We alive now great ships dry should I get started as you can info, and then the audience. If you haven’t typed in YouTube channel, please do so. And also, please register. If you haven’t registered yet, please go out, go to our official website and do the registration so that you’ll be notified on upcoming events. So hello, everyone, a very warm welcome to the seventh chapter our webinar series. Today we have a distinguished personality as he is an absolute delight to talk to Zachary Arden. Dr. Zachary Arden is a postdoctoral fellow in bacterial evolutionary genomics at the Wellcome Sanger Institute in Cambridge, UK, his PhD research studying experimental evolution in mice, conducted at the University of New Zealand. And since then, he has had postdoctoral positions at the Technical University of Munich, and Cosmo Institute of Technology. He’s particularly interested in our new names throughout evolutionary history, as well as broader philosophy, your question about directionality in evolution? Now over to your doctor? Definitely. Yes, thank you very much. It’s great to be here. And I hope that people have questions following on from this as well. So the question that I’m raising is how can life evolve new genes, which is one of the central questions and evolutionary theory? And I’m actually not going to answer it today. But I’m going to give some suggestions and just an overview of where research where recent research has been pointing to suggest that we’re starting to answer this the same question, but there’s, there’s a lot more work to do. So this is the topic How to Get genuinely Eugene, for an evolutionary process. So an evolutionary process is through descent with modification. So in some way, we would expect anything that’s new should be related to something that already existed. So how can you get something that’s a genuinely completely new gene out of an evolutionary process? And the answer that I’ll be suggesting is we don’t really know. But we are starting to find clues, and lots of interesting research directions that we’ll be opening up over the next few years. This is my backgrounds. In introductions, I studied both philosophy and biology, particularly in my undergrad. And since then I’ve been focused on molecular biology, and then specifically in my PhD in experimental evolution. So evolution over the short term, in yeast, in that case, and since then, I’ve been exploring various aspects of molecular evolution in you know, in culture, and now in Cambridge. The work that I’ll be referring to his lots of different co authors and collaborators. And here are some of them, just highlighting the great some of the different fields just to show the diversity of this kind of research areas, from various aspects of microbiology, proteomics, detecting proteins, evolutionary genetics, looking at changes across genomes and the processes of evolution. And a whole lot of PhD students who are also involved at some of them have recently completed their PhD. Okay, so the first part of the talk, I just want to introduce some ideas that will be useful later. So ideas that you may be familiar with, if you’re a biologist, but may still be helpful reminders, hopefully, for a lot of you just to set the scene for some of the details about the origin of new genes, which I’ll explore a little bit later. So firstly, a simple reminder, what is a gene what is a gene that codes for proteins specifically? And this is just a really simple example, which comes from the viruses cuff to which I’ll be mentioning a few times throughout the talk. And this just illustrates how the genetic sequence in this case RNA is read in triplets and discusses directly Read from the RNA because it’s an RNA virus. So even triplet codon triplets which encode amino acids, it’s nucleotide triplet and close one amino acid. This is probably obvious to most of you, but the reason why the details matter will become clear later. And amazingly, this single linear strands of nucleotides, because a linear strand of amino acids, this folds into this remarkable structure, and this is just part of the structure of this protein. So somehow we get from somehow in this linear sequence of nucleotides is encoded, this three dimensional protein structure, and it’s really quite a remarkable thing, which will be storing a little bit more of the next little while. Okay, so that’s, that’s what I’m talking about. When I’m talking about genes, I’m talking about sequences of nucleotides that encode proteins, particularly proteins that fold the three dimensional structures. What’s the genetic code? Well, this is the relationship between the nucleotides in green, and the amino acids and orange. And you probably, I guess, or know, something about this genetic code that it has this kind of arrangement where you have usually a couple more code on triplets in code, a single amino acid. So for instance, with with arginine, a bunch of these triplets encode a single amino acid, arginine. So you probably know that, but you might not be so well aware that the code actually has a structure. And the structure is important for biology, important for evolution and a bunch of ways but in the different aspects of the structural one aspect of Shawn with these colors. So these amino acids, now, colored according to the chemical properties, clustered into, I think, five groups here. So those amino acids are reasonably similar or clustered with the same color. And we see the interesting thing here is that the colors seem to clusters get together. So similar amino acids. Quite similar in terms of the nucleotides that they encode the nucleotide sequences can be seen as clustered together in a way that correlates with the clustering of the chemical properties of the amino acids. And that’s quite interesting. And it’s related to something that will turn out to be useful later in the talk. The CO heads are different structures, which which are interesting, which has explored a bit in some of my research, and turns out to be fundamental for evolution. Okay, so we have stretches of nucleotides, like here, how do we decide which nucleotide encoder gene and in the really simple cases, we just look for the long open reading frames. So an open reading frame is any stretch of triplets that starts with the start codon, and into the stop codon and doesn’t have any stop codons in the middle in the right set of triplets right reading frame. And kind of the classic, early way of deciding what counts as a genus, just look for the really long open reading frames. By chance you don’t expect overweening faces very long, because you expect you expect to stop going on roughly every 20 or so amino, roughly every 20 or so codons interrupt the sequence, if you get a really long stretch of like 100 codons, that’s really unexpected by chance. And that suggests that there’s something important happening there, that’s preventing stop codons that suggests that it’s actually encoding a protein. So this is the usual way. And then there might be some overlapping things that are short and usually traditionally assumed that there’s not genes and only the long ones. And James, I will find out soon, that’s not always actually necessarily the case. Because there’s a lot of short teams as well. You can define them in different ways, maybe less than 100 amino acids or less than 50 amino acids. But short chains like this are usually or nearly always, in the case of listen 50 amino acids missed by the usual methods that people have for deciding what counts as a true gene. There’s a lot of studies here, just a couple that are recently published regarding bacteria, and that there’s a lot of short chains that people have missed, because they were only looking for the long open reading frames at something. So that short James, but there’s also an overlapping genes. And this is probably the main focus of my research, and I’ll touch on a few times during this talk. So we’ve already seen this protein that actually doesn’t I mentioned that it’s the nucleo capsid protein in just one of the main protein sources come to a very important the most highly expressed protein is important for detecting With a someone has a Sound Capture as well. So this is standard protein very straightforward. But it turns out the same sequence of nucleotides and cause a different protein as well, which is really surprising. But the same sequence and a different reading frame to study with from different triplets encode a different protein, which also has a remarkable protein fold. And this is the protein off mine B, which has been studied stare mouse in is starting to understood. So it’s also a protein. And if you, if you just saw this, you might think this is the only protein that’s in Columbus region, but the same sequence also encodes the very important nucleic acid protein. So it turns out the same sequence of nucleotides and different reading frames can encode different proteins and this is the phenomenon non overlapping genes. I will discuss this in this blog post that you can look up down here on this website called very logical if you’re interested. Okay, so that’s one of the key concepts. And we’ll come back to it another case context, we’re almost finished the introduction now. But another key concept is the concept of orphan genes. off because open reading frame, and Gene, so orphan genes are those things that are only found. So this is, for instance, a picture of basically the whole tree of life. And an orphan gene is a gene that’s only found in a tiny little box, a tiny twig at the tips of the tree. So an orphan is only found a one small group within the tree of life. For instance, maybe it’s only found within a single species. And people can have slightly different definitions for this. Originally, an orphan gene was a gene that was just been a one genome. But as we’ve got a lot of genomes now that are very similar, people are using the term more broadly, for any gene is only found in a very small part of the tree of life. These raise all kinds of interesting questions, because basically, how did they get there? They only found in a tiny trace, trace. And this is basically the the the focus of this talk, how did these kinds of genes and really small taxonomic groups only restricted to a species or genus, or such, how do they end up with. So you could summarize the last three slides that proteins gotta catch them all, that there’s all these different kinds of proteins that are not the traditional long proteins, which are widely conserved through evolution, but some proteins are short, some proteins are overlapping, and some other proteins are tax and all must be restricted, that means they’re very young, only found in a very small part of the tree of life. And ideally, if we going to describe genomes accurately, we should catch all of these proteins. And most of these unusual ones have been missed. Finally, just a quick look at the Softcup to genome which we’ll come back to, and this is the for the genome, as you probably noticed 30,000 nucleotides and this is the end of the genome the last part of the first time versus a bunch of different proteins and non structural proteins with the last 8000 or so nucleotides in the genome encodes these these other proteins in blue the structural proteins in green in green the Okay. Okay, so yeah, and green. Okay. Okay, so yeah, in Green Bay accessory proteins, so these that don’t have core structural, they don’t form part of the structure of the virus once it’s formed, but they still have some other small kind of list. Maybe in some cases, less important role is structural, but they still do something functional. And then these other dates and a not so well understood these overlapping genes, which I’ll be discussing that more. For comparison, we see this similar virus, which caused the SARS outbreak in the early 2000s as causal southco. One is very similar genes. But as I’ll discuss a little bit more, there’s this general this potential gene or three d, which is missing from sounds cold, and there are some other slight differences as well. For instance, off eight you look at closely His hair is actually during the SARS pandemic was actually split into. So are these accessory James evolve relatively fast, they change relatively fast in between some of the genome some of these effects redeems will be different. Where’s the structural genes tend to be more conserved? Okay, so what about series obtain origin? How if people try to describe, try to explain how genes have begun to exist? Firstly, it’s important to note why why this is an important question why? It’s an interesting question, because genes are highly specific. In other words, you need to have a very specific sequence in order to fold into the kinds of structures that we saw before, most random sequences won’t fold into a useful structure, work for them to structure at all. And if you have a specific structure that has a particular function, the vast majority of sequences will not fall into that you need a very specific set of possible sequences to encode a given protein structure. And this is just a very technical analysis, which you can read up more on online if you’re interested. But to give the kind of numbers that we’re thinking about. This is called a co evolutionary analysis. This is one way of assessing how many sequences fold into a particular structure. But this is suggesting that maybe only one out of 10 to the 24 sequences would fold into just a very simple protein domain, that for some of the more complex domains, the numbers become ridiculously tiny, maybe only one out of 10. So there are 120. sequences, the exact details don’t matter. The point is just whichever kind of analysis you do, for some of these complicated structures, the same, only a tiny proportion of possible sequences would fold into the structure. So then it becomes an interesting question, how can evolution find given that there’s limited time and limited possibilities, helping evolution finds really complex structures like these kinds of things, and this is one of the great questions, evolutionary biology. And it’s, these numbers are discussed in this paper, which you can look up if you interested. So given this kind of high specificity of the genes, at the fact you need a very specific sequence, is generally the thought that evolution is just a tinkerer. So evolution doesn’t basically, this is a quote from a famous mix of biologists who won the Nobel Prize in 1965. And he suggested basically, the evolution doesn’t really invent new things. So he says evolution does not produce innovations from scratch, it works on what already exists by the transforming a system to their new function, or combining several systems to produce a more complex one. This has been the standard view of the evolution of genes, that every new gene is just descended from an old chain, and it’s modified somehow. And this is what most people have thought of different models for the possible origins of genes. So one model, illustrate, here’s the Big Bang model. This is the kind of classic model deck, people who think evolution is just a tinker effect that there were some ancestral genes. Somehow the origin of life in early life, and every other gene that’s existed, since it’s just, if you just follow the lines carefully, might be hard to see. But every gene that’s arisen, since it’s just been copying these genes and changing them slightly, as every gene at the tip is some descendants of one of these early genes. So that’s the first model, which has been called the Big Bang model, that there was an origin of proteins early in life. And ever since then, basically, all or nearly all of the proteins have just been descended from these original ones. The second model, as a continuous model, that actually you do get not just at the beginning, but also at other points in history. For instance, here, here, here, here, you get new genes originating and into some of these genes that we find in present day life, which is a person that these are descended from genes, which actually arose sometime during evolution that they were de novo, and then from nothing, or like original creations of the genes throughout life. That’s the second model. And the third model is that maybe even these new genes are somehow involved in the creation of new spaces or new genera, new taxonomic groups. So that’s a related kind of version of Model B, the continuous model that new genes continuously arising, and I think the data increasingly supports the second model, as well explain throughout the rest of the talk. Okay, so the first model, the Big Bang model, the idea that evolution is just a tinkerer is related to this idea of duplication and divergence. So it used to be thought that new protein coding genes only came about by copying and modifying old ones and original gene, at some point it gets copied you then you have two copies of the gene in those two copies can change over time, something new is is getting mutated, and this one, some of the other ones, he may say this, well, then they end up looking quite different at the end, if you just I mean, if you look at the end, you think these two genes are completely unrelated. But actually, if you trace it back, which you can do by comparing to other genomes, you can start to see actually how they actually came from the same starting point. So this was the the the kind of traditional model of how genes arose. All genes basically went through this kind of process of nearly all of the alternative model was called de novo gene origin. This is related to the continuous model that I mentioned before. Okay, look at the last universal common ancestor that the Western genes are beginning of, and through the process. We’ve got these different colors, these different genes represented by different colors. And the question is, how do these different genes get there? Again, either through divergence, like I said, we’ve had a little Blue Gene, and it changes into the green jeans and the yellow jeans, or no, Turner does Moto, which is that at some point, you really started with a sequence that didn’t encode the genomes, non coding just nucleotides. And then at some point, it began to code for a protein. So there was an open reading frame that got translated into a protein, at some point through evolution, and all the relatives don’t still don’t have the protein because the ancestor didn’t. But in one branch of the tree, there is a new version there. So this is the de novo emergence model. Okay, how do we study these kinds of things, I want to briefly touch on some different methods that are used. The first one that I’ve used a lot is called ribosome profiling, this doesn’t make this sort of tick thing, whether something is actually whether a nucleotide sequence is actually encoded in protein or not. This works, because when translation happens, approximately 30 nucleotides of RNA is protected by the ribozyme. At any given point, when the ribosome is actively translating, it turns out with some recent methods, and RNA sequencing, that you can actually find these bits of RNA that are covered by the ribosomes, and work out which makes insides are actively being translated. And this is illustrated here, which is covered in this kind of overview that we wrote a little while ago. And it basically just shows that you, you saw the river zone, you stop at translating, you harvest the cells, you break apart the cells and end up with these RNAs, covenant ribosomes, you degrade away the excess RNA that’s not protected by a rubber zone. And then you end up with the parts of the RNA that are just protected. And these can be basically selected for an end segments. And that’s a simple overview of the process of represent profiling, which leads you to tip for the really high accuracy, really high sensitivity, which parts of the genome actually been translated, this is more sensitive in some other methods like MS spectrometry, which has traditionally been used for. And so I use very sufficient water cases for detecting proteins. Another whole area is comparative to normal manuscript which you can look up, which shows this is just comparing different genomes. So this is a train comparing different different genomes within the genus pseudomonas, bacterial genus. And this is shows that if you if you look closely at sequence in a particular region of the genome, there’s no so the stock corners represented with black lines. If you look closely at just within this species, generous aeruginosa, there’s no stop codons. And that has been basically all the other genomes, there are stop codons there. So that suggests that this this gene is basically restricted for this species. And if you look at other species, just a couple of exceptions, which are just minor details. There are the gene isn’t present isn’t being translated. So this comparative genomics can help you see where they say how old the gene is, how how restricted okay. So, yeah, this is the another method, evolutionary sequence analysis and this shows the different ways of doing this, but this basically shows constrained in the sequence across the region, and this is a study looking at at So that overlapping gene and HIV, and we just used it illustrates this tool that we created a couple of years ago. In order to detect these overlapping genes by looking at excess constraints, that sort of fewer changes than you would expect. And given regions of the genomes that help you to tick, this is actually a protein coding gene rather than just random intergenic sequences and function. Really, in order to do this, well, you need to find a bunch of these different methods. So for instance, you can combine the RNA sequencing and the ribosome profiling and mass spectrometry to find the peptides of the protein. And then that gives you really clear, strong evidence that something has been translated. And this is the case of this overlapping gene that we found instead of that it’s in blue. It’s transcribed, translated according to both survivors and profiling by receipt and mass spectrometry, the peptides and the same is true for the other gene, which is already known about the other strands, whether it is also translated, if it slides and expressed with the RNA seek. So those are different methods of ends. You can also add in the kind of evolutionary analysis which shows sequence constraints. The yellow line compared to the white line shows something straight in this region, which suggests that that’s the true version. Okay, so yeah, these are the kinds of methods looking at both the expression and the evolution. The whole area of de novo gene Origins is possible because of the genomic revolution. And this is an old graph, it only goes to 2015, but already shows something like an exponential growth in the number of new genomes that are being created year by year. And this is only increased in recent years. Now, there’s hundreds of 1000s of different kinds of genomes and continues to increase a GSR is one example which works been impossible to think about just a few years ago, in the Saskatoon outbreak, the Wellcome Sanger Institute where I worked at sequence something like 700,000 for a subculture genome, which just shows a huge capacity that we have for sequencing new genomes now, which is allowing, allowing us to really explore the Tree of Life in huge detail, as well as looking in in detail at things like disease outbreaks. So there’s a lot of recent results in this whole area of de novo genes and new genes, which I won’t really go into in detail, but I just want to highlight that there’s a lot of work being done in this area. And this is just reminding you of what the de novo Argenis origin from something that was originally non coding and then becomes coding at some point. Again, there’s a lot of interesting work being done across different species and exploring specific examples of the evolutionary history of some very young genes that did arise from non coding sequences. There’s also interesting results, I’ve been focusing on proteins so far. But there’s also a bunch of interesting functional things that RNA does. In this interesting results, I think, related and really, really useful. And this is RNA structures. So there’s, there’s parts of the DNA that don’t encode a protein, but doing Howard folded RNA, which was does something useful. And it turns out, just to summarize that the consequence of a bunch of this work in RNA is showing that actually, the reason that these new RNA folds or folded elements are rising is not due to natural selection, but actually is due to bias. And the way that the sequences relate to the function. So that’s called the GP, the genotype phenotype. So this is one of the fundamentals of biology is how does these strings of nucleotides which have been showing, how do they actually relate to the folder sequence? It turns out, the reason that you can get these folded RNAs is because there’s strong bias in the way in that relationship and that mapping, which allows you to get functional, some of these functional folded structures more often than you would expect, and kind of naively just by chance. So this whole area of looking at these genotype phenotype maps, looking at the biases in the mapping between the single stranded sequence in the focus structures is an important part of this whole region of diverging origin. Okay, some specific details of SAS capture because that’s of interest to lots of people recently, of course, and we did some work on this and last year. Firstly, an important thing to note is there’s a lot of recording venation across the Saskatoon genome, which is illustrated in this really nice plot, where you’re comparing Softcup, to to all these different genomes, and you see that there’s some regions of the genome where there’s much less, there’s much lower similarity. Somebody here, in in some regions, but in other regions is high similarity. So it’s suggesting that there’s some there’s been some process, which is remarkably different in this part of the genome versus this time, the genome. And the basic foundation for this kind of passion is recombination with different genomes have basically been been exchanging parts of their nucleotide sequences, which ultimately ends up explaining the the penance of similarity that we see across genomes. So recombination is a really important process, particularly for some of these viruses, and the evolution. And I won’t be explaining it more. But this is just important to keep in mind that this is one of the main evolutionary forces or facts factors which is at play. Also, really important, like it, like a briefly mentioned before is the history genes. And in, in the genome, and this is just this picture just illustrates that there are if you look really carefully at this picture, there’s differences and this history comes across, quite closely related. So southco, one I think, is about 90%, similar to Saskatoon, which is up here. So these genomes are very similar, the nucleotide level, but they still actually have differences and the kinds of accessory genes that they encode. So let’s just show that the accessory genes are evolving really fast. Partly because of things like recombination, that sometimes they lost or gained in different genomes, they’re able to do that, because they’re not completely critical for the the replication of the virus, they just have some edit function, which is more or less useful in different circumstances. So yeah, the history genes are evolving really fast. And they make the sense of like rapidly, and for instance, these overlapping genes are restricted to just a couple of genomes, just a couple of strains of the virus, or versions of the virus. And most of them don’t have this, this lack of damage, as illustrated in yellow. The same is true for some of these accessory genes that are present or absent and different forms of the virus to some parts, the genome are evolving much more fast than others. And the origin of these pathogens is the easiest place to study de novo gene origin, because they’re because they’re evolving so quickly. One example of this, there has been studies have suggested that this all three a we’ll look at next as well, this all three a actually originated from a more famous protein, the input in the membrane protein. And basically, they this type of suggesting that offer a was ultimately copied in diverge a lot from the original importing. So that’s what evolutionary studies suggest, and how we originally how we got this extra accessory protein is ultimately was a copy of this policy. So that’s an example of the classic and a picture of gene evolution. Um, overlapping genes, illustrated before, off maybe is the most highly expressed and clearly the most studied one. But there are other examples as well. The ones that we focused on other ones that are overlapping, or three esteem they just mentioned, has these three potential James overlapping and we all have some evidence of expression and some potential evidence of functionality as well. In comparison to says cuff, one will say that all three C is present in all three days prisoners much longer, instead of one, witnesses cup two, it’s just it’s really short, Jane, and all three day is not present in Sam’s Club one. So the work that we were downsized to last year was looking into particularly these three genes, how they changed across related genomes, what’s the evidence that they’re actually true genes, what’s even the rest of the functional? Okay, actually, we’ll try and do better that because that was an important one. So I’ll just go back to the last slide which was, so this last slide was just showing one really interesting feature of this this thing, or these genes and this all three, a reason. So it turns out that these genes and offer a reason are actually or must be similar in some ways, which was quite unexpected. So if you look at the hydrophobicity, which is a chemical property of the amino acids, you look at a sliding window plot, you can average the hydrophobicity across a region, it turns out that whether you’re looking at all three A, which is shown as this frame green, or three C, which is shown in red, or 3d, which is showing, this two forms a shorter form and a longer form, these are shown in yellow. Because of that these chemical properties of the sequences, even though they’re in different reading frames, they encode completely different amino acids. In theory, they actually have correlated chemical properties. And this is something that’s actually a result of the genetic code, the genetic code biases, the way amino acids are encoded, so that they even if you shift the frame, instead, you get a completely different amino acid. But there’s a tendency for to have a similar chemical properties to the unshifted sequence amino acid encoded by the unshifted codons. So there’s this remarkable correlation and these different reading friends, which might explain why some regions that have this high correlation may be more likely to form functional, overlapping genes. So this is the theory but is supported by the kind of fundamentals of the genetic code, and we explore this a little bit in this paper, and some recent papers also starting to look into this really interesting correlation between the different reading frames. Okay, almost finished. So there’s a lot of questions which are still left open. We’re exploring the origin of new genes, what’s the rate of origin of the genes? Are they lots of no non functional genes, kind of in the process of creating new genes? People call this proto genes, maybe most of most things, most young genes are just non functional and get lost. Only a tiny proportion of them end up being functional and useful. This is renown clear whether this is the case, how many there are, what circumstances make them functional or not. There’s lots of open questions there. And the last thing I kind of hinted that maybe the structure of the genetic code actually helps some regions to be more likely to create new genes. Conclusions. genomes are really dynamic over evolutionary time, they’re changing a lot. Actually, quickly. It appears they often invent new functional components, new genes, and understanding all these things. It’s really important for understanding biology. Maybe it’s important for even understanding things like us as copter. If you like to understand more, particularly recommend a few books by Andreas Waghmare, particularly this, one arrival of fitness is explored. In a really accessible way, a bunch of these questions around how new stuff evolves and evolution can check out my research, some of which I referred to, or you can ask me questions if you like as well. So now I think there’s some time for questions. I look forward to any comments that people have. So you actually muted thank you so much for this wonderful talk. Thank you. We’re really grateful for the time for you to to share this with us. So audience can pose their questions in the comments. There’s a question from Caesar Hanson. As my excitement what were the challenges that he faced when exploring this field? Yeah, so that was an interesting question. With overlapping genes. One of the challenges was that this kind of controversial and people didn’t expect to find Many overlapping gene. So just convincing people that these things are real, it’s like difficult. Yeah, so maybe that was the main thing is just that it’s been hard to publish some of the research. It’s, it’s, it’s required a lot of revision and a lot of kind of extra proof that these genes are really including proteins and especially that they’re really functional is quite hard to show. Yeah, so it’s interesting whenever there’s something that’s really new, it can be controversial and it can be hard to prove or convince people that these things are real. But yeBioah, I think progress is the main now there’s, there’s a bunch of papers on short genes on overlapping genes on on young genes. I think a lot of people are saying just I think that’s okay. That’s fine. If people think of other things, or watch or explore and exist, you’re welcome to email me. And I’ll Yes. Thank you, doctor and thank you everyone who have tuned in with us today. So if you haven’t, yet to canopy thank you all. 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