in climate biotechnology. As your host, I will interview our sector's most creative voices, from scientists and entrepreneurs to policymakers and investors. Now in this episode, lauren and I are going to talk about his project, working on MMO. But before we go in, I really want to make sure that we give credit to the authors of the problem statement that led to Lauren's project. As you probably know, listening to Homeworld's podcast and the other things we do in the community, we're methodical about framing problems. You're going to hear Lauren talk about how Well-Framed Problems created his amazing work in neuroscience, and then he gives credit to. What got him into working on climate problems was a problem statement written by many experts in the field and put public on the Homeworld website. So I want to make sure we're giving credit to a paper called Develop a Sensitive and High-Throughput Assay for Methane Oxidation. This was written by Amy Rosenzweig, eli Hornstein, arjun Kakar, farina Kreichbomber, jonas Wilhelm, rachel Strickman and Paul Reginado. This was the inspiration that got Lauren, who's a relative outsider to methane, and his collaborator, who knows methanitros very well, to engage on a high-ambition de-risking project that Homeworld is very proud to share. So huge thank you to the authors for their time and expertise for creating the problems. We want to see more of a problem-centric culture in climate biotech broadly and this is a great example of experts framing work that needs to be done and then people picking that up and then getting it into action. So thank you to Rosenzweig et al and very excited now for this conversation with Lauren. We're thrilled to welcome Lauren Luger for a discussion about climate biotech. Now.
Speaker 2:This is a really special conversation for me because Lauren was, for me, was one of those mythical names that you see when you work in neuroscience and in my journey, it was one of those moments where meeting one of my heroes was actually a good thing. Lauren's landmark paper Chen 2013, is the paper that introduced calcium sensing, which is one of the most important contributions in neuroscience. It allowed scientists to watch the brain's neurons blink as they convey information. It's one of those papers that exudes a deep understanding of a problem. Yet this ferocious rigor necessary to make that tool and prove that this thing worked, I got to say.
Speaker 2:I don't really get intimidated much these days, but I remember leading up to my first meeting with Lauren where I was profoundly nervous. I was getting my guard up to get roasted like a supplemental figure 12, but then he gets on the call and he just immediately says hey, how y'all doing? Let's talk about proteins and let's fix the climate. And it always stuck with me because I just remember then having more conversations with Lauren. I pitched him my first idea of something and he just straight up said, oh, that sounds like a nightmare. And that was the start of our wonderful relationship. Because what you get with Lauren is you always get honesty, you get rigor, and then you also get the human journey behind science and a little bit of the formal introduction. Now for Lauren.
Speaker 2:Today, lauren is a professor of neurosciences at UC San Diego and an HHMI investigator. Previously he was a group leader at Janelia Farm and conducted postdoctoral work at Stanford and the Carnegie Institute of Plant Biology. He has been a mentor and collaborator to many, so we're happy to share that. Lauren is also a recent recipient of the Homeworld Garden Grant for their work titled Profiling In Vivo PMMO Activity and Lipid Order in Methanotropes Using Designed Fluorescent Biosensors. All right, let's get right to the man himself, lorne Luger. Who are you? Where did you grow up?
Speaker 1:Dan, I'm so happy to be here. Before I tell you who I am and where I grew up, let me just need to correct you on one thing we did not introduce calcium imaging in 2013. And this actually extends to most everything I do Calcium imaging had actually been around for 30 years. By that point. I would like to say we democratized calcium imaging. I don't want to be remembered as someone who came up with very clever ideas or someone who was very smart. I want to be remembered as someone who was useful and made stuff that just worked right out of the box. So I would say that's what we did in 2013. I just need to give people a tool that's going to work, and work well, and I don't really care like how we did it.
Speaker 2:So yeah, Straight into the rigor. This is exactly what I expected. Just to unpack that there was already calcium dyes. Right, you can already inject that.
Speaker 1:Well, there were already genetically, and the first genetically encoded calcium indicator was 1997 from Roger Chen. So we were already like 10 years late to the party for that. We take things and we make them better. To be fair, we do invent our fair share of things too. I would rather take someone else's invention and make it actually useful and something that some first year grad student can get amazing data with in their first week at the job, than come up with something that's super cute and super splashy and very clever, but ultimately not that useful.
Speaker 1:Flashy and very clever but ultimately not that useful. That's all I care about is I care about things that just actually work. I like grinding on things and making them better and making incremental gains and making lots of incremental gains, and then suddenly you have a tool that actually works, but funding agencies don't want to see an application that's like well, we're going to take this existing tool and we're going to make it 15% better. So I was very blessed to be at Janelia Farm where my director, jerry Rubin. I was like I'm going to take this tool and I'm going to make it 15% better, and then I'm going to do that a couple of times and suddenly we're going to have a tool that's 10 times better. And then we got tools that were 10 times better and we could never gotten that through like NIH.
Speaker 2:When I was a PhD student. I love photography and so I take my camera and I just offer free portraits for PhD students, and that was one of those things where the photos from my camera are maybe 20% better than a standard iPhone photo. And when we talk about calcium imaging today, if someone says, oh yeah, we're doing whole brain, zebrafish, calcium imaging, what they're saying is they're using your tool. Now it's kind of like your joke is very similar to Eric Schmidt's, which is Eric Schmidt said we weren't the first to search, google was last. So for people that don't have context, calcium imaging absolutely transformed the way we do neuroscience, because now you can actually see the entire brain dynamics for a living organism thinking, remembering things and so from the outside, looking in or maybe looking back, it seems obvious. But the field would not be the same if it wasn't for the work you and Roger Chen and all these other people did. So let's go back to Lauren. Who are you? Where did you grow up?
Speaker 1:Lauren, who are you? Where did you grow up? Yeah, so who am I? I grew up in Alabama, what at that point was a fairly rural part of Alabama, madison, alabama, next to Tennessee. I had an after-school job at NASA, which was in Huntsville, by ninth grade or so. On the one hand, it was like the boonies where I actually lived, like we lived next to a cotton field in a sewage lagoon. It was like a 30 minute drive to NASA, like two different worlds there.
Speaker 1:It was a great place to be a kid, like. There were crawdads and locusts and you could ride your bike for 10 miles and be just totally fine. I always knew that I wanted to be doing something like this, something in math and science. I have this very clear memory of being driven around in the massive family station wagon must have been like 1978. And I've got this little book of just multiplication problems. I didn't want to read Winnie the Pooh, I wanted to multiply three and four digit numbers. Yeah, I thought I was going to be a mathematician Then. I always thought I was going to be like a synthetic organic chemist.
Speaker 1:Never really considered being a biologist, certainly not a neuroscientist. Like definitely stumbled into all of that stuff. I only did and do neuroscience because that's what Janelia did. We're doing some climate work, but before too long I'd love to be doing all climate work because, honestly, neuroscience doesn't feel super important right now. We've got one last chance to save a planet where we can study neuroscience. It's important, don't get me wrong, but maybe we can do that later. Maybe we can circle back and solve those neuroscience questions in 50 or 100 years, after we have a planet that can support human and other life. Yeah, this is the field I'm actually choosing, whereas the scientific journey before that was really a lot of random interactions, like a kind of a random walk through subject space.
Speaker 2:And there's nothing wrong with that either, I would say especially in kind of the early years of a career. I think sometimes getting a mission too early can actually be a fault like a red herring.
Speaker 1:Yeah, I definitely am glad that I started with math and chemistry and physics and then made it to the squishier stuff rather than the other way around. So I think it really laid a good foundation, even though I used next to nothing from the math curriculum that I took. The first grad school program I attended was math. I did a year of math grad school at Berkeley because I was like I'm going to follow my dream, I'm going to be a mathematician, and it took about three months of actually being knee deep in that stuff and with the people and I was like it's not the life I want. I just dropped out and, through random interactions, ended up in a biochemistry PhD program that I finished.
Speaker 2:At Stanford right.
Speaker 1:Stanford was undergrad, phd program was at Duke.
Speaker 2:Got it.
Speaker 1:I only ended up there because that's where my girlfriend was. So yeah, and I only ended up in my PhD lab because it was like a friend of a friend. And then I only ended up in my postdoc because I was like the craziest thing ever. I was on Caltrain, going down to interview at what was then called Stanford Research Institute, and cell phone rings and it's this guy with this thick German accent and he's talking really fast. I'm barely understanding him and I eventually make out that he has been trying to reach my PhD advisor for a couple years.
Speaker 1:Guy never answered the phone. He called the lab I was first author on the paper on the computational design of proteins. They gave him my cell phone number. He calls me and he's hey, I'm at stanford. And I was like, oh yeah, that's actually the next stop. So I got off of cal train at the next stop, went, walked over to stanford, gave an interview, talk, was offered a job, accepted it on the spot, then went back to cal train and continued on to my second interview. I was like I already accepted a job like 30 minutes ago from this guy I'd never heard of that just randomly called my cell phone and that's how a lot of my career has been only ended up at Janelia because, like a friend of a friend had heard of it. So yeah, kids out there, you don't have to have a plan and you can adjust your plan on the fly, and lots of times if you're just open-minded like things really can fall into place.
Speaker 2:Totally Linear, intentional journey, like things really can fall into place. Totally Linear, intentional journey, like professional journeys, is only for writing grants. Everything else, I think, is you got to be open to chance. So thanks for breaking protocol. Because the second question we always ask is did you always know you'd be building tools to probe complex biological systems? And in this case there's a one word answer, which is no, absolutely not. So this is amazing. First attempt as a PhD was math at Berkeley, which is, I think, still one of the top, like the best. It was great, so way to drop out of that and then did a PhD at Duke.
Speaker 1:Because of a girlfriend and that was in biochemistry Got it.
Speaker 2:And then you did a postdoc at Stanford and then that led to Janelia, where I think a lot of the major work that people know when they first hear Lorne Luger is probably from that era. This is setting up just a brief conversation about how biology has changed so much and I think you're really like a reference class of that, like, I think, the way biology has changed and you've been, I think, on the front of that, of how biology has been shifted from a descriptive science to an engineerable medium. I think we need to. So that's what I know, but I think it's really great to just set that up a little bit for the audience. You did some of the early protein engineering in 2004, right, and then you go to Janelia and I'm so curious how did you drop in there? How did that end up leading to GCAMP and the subsequent work?
Speaker 1:Yeah, no, yeah, it was pretty much when, I hit Janelia.
Speaker 1:That was 06, like right after they opened. I showed up at Janelia in October 06. I was told that pretty much the power outlets had reliably started working like the week before I arrived. So, yeah, we were pretty much on the ground. As it started to ramp up, I just decided that I was going to change the way I did things. And PhD and postdoc. Pretty much the boss said, hey, this is the problem, this is what you work on and I choose how I work on it. Then suddenly, running the show, I get to choose and suddenly you're running the show and get to choose.
Speaker 1:I was fortunate to realize that if I came up with ideas of what we should work on, they would probably be bad. But this happens with a lot of tool making. Someone makes a tool and then you bring it to the people that could use it and they're like that's actually not what we want. You're close, but it actually needs to do. This other thing I literally grabbed one of those yellow legal pads of paper and I just walked around the building and I made a column for each.
Speaker 1:I started with the PIs, but then I would go down the org chart and I just said tell me the five most important tools that could change the way you do science. And I just started like tabulating them and pretty soon it became apparent that calcium indicators were gonna be really important. But I was like breathtakingly ignorant about why these would be important. I remember when Carl Svoboda he's the one I went to first and he's like, yeah, we need calcium sensors, of course.
Speaker 1:And I said what you mean teeth and bones and he's like, oh, we need to talk about like how cells actually work. So I'd gotten that, hey, we need to talk about like how cells actually work. So I'd gotten that far, like I'd gotten the job I was starting and I still didn't really know that. I didn't know what calcium ions were doing in cells. But people started telling me like we need these calcium indicators and that was a runaway number one on the tabulated list and so it's hey, team, we're making calcium indicators. When the rest of the research program also just came out of that like people wanted neurotransmitter indicators.
Speaker 1:Optogenetics wasn't really a thing when I started in 06. Like there were some whispers about it. Chynarodopsin paper the big one came out later that year. People were like, oh, we need some of that and we need these fluorescent proteins for super resolution imaging. Pretty much like the first decade at Janelia. I was just making my way through that first list we made from interviewing people and getting their top things and I'm proud that we actually made pretty decent progress through that list.
Speaker 2:That's amazing. You've mentored me and Paul Reginato because we talk about problems so specifically at Homeworld. Right, you've got to have a really clear view on a problem before you can do any work that matters. I think it's worth just. This is obviously not a calcium imaging conversation, but just briefly understanding why it was so hard, and there was a really beautiful insight. I don't know if that was you or the GCaMP2 team to come up with the idea of the circular permutation of GFP tied to the calcium sensor.
Speaker 1:That, of course, was Roger Chen. Roger Chen and Atsushi Miyawaki were the first people to circularly permute proteins and even turn them into genetically encoded calcium indicators.
Speaker 2:Those ideas were in that transition of just describing biology to seeing what you can do when you take how biology works and modify it. That's just one of those. At least, when I stumbled across that idea of circular permuting GFP, that was one of those like oh my God, that is that's. It's like when I learned the Fourier transform, actually, I remember just running away from the lecture being like wow, like I totally view things differently, and then you've used that. So that core trick, I think, is that the way you created them. The glutamate sensor.
Speaker 1:We made glue sniffer, like we told people, where we said, hey, this is just GCAMP, but for glutamate we just had a really nice hammer and we just hammered a bunch of nails, so hammered like 20 or 30 nails that looked pretty similar and were amenable to that hammer. The hammer has changed slightly over time. There are other tricks you can do, but that one turns out to really get you a long way. Get you a long way.
Speaker 2:Yeah, okay. So let me trigger a Lauren Lugar rant here, which is? It comes down to this idea of biology I think used to be very descriptive, right which is learning it like you would geography, oh, this idea, this thing, this mitochondria looks like this, blah, blah, blah. And then something happens in which now it's an engineerable medium and you can come up with weird ideas like, oh, let's hack a glutamate binder and seeing that transition, you lived it and I'm very curious to hear your reaction. And I think people now come to this like post alpha fold, right, they're like oh, we've got alpha fold so we can make any protein, right. And so, first of all, does this drive with your view of how biology has shifted from descriptive to engineerable, and why isn't alpha fold enough?
Speaker 1:Yeah, so yeah, let me unpack that. Yes, that's definitely my experience. Again, you give me way too much credit for that. The first protein engineering papers, I think you essentially go back to late 70s, early 80s. Protein engineering had been around a long time.
Speaker 1:Before we had a genome, which to me is very interesting oh, long before we had a genome, really pretty much as soon as we had like restriction enzymes. Then we had protein engineering. It was an obvious enough thing. So I take zero credit for the invention of protein engineering. Maybe I will take a little credit for that of just trying to make things that again broken record here, things that just really work. The first time so many tools existed were out there.
Speaker 1:But you get them and you try them and you're like what the hell? It's not really. And then it turns out like there's some special sauce you have to do and the figure in the paper was the best replicate ever out of a thousand and it like never looks like that. Like I wanted to make tools that you could just your first experiment on day one. You would get a trace that looks like it is in the paper and if not, then I was very clear to people. I was like if it doesn't work, you call me and we will figure out. Maybe you're doing something dumb. Maybe your problem is like different right, and this is not the tool for that. Maybe this is biological and we need to rethink how we're going to do it.
Speaker 1:Or maybe my tool is not good enough and I need to make a 2.0, a 3.0 for you. The feedback from people over the years has been critical in telling us again what to make, but also just giving us feedback. We learned so many things that never occurred to us about how people are actually using our tools and what properties they need to have.
Speaker 2:Yeah, and what I love so much about your story is that you happen to be in one of the best spaces in the world, filled with a bunch of great labs, filled with a bunch of people on the same mission of let's map the brain. The domain is neuroscience. The problem is dynamics. You get to calcium, so shifting into climate. Now, this is what we've worked so hard on, right? Okay, what's the neuroscience equivalent? Okay, methane. Let's talk about how you remove methane from the atmosphere. And then, inside that, what are the main areas? And so that's how we came across the MMO enzyme and its importance to climate, and that's why we're so excited to fund you and Michael Konopka on this project. So, mmos right, what are you building here? And let's start with the problem formulation what is MMO? Even.
Speaker 1:Yeah, yeah, mmo, methane monooxygenase. So methane, quote unquote natural gas sits everywhere. It is a super stable molecule and so really hard to degrade. It's also a super potent greenhouse gas, depending on how you count like over what time scale. But ballpark 80 times worse per molecule than carbon dioxide as greenhouse gas, even though if you say global warming, climate change, temperature increase, greenhouse gas effect, everyone thinks carbon dioxide. Of course that's the boogeyman. But there's all sorts of other stuff in there that is, per molecule, much worse, worse, but the concentrations are much, much lower. And methane is one of these things. So it's in there. It's doing bad stuff. It's increasing at alarming rates. We got to figure out how to get rid of it. Again, we shamelessly steal from nature, right? So nature has figured out how to eat methane, because anything that exists in the world some microbes somewhere will figure out how to live off of it. That's just. That is the basic principle of evolution and it's like the prime directive Life finds a way.
Speaker 2:Life finds the way exactly.
Speaker 1:So life found a way to eat methane. So they have this set of enzymes methane monooxygenase. Michael and I are working on one subclass of that, particulate methane monooxygenase, pmmo. You don't have to really concern yourself with the details of that, but, as you can imagine, you've got this molecule, methane, super, super stable. You have got to do hella wicked chemistry to break that guy down, to break those ch bonds that are so stable, to turn it into carbon dioxide, which you're like hey, wait, we're trying to minimize carbon dioxide. Why do we want to do that? But hey, it's 80 times better than methane. So we cut it down by almost two orders of magnitude when we ran this reaction.
Speaker 2:Just if I could jump in for a little bit of color context, because even for me I was like why do we need to do methane? And the two reasons that got me most excited about it is that methane is much more driven by positive feedback loops. And the other thing that I think is so interesting about why methane is hard is that there's very few sinks, that I understand.
Speaker 2:There's very few engineerable sinks to methane, aside from just twiddle your thumbs and hope it goes away, and so when you find things that could be engineerable sinks, you have to go all in. So, if you're a biotechnologist, like everyone listening to this is, you'll Google how does biology break down methane, and you'll get to that MO right. But why is it hard? Why don't we just alpha fold it and make it work in a massive bioreactor? How hard could it be?
Speaker 1:Yeah, anything that needs to be done, nature finds a way. Like bioluminescence has evolved hundreds, maybe even thousands of times, like nature needs to communicate in the dark, it finds a way, turns out to be really easy. Chemistry, boom. So many different things. We can engineer them. They're so robust. Methane combustion, methane degradation, is so hard that this is one of the few things in nature that have been done, as far as we can tell, exactly once.
Speaker 1:There's only a handful of these things. There is one enzyme in nature that breaks down methane and it is MO, and there is no other way. There is one way to do it. Two if you count PMMO or SMMO, but basically there's one way to do it. Two if you count PMMO or SMMO, but basically there's one way to do it. So it's super, super hard and it involves this horrible multi-component enzymes. So you get different protein subunits. You have this horrible like activated metal centers. It's a membrane dissociated protein. So basically we know the guy, we know the thing. It's mmo, but it is a tricky protein and so we end up even though this was probably ploned 20, 25 years ago like there's some old papers about MMO and what's going on, but we know precious little of the details.
Speaker 1:So most things like GFP jellyfish expresses a GFP so it can see other jellyfish in the dark. You can take GFP, you can clone it into whatever Almost always works.
Speaker 1:And if it doesn't work, it almost certainly you screwed something up and it will work when you do it again. Oh my God, it's a small protein. It's really easy to understand. You solve the crystal structure and you look at it and you're like, of course, that's what it looks like, I can engineer it to do.
Speaker 1:This MMO is the exact opposite. Like we don't really have good, even structural, models of what the whole complex looks like. We've got bits and pieces but we don't really have the whole thing, certainly not with the lipids and certainly not with all the metal centers. We're not even really sure what set of metals it can and can't use. We don't know if it uses NAD or NADP or neither. There's so much basic biochemistry that you just take for granted with any given enzyme like lysozyme we know every single thing about lysozyme, three decimal point precision. We don't even really know the full set of what Legos are sticking together to give you a working MMO. So let me just give a little bit of background to people.
Speaker 1:Nature found a way. These bugs, these methanotrophs, microbes, figured out how to eat methane. So it's great you know that they eat methane. They have really great catalytic rates. Methanotrophs they're really slow growing. They're like, a lot of them are anaerobic, which is a pain. You really can't scale this up. You can't say, okay, just grow huge vats of these MMO producing methanotrophs and boom, problem solved. That is dead end. So we need something at scale that's out there. So the best idea people have at this point is maybe express it in plants and trees and stuff. Imagine like a pine tree expressing MMO in the needles. Then you've got great surface to area, you've got great scale.
Speaker 1:But the problem is right now you can express MMO to some extent in some of these other organisms, but it doesn't work. You can tag it with GFP and be like, okay, there's something there, but it just doesn't work. Or sometimes works, but it's one thousandth the activity, so something's wrong. But we don't know what that is. Is it the metal? Are we not supplying the right ion? Is it something else? Is it the lipid? So what Michael and I are trying to do and again, this is not our idea we shamelessly stole the problem statement from your set of world experts that you guys had that awesome workshop and they came up with the problem statement and we're like, yeah, that's the one we want to do, right there.
Speaker 2:That's what it's there for.
Speaker 1:So I'm the protein engineer, michael is an expert on methanotrophs and MMO and he can grow these guys, he can measure methane, mmo turnover rates, all these things. So we figure a really good team. We are just trying to come up with a set of tools that can help us answer basic biological questions about MMO. Is it using NAD? Like, what is the pH range at which it functions?
Speaker 1:And first project, we wanted to get the parts list, get some basic information, because then the dream is once you have this toolkit is once you have this toolkit, then you can do things like okay, now let's treat it like a calcium indicator, treat it like a G camp, but instead of a fluorescence increase to calcium, now it's methane oxidation rate and let's just make libraries, let's do directed evolution, let's make it a thousand times better, let's put it in a tobacco leaf and figure out why it's not working, let's figure out what we have to supplement. But again, this is just the beginning. It's a really hard project. I think we have to have pretty modest goals all the subunits labeled, figure out where things are going, figure out a bit about ion and cofactor selectivity, stoichiometry, et cetera.
Speaker 2:Okay, so let's. I'm so glad we're deep in it. Now we can poke around All the subunits labeled, meaning you want to have antibodies flown in to see if you can label it that way. Tag it with dyes. What does it mean to label?
Speaker 1:Yeah, no, that's a great question. Dies. What does it mean to label? Yeah, I know that's a great question. Probably all of those things would be useful if we GFP tag all these things or maybe epitope tag where we can then use a really robust antibody.
Speaker 2:And Lauren, just for context, when you think about epitope tagging, you add like an eight amino acid sequence to something that you think is on the surface, and then you can.
Speaker 1:Exactly the notion is GFP is great to add, because then you can see it instantaneously, but GFP is big.
Speaker 1:It's not super big but it's like 200 amino acids so it can maybe double the size of your whole protein. Could screw it up. Yeah, this is eight to 10 amino acids, small and wiggly, probably not going to screw up your protein. And then you come in with an antibody. Obviously then it's usually not. You have to disturb your sample to do antibody labeling so you can sometimes fool yourself a little bit. So it's great to do live imaging to see where all the subunits are. Are they finding each other? Is that the failure mode? Is it like right out of the gate, like we're not making subunit two, or we're making all three of the subunits but subunit three gets lost and just goes to the lysosome and gets degraded? Or are they all getting assembled? We just don't have the right metal and so then you don't have a functional enzyme. Super, super basic and logical questions here we want to answer.
Speaker 2:And we're talking about getting into an easy to work with model. So this is to get into E coli.
Speaker 1:Yeah, that's a great question, so we want to tackle it both ways. That's a great question, so we want to tackle it both ways. We want to. So methanotrophs even though they're weirdo bugs, they're slow growing. Ideally, you're feeding them methane, which is both complicated and dangerous. Now suddenly you can explode the lab if your experiment goes poorly and they grow super like.
Speaker 2:the doubling time is like 48 hours.
Speaker 1:Yeah, you can sometimes treat them, feed them methanol. I think they grow even more slowly. They're a little annoyed, but they will grow on methanol, but it's a lot safer and easier. Other people have started to lay the foundation for this by developing plasmids and promoters and antibiotic selection systems and etc.
Speaker 1:It's obviously not E coli level so it's a little bit more of a wild west, but I think it's not the craziest thing to go right into expressing in methanotrophs, but then, yes, in parallel to pull it out and put it in some workhorse bug like E coli or B subtilis, or talking about endogenous, basically endogenous MMO, and the methanochromy already grows.
Speaker 2:One of the things that is disheartening to me about working with methane is the solubility is terrible. Is that part of the reason that, when you think about, do you work with the particulate, which is membrane bound, versus the soluble MMO? Do we? Think that's just the reason that particulate works better.
Speaker 1:Is that you're more likely to bump into methane on a surface? That is an interesting question. I had always thought that it was just maybe a protein phenomenon, but thinking about the substrate, now, that's actually really interesting and something that we really need to dig into. It's a great point.
Speaker 2:So the idea is that you're going to grow these things and then you're going to do basically no disrespect to the methanotroph world, but you're basically doing gold standard neuroscience toolkit stuff on an organism that has had way less attention right Like we've got optogenetics, which is making the brain fire, we've got calcium imaging watching the brain dynamics, because there's huge amounts of money and a whole institute Janelia.
Speaker 2:Falkham, as well as others really into brain mapping right, whereas, like methane, now we're here and everyone's like, yeah, this is probably one of the most important things we can do and all the research has been separated. So now, when I think about the work that you're doing and why we're so excited to support it is, it feels like taking the toolkit and the approach of precise biochemistry in the neuro context and then putting it on probably the single highest leverage biochem problem in climate, which is MMO.
Speaker 1:But again, we're definitely starting at the ground floor here in terms of tools for methanotrophs and. Mmo. Yeah, we're starting this G-Camp 1 level technology that we're making here, but that's what we need. There have been zero functional imaging experiments in methanotrophs to date. We want to do. We want to get that number to 10 or 20. Measure some pH, measure some NAD or 20. Measure some pH, measure some NAD. Figure out, if we even just know which parts then we can know what to start tinkering with.
Speaker 2:What does it mean? I'm so excited to always just geek out, so I'm asking you, I think, level one questions what does it mean to be membrane dissociated? Membrane associated so it's bound, or does it mean it has to be inside a membrane?
Speaker 1:Yeah, so methanotrophs? They're in addition to the particulate or soluble MMO. There's a whole phylogenetics of this stuff, so there's type 1, type 2, p MMO exists in this weird organelle that, as far as I'm aware, has only evolved in methanotrophs. It's called this intracellular membrane. So it's basically like a huge Golgi that just tiles the cell. So you get basically these layers of this intermembrane space and MMO is packed in there.
Speaker 2:Wow.
Speaker 1:We don't know why, A leading theory is that. Remember I said the concentration of methane is very low, so that's not good for doing chemistry right. If your substrate is very low concentration, the first thing these microbes had to figure out how to do was they had to figure out how to concentrate it. And maybe the purpose of these things is to somehow import the methane into this little luminal space where you can probably increase local concentrations by a couple orders of magnitude. Then suddenly you're in business to do.
Speaker 1:Chemistry maybe this is primary, a methane concentrating organelle, and it's just to make it a great place to do chemistry from the substrate perspective, probably that almost certainly that's not the whole story. The experts that wrote the problem statement pointed out that the exact lipid concentration of these intracellular membrane spaces really seems to control chemistry. Maybe that has to do with the methane concentration mechanism. I would say that the literature we have on other enzymes that really depend on the lipid concentration, there's probably something going on with the membrane at the lipid surface, the protein, the enzyme at the lipid surface, the protein, the enzyme at the lipid eye layer, probably some critical interaction with these fatty acids to lock it into the most active state.
Speaker 2:This is like a C4 concentrating mechanism that we see in plants right, but for carbon dioxide.
Speaker 1:Yeah, wow, yep.
Speaker 2:Wow, and so it makes me think of the nitrosome that came out last year. I'm going to butcher that description so hopefully people know what I'm half referring to. So there is a organelle in methanotrophs that nobody understands what it does, what it's got on the membrane, what it's actually potentially concentrating inside, what it's got on the membrane, what it's actually potentially concentrating inside, which we don't know which way the MMO is oriented.
Speaker 1:Is it?
Speaker 2:pointing in, and is the pointy end on the inside or the outside?
Speaker 1:Exactly.
Speaker 2:Wow, this is one of my moments of wow. We don't know anything about biology. And for people who are sitting there thinking I've done cloning experiments, how hard could it be just to staple all the pieces together, shove it in E coli? People have done it right, but can you just the technicalities here.
Speaker 1:it's like it's three subunits. It's three, but again people have made some progress using modern protein engineering tools to reduce that number of subunits. So you can like combine them, so you can get it down to. I believe people have got it down to two subunits. I don't know if a single, I don't know if we got it down to one, because then you know, but that may not be possible. If your stoichiometry is not one-to-one-to-one, that may be a pretty unaddressable. You may actually do yourself a disservice if you enforce inappropriate stoichiometry.
Speaker 1:But we don't know all these things yet. But the good news is, with protein engineering and protein design and I tell all the first year postdocs and grad students this, I was like protein design in many ways is easy because of two things you only need one thing to work and in the end you don't have to understand why it works. Obviously, it's better if you have more than one thing and it's better if you understand how it works, but you actually don't need either of those to be true. If we can get to a point where people can start doing directed evolution and make this better, then honestly it doesn't really matter if we have a complete understanding of all the parts. I want to be useful. I don't need to understand everything. I just need to understand enough of the system to be useful and help make a thing that does the job.
Speaker 2:I love it. And one more, just technical question. I'm sure you've already turned over this rock which is non homology search is pretty much a go-to approach now and there's a bunch of different tools for this. In my brief I have not done this, so I'm just speaking as a fake expert here. But if you can understand the pointy end of the enzyme, or like where the active site for taking in methane and spitting out broken up methane, do you think it's possible to do a non-homology search over all the genomic data sets and metagenomic data sets we have?
Speaker 1:Maybe I would argue that this is probably one of those systems where there are so many intangibles, the concentratingAD NADP question that honestly. I think, the way that people have made the most gains so far is just getting random methanotrophs and screening them. Just look, just culture them from somewhere, isolate it, sequence it, make sure you've got a thing that you understand and then just test it. And again, somehow, what we produce is we screen 500 different methanotrophs and we get one. That's like super awesome, with little to no understanding.
Speaker 2:Like I'll still call that that's a win and I think this the humility with this project is. It reminds me of the oxfos wars in the 70s. Right, the race was what are all the metabolites from breaking down sugar? Right, and there was this one eccentric genius who was like it's not a metabolite. You're looking for a chemical answer to what I think is a spatial solution, then, and that was the proton gradient.
Speaker 2:And I'll be. I'm totally guilty of this, where, when I first started thinking about MMO, I was just thinking about it as a protein problem, but it seems like it's not, or it's not just a protein problem and there's a multimodal view of biology which requires a bunch of tools and sensors and people to figure it out.
Speaker 1:Yeah, and protein gradient may be the answer to this one too. That's one of the first things we're going to test is we're going to put our pH sensors in there and our redox potential sensors. Membrane potential yeah, we don't know the voltage across these membranes, in this intracellular membrane space.
Speaker 2:Maybe it's non-stoichiometric on the way the subunits bind too right. Absolutely yeah, absolutely so let's leave this on a teaser note. More stuff coming soon and just to flag for the audience, there are so many other cool things that your lab does. We didn't talk about psychedelics. We didn't talk about really beautiful parts of frankly. To me, my bucket is brain metabolism Right.
Speaker 1:So many cool things your lab does, I hope people look you up, but we do.
Speaker 2:We finish here with four rapid fire questions and then help people find you. First rapid fire question. What is a single book, paper, art piece or idea that just blew your mind and shaped your development as a scientist?
Speaker 1:Oh, shape my development as a scientist. That is an interesting one, maybe when this will reveal my age, but Gödel Escher Bach. Oh yeah, that was pretty formative back in the I think that was early 90s.
Speaker 2:Wow, and the brain right.
Speaker 1:Yeah, more about, just like philosophy of math and how to approach problems, how to think about hard problems and to find connections between seemingly disparate realms of scientific endeavor Great.
Speaker 2:What is the best advice line that a mentor gave you seemingly disparate realms of scientific endeavor Great.
Speaker 1:What is the best advice line that a mentor gave you? Oh okay, these are tough ones, wow. So I'm going to give you a non-traditional answer to this and I'm going to say that the best line of advice I got was advice that I completely ignored of everything we made away for free, instantaneously, like as soon as we invented it, years before pre-publication, years before pre-printing even, and people said you can't do that. That was the best advice I got because I was like, yeah, I can.
Speaker 1:Yeah, I can and yeah, I will, and I'd like to think that's one of the things where I think I'm proudest. Where I do think we actually did manage to move the needle a bit was in taking the culture of toolmaking. Toolmaking used to be the weirdest scientific endeavor, because these people would make these tools, they would publish papers showing that they were useful and you could use them to address scientific questions, and then they would keep them to themselves. And it's what the fuck are you doing?
Speaker 1:Like you made this tool and you showed it could be useful, and now you're not even giving it to people. But I was like give it away to everybody and if we get scooped every now and then, so be it. Like it's going to be more than repaid by stuff that's going to happen good things that are going to happen, and I think that's been completely borne out.
Speaker 1:So you know that's my answer is people saying you can't do that, don't do that, it's foolish to do that. No one in the early days, no one in 06, was supportive of me giving things out very soon, very free, with no restrictions. I had zero backup from above or below, but I just was like fuck it. This is really important to me. This is something I'm going to make happen come hell or high water, and so I'm glad people told me I couldn't do it.
Speaker 2:Dude, I love it, so you interpreted best as highest magnitude.
Speaker 1:Yeah.
Speaker 2:That's perfect.
Speaker 1:Most impactful advice.
Speaker 2:Question three if you had a magic wand to get more attention and resources into one part of biology, what would it be?
Speaker 1:I think it has to be climate biotech right. We're facing. That's the only like really addressable existential threat to the species and I do think it tell you people listening like you can switch into climate. Don't be intimidated that you don't have formal training in this. I had zero formal training in neuroscience and I was a neuroscientist. I've taken zero climate classes and now I want to be a climate scientist. You can do this and yeah, biotech, protein engineering, direct evolution, ai, all the tools. I think it's a realm that's ripe for. We're ready to make hay and the time is now.
Speaker 2:Awesome For the listeners. I did not pay him to say that, but I, of course, like the answer. All right, fourth question, and we're done. What is one aspect of personal development that you think biotechnologists need to spend more time on?
Speaker 1:Personal development. Yeah, these are tough questions, you know. The field's come a long way, and particularly our little way, and particularly our little community, our little corner of it. But I still think that everyone can always afford to be reminded that you should share everything. You should share all your data, all your reagents, all your failure modes, because in the end it just turns out to be very short-sighted to think that, oh, I'm going to hold on to this thing and I'm going to this is going to be my little corner.
Speaker 1:You can have a bigger impact in the big picture. If you let the bird out of its cage and let it fly around and be useful for a jillion people, you will get more credit, you will be more famous, you will be more kindly thought of. That would be my advice Learn to let go of things and not insist on receiving personal credit. Yeah, I'll just leave you with one last thought.
Speaker 1:Like the thing that I love most about GCaMP and having let it free to the world, I love it when I see a paper that's we used calcium imaging to do this. It doesn't say it was GCaMP6 or 8. It doesn't even say it was GCaMP. It just says we use calcium imaging. To me, that is the ultimate credit, receiving no credit at all, not even acknowledged. That was something that was useful. So it's just we use calcium imaging and that's what I want to see for things like climate, biotech, where stuff becomes like so turnkey and so ubiquitous that you don't even have to describe what it is anymore because it's just a thing that always works and everyone takes for granted.
Speaker 2:That is so on brand you. It's kind of like nobody cites PCR anymore. We're not giving credit to Tim Berners-Lee or Al Gore for inventing the internet.
Speaker 1:These things just work and they're everywhere. Yeah, it's part of the, it's just in the air now.
Speaker 2:Wow, so we get. This is a great place to end it. I want to make sure that people know where to find you If you want to steer them towards anything. The last thing that people will hear from me is just a huge thank you to Lauren. You've been an amazing mentor and I think you've led from the front in a lot of the great cultural points that we want to see more of in bio. So for me, thank you very much.
Speaker 1:Well and Dan, thank you. You guys and your organization, like you guys, are one of the few things that like keep me hopeful in this nightmarish hellscape we live in. There's so much to be depressed about and so many reasons just to give up and walk away. But your enthusiasm, your can-do attitude, I just love it. And every time it gets me like I'm like actually, yeah, fuck, we can do this, like there's still time to save this planet. Let's fucking go.
Speaker 2:So that's all, and so would you send people to Twitter or X. Is there any resources? Or just read your papers?
Speaker 1:Honestly, I'd say just email me. Just, you can easily find any of my emails and just email me, and if you don't get a reply, just email me again. I actually love it when people email me and are like hey, buddy, because I'm like oh shit, sorry, I forgot, I was super excited, but then I just I whiffed it.
Speaker 2:Amazing, amazing.
Speaker 1:All right.
Speaker 2:Lauren, thank you so much for taking the time. Thank you for coming on the podcast and for all the great work that you do out in the community.
Speaker 1:Yeah, thanks you too.
Speaker 2:Dan, my pleasure. Thank you so much for tuning into this episode of the Climate Biotech Podcast. We hope this has been educational, inspirational and fun for you as you navigate your own journey and bring the best of biotech into planetary scale solutions. We'll be back with another one soon and in the meantime, stay in touch with Homeworld Collective on LinkedIn, twitter or Blue Sky. Links are all in the show notes. Huge thanks to our producer, dave Clark, and operations lead, paul Himmelstein, for making these episodes happen. Catch you on the next one.