Podcast appearances and mentions of Sydney Brenner

“Eventually, my dream would be to simulate a virtual cell.”—Demis HassabisThe aspiration to build the virtual cell is considered to be equivalent to a moonshot for digital biology. Recently, 42 leading life scientists published a paper in Cell on why this is so vital, and how it may ultimately be accomplished. This conversation is with 2 of the authors, Charlotte Bunne, now at EPFL and Steve Quake, a Professor at Stanford University, who heads up science at the Chan-Zuckerberg Initiative The audio (above) is available on iTunes and Spotify. The full video is linked here, at the top, and also can be found on YouTube.TRANSCRIPT WITH LINKS TO AUDIO Eric Topol (00:06):Hello, it's Eric Topol with Ground Truths and we've got a really hot topic today, the virtual cell. And what I think is extraordinarily important futuristic paper that recently appeared in the journal Cell and the first author, Charlotte Bunne from EPFL, previously at Stanford's Computer Science. And Steve Quake, a young friend of mine for many years who heads up the Chan Zuckerberg Initiative (CZI) as well as a professor at Stanford. So welcome, Charlotte and Steve.Steve Quake (00:42):Thanks, Eric. It's great to be here.Charlotte Bunne:Thanks for having me.Eric Topol (00:45):Yeah. So you wrote this article that Charlotte, the first author, and Steve, one of the senior authors, appeared in Cell in December and it just grabbed me, “How to build the virtual cell with artificial intelligence: Priorities and opportunities.” It's the holy grail of biology. We're in this era of digital biology and as you point out in the paper, it's a convergence of what's happening in AI, which is just moving at a velocity that's just so extraordinary and what's happening in biology. So maybe we can start off by, you had some 42 authors that I assume they congregated for a conference or something or how did you get 42 people to agree to the words in this paper?Steve Quake (01:33):We did. We had a meeting at CZI to bring community members together from many different parts of the community, from computer science to bioinformatics, AI experts, biologists who don't trust any of this. We wanted to have some real contrarians in the mix as well and have them have a conversation together about is there an opportunity here? What's the shape of it? What's realistic to expect? And that was sort of the genesis of the article.Eric Topol (02:02):And Charlotte, how did you get to be drafting the paper?Charlotte Bunne (02:09):So I did my postdoc with Aviv Regev at Genentech and Jure Leskovec at CZI and Jure was part of the residency program of CZI. And so, this is how we got involved and you had also prior work with Steve on the universal cell embedding. So this is how everything got started.Eric Topol (02:29):And it's actually amazing because it's a who's who of people who work in life science, AI and digital biology and omics. I mean it's pretty darn impressive. So I thought I'd start off with a quote in the article because it kind of tells a story of where this could go. So the quote was in the paper, “AIVC (artificial intelligence virtual cell) has the potential to revolutionize the scientific process, leading to future breakthroughs in biomedical research, personalized medicine, drug discovery, cell engineering, and programmable biology.” That's a pretty big statement. So maybe we can just kind of toss that around a bit and maybe give it a little more thoughts and color as to what you were positing there.Steve Quake (03:19):Yeah, Charlotte, you want me to take the first shot at that? Okay. So Eric, it is a bold claim and we have a really bold ambition here. We view that over the course of a decade, AI is going to provide the ability to make a transformative computational tool for biology. Right now, cell biology is 90% experimental and 10% computational, roughly speaking. And you've got to do just all kinds of tedious, expensive, challenging lab work to get to the answer. And I don't think AI is going to replace that, but it can invert the ratio. So within 10 years I think we can get to biology being 90% computational and 10% experimental. And the goal of the virtual cell is to build a tool that'll do that.Eric Topol (04:09):And I think a lot of people may not understand why it is considered the holy grail because it is the fundamental unit of life and it's incredibly complex. It's not just all the things happening in the cell with atoms and molecules and organelles and everything inside, but then there's also the interactions the cell to other cells in the outside tissue and world. So I mean it's really quite extraordinary challenge that you've taken on here. And I guess there's some debate, do we have the right foundation? We're going to get into foundation models in a second. A good friend of mine and part of this whole I think process that you got together, Eran Segal from Israel, he said, “We're at this tipping point…All the stars are aligned, and we have all the different components: the data, the compute, the modeling.” And in the paper you describe how we have over the last couple of decades have so many different data sets that are rich that are global initiatives. But then there's also questions. Do we really have the data? I think Bo Wang especially asked about that. Maybe Charlotte, what are your thoughts about data deficiency? There's a lot of data, but do you really have what we need before we bring them all together for this kind of single model that will get us some to the virtual cell?Charlotte Bunne (05:41):So I think, I mean one core idea of building this AIVC is that we basically can leverage all experimental data that is overall collected. So this also goes back to the point Steve just made. So meaning that we basically can integrate across many different studies data because we have AI algorithms or the architectures that power such an AIVC are able to integrate basically data sets on many different scales. So we are going a bit away from this dogma. I'm designing one algorithm from one dataset to this idea of I have an architecture that can take in multiple dataset on multiple scales. So this will help us a bit in being somewhat efficient with the type of experiments that we need to make and the type of experiments we need to conduct. And again, what Steve just said, ultimately, we can very much steer which data sets we need to collect.Charlotte Bunne (06:34):Currently, of course we don't have all the data that is sufficient. I mean in particular, I think most of the tissues we have, they are healthy tissues. We don't have all the disease phenotypes that we would like to measure, having patient data is always a very tricky case. We have mostly non-interventional data, meaning we have very limited understanding of somehow the effect of different perturbations. Perturbations that happen on many different scales in many different environments. So we need to collect a lot here. I think the overall journey that we are going with is that we take the data that we have, we make clever decisions on the data that we will collect in the future, and we have this also self-improving entity that is aware of what it doesn't know. So we need to be able to understand how well can I predict something on this somewhat regime. If I cannot, then we should focus our data collection effort into this. So I think that's not a present state, but this will basically also guide the future collection.Eric Topol (07:41):Speaking of data, one of the things I think that's fascinating is we saw how AlphaFold2 really revolutionized predicting proteins. But remember that was based on this extraordinary resource that had been built, the Protein Data Bank that enabled that. And for the virtual cell there's no such thing as a protein data bank. It's so much more as you emphasize Charlotte, it's so much dynamic and these perturbations that are just all across the board as you emphasize. Now the human cell atlas, which currently some tens of millions, but going into a billion cells, we learned that it used to be 200 cell types. Now I guess it's well over 5,000 and that we have 37 trillion cells approximately in the average person adult's body is a formidable map that's being made now. And I guess the idea that you're advancing is that we used to, and this goes back to a statement you made earlier, Steve, everything we did in science was hypothesis driven. But if we could get computational model of the virtual cell, then we can have AI exploration of the whole field. Is that really the nuts of this?Steve Quake (09:06):Yes. A couple thoughts on that, maybe Theo Karaletsos, our lead AI person at CZI says machine learning is the formalism through which we understand high dimensional data and I think that's a very deep statement. And biological systems are intrinsically very high dimensional. You've got 20,000 genes in the human genome in these cell atlases. You're measuring all of them at the same time in each single cell. And there's a lot of structure in the relationships of their gene expression there that is just not evident to the human eye. And for example, CELL by GENE, our database that collects all the aggregates, all of the single cell transcriptomic data is now over a hundred million cells. And as you mentioned, we're seeing ways to increase that by an order of magnitude in the near future. The project that Jure Leskovec and I worked on together that Charlotte referenced earlier was like a first attempt to build a foundational model on that data to discover some of the correlations and structure that was there.Steve Quake (10:14):And so, with a subset, I think it was the 20 or 30 million cells, we built a large language model and began asking it, what do you understand about the structure of this data? And it kind of discovered lineage relationships without us teaching it. We trained on a matrix of numbers, no biological information there, and it learned a lot about the relationships between cell type and lineage. And that emerged from that high dimensional structure, which was super pleasing to us and really, I mean for me personally gave me the confidence to say this stuff is going to work out. There is a future for the virtual cell. It's not some made up thing. There is real substance there and this is worth investing an enormous amount of CZIs resources in going forward and trying to rally the community around as a project.Eric Topol (11:04):Well yeah, the premise here is that there is a language of life, and you just made a good case that there is if you can predict, if you can query, if you can generate like that. It is reminiscent of the famous Go game of Lee Sedol, that world champion and how the machine came up with a move (Move 37) many, many years ago that no human would've anticipated and I think that's what you're getting at. And the ability for inference and reason now to add to this. So Charlotte, one of the things of course is about, well there's two terms in here that are unfamiliar to many of the listeners or viewers of this podcast, universal representations (UR) and virtual instrument (VIs) that you make a pretty significant part of how you are going about this virtual cell model. So could you describe that and also the embeddings as part of the universal representation (UR) because I think embeddings, or these meaningful relationships are key to what Steve was just talking about.Charlotte Bunne (12:25):Yes. So in order to somewhat leverage very different modalities in order to leverage basically modalities that will take measurements across different scales, like the idea is that we have large, may it be transformer models that might be very different. If I have imaging data, I have a vision transformer, if I have a text data, I have large language models that are designed of course for DNA then they have a very wide context and so on and so forth. But the idea is somewhat that we have models that are connected through the scales of biology because those scales we know. We know which components are somewhat involved or in measurements that are happening upstream. So we have the somewhat interconnection or very large model that will be trained on many different data and we have this internal model representation that somewhat capture everything they've seen. And so, this is what we call those universal representation (UR) that will exist across the scales of biology.Charlotte Bunne (13:22):And what is great about AI, and so I think this is a bit like a history of AI in short is the ability to predict the last years, the ability to generate, we can generate new hypothesis, we can generate modalities that we are missing. We can potentially generate certain cellular state, molecular state have a certain property, but I think what's really coming is this ability to reason. So we see this in those very large language models, the ability to reason about a hypothesis, how we can test it. So this is what those instruments ultimately need to do. So we need to be able to simulate the change of a perturbation on a cellular phenotype. So on the internal representation, the universal representation of a cell state, we need to simulate the fact the mutation has downstream and how this would propagate in our representations upstream. And we need to build many different type of virtual instruments that allow us to basically design and build all those capabilities that ultimately the AI virtual cell needs to possess that will then allow us to reason, to generate hypothesis, to basically predict the next experiment to conduct to predict the outcome of a perturbation experiment to in silico design, cellular states, molecular states, things like that. And this is why we make the separation between internal representation as well as those instruments that operate on those representations.Eric Topol (14:47):Yeah, that's what I really liked is that you basically described the architecture, how you're going to do this. By putting these URs into the VIs, having a decoder and a manipulator and you basically got the idea if you can bring all these different integrations about which of course is pending. Now there are obviously many naysayers here that this is impossible. One of them is this guy, Philip Ball. I don't know if you read the language, How Life Works. Now he's a science journalist and he's a prolific writer. He says, “Comparing life to a machine, a robot, a computer, sells it short. Life is a cascade of processes, each with a distinct integrity and autonomy, the logic of which has no parallel outside the living world.” Is he right? There's no way to model this. It's silly, it's too complex.Steve Quake (15:50):We don't know, alright. And it's great that there's naysayers. If everyone agreed this was doable, would it be worth doing? I mean the whole point is to take risks and get out and do something really challenging in the frontier where you don't know the answer. If we knew that it was doable, I wouldn't be interested in doing it. So I personally am happy that there's not a consensus.Eric Topol (16:16):Well, I mean to capture people's imagination here, if you're successful and you marshal a global effort, I don't know who's going to pay for it because it's a lot of work coming here going forward. But if you can do it, the question here is right today we talk about, oh let's make an organoid so we can figure out how to treat this person's cancer or understand this person's rare disease or whatever. And instead of having to wait weeks for this culture and all the expense and whatnot, you could just do it in a computer and in silico and you have this virtual twin of a person's cells and their tissue and whatnot. So the opportunity here is, I don't know if people get, this is just extraordinary and quick and cheap if you can get there. And it's such a bold initiative idea, who will pay for this do you think?Steve Quake (17:08):Well, CZI is putting an enormous amount of resources into it and it's a major project for us. We have been laying the groundwork for it. We recently put together what I think is if not the largest, one of the largest GPU supercomputer clusters for nonprofit basic science research that came online at the end of last year. And in fact in December we put out an RFA for the scientific community to propose using it to build models. And so we're sharing that resource within the scientific community as I think you appreciate, one of the real challenges in the field has been access to compute resources and industry has it academia at a much lower level. We are able to be somewhere in between, not quite at the level of a private company but the tech company but at a level beyond what most universities are being able to do and we're trying to use that to drive the field forward. We're also planning on launching RFAs we this year to help drive this project forward and funding people globally on that. And we are building a substantial internal effort within CZI to help drive this project forward.Eric Topol (18:17):I think it has the looks of the human genome project, which at time as you know when it was originally launched that people thought, oh, this is impossible. And then look what happened. It got done. And now the sequence of genome is just a commodity, very relatively, very inexpensive compared to what it used to be.Steve Quake (18:36):I think a lot about those parallels. And I will say one thing, Philip Ball, I will concede him the point, the cells are very complicated. The genome project, I mean the sort of genius there was to turn it from a biology problem to a chemistry problem, there is a test tube with a chemical and it work out the structure of that chemical. And if you can do that, the problem is solved. I think what it means to have the virtual cell is much more complex and ambiguous in terms of defining what it's going to do and when you're done. And so, we have our work cut out for us there to try to do that. And that's why a little bit, I established our North Star and CZI for the next decade as understanding the mysteries of the cell and that word mystery is very important to me. I think the molecules, as you pointed out earlier are understood, genome sequenced, protein structure solved or predicted, we know a lot about the molecules. Those are if not solved problems, pretty close to being solved. And the real mystery is how do they work together to create life in the cell? And that's what we're trying to answer with this virtual cell project.Eric Topol (19:43):Yeah, I think another thing that of course is happening concurrently to add the likelihood that you'll be successful is we've never seen the foundation models coming out in life science as they have in recent weeks and months. Never. I mean, I have a paper in Science tomorrow coming out summarizing the progress about not just RNA, DNA, ligands. I mean the whole idea, AlphaFold3, but now Boltz and so many others. It's just amazing how fast the torrent of new foundation models. So Charlotte, what do you think accounts for this? This is unprecedented in life science to see foundation models coming out at this clip on evolution on, I mean you name it, design of every different molecule of life or of course in cells included in that. What do you think is going on here?Charlotte Bunne (20:47):So on the one hand, of course we benefit profits and inherit from all the tremendous efforts that have been made in the last decades on assembling those data sets that are very, very standardized. CELLxGENE is very somehow AI friendly, as you can say, it is somewhat a platform that is easy to feed into algorithms, but at the same time we actually also see really new building mechanisms, design principles of AI algorithms in itself. So I think we have understood that in order to really make progress, build those systems that work well, we need to build AI tools that are designed for biological data. So to give you an easy example, if I use a large language model on text, it's not going to work out of the box for DNA because we have different reading directions, different context lens and many, many, many, many more.Charlotte Bunne (21:40):And if I look at standard computer vision where we can say AI really excels and I'm applying standard computer vision, vision transformers on multiplex images, they're not going to work because normal computer vision architectures, they always expect the same three inputs, RGB, right? In multiplex images, I'm measuring up to 150 proteins potentially in a single experiment, but every study will measure different proteins. So I deal with many different scales like larger scales and I used to attention mechanisms that we have in usual computer vision. Transformers are not going to work anymore, they're not going to scale. And at the same time, I need to be completely flexible in whatever input combination of channel I'm just going to face in this experiment. So this is what we right now did for example, in our very first work, inheriting the design principle that we laid out in the paper AI virtual cell and then come up with new AI architectures that are dealing with these very special requirements that biological data have.Charlotte Bunne (22:46):So we have now a lot of computer scientists that work very, very closely have a very good understanding of biologists. Biologists that are getting much and much more into the computer science. So people who are fluent in both languages somewhat, that are able to now build models that are adopted and designed for biological data. And we don't just take basically computer vision architectures that work well on street scenes and try to apply them on biological data. So it's just a very different way of thinking about it, starting constructing basically specialized architectures, besides of course the tremendous data efforts that have happened in the past.Eric Topol (23:24):Yeah, and we're not even talking about just sequence because we've also got imaging which has gone through a revolution, be able to image subcellular without having to use any types of stains that would disrupt cells. That's another part of the deep learning era that came along. One thing I thought was fascinating in the paper in Cell you wrote, “For instance, the Short Read Archive of biological sequence data holds over 14 petabytes of information, which is 1,000 times larger than the dataset used to train ChatGPT.” I mean that's a lot of tokens, that's a lot of stuff, compute resources. It's almost like you're going to need a DeepSeek type of way to get this. I mean not that DeepSeek as its claim to be so much more economical, but there's a data challenge here in terms of working with that massive amount that is different than the human language. That is our language, wouldn't you say?Steve Quake (24:35):So Eric, that brings to mind one of my favorite quotes from Sydney Brenner who is such a wit. And in 2000 at the sort of early first flush of success in genomics, he said, biology is drowning in a sea of data and starving for knowledge. A very deep statement, right? And that's a little bit what the motivation was for putting the Short Read Archive statistic into the paper there. And again, for me, part of the value of this endeavor of creating a virtual cell is it's a tool to help us translate data into knowledge.Eric Topol (25:14):Yeah, well there's two, I think phenomenal figures in your Cell paper. The first one that kicks across the capabilities of the virtual cell and the second that compares the virtual cell to the real or the physical cell. And we'll link that with this in the transcript. And the other thing we'll link is there's a nice Atlantic article, “A Virtual Cell Is a ‘Holy Grail' of Science. It's Getting Closer.” That might not be quite close as next week or year, but it's getting close and that's good for people who are not well grounded in this because it's much more taken out of the technical realm. This is really exciting. I mean what you're onto here and what's interesting, Steve, since I've known you for so many years earlier in your career you really worked on omics that is being DNA and RNA and in recent times you've made this switch to cells. Is that just because you're trying to anticipate the field or tell us a little bit about your migration.Steve Quake (26:23):Yeah, so a big part of my career has been trying to develop new measurement technologies that'll provide insight into biology. And decades ago that was understanding molecules. Now it's understanding more complex biological things like cells and it was like a natural progression. I mean we built the sequencers, sequenced the genomes, done. And it was clear that people were just going to do that at scale then and create lots of data. Hopefully knowledge would get out of that. But for me as an academic, I never thought I'd be in the position I'm in now was put it that way. I just wanted to keep running a small research group. So I realized I would have to get out of the genome thing and find the next frontier and it became this intersection of microfluidics and genomics, which as you know, I spent a lot of time developing microfluidic tools to analyze cells and try to do single cell biology to understand their heterogeneity. And that through a winding path led me to all these cell atlases and to where we are now.Eric Topol (27:26):Well, we're fortunate for that and also with your work with CZI to help propel that forward and I think it sounds like we're going to need a lot of help to get this thing done. Now Charlotte, as a computer scientist now at EPFL, what are you going to do to keep working on this and what's your career advice for people in computer science who have an interest in digital biology?Charlotte Bunne (27:51):So I work in particular on the prospect of using this to build diagnostic tools and to make diagnostics in the clinic easier because ultimately we have somewhat limited capabilities in the hospital to run deep omics, but the idea of being able to somewhat map with a cheaper and lighter modality or somewhat diagnostic test into something much richer because a model has been seeing all those different data and can basically contextualize it. It's very interesting. We've seen all those pathology foundation models. If I can always run an H&E, but then decide when to run deeper diagnostics to have a better or more accurate prediction, that is very powerful and it's ultimately reducing the costs, but the precision that we have in hospitals. So my faculty position right now is co-located between the School of Life Sciences, School of Computer Science. So I have a dual affiliation and I'm affiliated to the hospitals to actually make this possible and as a career advice, I think don't be shy and stick to your discipline.Charlotte Bunne (28:56):I have a bachelor's in biology, but I never only did biology. I have a PhD in computer science, which you would think a bachelor in biology not necessarily qualifies you through. So I think this interdisciplinarity also requires you to be very fluent, very comfortable in reading many different styles of papers and publications because a publication in a computer science venue will be very, very different from the way we write in biology. So don't stick to your study program, but just be free in selecting whatever course gets you closer to the knowledge you need in order to do the research or whatever task you are building and working on.Eric Topol (29:39):Well, Charlotte, the way you're set up there with this coalescence of life science and computer science is so ideal and so unusual here in the US, so that's fantastic. That's what we need and that's really the underpinning of how you're going to get to the virtual cells, getting these two communities together. And Steve, likewise, you were an engineer and somehow you became one of the pioneers of digital biology way back before it had that term, this interdisciplinary, transdisciplinary. We need so much of that in order for you all to be successful, right?Steve Quake (30:20):Absolutely. I mean there's so much great discovery to be done on the boundary between fields. I trained as a physicist and kind of made my career this boundary between physics and biology and technology development and it's just sort of been a gift that keeps on giving. You've got a new way to measure something, you discover something new scientifically and it just all suggests new things to measure. It's very self-reinforcing.Eric Topol (30:50):Now, a couple of people who you know well have made some pretty big statements about this whole era of digital biology and I think the virtual cell is perhaps the biggest initiative of all the digital biology ongoing efforts, but Jensen Huang wrote, “for the first time in human history, biology has the opportunity to be engineering, not science.” And Demis Hassabis wrote or said, ‘we're seeing engineering science, you have to build the artifact of interest first, and then once you have it, you can use the scientific method to reduce it down and understand its components.' Well here there's a lot to do to understand its components and if we can do that, for example, right now as both of AI drug discoveries and high gear and there's umpteen numbers of companies working on it, but it doesn't account for the cell. I mean it basically is protein, protein ligand interactions. What if we had drug discovery that was cell based? Could you comment about that? Because that doesn't even exist right now.Steve Quake (32:02):Yeah, I mean I can say something first, Charlotte, if you've got thoughts, I'm curious to hear them. So I do think AI approaches are going to be very useful designing molecules. And so, from the perspective of designing new therapeutics, whether they're small molecules or antibodies, yeah, I mean there's a ton of investment in that area that is a near term fruit, perfect thing for venture people to invest in and there's opportunity there. There's been enough proof of principle. However, I do agree with you that if you want to really understand what happens when you drug a target, you're going to want to have some model of the cell and maybe not just the cell, but all the different cell types of the body to understand where toxicity will come from if you have on-target toxicity and whether you get efficacy on the thing you're trying to do.Steve Quake (32:55):And so, we really hope that people will use the virtual cell models we're going to build as part of the drug discovery development process, I agree with you in a little of a blind spot and we think if we make something useful, people will be using it. The other thing I'll say on that point is I'm very enthusiastic about the future of cellular therapies and one of our big bets at CZI has been starting the New York Biohub, which is aimed at really being very ambitious about establishing the engineering and scientific foundations of how to engineer completely, radically more powerful cellular therapies. And the virtual cell is going to help them do that, right? It's going to be essential for them to achieve that mission.Eric Topol (33:39):I think you're pointing out one of the most important things going on in medicine today is how we didn't anticipate that live cell therapy, engineered cells and ideally off the shelf or in vivo, not just having to take them out and work on them outside the body, is a revolution ongoing, and it's not just in cancer, it's in autoimmune diseases and many others. So it's part of the virtual cell need. We need this. One of the things that's a misnomer, I want you both to comment on, we keep talking about single cell, single cell. And there's a paper spatial multi-omics this week, five different single cell scales all integrated. It's great, but we don't get to single cell. We're basically looking at 50 cells, 100 cells. We're not doing single cell because we're not going deep enough. Is that just a matter of time when we actually are doing, and of course the more we do get down to the single or a few cells, the more insights we're going to get. Would you comment about that? Because we have all this literature on single cell comes out every day, but we're not really there yet.Steve Quake (34:53):Charlotte, do you want to take a first pass at that and then I can say something?Charlotte Bunne (34:56):Yes. So it depends. So I think if we look at certain spatial proteomics, we still have subcellular resolutions. So of course, we always measure many different cells, but we are able to somewhat get down to resolution where we can look at certain colocalization of proteins. This also goes back to the point just made before having this very good environment to study drugs. If I want to build a new drug, if I want to build a new protein, the idea of building this multiscale model allows us to actually simulate different, somehow binding changes and binding because we simulate the effect of a drug. Ultimately, the redouts we have they are subcellular. So of course, we often in the spatial biology, we often have a bit like methods that are rather coarse they have a spot that averages over certain some cells like hundreds of cells or few cells.Charlotte Bunne (35:50):But I think we also have more and more technologies that are zooming in that are subcellular where we can actually tag or have those probe-based methods that allow us to zoom in. There's microscopy of individual cells to really capture them in 3D. They are of course not very high throughput yet, but it gives us also an idea of the morphology and how ultimately morphology determine certain somehow cellular properties or cellular phenotype. So I think there's lots of progress also on the experimental and that ultimately will back feed into the AI virtual cell, those models that will be fed by those data. Similarly, looking at dynamics, right, looking at live imaging of individual cells of their morphological changes. Also, this ultimately is data that we'll need to get a better understanding of disease mechanisms, cellular phenotypes functions, perturbation responses.Eric Topol (36:47):Right. Yes, Steve, you can comment on that and the amazing progress that we have made with space and time, spatial temporal resolution, spatial omics over these years, but that we still could go deeper in terms of getting to individual cells, right?Steve Quake (37:06):So, what can we do with a single cell? I'd say we are very mature in our ability to amplify and sequence the genome of a single cell, amplify and sequence the transcriptome of a single cell. You can ask is one cell enough to make a biological conclusion? And maybe I think what you're referring to is people want to see replicates and so you can ask how many cells do you need to see to have confidence in any given biological conclusion, which is a reasonable thing. It's a statistical question in good science. I think I've been very impressed with how the mass spec people have been doing recently. I think they've finally cracked the ability to look at proteins from single cells and they can look at a couple thousand proteins. That was I think one of these Nature method of the year things at the end of last year and deep visual proteomics.Eric Topol (37:59):Deep visual proteomics, yes.Steve Quake (38:00):Yeah, they are over the hump. Yeah, they are over the hump with single cell measurements. Part of what's missing right now I think is the ability to reliably do all of that on the same cell. So this is what Charlotte was referring to be able to do sort of multi-modal measurements on single cells. That's kind of in its infancy and there's a few examples, but there's a lot more work to be done on that. And I think also the fact that these measurements are all destructive right now, and so you're losing the ability to look how the cells evolve over time. You've got to say this time point, I'm going to dissect this thing and look at a state and I don't get to see what happens further down the road. So that's another future I think measurement challenge to be addressed.Eric Topol (38:42):And I think I'm just trying to identify some of the multitude of challenges in this extraordinarily bold initiative because there are no shortage and that's good about it. It is given people lots of work to do to overcome, override some of these challenges. Now before we wrap up, besides the fact that you point out that all the work has to be done and be validated in real experiments, not just live in a virtual AI world, but you also comment about the safety and ethics of this work and assuming you're going to gradually get there and be successful. So could either or both of you comment about that because it's very thoughtful that you're thinking already about that.Steve Quake (41:10):As scientists and members of the larger community, we want to be careful and ensure that we're interacting with people who said policy in a way that ensures that these tools are being used to advance the cause of science and not do things that are detrimental to human health and are used in a way that respects patient privacy. And so, the ethics around how you use all this with respect to individuals is going to be important to be thoughtful about from the beginning. And I also think there's an ethical question around what it means to be publishing papers and you don't want people to be forging papers using data from the virtual cell without being clear about where that came from and pretending that it was a real experiment. So there's issues around those sorts of ethics as well that need to be considered.Eric Topol (42:07):And of those 40 some authors, do you around the world, do you have the sense that you all work together to achieve this goal? Is there kind of a global bonding here that's going to collaborate?Steve Quake (42:23):I think this effort is going to go way beyond those 40 authors. It's going to include a much larger set of people and I'm really excited to see that evolve with time.Eric Topol (42:31):Yeah, no, it's really quite extraordinary how you kick this thing off and the paper is the blueprint for something that we are all going to anticipate that could change a lot of science and medicine. I mean we saw, as you mentioned, Steve, how that deep visual proteomics (DVP) saved lives. It was what I wrote a spatial medicine, no longer spatial biology. And so, the way that this can change the future of medicine, I think a lot of people just have to have a little bit of imagination that once we get there with this AIVC, that there's a lot in store that's really quite exciting. Well, I think this has been an invigorating review of that paper and some of the issues surrounding it. I couldn't be more enthusiastic for your success and ultimately where this could take us. Did I miss anything during the discussion that we should touch on before we wrap up?Steve Quake (43:31):Not from my perspective. It was a pleasure as always Eric, and a fun discussion.Charlotte Bunne (43:38):Thanks so much.Eric Topol (43:39):Well thank you both and all the co-authors of this paper. We're going to be following this with the great interest, and I think for most people listening, they may not know that this is in store for the future. Someday we will get there. I think one of the things to point out right now is the models we have today that large language models based on transformer architecture, they're going to continue to evolve. We're already seeing so much in inference and ability for reasoning to be exploited and not asking for prompts with immediate answers, but waiting for days to get back. A lot more work from a lot more computing resources. But we're going to get models in the future to fold this together. I think that's one of the things that you've touched on the paper so that whatever we have today in concert with what you've laid out, AI is just going to keep getting better.Eric Topol (44:39):The biology that these foundation models are going to get broader and more compelling as to their use cases. So that's why I believe in this. I don't see this as a static situation right now. I just think that you're anticipating the future, and we will have better models to be able to integrate this massive amount of what some people would consider disparate data sources. So thank you both and all your colleagues for writing this paper. I don't know how you got the 42 authors to agree to it all, which is great, and it's just a beginning of something that's a new frontier. So thanks very much.Steve Quake (45:19):Thank you, Eric.**********************************************Thanks for listening, watching or reading Ground Truths. Your subscription is greatly appreciated.If you found this podcast interesting please share it!That makes the work involved in putting these together especially worthwhile.All content on Ground Truths—newsletters, analyses, and podcasts—is free, open-access, with no ads..Paid subscriptions are voluntary and all proceeds from them go to support Scripps Research. They do allow for posting comments and questions, which I do my best to respond to. Many thanks to those who have contributed—they have greatly helped fund our summer internship programs for the past two years. And such support is becoming more vital In light of current changes of funding by US biomedical research at NIH and other governmental agencies.Thanks to my producer Jessica Nguyen and to Sinjun Balabanoff for audio and video support at Scripps Research. Get full access to Ground Truths at erictopol.substack.com/subscribe

In this podcast, Thomas Czech, Distinguished Professor at the University of Colorado, Boulder, with a lineage of remarkable contributions on RNA, ribozyme, and telomeres, discuss why RNA is so incredibly versatile.Video snippet from our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.Transcript with links to the audio and external linksEric Topol (00:07):Well, hello, this is Eric Topol from Ground Truths, and it's really a delight for me to welcome Tom Cech who just wrote a book, the Catalyst, and who is a Nobel laureate for his work in RNA. And is at the University of Colorado Boulder as an extraordinary chemist and welcome Tom.Tom Cech (00:32):Eric, I'm really pleased to be here.The RNA GuyEric Topol (00:35):Well, I just thoroughly enjoyed your book, and I wanted to start out, if I could, with a quote, which gets us right off the story here, and let me just get to it here. You say, “the DNA guy would need to become an RNA guy. Though I didn't realize it at the time, jumping ship would turn out to be the most momentous decision in my life.” Can you elaborate a bit on that?Tom Cech (01:09):As a graduate student at Berkeley, I was studying DNA and chromosomes. I thought that DNA was king and really somewhat belittled the people in the lab next door who were working on RNA, I thought it was real sort of second fiddle material. Of course, when RNA is acting just as a message, which is an important function, a critical function in all life on earth, but still, it's a function that's subservient to DNA. It's just copying the message that's already written in the playbook of DNA. But little did I know that the wonders of RNA were going to excite me and really the whole world in unimaginable ways.Eric Topol (02:00):Well, they sure have, and you've lit up the world well before you had your Nobel Prize in 1989 was Sid Altman with ribozyme. And I think one of the things that struck me, which are so compelling in the book as I think people might know, it's divided in two sections. The first is much more on the biology, and the second is much more on the applications and how it's changing the world. We'll get into it particularly in medicine, but the interesting differentiation from DNA, which is the one trick pony, as you said, all it does is store stuff. And then the incredible versatility of RNA as you discovered as a catalyst, that challenging dogma, that proteins are supposed to be the only enzymes. And here you found RNA was one, but also so much more with respect to genome editing and what we're going to get into here. So I thought what we might get into is the fact that you kind of went into the scum of the pond with this organism, which by the way, you make a great case for the importance of basic science towards the end of the book. But can you tell us about how you, and then of course, many others got into the Tetrahymena thermophila, which I don't know that much about that organism.Tom Cech (03:34):Yeah, it's related to Tetrahymena is related to paramecium, which is probably more commonly known because it's an even larger single celled animal. And therefore, in an inexpensive grade school microscope, kids can look through and see these ciliated protozoa swimming around on a glass slide. But I first learned about them when I was a postdoc at MIT and I would drive down to Joe Gall's lab at Yale University where Liz Blackburn was a postdoc at the time, and they were all studying Tetrahymena. It has the remarkable feature that it has 10,000 identical copies of a particular gene and for a higher organism, one that has its DNA in the nucleus and does its protein synthesis in the cytoplasm. Typically, each gene's present in two copies, one from mom, one from dad. And if you're a biochemist, which I am having lots of stuff is a real advantage. So 10,000 copies of a particular gene pumping out RNA copies all the time was a huge experimental advantage. And that's what I started working on when I started my own lab at Boulder.Eric Topol (04:59):Well, and that's where, I guess the title of the book, the Catalyst ultimately, that grew into your discovery, right?Tom Cech (05:08):Well, at one level, yes, but I also think that the catalyst in a more general conversational sense means just facilitating life in this case. So RNA does much more than just serve as a biocatalyst or a message, and we'll get into that with genome editing and with telomerase as well.The Big Bang and 11 Nobel Prizes on RNA since 2000Eric Topol (05:32):Yes, and I should note that as you did early in the book, that there's been an 11 Nobel prize awardees since 2000 for RNA work. And in fact, we just had Venki who I know you know very well as our last podcast. And prior to that, Kati Karikó, Jennifer Doudna who worked in your lab, and the long list of people working RNA in the younger crowd like David Liu and Fyodor Urnov and just so many others, we need to have an RNA series because it's just exploding. And that one makes me take you back for a moment to 2007. And when I was reading the book, it came back to me about the Economist cover. You may recall almost exactly 17 years ago. It was called the Biology's Big Bang – Unravelling the secrets of RNA. And in that, there was a notable quote from that article. Let me just get to that. And it says, “it is probably no exaggeration to say that biology is now undergoing its neutron moment.”(06:52):This is 17 years ago. “For more than half a century the fundamental story of living things has been a tale of the interplay between genes, in the form of DNA, and proteins, which is genes encode and which do the donkey work of keeping living organisms living. The past couple of years, 17 years ago, however, has seen the rise and rise of a third type of molecule, called RNA.” Okay, so that was 2007. It's pretty extraordinary. And now of course we're talking about the century of biology. So can you kind of put these last 17 years in perspective and where we're headed?Tom Cech (07:34):Well, Eric, of course, this didn't all happen in one moment. It wasn't just one big bang. And the scientific community has been really entranced with the wonders of RNA since the 1960s when everyone was trying to figure out how messenger RNA stored the genetic code. But the general public has been really kept in the dark about this, I think. And as scientists, were partially to blame for not reaching out and sharing what we have found with them in a way that's more understandable. The DNA, the general public's very comfortable with, it's the stuff of our heredity. We know about genetic diseases, about tracing our ancestry, about solving crimes with DNA evidence. We even say things like it's in my DNA to mean that it's really fundamental to us. But I think that RNA has been sort of kept in the closet, and now with the mRNA vaccines against Covid-19, at least everyone's heard of RNA. And I think that that sort of allowed me to put my foot in the door and say, hey, if you were curious about the mRNA vaccines, I have some more stories for you that you might be really interested in.RNA vs RNAEric Topol (09:02):Yeah, well, we'll get to that. Maybe we should get to that now because it is so striking the RNA versus RNA chapter in your book, and basically the story of how this RNA virus SARS-CoV-2 led to a pandemic and it was fought largely through the first at scale mRNA nanoparticle vaccine package. Now, that takes us back to some seminal work of being able to find, giving an mRNA to a person without inciting massive amount of inflammation and the substitution of pseudouridine or uridine in order to do that. Does that really get rid of all the inflammation? Because obviously, as you know, there's been some negativism about mRNA vaccines for that and also for the potential of not having as much immune cell long term activation. Maybe you could speak to that.Tom Cech (10:03):Sure. So the discovery by Kati Karikó and Drew Weissman of the pseudouridine substitution certainly went a long way towards damping down the immune response, the inflammatory response that one naturally gets with an RNA injection. And the reason for that is that our bodies are tuned to be on the lookout for foreign RNA because so many viruses don't even mess with DNA at all. They just have a genome made of RNA. And so, RNA replicating itself is a danger sign. It means that our immune system should be on the lookout for this. And so, in the case of the vaccination, it's really very useful to dampen this down. A lot of people thought that this might make the mRNA vaccines strange or foreign or sort of a drug rather than a natural substance. But in fact, modified nucleotides, nucleotides being the building blocks of RNA, so these modified building blocks such as pseudoU, are in fact found in natural RNAs more in some than in others. And there are about 200 modified versions of the RNA building blocks found in cells. So it's really not an unusual modification or something that's all that foreign, but it was very useful for the vaccines. Now your other question Eric had to do with the, what was your other question, Eric?Eric Topol (11:51):No, when you use mRNA, which is such an extraordinary way to get the spike protein in a controlled way, exposed without the virus to people, and it saved millions of lives throughout the pandemic. But the other question is compared to other vaccine constructs, there's a question of does it give us long term protective immunity, particularly with T cells, both CD8 cytotoxic, maybe also CD4, as I know immunology is not your main area of interest, but that's been a rub that's been put out there, that it isn't just a weaning of immunity from the virus, but also perhaps that the vaccines themselves are not as good for that purpose. Any thoughts on that?Tom Cech (12:43):Well, so my main thought on that is that this is a property of the virus more than of the vaccine. And respiratory viruses are notoriously hard to get long-term immunity. I mean, look at the flu virus. We have to have annual flu shots. If this were like measles, which is a very different kind of virus, one flu shot would protect you against at least that strain of flu for the rest of your life. So I think the bad rap here is not the vaccine's fault nearly as much as it's the nature of respiratory viruses.RNA And Aging Eric Topol (13:27):No, that's extremely helpful. Now, let me switch to an area that's really fascinating, and you've worked quite a bit on the telomerase story because this is, as you know, being pursued quite a bit, has thought, not just because telomeres might indicate something about biologic aging, but maybe they could help us get to an anti-aging remedy or whatever you want to call it. I'm not sure if you call it a treatment, but tell us about this important enzyme, the role of the RNA building telomeres. And maybe you could also connect that with what a lot of people might not be familiar with, at least from years ago when they learned about it, the Hayflick limit.Tom Cech (14:22):Yes. Well, Liz Blackburn and Carol Greider got the Nobel Prize for the discovery of telomerase along with Jack Szostak who did important initial work on that system. And what it does is, is it uses an RNA as a template to extend the ends of human chromosomes, and this allows the cell to keep dividing without end. It gives the cell immortality. Now, when I say immortality, people get very excited, but I'm talking about immortality at the cellular level, not for the whole organism. And in the absence of a mechanism to build out the ends of our chromosomes, the telomeres being the end of the chromosome are incompletely replicated with each cell division. And so, they shrink over time, and when they get critically short, they signal the cell to stop dividing. This is what is called the Hayflick limit, first discovered by Leonard Hayflick in Philadelphia.(15:43):And he, through his careful observations on cells, growing human cells growing in Petri dishes, saw that they could divide about 50 times and then they wouldn't die. They would just enter a state called senescence. They would change shape, they would change their metabolism, but they would importantly quit dividing. And so, we now see this as a useful feature of human biology that this protects us from getting cancer because one of the hallmarks of cancer is immortality of the tumor cells. And so, if you're wishing for your telomeres to be long and your cells to keep dividing, you have to a little bit be careful what you wish for because this is one foot in the door for cancer formation.Eric Topol (16:45):Yeah, I mean, the point is that it seems like the body and the cell is smart to put these cells into the senescent state so they can't divide anymore. And one of the points you made in the book that I think is worth noting is that 90% of cancers have the telomerase, how do you say it?Tom Cech (17:07):Telomerase.Eric Topol (17:08):Yeah, reactivate.Tom Cech (17:09):Right.Eric Topol (17:10):That's not a good sign.Tom Cech (17:12):Right. And there are efforts to try to target telomerase enzyme for therapeutic purposes, although again, it's tricky because we do have stem cells in our bodies, which are the exception to the Hayflick limit rule. They do still have telomerase, they still have to keep dividing, maybe not as rapidly as a cancer cell, but they still keep dividing. And this is critical for the replenishment of certain worn out tissues in our such as skin cells, such as many of our blood cells, which may live only 30 days before they poop out. That's a scientific term for needing to be replenished, right?Eric Topol (18:07):Yeah. Well, that gets me to the everybody's, now I got the buzz about anti-aging, and whether it's senolytics to get rid of these senescent cells or whether it's to rejuvenate the stem cells that are exhausted or work on telomeres, all of these seem to connect with a potential or higher risk of cancer. I wonder what your thoughts are as we go forward using these various biologic constructs to be able to influence the whole organism, the whole human body aging process.Tom Cech (18:47):Yes. My view, and others may disagree is that aging is not an affliction. It's not a disease. It's not something that we should try to cure, but what we should work on is having a healthy life into our senior years. And perhaps you and I are two examples of people who are at that stage of our life. And what we would really like is to achieve, is to be able to be active and useful to society and to our families for a long period of time. So using the information about telomerase, for example, to help our stem cells stay healthy until we are, until we're ready to cash it in. And for that matter on the other side of the coin, to try to inhibit the telomerase in cancer because cancer, as we all know, is a disease of aging, right? There are young people who get cancer, but if you look at the statistics, it's really heavily weighted towards people who've been around a long time because mutations accumulate and other damage to cells that would normally protect against cancer accumulates. And so, we have to target both the degradation of our stem cells, but also the occurrence of cancer, particularly in the more senior population. And knowing more about RNA is really helpful in that regard.RNA DrugsEric Topol (20:29):Yeah. Well, one of the things that comes across throughout the book is versatility of RNA. In fact, you only I think, mentioned somewhere around 12 or 14 of these different RNAs that have a million different shapes, and there's so many other names of different types of RNAs. It's really quite extraordinary. But one of the big classes of RNAs has really hit it. In fact, this week there are two new interfering RNAs that are having extraordinary effects reported in the New England Journal on all the lipids, abnormal triglycerides and LDL cholesterol, APOC3. And can you talk to us about this interfering the small interfering RNAs and how they become, you've mentioned in the book over 400 RNAs are in the clinic now.Tom Cech (21:21):Yeah, so the 400 of course is beyond just the siRNAs, but these, again, a wonderful story about how fundamental science done just to understand how nature works without any particular expectation of a medical spinoff, often can have the most phenomenal and transformative effects on medicine. And this is one of those examples. It came from a roundworm, which is about the size of an eyelash, which a scientist named Sydney Brenner in England had suggested would be a great experimental organism because the entire animal has only about a thousand cells, and it's transparent so we can look at, see where the cells are, we can watch the worm develop. And what Andy Fire and Craig Mello found in this experimental worm was that double-stranded RNA, you think about DNA is being double-stranded and RNA as being single stranded. But in this case, it was an unusual case where the RNA was forming a double helix, and these little pieces of double helical RNA could turn off the expression of genes in the worm.(22:54):And that seemed remarkable and powerful. But as often happens in biology, at least for those of us who believe in evolution, what goes for the worm goes for the human as well. So a number of scientists quickly found that the same process was going on in the human body as a natural way of regulating the expression of our genes, which means how much of a particular gene product is actually going to be made in a particular cell. But not only was it a natural process, but you could introduce chemically synthesized double helical RNAs. There are only 23 base pairs, 23 units of RNA long, so they're pretty easy to chemically synthesize. And that once these are introduced into a human, the machinery that's already there grabs hold of them and can be used to turn off the expression of a disease causing RNA or the gene makes a messenger RNA, and then this double-stranded RNA can suppress its action. So this has become the main company that is known for doing this is Alnylam in Boston, Cambridge. And they have made quite a few successful products based on this technology.Eric Topol (24:33):Oh, absolutely. Not just for amyloidosis, but as I mentioned these, they even have a drug that's being tested now, as you know that you could take once or twice a year to manage your blood pressure. Wouldn't that be something instead of a pill every day? And then of course, all these others that are not just from Alnylam, but other companies I wasn't even familiar with for managing lipids, which is taking us well beyond statins and these, so-called PCSK9 monoclonal antibodies, so it's really blossoming. Now, the other group of RNA drugs are antisense drugs, and it seemed like they took forever to warm up, and then finally they hit. And can you distinguish the antisense versus the siRNA therapeutics?Tom Cech (25:21):Yes, in a real general sense, there's some similarity as well as some differences, but the antisense, what are called oligonucleotides, whoa, that's a big word, but oligo just means a few, right? And nucleotides is just the building blocks of nucleic acid. So you have a string of a few of these. And again, it's the power of RNA that it is so good at specifically base pairing only with matching sequences. So if you want to match with a G in a target messenger RNA, you put a C in the antisense because G pairs with C, if you want to put an A, if want to match with an A, you put a U in the antisense because A and U form a base pair U is the RNA equivalent of T and DNA, but they have the same coding capacity. So any school kid can write out on a notepad or on their laptop what the sequence would have to be of an antisense RNA to specifically pair with a particular mRNA.(26:43):And this has been, there's a company in your neck of the woods in the San Diego area. It started out with the name Isis that turned out to be the wrong Egyptian God to name your company after, so they're now known as Ionis. Hopefully that name will be around for a while. But they've been very successful in modifying these antisense RNAs or nucleic acids so that they are stable in the body long enough so that they can pair with and thereby inhibit the expression of particular target RNAs. So it has both similarities and differences from the siRNAs, but the common denominator is RNA is great stuff.RNA and Genome EditingEric Topol (27:39):Well, you have taken that to in catalyst, the catalyst, you've proven that without a doubt and you and so many other extraordinary scientists over the years, cumulatively. Now, another way to interfere with genes is editing. And of course, you have a whole chapter devoted to not just well CRISPR, but the whole genome editing field. And by the way, I should note that I forgot because I had read the Codebreaker and we recently spoke Jennifer Doudna and I, that she was in your lab as a postdoc and you made some wonderful comments about her. I don't know if you want to reflect about having Jennifer, did you know that she was going to do some great things in her career?Tom Cech (28:24):Oh, there was no question about it, Eric. She had been a star graduate student at Harvard, had published a series of breathtaking papers in magazines such as Science and Nature already as a graduate student. She won a Markey fellowship to come to Colorado. She chose a very ambitious project trying to determine the molecular structures of folded RNA molecules. We only had one example at the time, and that was the transfer RNA, which is involved in protein synthesis. And here she was trying these catalytic RNAs, which we had discovered, which were much larger than tRNA and was making great progress, which she finished off as an assistant professor at Yale. So what the general public may not know was that in scientific, in the scientific realm, she was already highly appreciated and much awarded before she even heard anything about CRISPR.Eric Topol (29:38):Right. No, it was a great line you have describing her, “she had an uncanny talent for designing just the right experiment to test any hypothesis, and she possessed more energy and drive than any scientist I'd ever met.” That's pretty powerful. Now getting into CRISPR, the one thing, it's amazing in just a decade to see basically the discovery of this natural system to then be approved by FDA for sickle cell disease and beta thalassemia. However, the way it exists today, it's very primitive. It's not actually fixing the gene that's responsible, it's doing a workaround plan. It's got double strand breaks in the DNA. And obviously there's better ways of editing, which are going to obviously involve RNA epigenetic editing, if you will as well. What is your sense about the future of genome editing?Tom Cech (30:36):Yeah, absolutely, Eric. It is primitive right now. These initial therapies are way too expensive as well to make them broadly applicable to the entire, even in a relatively wealthy country like the United States, we need to drive the cost down. We need to get them to work, we need to get the process of introducing them into the CRISPR machinery into the human body to be less tedious and less time consuming. But you've got to start somewhere. And considering that the Charpentier and Doudna Nobel Prize winning discovery was in 2012, which is only a dozen years ago, this is remarkable progress. More typically, it takes 30 years from a basic science discovery to get a medical product with about a 1% chance of it ever happening. And so, this is clearly a robust RNA driven machine. And so, I think the future is bright. We can talk about that some more, but I don't want to leave RNA out of this conversation, Eric. So what's cool about CRISPR is its incredible specificity. Think of the human genome as a million pages of text file on your computer, a million page PDF, and now CRISPR can find one sentence out of that million pages that matches, and that's because it's using RNA, again, the power of RNA to form AU and GC base pairs to locate just one site in our whole DNA, sit down there and direct this Cas9 enzyme to cut the DNA at that site and start the repair process that actually does the gene editing.Eric Topol (32:41):Yeah, it's pretty remarkable. And the fact that it can be so precise and it's going to get even more precise over time in terms of the repair efforts that are needed to get it back to an ideal state. Now, the other thing I wanted to get into with you a bit is on the ribosome, because that applies to antibiotics and as you call it, the mothership. And I love this metaphor that you had about the ribosome, and in the book, “the ribosome is your turntable, the mRNA is the vinyl LP record, and the protein is the music you hear when you lower the needle.” Tell us more about the ribosome and the role of antibiotics.Tom Cech (33:35):So do you think today's young people will understand that metaphor?Eric Topol (33:40):Oh, they probably will. They're making a comeback. These records are making a comeback.Tom Cech (33:44):Okay. Yes, so this is a good analogy in that the ribosome is so versatile it's able to play any music that you feed at the right messenger RNA to make the music being the protein. So you can have in the human body, we have tens of thousands of different messenger RNAs. Each one threads through the same ribosome and spills out the production of whatever protein matches that mRNA. And so that's pretty remarkable. And what Harry Noller at UC Santa Cruz and later the crystallographers Venki Ramakrishnan, Tom Steitz, Ada Yonath proved really through their studies was that this is an RNA machine. It was hard to figure that out because the ribosome has three RNAs and it has dozens of proteins as well. So for a long time people thought it must be one of those proteins that was the heart and soul of the record player, so to speak.RNA and Antibiotics(34:57):And it turned out that it was the RNA. And so, when therefore these scientists, including Venki who you just talked to, looked at where these antibiotics docked on the ribosome, they found that they were blocking the key functional parts of the RNA. So it was really, the antibiotics knew what they were doing long before we knew what they were doing. They were talking to and obstructing the action of the ribosomal RNA. Why is this a good thing for us? Because bacterial ribosomes are just enough different from human ribosomes that there are drugs that will dock to the bacterial ribosomal RNA, throw a monkey wrench into the machine, prevent it from working, but the human ribosomes go on pretty much unfazed.Eric Topol (36:00):Yeah, no, the backbone of our antibiotics relies on this. So I think people need to understand about the two subunits, the large and the small and this mothership, and you illuminate that so really well in the book. That also brings me to phage bacteria phage, and we haven't seen that really enter the clinic in a significant way, but there seems to be a great opportunity. What's your view about that?Tom Cech (36:30):This is an idea that goes way back because since bacteria have their own viruses which do not infect human cells, why not repurpose those into little therapeutic entities that could kill, for example, what would we want to kill? Well, maybe tuberculosis has been very resistant to drugs, right? There are drug resistant strains of TB, yes, of TB, tuberculosis, and especially in immunocompromised individuals, this bug runs rampant. And so, I don't know the status of that. It's been challenging, and this is the way that biomedicine works, is that for every 10 good ideas, and I would say phage therapy for bacterial disease is a good idea. For every 10 such ideas, one of them ends up being practical. And the other nine, maybe somebody else will come along and find a way to make it work, but it hasn't been a big breakthrough yet.RNA, Aptamers and ProteinsEric Topol (37:54):Yeah, no, it's really interesting. And we'll see. It may still be in store. What about aptamers? Tell us a little bit more about those, because they have been getting used a lot in sorting out the important plasma proteins as therapies. What are aptamers and what do you see as the future in that regard?Tom Cech (38:17):Right. Well, in fact, aptamers are a big deal in Boulder because Larry Gold in town was one of the discoverers has a company making aptamers to recognize proteins. Jack Szostak now at University of Chicago has played a big role. And also at your own institution, Jerry Joyce, your president is a big aptamer guy. And you can evolution, normally we think about it as happening out in the environment, but it turns out you can also make it work in the laboratory. You can make it work much faster in the laboratory because you can set up test tube experiments where molecules are being challenged to perform a particular task, like for example, binding to a protein to inactivate it. And if you make a large community of RNA molecules randomly, 99.999% of them aren't going to know how to do this. What are the odds? Very low.(39:30):But just by luck, there will be an occasional molecule of RNA that folds up into a shape that actually fits into the proteins active sighting throws a monkey wrench into the works. Okay, so now that's one in a billion. How are you going to find that guy? Well, this is where the polymerase chain reaction, the same one we use for the COVID-19 tests for infection comes into play. Because if you can now isolate this needle in a haystack and use PCR to amplify it and make a whole handful of it, now you've got a whole handful of molecules which are much better at binding this protein than the starting molecule. And now you can go through this cycle several times to enrich for these, maybe mutagen it a little bit more to give it a little more diversity. We all know diversity is good, so you put a little more diversity into the population and now you find some guy that's really good at recognizing some disease causing protein. So this is the, so-called aptamer story, and they have been used therapeutically with some success, but diagnostically certainly they are extremely useful. And it's another area where we've had success and the future could hold even more success.Eric Topol (41:06):I think what you're bringing up is so important because the ability to screen that tens of thousands of plasma proteins in a person and coming up with as Tony Wyss-Coray did with the organ clocks, and this is using the SomaLogic technology, and so much is going on now to get us not just the polygenic risk scores, but also these proteomic scores to compliment that at our orthogonal, if you will, to understand risk of people for diseases so we can prevent them, which is fulfilling a dream we've never actually achieved so far.Tom Cech (41:44):Eric, just for full disclosure, I'm on the scientific advisory board of SomaLogic in Boulder. I should disclose that.Eric Topol (41:50):Well, that was smart. They needed to have you, so thank you for mentioning that. Now, before I wrap up, well, another area that is a favorite of mine is citizen science. And you mentioned in the book a project because the million shapes of RNA and how it can fold with all hairpin terms turns and double stranded and whatever you name it, that there was this project eteRNA that was using citizen scientists to characterize and understand folding of RNA. Can you tell us about that?RNA Folding and Citizen ScienceTom Cech (42:27):So my friend Rhiju Das, who's a professor at Stanford University, sort of adopted what had been done with protein folding by one of his former mentors, David Baker in Seattle, and had repurposed this for RNA folding. So the idea is to come up with a goal, a target for the community. Can you design an RNA that will fold up to look like a four pointed cross or a five pointed star? And it turned out that, so they made it into a contest and they had tens of thousands of people playing these games and coming up with some remarkable solutions. But then they got a little bit more practical, said, okay, that was fun, but can we have the community design something like a mRNA for the SARS-CoV-2 spike protein to make maybe a more stable vaccine? And quite remarkably, the community of many of whom are just gamers who really don't know much about what RNA does, were able to find some solutions. They weren't enormous breakthroughs, but they got a several fold, several hundred percent increase in stability of the RNA by making it fold more tightly. So I just find it to be a fascinating approach to science. Somebody of my generation would never think of this, but I think for today's generation, it's great when citizens can become involved in research at that level.Eric Topol (44:19):Oh, I think it's extraordinary. And of course, there are other projects folded and others that have exemplified this ability for people with no background in science to contribute in a meaningful way, and they really enjoy, it's like solving a puzzle. The last point is kind of the beginning, the origin of life, and you make a pretty strong case, Tom, that it was RNA. You don't say it definitively, but maybe you can say it here.RNA and the Origin of LifeTom Cech (44:50):Well, Eric, the origin of life happening almost 4 billion years ago on our primitive planet is sort of a historical question. I mean, if you really want to know what happened then, well, we don't have any video surveillance of those moments. So scientists hate to ever say never, but it's hard to sort of believe how we would ever know for sure. So what Leslie Orgel at the Salk Institute next to you taught me when I was a starting assistant professor is even though we'll never know for sure, if we can recapitulate in the laboratory plausible events that could have happened, and if they make sense chemically and biologically, then that's pretty satisfying, even if we can never be absolutely sure. That's what a number of scientists have done in this field is to show that RNA is sort of a, that all the chemistry sort of points to RNA as being something that could have been made under prebiotic conditions and could have folded up into a way that could solve the greatest of all chicken and egg problems, which came first, the informational molecule to pass down to the next generation or the active molecule that could copy that information.(46:32):So now that we know that RNA has both of those abilities, maybe at the beginning there was just this RNA world RNA copying itself, and then proteins came along later, and then DNA probably much more recently as a useful but a little bit boring of genetic information, right?Eric Topol (46:59):Yeah. Well, that goes back to that cover of the Economist 17 years ago, the Big Bang, and you got me convinced that this is a pretty strong story and candidate. Now what a fun chance to discuss all this with you in an extraordinary book, Tom. Did I miss anything that you want to bring up?Tom Cech (47:21):Eric, I just wanted to say that I not only appreciate our conversation, but I also appreciate all you are doing to bring science to the non-scientist public. I think people like me who have taught a lot of freshmen in chemistry, general chemistry, sort of think that that's the level that we need to aim at. But I think that those kids have had science in high school year after year. We need to aim at the parents of those college freshmen who are intelligent, who are intellectually curious, but have not had science courses in a long time. And so, I'm really joining with you in trying to avoid jargon as much as possible. Use simple language, use analogies and metaphors, and try to share the excitement of what we're doing in the laboratory with the populace.Eric Topol (48:25):Well, you sure did that it was palpable. And I thought about it when I read the book about how lucky it would be to be a freshman at the University of Boulder and be having you as the professor. My goodness. Well, thank you so much. This has been so much fun, Tom, and I hope everybody's going to get out there and read the Catalyst to get all the things that we didn't even get a chance to dive into. But this has been great and look forward to future interactions with you.Tom Cech (48:53):Take care, Eric.*********************Thanks for listening or reading this edition of Ground Truths.Please share this podcast with your friends and network. That tells me you found it informative and makes the effort in doing these worthwhile.All Ground Truths newsletters and podcast are free. Voluntary paid subscriptions all go to support Scripps Research. Many thanks for that—they greatly helped fund our summer internship programs for 2023 and 2024.Thanks to my producer Jessica Nguyen and Sinjun Balabanoff for audio and video support at Scripps Research.Note: you can select preferences to receive emails about newsletters, podcasts, or all I don't want to bother you with an email for content that you're not interested in. Get full access to Ground Truths at erictopol.substack.com/subscribe

  For the first time ever, parents going through IVF can use whole genome sequencing to screen their embryos for hundreds of conditions. Harness the power of genetics to keep your family safe, with Orchid. Check them out at orchidhealth.com. Today Razib talks to geneticist Erich Schwarz, a Research Professor in the Department of Molecular Biology and Genetics at Cornell University  since 2012. Schwarz has a molecular biology degree from Harvard and a Ph.D. from Caltech. After working with the fruit fly Drosophila melanogaster in graduate school, he switched to the nematode Caenorhabditis elegans, and has continued studying nematodes ever since. After helping to found the C. elegans genome database WormBase (wormbase.org) in the early 2000s, he began sequencing and characterizing the genomes of several nematode worms other than C. elegans, either because they are biologically informative or because they are worldwide parasites. His current work includes using the genome of Ancylostoma ceylanicum to help devise an anti-hookworm vaccine. Schwarz explains why C. elegans, often called “the worm,” has been so useful in developmental and molecular genetics, and its role in the career of the late Nobel laureate Sydney Brenner. With a simple anatomical structure, every single one of the 1,000 cells of  C. elegans has been mapped and detailed. Despite its small size, this organism has spawned a research community of thousands, documented in Andrew Brown's In the Beginning Was the Worm: Finding the Secrets of Life in a Tiny Hermaphrodite. In the age of hundreds of thousands of human genomes, Schwarz explains the decades-long period in the late 20th century when biological research was dominated by “model organisms,” simple and easy-to-experiment-on animals, plants and bacteria that could eloquently and plainly elucidate universal and essential mechanisms of function and structure.  Razib and Schwarz also discuss the future of model organisms in a genomic future, when high-throughput data analysis can supercharge decades-long experimental projects. Ultimately, the future is not likely to see model organisms set aside, but rather to witness them merged into the broader research community in human and medical genomics which has been driving technological changes in sppedspeed and volume of data collection.

El doctor Sergio Moreno es, sin duda, uno de los investigadores españoles mas reconocidos internacionalmente en el área de la regulación de la división celular. Trabajó durante 7 años en el laboratorio de Paul Nurse en Londres y Oxford, brillante científico y ganador del Premio Nobel de Medicina y Fisiología en el 2001. Actualmente el doctor Moreno trabaja en el Instituto de Biología Funcional y Genómica del CSIC/Universidad de Salamanca. En este podcast conversamos sobre su formación, sus recuerdos de Inglaterra con Paul Nurse, sobre la división celular y sus mecanismos de regulación, sobre la importancia de los modelos simples como las levaduras en la investigación científica, cómo mejorar la investigación en España y otros temas mas trascendentales como la vida, la muerte, o las claves de la felicidad. 1:00 Buenos recuerdos de la Universidad Laboral de Las Palmas. 5:00 Por qué Farmacia. Miguel Angel Falcón. 9:00 Salamanca. Tesis sobre secreción de proteínas. Luis Rodríguez Domínguez. 14:00 Las levaduras como modelo de estudio. Conservación biológica. 18:00 Traslado a Inglaterra al laboratorio de Paul Nurse 25:30 Descubrimiento del cdc2, gen relevante en la división celular 31:00 Oxford. Diferencia con España en la contratación de Investigadores 35:00 Premio Nobel 2001 39:00 División Celular: importancia, regulación, comentarios. 49:00 Mis dos grandes momentos Eureka 57:00 Consejos y atributos del joven científico 1:02:30 Reflexiones sobre cómo mejorar la Investigación en España 1:10:00 Científicos que me han inspirado. Sydney Brenner. 1:12:30 Aficiones. Deportes. Cocina y similitudes con el laboratorio 1:17:30 La vida y la muerte. Reflexiones 1:24:00 Claves de la Felicidad. Viajes. Japon.

Express VPN 12 Months 35% off!! Rafael Yuste is a Professor of Biological Sciences at Columbia University and directs its Neurotechnology Center. He led a small group of scientists that inspired the US BRAIN Initiative, announced by President Barack Obama in 2013, and helped form the International Brain Initiative (IBI) in 2017. He also co-led the Morningside Group, a global consortium of interdisciplinary experts advocating for the ethical use of neurotechnology and artificial intelligence. Additionally, Rafael Yuste is a Co-Director of the Kavli Institute for Brain Science at Columbia University, and is a visiting researcher at the Donostia International Physics Center in San Sebastian, Spain. Rafael Yuste was born and educated in Madrid, where he obtained his MD at the Universidad Autónoma in the Fundación Jimenez Diaz Hospital. After a brief research period in Sydney Brenner's group at the LMB in Cambridge, UK, he performed PhD studies with Larry Katz in Torsten Wiesel's laboratory at Rockefeller University in New York. He then moved to Bell Labs, where he was a postdoctoral student of David Tank and Winfried Denk. In 2018, he was awarded the Eliasson Global Leadership Prize by the Tällberg Foundation for his seminal work in inspiring the US and International BRAIN initiatives and for his efforts toward building ethical guidelines for neurotechnology and artificial intelligence. If you haven't already and you enjoyed this episode, please subscribe to this podcast and our mailing list, and don't forget, my book, Brexit: The Establishment Civil War, is now out, you'll find the links in the description below. Watch Us On Odysee.com - Sign up and watch videos to earn crypto-currency! Amazon Music 3 Months Free ORDER BREXIT:THE ESTABLISHMENT CIVIL WAR HERE Get 25% off podcast hosting with Podiant Order GameStop T-shirts Here! RESOURCES https://twitter.com/yusterafa https://nri.ntc.columbia.edu/people/professor-rafael-yuste https://bigthink.com/mind-brain/neuro-rights https://officialblogofunio.com/2021/01/12/neuro-rights/ https://www.scmp.com/lifestyle/gadgets/article/3132344/real-life-inception-why-neuro-rights-laws-protect-peoples-brains Follow us on Twitter or sign up for our mailing list here to get information on my book, Brexit: The Establishment Civil War. Music from Just Jim - https://soundcloud.com/justjim

Express VPN 12 Months 35% off!! Rafael Yuste is a Professor of Biological Sciences at Columbia University and directs its Neurotechnology Center. He led a small group of scientists that inspired the US BRAIN Initiative, announced by President Barack Obama in 2013, and helped form the International Brain Initiative (IBI) in 2017. He also co-led the Morningside Group, a global consortium of interdisciplinary experts advocating for the ethical use of neurotechnology and artificial intelligence. Additionally, Rafael Yuste is a Co-Director of the Kavli Institute for Brain Science at Columbia University, and is a visiting researcher at the Donostia International Physics Center in San Sebastian, Spain. Rafael Yuste was born and educated in Madrid, where he obtained his MD at the Universidad Autónoma in the Fundación Jimenez Diaz Hospital. After a brief research period in Sydney Brenner's group at the LMB in Cambridge, UK, he performed PhD studies with Larry Katz in Torsten Wiesel’s laboratory at Rockefeller University in New York. He then moved to Bell Labs, where he was a postdoctoral student of David Tank and Winfried Denk. In 2018, he was awarded the Eliasson Global Leadership Prize by the Tällberg Foundation for his seminal work in inspiring the US and International BRAIN initiatives and for his efforts toward building ethical guidelines for neurotechnology and artificial intelligence. If you haven’t already and you enjoyed this episode, please subscribe to this podcast and our mailing list, and don’t forget, my book, Brexit: The Establishment Civil War, is now out, you’ll find the links in the description below. Watch Us On Odysee.com - Sign up and watch videos to earn crypto-currency! Amazon Music 3 Months Free ORDER BREXIT:THE ESTABLISHMENT CIVIL WAR HERE Get 25% off podcast hosting with Podiant Order GameStop T-shirts Here! RESOURCES https://twitter.com/yusterafa https://nri.ntc.columbia.edu/people/professor-rafael-yuste https://bigthink.com/mind-brain/neuro-rights https://officialblogofunio.com/2021/01/12/neuro-rights/ https://www.scmp.com/lifestyle/gadgets/article/3132344/real-life-inception-why-neuro-rights-laws-protect-peoples-brains Follow us on Twitter or sign up for our mailing list here to get information on my book, Brexit: The Establishment Civil War. Music from Just Jim - https://soundcloud.com/justjim

After the discovery of the double-helix structure of DNA in the 1950s, South African biologist Sydney Brenner was searching for a model animal to help him tease out the genes involved in human behaviour and human development from egg to adult. Brenner chose a tiny nematode worm called caenorhabditis elegans (c.elegans for short), whose biological clockwork can be observed in real time under a microscope through its transparent skin. The worm has since been at the heart of all sorts of discoveries about how our bodies work and fail. Sue Armstrong has been speaking to people who knew and worked with Sydney Brenner. This programme is a Ruth Evans Production. Photo: the c. elegans worm. Credit: Science Photo Library

The Whole View, Episode 440: COVID-19 Vaccines Part 1 - mRNA Vaccine Technology Welcome back to episode 440 of the Whole View. (0:27)    Stacy explains that today's topic is one she and Sarah have received the most questions on possibly ever. Stacy also lets the audience know that this show will be a 2-parter, possibly a 3-parter depending on how deep in they get. This show has been long in the making because she and Sarah had to wait for the research publication. Then Sarah has done her own research on top of it to prepare for this show. Sarah shares that she's been following this topic for about a year now: ever since the novel coronavirus was sequenced. It's important they lay out the science for listeners, look at the technology and history of vaccines, answer the frequently asked questions, and bust the myths surrounding this topic. (2:08) She and Stacy decided to divide the show into multiple parts to take their time and do the subject justice. Stacy takes a minute to address how polarizing the word "vaccine" can be. And she and Sarah are aware of this. She wants to assure listeners they understand vaccines are a personal decision for everyone, just like every other health and medical choices are. Stacy and Sarah are here to provide the information you need to be an informed consumer.   Note On Vaccines In this episode, they will discuss the mRNA vaccine technology in the history of vaccines. (2:40) Next week's episode, Sarah and Stacy will go over the safety and efficacy data for the first two vaccines, Emergency Use Authorization, the Pfizer/BioNTech vaccine, and the Moderna vaccine. Sarah and Stacy will discuss their thoughts on vaccinations going forward. But Stacy reminds listeners that it's never aimed at telling others what to do.  She also reminds listeners that she and Sarah are not medical professionals. If you have questions regarding the vaccine for yourself or your family, discuss them with your doctor. There is a lot of information that is both true and not true floating around on the web.  Stacy is very excited to talk about the science and breakdown behind these vaccines and gives a little background on herself for context. Both Stacy and her mother have anaphylactic reactions to things like gluten due to multiple autoimmune disorders. Stacy has brought up to Sarah whether or not she thinks getting the vaccine is a good idea for someone with health issues like Stacy's mom. Stacy also wonders how having the coronavirus, but not having the antibodies, will affect her if given the vaccine.   Listener Questions Sarah reiterates just how many questions they've received from listeners around this subject. (5:10) She takes a moment to share a few she thinks accurately sum up what they want to cover in this episode. Mae wrote: I am sure you don't want to cover this topic, but you are a source I highly trust as I am sure a lot of your other followers do. Would you consider doing a show about the Covid vaccines out there? It's so hard to know what to believe these days.....Not looking to be told what to do, but merely to be presented the information as you do so well in breaking down the real science that is not filtered through such a biased lens. Meghan added: Can you please do an episode explaining the science behind vaccines, and explaining how they really work, including the new Covid one. You always do an excellent job of explaining things well in a relatively easy to understand way without shortcutting good science. Stacy assures listeners that they will do their very best to break everything down. However, you might still have questions or have heard something different that might conflict with prior information. Stacy encourages you to reach out via the contact forms on the website for any follow-up. If you're part of the Patreon family, use direct access to talk with Sarah and Stacy there. She also encourages listeners not to attack the topic on social media or to put too much emphasis on things you hear without any sources cited.   A Brief History of Vaccine Technology Sarah starts off by going way back into the history of vaccines. (8:27) The very first form of inoculation was called variolation. The first variolation for smallpox dates to the 1600s in China and Ottoman Empire and practiced first in Britain and colonial Massachusetts in 1721. They took the pus from someone suffering a natural smallpox infection. And then they'd would then rub it onto punctured or cut skin of someone who had never been exposed. If the procedure didn't kill you, you'd have immunity to the illness. However, Sarah noted it was pretty successful in terms of early inoculation. Sarah explains briefly how cell memory aids in fighting episodes of re-exposures. This is what gives us immunity or less a severe immune response when exposed. Development Of A Smallpox Vaccine Dr. Edward Jenner is considered the founder of vaccinology in the West. He noticed many milkmaids were immune to smallpox. He realized they were getting infected with cowpox (a related variola virus that is relatively harmless to humans), and the infection built an immunity to smallpox. In 1796, he inoculated his gardener's 8-year-old son by variolating cowpox pus from a milkmaid's hand. Jenner then demonstrated this immunity to smallpox by exposing the boy to smallpox 6 weeks later, and he didn't get sick. That's a lot of confidence! And also, not cool. Jenner then repeated this experiment multiple times over a couple of years with different people and published his methodology in 1798. He named his process vaccination because the cowpox virus is called vaccinia. Doctors started administering this as a smallpox vaccine all over the world in 1798. This is the first instance of understanding that exposing the body to a weaker version of a virus could stimulate enough of an immune response to tricker cellular memory. Over the 18th and 19th centuries, systematic implementation of mass smallpox immunization culminated in its global eradication in 1979. It took just about 200 years from the start of this vaccine to the eradication of smallpox. Other Vaccine Development Louis Pasteur's experiments spearheaded the development of live attenuated cholera vaccine in 1897. And then an inactivated anthrax vaccine in 1904.  Plague vaccine was also invented in the late 19th Century.  Between 1890 and 1950, bacterial vaccine development proliferated, including the Bacillis-Calmette-Guerin (BCG) vaccination, which is still in use today.  In 1923, Alexander Glenny perfected a method to inactivate tetanus toxin with formaldehyde. The same method was used to develop a vaccine against diphtheria in 1926. Pertussis vaccine development took considerably longer, and a whole-cell vaccine was first licensed for use in the US in 1948. mRNA vaccine technology Sarah tells the audience that many of the childhood vaccines given to children today were developed 70 – 100 years ago. There have been advancements in the vial today that are different from what was in the vial back then. However, the vaccine technology is pretty much the same now, and it was that then. Sarah underlines that mRNA vaccine technology was one of the biggest advancements since Jenner and Pasteur's experiments.   Modern Vaccines When looking at vaccines today, they all have the same basic ingredients (18:20) They all work by stimulating an immune response against what's called an antigen. An antigen is a bad thing that makes us sick. The body develops immunological memory by the adaptive immune system in response to the antigen.  It's the same way our immune system develops memory when we've been naturally infected.  But because vaccines use weaker viruses, it goes without the danger of natural infection. Vaccinations are very costly and big investments to undertake. So we really only develop vaccines against illnesses that are very, very bad and have a huge impact on society. Up until now, mRNA vaccine technology hasn't changed much since the 50s. Traditional vaccines contain three components: antigen, adjuvant, and additives to preserve/stabilize. AntigenThis is the thing we're developing immunity against. Antigens come in various types: live, attenuated virus; inactivated virus; inactivated toxins for bacterial diseases where toxins generated by the bacteria cause the illness; or parts of a virus-like split, subunit, or conjugate.   Adjuvants Stacy asks about adjuvants and what they do to cause the stimulation. (20:00) Sarah explains that adding a little bit of dead virus to our arm tissue isn't usually enough to trigger an immune response. An adjuvant is a compound (most commonly aluminum) that stimulates the immune system. And helps to develop a more robust immune response and stronger immunity against the antigen.  Adjuvants are why people often feel sick after a vaccine. It's not the virus causing the side effects, but rather the ramped-up immune system caused by the adjuvant. It's also why many people with autoimmune diseases experience a temporary flare after vaccination.  If you already have an immune system in overdrive due to an autoimmune system, it makes sense why autoimmune suffers would have more adverse reactions. Sarah feels it's important to note there is no science showing vaccines cause autoimmune diseases. However, because they're meant to cause an immune response, vaccines can make autoimmune diseases more noticeable. Sarah recommends this article as a source of more information about adjuvants. Additives Additives are preservatives, stabilizers, and residuals included in the vaccine. Sarah explains this is where there can sometimes be egg protein as a residual. So there are certain vaccines out there that people with egg allergies can't have. Sarah notes there is still one vaccine out there that uses Thimerosal as a preservative. But it has been mostly phased out since the 1980s. This is because Thimerosal contains traces of mercury. Stacy circles back to heavy metals and how often they talk about those as being bad.  She feels it's important to note that going through normal daily life, we encounter things like heavy metals in food and water. This is why we have livers: so we can flush them out of our systems naturally. It's why she and Sarah talk so much about taking care of our liver. So when we hear things like, "there's aluminum in this vaccine," it might come off as a red flag. We don't want to put that in our bodies. Stacy explains why these vaccines work to achieve the response it needs because you're right: your body does not want that aluminum in there. So it gets agitated and works a little bit harder to flush it out. And that's how the vaccine is able to create the body's immune response. Stacy shares one way she helps her body is to take extra care of her liver the weeks before getting a vaccine. That way, she could optimize her body's ability to flush out the substances it doesn't want in there. Sarah agrees that a great practice is to practice self-care, such as getting enough sleep and eating right before and after getting a vaccination.   Always a Cost-Benefit Analysis  Sarah explains that Stacy brought up a great point: there is always a cost-benefit to mRNA vaccine technology and other types of vaccines. (28:45) Sarah believes we are at a point now where most of us are disconnected from the actual impacts of viruses like polio and whooping cough. She shares that her grandfather survived polio when he was 14-years-old. He was wheelchair-bound for 2 years and walked with a cane or walker for the rest of his life. He also developed post-polio syndrome in old age, which caused heart failure. For Sarah, she is at the tail-end of people's age with a personal connection with some of these illnesses that we heard about. Gen X and younger generally don't understand a lot of the consequences that come with a lot of these diseases. Over a century ago, the infant mortality rate was over 20%. And the childhood mortality rate before age five was an additional disconcerting 20%. That's what vaccination has been able to do for us and society: give us more than a near 50% chance of reaching our 5th birthday. We only invest in vaccines for diseases with high mortality and/or morbidity. Sarah explains that mortality equals death.  Morbidity, on the other hand, anything bad that happens that's not death. It includes severe illness, complications, and lifelong health problems. For example, morbidity from mumps is basically zero. But 1 in 300 get encephalitis (or brain inflammation) while 1 in 10 men get orchitis (testicle inflammation) Measles mortality is 1 in 500, blindness is 1 in 500, encephalitis is 1 in 1000, and pneumonia is 1 in 20. So vaccinations aren't just reserved for high-mortality diseases, but also ones that have a high rate of complications that can impact the quality of life long-term. Safety Of Vaccine Technology Safety standards are much higher for vaccines than most medications because we give vaccines to healthy people.   Some of this was learned the hard way. For example, in April 1955, more than 200,000 children in five Western and mid-Western USA states received a polio vaccine in which was basically a bad batch.  The process of inactivating the live virus proved to be defective, so rather in inoculating the children from polio, it ended up giving them polio instead.  Within days there were reports of paralysis, and within a month, the first mass vaccination program against polio had to be abandoned. This became a huge issue in the medical community. And it ended up enacting a lot of change in terms of what was acceptable safety standards.  Sarah explains that now vaccine technology is at the safest point it's ever been. But there is such a thing as vaccine-induced injury. Vaccine-Induced Injury  Stacy thinks the realities of the few cases of negative outcomes of vaccines need to be explored. (34:35) Especially since they risk being taken out of context or misunderstood.  She wonders what Sarah knows about the frequency of these negative outcomes. And what the science sense about the risk of injury.  Sarah explains this is extremely well-tracked and well-studied. The phenomenon of vaccine-related injury is incredibly rare. But she explains we do need to acknowledge it exists. She attributes social media for taking these few and far between cases and inflaming them in public. This, in turn, has destabilized a lot of the trust the public has in vaccines, which can be very harmful. She explains that an adverse reaction is usually something like soreness near the injection site or a bruise, maybe a headache, or anything that doesn't feel normal. A serious adverse reaction is something that requires medical care and could potentially result in death. Because of this risk, Sarah believes it's very important to be aware of serious adverse reactions to ensure you're making decisions that are medically in your best interest. Sarah takes a few moments to summarize some of the more serious adverse reactions from commonly administered vaccines and the odds of experiencing one. Stacy feels it's super important to address the elephant in the room. And there is no science showing any sort of link between vaccines and autism.  Adverse reactions can occur from vaccination, but a huge amount of scientific information has really conclusively shown autism is not one of them.  For more on Vaccine-Induced Injury, Sarah recommends checking here for additional information. Vaccine And Autoimmune Diseases Stacy explains that in autoimmune diseases, we often see them "activate" due to an immune system flare up- for example, during pregnancy or nursing. This isn't to say that pregnancy or nursing caused the autoimmune disease. But rather, it triggered it to activate, and that's why we start noticing the symptoms around that time. She explains that this holds true with vaccines as well. If someone starts to notice autoimmune systems after receiving a vaccine, that vaccine itself didn't "cause" the immune disease. Rather, it agitated the immune system. And that agitation triggered the symptoms of an autoimmune disease that was already lying latent inside the body. Sarah adds there's no evidence saying people with autoimmune diseases should avoid vaccines. If anything, they may need more booster vaccines to reach adequate immunity due to the immune system already not functioning optimally. The Importance of Herd Immunity Sarah also reminds listeners that vaccines aren't actually about individual protection at all. (46:10) They protect you individually, sure, but the reason vaccines are so amazing (and why smallpox was able to be eradicated) is because of the creation of herd immunity. Herd immunity means enough of a community is immune to an illness (cannot get it and cannot pass it) that if there is an individual infection, the illness has nowhere to go. It's stuck. Herd immunity limits the path for the virus to spread and can be much more easily contained. Herd immunity also protects members in our community who might have some sort of medical issue that prevents them from being vaccinated themselves.  Sarah cites children with cancer are unable to get vaccinated due to their health issues. So being surrounded by people who cannot spread a life-threatening illness is very beneficial to their health and wellness. Smallpox, which had an incredibly high mortality rate and permanent scarring, no longer exists anywhere in the world because of vaccines! So while we might want the covid vaccine for individual protection, that's not the primary goal. The primary goal of vaccination is community protection.   How mRNA Vaccines Work mRNA vaccines are the biggest advance in vaccine technology since Louis Pasteur and Edward Jenner. (50:35) It can revolutionize not just immunizations but also cancer therapy and other drug development. Brief History of mRNA Vaccine mRNA stands for messenger RiboNucleic Acid. Our cells make as an intermediary between the DNA in our cell's nucleus and a protein.  It also functions as a set of instructions to make protein, which is the intermediate step between DNA and the protein it encodes The steps are: DNA - transcription -> RNA - translation -> protein Translation may occur at ribosomes free-floating in the cytoplasm. Or directed to the endoplasmic reticulum by the signal recognition particle. mRNA was first discovered in 1961 by Sydney Brenner at Cambridge and James Watson at Harvard. The concept of mRNA-based drugs occurred in 1989 when Malone demonstrated that mRNA could be successfully transfected and expressed in various eukaryotic cells under a cationic (positively charged) lipid package.  In 1990, in vitro-transcribed mRNA was sufficiently expressed in mouse skeletal muscle cells through direct injection. This became the first successful proof of the feasibility of mRNA vaccines. After the first mRNA-based drug company was established in 1997, many groups began to research and develop mRNA-based drugs.  So far, over twenty mRNA-based candidate drugs have entered the clinical trial stage. A big advance in 2005 when Katalin Karilo and Drew Weissman at the University of Pennsylvania showed how to modify mRNA to get into human cells without triggering an immune response. Major advances in lipid nanoparticle technology for the mRNA envelope over the last 4-5 years.   Last 4-5 years, improvements in mRNA vaccines increase protein translation, modulate innate, adaptive immunogenicity, and improve delivery. This mRNA vaccine technology has been perfected in just the last few years. This is why the Covid-19 vaccine was able to be developed so quickly. The technology we needed to create this vaccine was already primed and ready to go. How Do mRNA Vaccines Work? Sarah explains that the coolest part of mRNA vaccine is that they do not use adjuvants! (58:01) This is because adding the RNA to the cell nucleus is enough to trigger it to replicate. It doesn't need anything additional to trigger the immune response.  Two major types of RNA are currently studied as vaccines:  non-replicating mRNA which is what's in both the Pfizer/BioNTech covid-19 vaccine and the Moderna covid-19 vaccine virally derived, self-amplifying RNA.  Conventional mRNA-based vaccines encode the antigen of interest and contain 5′ and 3′ untranslated regions (UTRs).  Self-amplifying RNAs encode the antigen and the viral replication machinery that enables intracellular RNA amplification and abundant protein expression. The lipid envelope facilitates entrance into the cell via endocytosis and exit from endosome into cytoplasm This molecule provides the template in the cytoplasm of a cell for translation by the ribosome.  And tRNA into the encoded protein, making multiple copies of the protein from each mRNA template. The protein can then be presented to the immune system through MHC or, like both Pfizer/BioNTech and Moderna vaccine, the protein is transmembrane, so it presents itself!  Sarah explains that there were some human trials using mRNA vaccines to treat cancer patients.  So yes, as Stacy brings us, the technology is still pretty new. But this isn't the first time we're using mRNA technology. It's the first opportunity we've had to utilize the discoveries large-scale. Ingredients Of mRNA Vaccines Sarah explains that what makes this new vaccine technology so cool is how few ingredients it requires to make. (1:05:20) mRNA (rather than a live attenuated virus, dead virus, or split virus) Lipid nanoparticle envelope (rather than viral particles floating around a solution or viral vector-like adenovirus)LNPs often consist of four components:  an ionizable cationic lipid, which promotes self-assembly into virus-sized (~100 nm) particles and allows endosomal release of mRNA to the cytoplasm;  lipid-linked polyethylene glycol (PEG), which increases the half-life of formulations; cholesterol, a stabilizing agent;  and naturally occurring phospholipids, which support lipid bilayer structure.  It requires no adjuvant, which is SO COOL! Adding an adjuvant to the lipid envelope has been studied, but it doesn't seem to be necessary. This is because foreign mRNA and viral proteins are really good at eliciting an immune response. mRNA has self-adjuvant properties which activate strong and long-lasting adaptive immune responses through tumor necrosis factor-α(TNF-α), interferon-α(IFN-α), and other cytokines secretion by immune cells The foreign viral proteins are presented via MHC-I Lipid nanoparticles may have a little adjuvant activity in some circumstances. But basically, all of the immune stimulation is targeted against the foreign viral protein and mRNA! For example, here are all the ingredients for the Moderna Vaccine: The vaccine contains a synthetic messenger ribonucleic acid (mRNA) encoding the pre-fusion stabilized spike glycoprotein (S) of SARS-CoV-2 virus.  lipids (SM-102, 1,2-dimyristoyl-rac-glycero-3-methoxypolyethylene glycol-2000 [PEG2000-DMG], cholesterol, and 1,2-distearoyl-sn-glycero-3-phosphocholine [DSPC]),  pH Buffering agents: tromethamine, tromethamine hydrochloride, (both drugs for metabolic acidosis) acetic acid, sodium acetate, (both naturally found in our blood) Cryo-stabilizer: sucrose Sarah jokes about how much she's nerding out about it.  Advantages Over the past decade, major technological innovation and research investment have enabled mRNA to become a promising therapeutic tool in vaccine development and protein replacement therapy.  The use of mRNA has several beneficial features over subunit, killed, live attenuated virus, and DNA-based vaccines.  Safety As mRNA is a non-infectious, non-integrating platform, there is no potential risk of infection or insertional mutagenesis.  Additionally, mRNA is degraded by normal cellular processes. And it's in vivo half-life can be regulated through the use of various modifications and delivery methods 9,10,11,12.  The inherent immunogenicity of the mRNA can be down-modulated to further increase the safety profile9,12,13.  2: Efficacy Various modifications make mRNA more stable and highly translatable9,12,13.  Efficient in vivo delivery can be achieved by formulating mRNA into carrier molecules, allowing rapid uptake and expression in the cytoplasm (reviewed in Refs 10,11).  mRNA is the minimal genetic vector; therefore, anti-vector immunity is avoided, and mRNA vaccines can be administered repeatedly.  mRNA vaccines expressing antigen of infectious pathogen induce both strong and potent T cell and humoral immune responses Even better for viruses requiring cellular immunity like coronaviruses. (Click here for more!) Production  mRNA vaccines have the potential for rapid, inexpensive, and scalable manufacturing, mainly owing to the high yields of in vitro transcription reactions. They are really fast to make. Moderna took 2 days to create the RNA sequence to produce the spike protein after sequencing the virus genome in January. Then shipped its first vial of vaccine to NIH for trials 41 days after that. This will also mean the vaccine can be modified for new strains (so far, not necessary), and we can get a vaccine even faster in the event of another pandemic! Myths About the mRNA Vaccines One of the biggest myths many people believe is that the vaccines were rushed. So we don't know if they're safe. (1:07:30) The unprecedented investment (funding) allowed for tests normally done serially to be done in parallel. And it allowed for manufacture (normally 6 months to a year) to be done during clinical trials rather than after. These vaccines build upon vaccine research from SARS and MERS and the knowledge base about coronaviruses from that research. So we've been researching it longer than people have known about the novel coronavirus. For example, it was already known that the spike protein bound with ACE2. And that's how SARS-CoV-2 infects cells. It also builds upon a tremendous amount of mRNA vaccine research and clinical trials of mRNA vaccines for cancer. mRNA vaccine technology allows for a fast process. It's also very inexpensive to make. Yay science! Vaccines have some of the most stringent safety standards in all of pharmaceutical development! They are given to healthy people, not sick, so tolerance for serious reactions is lower. Also, these vaccines were tested thoroughly and have exceeded the standards. No corners were cut! Yes, there are still some things we don't know (like whether or not you can get an asymptomatic case after you've been vaccinated and then spread the virus or how long immunity will last), but we do know that the safety profile is excellent. It's approved for 16 and over because they did tests on adults before children. In fact, the 12-15 age groups are being tested now. Final Thoughts One of the biggest reasons these vaccines were able to be produced so fast is because of the timing. (1:10:42) Scientists have been working on vaccine technology for centuries. And major advancements in the last 30 years have made it possible to produce both efficient and safe vaccines. This is why basic science funding is so, so important.  Sarah goes into why this basic funding is so important. Most funding is going to direct human relevance.  The science that these vaccines are based on comes down to a basic discovery and expanding human knowledge.  And only after the fact, we understood how it could be applied to improving human life.  So increasing funding for basic science discovery is very important to Sarah. Stacy also circles back to how mind-blowing that this basic science discovery could also further our advancement toward a cure for cancer.  She reminds listeners that there are two vaccines approved for disruption in the US. Next week, Sarah and Stacy with dive into the science and myths on those to bring you all the info you need to make your own decision. If you're curious how Sarah and Stacy really feel about this topic, pop on over to Patreon for more science talk and bonus content.   See you next week!  

Hoy hacemos programa temático alrededor de la Organización Europea de Biología Molecular. El Doctor David del Álamo, Director de becas de EMBO, nos hablará de su transición del laboratorio a trabajar en la Organización, además de informarnos de las actividades que llevan a cabo. Por otro lado, rendiremos homenaje al doctor Sydney Brenner, ganador del Nobel de Medicina de 2002 y uno de los fundadores de EMBO que falleció el pasado mes de Abril.

Matthew Meselson organized the Herbicide Assessment Commission in 1970, which investigated the use of Agent Orange and other defoliants in Vietnam. The work of the commission helped to end Operation Ranch Hand, in which the United States sprayed nearly 20 million gallons – about 73 million liters - of herbicides and defoliants over the rainforest and mangrove forest canopies of Vietnam, Laos and Cambodia. I called Meselson to ask about his role in the Herbicide Assessment Commission, along with a host of other fascinating investigations to do with chemical and biological weapons, such as the anthrax accident in the Soviet Union and the yellow rain incident in Laos.  I also asked him about the U.S. Army’s insane plan in 1969 to ship 800 railroad cars filled with 27,000 tons of poison-gas weapons from the Rocky Mountain Arsenal to New Jersey for disposal at sea. Meselson completed his Ph.D. in 1957 under Linus Pauling at CalTech.  In 1958, in a classic experiment, he and Frank Stahl showed that DNA is replicated semi-conservatively, and in 1961 he along with Francois Jacob and Sydney Brenner discovered messenger RNA.  Meselson also made fundamental discoveries in DNA repair, the recognition and destruction of foreign DNA in cells, and, along with Werner Arber, he discovered restriction enzymes.  Meselson received his appointment as an Associate Professor of biology at Harvard in 1960 and his full professorship in 1964.  He has been at Harvard ever since.  Meselson has received many prominent awards throughout his career, including from the National Academy of Sciences, the Federation of American Scientists, the New York Academy of Sciences, and the Genetics Society of America, as well as the Guggenheim Fellowship and MacArthur Fellows Program Genius Award.

Con este episodio completamos el homenaje a Sydney Brenner que, por razones de tiempo, no pudimos desarrollar como queríamos en el episodio anterior. Hemos preparado un programa de duración excepcional en el que, como si se tratara de uno de los discos de un doble LP, la mitad está dedicado a recordar la vida y obra de este científico tan singular y genial. En su recuerdo, también hemos prescindido de la numeración habitual y hemos denominado al episodio BS#SB, sustituyendo el ordinal 4 por las iniciales S(sydney) B(renner). Y qué mejor punto de partida para comentar la vida de Brenner que su propia autobiografía ("Mi vida en la ciencia"), traducida, en España, por los profesores Juli Peretó y Emilia Matallana. Durante casi 2 horas hemos conversado con ellos y también con Ginés Morata, José M. Bautista, Luís Corrochano y Enrique Cerdá, todos ellos reconocidos científicos españoles que mantuvieron una estrecha relación con Sydney. Así rendimos, por fin, nuestro pequeño homenaje a un hombre que, además de ser uno de los miembros fundacionales de la Biología Molecular ha dejado una huella personal y una obra científica fundamental -difícilmente superable- en la historia de la biología. En la tertulia comentamos tres artículos recientes de gran interés. El primero nos lo comenta Silvana Tapia desde Brasil, donde se encontraba en un congreso de microbiología: un equipo americano ha investigado el microbioma presente en las úlceras asociadas al pie diabético, en un intento por mejorar el diagnóstico y el tratamiento de esta patología. Pepe Lozano, por su parte, nos presenta los detalles de un trabajo publicado por el grupo de Mariano Barbacid que ha tenido bastante repercusión en los medios de comunicación: la eliminación, por primera vez, de adenocarcinomas pancreáticos mediante el bloqueo combinado de las dianas moleculares EGFR y RAF. El hallazgo se ha obtenido en un modelo animal pero su relevancia garantiza, sin duda, el diseño de nuevos inhibidores selectivos y ensayos clínicos. Para conocer más detalles de este trabajo, hemos entrevistado a Teresa Blasco, primera autora del artículo y al propio Mariano Barbacid. Por último, Francis Villatoro nos comenta, no uno, ni dos, sino tres artículos que investigan la fidelidad de las herramientas editoras de genes más utilizadas en la actualidad: CRISPR-Cas y los editores de base. Y, como siempre, nuestros estudiantes más dicharacheros, Belén Delgado e Íker Puerto, nos presentan su selección de (bio)noticias. Bienvenidos al episodio especial BS#SB de Biosíntesis.

シドニー・ブレナー博士特集回（後半）では、顕微鏡を用いた線虫の全細胞系譜追跡の偉業を振り返るとともに、CRISPR-Cas9ゲノム編集法やイメージング技術を用いた最新の細胞系譜追跡技術、技術開発にまつわる世代を超えたアイデアの伝搬とその哲学、顕微鏡（光学系）を使わないイメージング技術の台頭などについて話しました。Show notes The embryonic cell lineage of the nematode Caenorhabditis elegans. Developmental Biology, 1983…Sulstonらによって線虫の全細胞における細胞系譜が初めて明らかにされた記念碑的な論文。Sulstonは4時間に及ぶ顕微鏡観察を毎日2回繰り返すことを何年も続け、959個の細胞系譜を明らかにした。 Jey Shendre…HarvardのGeorge Churchのlabで博士課程の学生だった頃、一発の実験で細胞系譜を一斉に追跡できるテクノロジーを作れ、と言われ無茶苦茶だと思いつつ取り組んだがうまく行かなかった。独立後、ラボメンバーとともにCRISPR-Cas9によるゲノム編集を用いることで当時は不可能だったアイデアを実現させ、それがGESTALT法 (Science 2016)となった。 Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science, 2016…Jey Shendreによる全く新しい細胞系譜の追跡方法。CRISPR-Cas9によるゲノム編集を人工的な配列に引き起こすことにより、生体内で変異パタンを生成させ、このパタンの系統樹を描くことにより細胞系譜を追跡するGESTALT法。ゼブラフィッシュにおける複雑な細胞系譜が顕微鏡を使わず、一度のシーケンシングによって再構成できることを世界で初めて示した。このアイデアに世界中の研究者らが触発され、多くの関連技術群が開発される契機となった。 Synthetic recording and in situ readout of lineage information in single cells. Nature, 2016…Long CaiとMichael ElowitzらによるCRISPR-Cas9と一分子RNA FISH法を組み合わせた細胞系譜の追跡技術、MEMOIR法。人工的な配列が時間経過とともにゲノム編集によって破壊され、一分子RNA FISHの蛍光が減弱するパタンを利用して、細胞系譜を追跡することができる方法。 light sheet fluorescence microscopy…Light sheet fluorescence microscopyのアイデア自体は古く、100年近く遡る。最近の光学系とカメラ、制御系の発展により、大きな分野となりつつある。 Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous system. Livet et al. Nature, 2007…Brainbowのオリジナル論文。Cre-LoxPによるDNA組み換え酵素によって複数の蛍光タンパク質をランダムに発現させ、その多色のパタンによってクローンを標識・追跡が可能となる。 Sequencing the connectome. Zador et al. PLos Biol., 2012- ZadorらによるDNAバーコードを用いたコネクトーム計測のアイデア論文。大規模な空間情報を顕微鏡を使わずにいかにシーケンシングによって明らかにするか？という全く新しい発想を提示した一方、このアイデアがどのように実現できるのか、この当時はまだ自明でなかった（今も）。 Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Lieberman-Aiden et al. Science, 2009…Hi-C法のオリジナル論文。この論文を起点として、核内における染色体高次構造、クロマチン構造解析が爆発的に進むようになった。 Capturing chromosome conformation. Dekker et al. Science, 2002…Hi-C法の基礎となった3C法のオリジナル論文。ホルムアルデヒドによる固定、制限酵素による切断、ライゲーション、PCR増幅という分子細胞生物学に必須ないくつかの基本的なテクニックを組み合わせることで、Job Dekkerはこの天才的なアイデアを生み出した。3C法の発明は分子細胞生物学、ゲノミクス研究の歴史において特異点的な偉業である。Job Dekkerはこの方法論にたどり着いた理由について、彼自身が博士時代にNMRの研究を行なっていたこと、分子生物学の実験でPCRだけはうまくできたこと、実験初期にすぐにうまくいったことだと、以前tadasuに語ってくれた。しかしその特異さゆえ、2009年のHi-C論文が世に出るまではなかなか評価されることはなかった。 Turning point: Job Dekker…Job Dekkerのポスドク回顧録。 DNA microscopy: Optics-free spatio-genetic imaging by a stand-alone chemical reaction…Avivによる光学系を全く用いない顕微鏡のアイデアであるDNA microscopy法。この技術の行方は今後も注視する必要がある。 low-input, high-throughput, no-output biology…Brennerが2008年に残した言葉。 “Progress in science depends on new techniques, new discoveries and new ideas, probably in that order”というSydney Brennerの言葉

En este episodio rendimos un humilde homenaje al gran Sydney Brenner, genético molecular (como él mismo gustaba de llamarse) y premio Nobel quien, tristemente, fallecía tres días antes de la grabación de este episodio. Su legado científico es enorme y su carisma inolvidable. Con él se nos va el penúltimo superviviente de aquella generación de investigadores geniales que fundaron la Biología Molecular. En la tertulia comentamos tres articulos publicados la pasada semana, dos con implicaciones biomédicas y uno de investigación básica. En el primero discutimos las causas moleculares que podrían explicar el hecho epidemiológico de que el cáncer de hígado sea más frecuente en hombres que en mujeres. En el segundo, comentamos resultados derivados de un estudio de microfluídica sobre el movimiento ameboide de los leucocitos en microcircuitos impresos. El tercer trabajo es un metaanálisis de las microbiotas asociadas al cáncer de colon, un intento por identificar firmas metagenómicas específicas de esta enfermedad. Nuestro libro de la semana está, precisamente, dedicado a este asunto y presentamos la reseña de “Microbiota: los microbios de tu organismo”, acompañada de una entrevista a su autor, el microbiólogo Ignacio López-Goñi.

Pictured: Sydney Brenner Matthew Bannister on Sydney Brenner, the Nobel Prize-winning biologist who worked with Francis Crick to map DNA. Dan Robbins, the artist who invented painting by numbers. Edda Tasiemka, the archivist who kept a comprehensive newspaper and magazine cuttings collection in her North London home. Ian McDonald, the Ministry of Defence spokesman during the Falklands War who became a familiar face on TV and was known for his sonorous delivery. Interviewed guest: Professor Jonathan Hodgkin Interviewed guest: Larry Robbins Interviewed guest: Robert Lacey Interviewed guest: Ian Mather Interviewed guest: Revel Barker Producer: Paula McGinley

Krishna babbles about the latest news in the week, including Mr. Sebi, Sydney Brenner, Deepak Chopra, MDMA in old people, and the latest in HPV vaccine outcomes.

Sydney Brenner was one of the 20th Century’s greatest biologists. Born 90 years ago in South Africa to impoverished immigrant parents, Dr Brenner became a leading figure in the biological revolution that followed the discovery of the structure of DNA by Crick and Watson, using data from Rosalind Franklin, in the 1950s. Brenner’s insights and inventive experiments laid foundation stones for new science of molecular biology and the genetic age in which we live today, from the Human Genome Project to gene editing. Sydney Brenner talks to biologist and historian Matthew Cobb of the University of Manchester about this thrilling period in biological science, and Dr Brenner’s 20 year-long collaboration with DNA pioneer Francis Crick: a friendship which generated some of their most creative research. Producer: Andrew Luck-Baker Picture: Sydney Brenner, Credit: Cold Spring Harbor Lab Archive

SynTalk thinks about dying & death from medical, ethical, existential, legal, & sociocultural perspectives, while constantly wondering how & why death is important. Is death ‘master-able’? The concepts are derived off / from Socrates, Glaucon, Epicurus, Jesus Christ, Hobbes, Stalin, Sydney Brenner, Bill Gates, Melinda Gates, Woody Allen, & Aruna Shanbaug, among others. How the hope for immortality is conceptually similar to the hope for justice? Can we avoid death before old age? How difficult is it to call someone dead, & is death an objective event? How life has changed from being ‘brutish, nasty and short’ a few centuries ago, & how the 20th century was in many ways the century of life. How is mortality different across age groups, and the role played by sanitation, vaccination, and oral rehydration over the years? Is death becoming more medicalized and protracted? Are more people now dying in hospitals? Why is it important to fight child mortality, and why is it likely that this global battle might be won or lost in the districts of India? Why the first month after birth is the most important to prevent avoidable death? Why the inevitability of death need (& should) not prevent appropriate public policy actions. How there is an opposition between life & death. What do we write on the death certificate, and why the cardio-respiratory arrest (for example) as a cause is not sufficient? How ‘extreme old age’ caused the death of Queen Mother? The difference and links between between physician assisted suicide, gradual withdrawal of care, (passive & active) euthanasia, medical care system, oral opiates, life support, & brain death. Why is the brain (stem) death becoming more popular, & possible links with organ transplant. Can there be a technology for death? How differently do people die? Can one prepare oneself to die (via philosophizing?)? What would leading oncologists do when they themselves face a terminal case of cancer? How suicide is the opposite of capital punishment. Do only human beings commit suicide; Why? Is death available to the ‘self’, in a moment when the self knows that it is no longer? How is the post-operative death different? Is the living cell programmed to die? Do we know what life is only through the occurrence of death, & is death a summation of life in some way? Is it important to not allow Market to take over death, just as it has taken over life? The importance of care for the dying? The role of the state in minimizing ‘bad luck’ deaths. How death is increasingly becoming banal and matter-of-fact, but is still (somehow) repressed culturally. Is it alright to have a cemetery in the middle of a university? How to die beautifully? The SynTalkrs are: Dr. Saitya Brata Das (philosophy, JNU, Delhi), Prof. Prabhat Jha (epidemiology, CGHR, St. Michael’s Hospital, Toronto), & Dr. Sanjay Nagral (surgery, Jaslok Hospital, Mumbai).

Dr. Sydney Brenner, Senior Distinguished Fellow of the Crick-Jacobs Center at the Salk Institute, talks about his life and career with Dr. Aravinda Chakravarti, Director of the Center for Complex Disease Research at the McKusick-Nathans Institute of Genetic Medicine, part of the Johns Hopkins University School of Medicine, and co-Editor of the Annual Review of Genomics and Human Genetics. Dr. Brenner recounts his early life in South Africa, and how he became interested in molecular biology, came to work with Francis Crick at Cambridge University, proposed the existence of messenger RNA, and studied Caenorhabditis elegans as a model of neural development. The latter earned him the 2002 Nobel Prize in Physiology and Medicine.

Charles Sabine & Sydney Brenner speak at the Gairdner Symposium on The Personalized Genome in Toronto (October 30, 2009)

Charles Sabine & Sydney Brenner speak at the Gairdner Symposium on The Personalized Genome in Toronto (October 30, 2009)

Charles Sabine & Sydney Brenner speak at the Gairdner Symposium on The Personalized Genome in Toronto (October 30, 2009)

Dr. Sydney Brenner