Hello, my name is Eriko Takano and I'm from the University of Manchester. And today, I'd like to speak to you about synthetic biology for the production of high-value chemicals. So as you all know, antibiotic resistance is a very big problem, and it's a worldwide problem. You can see here there's methicillin-resistant Staphylococcus aureus increasing, even enterococci that are resistant to vancomycin has emerged and is on the increase. There are many hospital infections and of course resistance is absolutely increasing. You can see also from this European map as well, that the proportion of MRSA which has close to 50% that there are many countries that do. Now, antibiotics are produced by actinomycetes and they're gram positive, soil dwelling bacteria. You can see spores here, this is scanning EM picture with spores growing up from the soil. Here another picture of streptomyces colony taken from the top with blue blobs, which are all antibiotics that are being secreted out from the cells. Another cross-section here, you can see this is a red antibiotic being produced and this is being retained in the cell. Now you can also see some chemical structures here, and some very simple structures like penicillin to some very, very large and complex structures like daptomycin. So you can see that these antibiotics have a very diverse chemical structure. Now, the first antibiotic to be discovered was penicillin. In those days it was called the antibiotic — the miracle drug. And in the 1960's, as you can see her, there was a huge peak of antibiotic, novel antibiotics being discovered. But ever since then, there's been a decrease. And even now, the most recent antibiotic to be found was daptomycin. But as you can see, resistance is increasing. So why do we have this decrease? Well some people say that it's because the industry is no longer interested in looking for natural products. Why? Well, it's not very profitable. Every drug takes a lot of money to actually get it through the to the market, but then antibiotics you only take it for two weeks. So of course, it's not as profitable as other drugs, compared to other drugs. Some other people say that well, there's no antibiotics to be found anymore in nature. And we know that's not true. Because recently, we've genome sequenced an actinomyces called Streptomyces clavuligerus. Clavuligerus is a commercial producer of beta-lactamase inhibitor. It's being used currently, as well, together with beta-lactams to induce its activity. Now when we genome sequenced this and analyzed it, we found there's more than 50 secondary metabolite gene clusters there. Now clavuligerus is known to produce 4-5 compounds, so all the rest, about 45 of them, they're all either asleep or they're being produced in such small quantities that we just cannot detect them. Now, it's not atypical for clavuligerus because we've recently found a global microbial genome analysis of all secondary metabolite pathways. Now if you look at this green part here. This bar, the height of the bar tells you how many secondary metabolites there are on the genome. Now if you look at the actinomyces, you have quite a few, but if you look at all the other bacterias as well, they have quite a few as well. You can imagine there's a load of secondary metabolite gene clusters out there waiting to be discovered. Now we've done some proof of concept studies, as well, to awake some of these clusters. Here we've deleted this repressor and you can now see a yellow compound being produced by this mutant. This is the parent here, which normally produces a blue color. And we've also been able to show that this gene cluster is responsible for producing a compound with antimicrobial activity, and now this yellow compound the chemical structure has been elucidated. And you can see, it's a complete novel compound. So if you think all of these potential secondary metabolites, if they were all awakened, we're absolutely sure there should be new chemical structures in there. We can find diversity and we will also be able to find novel antibiotic — antimicrobial antibiotics. Well, but to awaken all this, if we tried to do it like I did before, deleting one gene, overexpressing a promoter, this takes much too long. We need to be ahead of the game of the antibiotic resistant bacterias. So we need something much more systematic, high-throughput, much faster. And so that's why we want to use synthetic biology. What's synthetic biology, it's to engineer new life forms with unrestrained versatility, which means your imagination is really the limit. And it could be the next industrial revolution. And it's using the concept of engineering, of design, build, and test, and learn, and bringing it together with biology. So here are some examples of synthetic biology, for example, a total synthesis of a functional designer eukaryotic chromosome from yeast. Another example which I personally quite like very much is projects from the iGEM competition. iGEM is the International Genetically Engineered Machine competition. We have this every year, all the undergraduate students from all over the world participate. There are about 300 teams and they make new engineered microbes using standard parts. So here's one example, which is quite nice. In 2013, Heidelberg was the world champion, and what they did was try to recycle gold from electronic waste. Another example would be using bacillus as a biosense for meat spoilage. So bacillus turns blue if the meat's spoiled. Another example, of course, is the famous example from artemisinin. So Jay Keasling's lab has used all these enzymes from yeast and also from plants to produce artemisinin in Saccharomyces cerevisiae, where originally it was produced in a plant. So this is great. You know, being able to produce artemisinin in a non-natural host. But can we do something more than that? Can we take this one level higher? Can we use synthetic biology to produce compounds that nature has never seen before? And to use it to awaken all those antibiotic biosynthesis gene clusters. And we think we can. And in fact, so this is how we can actually do this? Well, first of all we can look at all the genome sequences from all kingdoms of life. Not just microbes, it can be even from humans if it needs to be. We can identify all the secondary metabolite gene clusters. We can even use enzymes that have very special activity. Or even change the enzyme. Redesign the protein specificity, or even the active sites as well. Once we find the enzymes that we require, we can then put them into a gene cluster, and at this point together with promoters and ribosomal binding sites, we want to completely rewrite the DNA. Once we have this gene cluster together, we can put this into a screening host and screen for the novel drug of your choice. And once you've found that novel drug, we then want to put this whole gene cluster into a production host. Because the production host is going to be completely different from a screening host in that the primary metabolism is going to be completely re-engineered so that it's geared to produce that compound of your choice. So, actually, antibiotics are perfect for using synthetic biology because it's naturally modular. Here's an example of how an antibiotic is produced, in fact, erythromycin, it has three huge open reading frames. Within the open reading frames, you have modules. Within the modules, you have these domains. And each of these circles are the domains that have the catalytic activity. So it's very similar to how fatty acids are being synthesized. You start off with C3 unit, it gets loaded onto the loading module, another C3 unit will be loaded onto the module 1, and another C3 to module 2, so on and so forth, to get this long chain of fatty acids. And in the end, it's cleaved off and cyclized to make this core structure. And of course, you have modifying enzymes to make the erythromycin the final compound. But if you look at this, you can see different levels where modularity occurs in the very high level, in the module levels, and in the domain levels. Which means that by changing, swapping these domains and modules around you can get a lot of chemical diversity. So how can we put these synthetic pathways together? You need ribosome binding sites, promoters, you need several of them. So you need libraries. But of course, not just promoters, ribosomal binding sites, but you also need enzymes, the parts, the bits that's going to be the most important to make your pathway. So we tried to see if we could actually do this. Can we make libraries of enzymes where we can swap around? And to do this, we've taken an antibiotic called calcium dependent antibiotic as an example. This is a peptide antibiotic and it uses a special amino acid, which is called L-hydroxyphenylglycine. Here's the biosynthesis pathway here. Because it's a special amino acid, you need all these genes within the biosynthesis cluster to produce this HPG in the end, and then it's incorporated into the final structure. Now we looked at this enzyme here called Hmo or MdlB, we tried to see if we could find homologs or orthologs. And to see if we could swap them around and will it still make CDA? Indeed we found some homologs, here we found three homologs for Hmo, and then we found some orthologs. So these genes, or enzymes, are not involved in actual HPG biosynthesis, but are involved in mandelate catabolism. So, to understand whether they can actually produce CDA, what we did was to delete the Hmo in the natural producer. And then complement it with all of these genes, to see if they could actually produce CDA. Now CDA's an antibiotic, so here you can see we've done some bioactivity tests and they do. You can see some halos here like this, which means it has activity. Well, this isn't good enough. We don't know if it's actually CDA. So what we've done further is to prove that it is CDA by doing HPLC analysis and LC mass spec. So this tells us that yes, we can make enzyme libraries and we can swap enzymes around to start producing antibiotics. Once you have the enzyme parts, you need to put them together. You need to refactor them, you need to build the pathway. But what's the best way of doing that? Is there an order that we should follow? Is one way better another? To understand this, we decided to use this six gene pathway that makes aloesapnarin II. Now, if you — this is the natural organization of the genes the six genes, you can see they're mostly coupled, transcriptionally and translationally coupled. But can we uncouple them? What's the best way? You can see some examples here. This would be the most simplified way of having one promoter, one ribosomal binding site, for one gene. But is this the best way of transcribing, translating this pathway? Maybe it's not. But if you start to think about all the combinations, it's a bit too much to do. So we went back to nature to see if there are some rules. And indeed, we did find some rules. Here are five different pathways, which are all similar in that they produce this compound here. What we found, in fact, was that there were two genes, these light green two here. They're always transcriptionally and translationally coupled. And we further found out that if we uncouple them, this pathway just does not work. So, we always know that this ketosynthase and this chain length factor always have to be transcriptionally and translationally coupled. So now we can start looking at different combinations, see which organization of the genes, the promoters, and the ribosomal binding sites are the best way to transcribe and translate the pathway. Okay, so just rewriting DNA is not about synthetic biology. We can think about other things, about the cell itself. So here, we're thinking about spatial control of biosynthetic pathways. What do I mean by that? Well, how about thinking about trying to put synthetic protein scaffolds? Or you can make compartments within the cell. And in both of these cases, it's enhancing enzyme activity. We can also think about a bigger spatial control. How about microbial consortia? We can have one cell producing a specific compound for the other cell. For example, you could have lignin, filamentous fungi produce using lignin to produce glucose and E. coli, which produces biofilm using that glucose. And you could grow them together. Synthetic bacterial organelles are something that we're very interested in. Especially using bacterial microcompartments. These are made from proteins, not from lipids and they can be found in E. coli and some gram negative bacteria. What's so good about them? Well, if you think that you don't have these BMCs and you don't encapsulate them into these BMCs, all the pathways you could have degradation of substrates, you could have toxic intermediates which can damage the cell. Once these pathways are embedded into the BMCs, the substrates will no longer have competition it will not be degraded. Even if it produces a toxic intermediate or even end compound, it will no longer affect the cell. So you can produce much more. So we're quite interested in this BMC and pursuing further with this. How about temporal control? Of course this is one of the most major things that a lot of the groups are doing. We can have very fast temporal control, for example, allosteric control. Or just-in-time expression, where the genes are only transcribed exactly when they're needed. One could also think about using signaling molecules to synchronize molecular clocks, but we know how heterogeneous even single cells can be, by using signaling molecules we could make absolutely sure that all the products are being produced at the same time in the cells. One could also use signaling molecules to adjust pathway expression. For example, I talked to you earlier about how antibiotics have these huge open reading frames, those were the core enzymes. And you need the core enzymes first, so you can use the signaling molecules to, for example, express these core enzymes first and then another signaling molecule to express the modifying genes. We are in fact working on these kind of signaling molecules. These are called gamma butyrolactones, which are found in streptomyces and are involved in antibiotic production. And we'd like to use this and produce new regulatory circuits we can use for synthetic biology. Okay, we've talked a lot about putting synthetic pathways together. Of course about building libraries of parts, promoters, ribosome binding sites. But actually, how do we make these libraries? Do we just go out and hunt it by hand? Absolutely not. So what we've done is to design softwares to actually find secondary metabolite gene clusters. It's called antiSMASH and it's already on our version 3. What it does is rapidly detect and annotate secondary metabolite biosynthesis gene clusters. Here's a snapshot of what it would look like. You can put in a whole genome, you can put in parts of the genes that are in the sequence, you'll find the software — the software will find all the possible secondary metabolite gene clusters. In this case they found 25, and then it will show you the open reading frames and it can even deduce the chemical structures, possible chemical structures. Another software we've developed is called multigene BLASt, and in this case we're not just looking for antibiotic synthesis gene clusters. Anything gene clusters that are in an operon and conserved, we can look for them. You can see here how these operons are all found in different strains. And not everything is conserved, but you can still find them. Another software we've developed is called Pep2Path. This is combining the antiSMASH together with any mass spec data that you have on peptides. So basically what we're doing is, if you have some peptide data, we can actually find the antibiotic biosynthesis cluster that you're looking for. We've also gone off to do some more modeling as well, so in this case we've found some comparative metabolite modeling. This is a constraint-based model, which is just using the genome sequence. And what we've done here is actually looked at all these different actinomyces species and asked which species would be the best to express all these different classes of antibiotics? White colors or the lighter colors mean that it's a good host, the darker colors mean it's a very bad host for expression. Now up here in this box, they are all streptomyces, remember that I said at the beginning that streptomyces are the natural producers for antibiotics. And if you look at the streptomyces species three here, they're not the best host for all of the classes. now if you go further down here to the mycobacterium species, these are natural mycobacterium species. And you can see it's completely white for all throughout. Which means it's a very good host for all kinds of antibiotics. Of course this is in silico analysis, so we need to prove this. And we'd like to do that very much, because this could be a very good chassy for expression. We've also done some more modeling, some on the regulatory networks to show that in fact, these gamma-butyrolactones have regulatory circuits that are a bistable switch for gene antibiotic production. We've also gone off to use the metabolite model to understand how the flux are being used. So in this case, we combine the metabolite model to transcription analysis. We had transcriptome data from a low producer of beta-lactamase inhibitor and a high producer of beta-lactamase inhibitor, and looked to see which pathways were redundant or essential? And in fact, all the green pathways here are basically redundant to produce lots of these beta-lactamase inhibitors. Which means we can now actually minimize the metabolite pathway and also redirect flux so that it produces a lot of these beta-lactones. Now, having done all this. We've made our cell, we've put our pathways in, it's supposed to work perfect. But of course, it doesn't. As with any engineered products, even cars or even computers, sometimes it just doesn't work very well. You need to fine tune it. And we use metabolomics as a debugging routine. And especially the metabolomics that we use is using LCMS method. So here, we're using an untargeted metabolite analysis, in fact, here are two growth curves. One has been induced with antisense RNAs, so it stops growth. While the other is continuing growth. And we took samples from all these time points and compared what kind of metabolites are being accumulated. Now of course, with biology, everything tends to be a little bit difficult to look at. But we did a lot of replicates, as you can see here, we have biological replicates, we have five different time points on the growth curve, we have two LC columns, plus positive and negative ionization modes with three technical replicates for each of them. But by doing all these replicates, we've been able to see a trend. Here you can see two compounds, which are immediately responding to the induction of these antisense RNA. While here, another compound here is not immediately responding, but rather responding to the stop of growth. So, we found lots of compounds actually metabolized, that seems to be going up and down. But remember, what we perturbed is this little blue spot here. It's just this enzyme. So why is it that it's making so much perturbation in other metabolites? We're not really sure why. But one thing we do understand from this metabolomics analysis is, metabolomics is a great tool to look at the cell as a whole. What's happening within the cell. So it's a great debugging routine. So I hope I've been able to show you today that what kind of tools are needed for antibiotic discovery and design. First, of course you need all these different pathways, we could engineer the chassies, we need to have regulatory circuits, we need to also control genes not just transcription and translation, we need lots of computational softwares doing modeling to help us understand where we should do the next experiment. And last, but not least, the debugging is a very, very important tool as well. And in fact, if you look at this, we can put them into the build, design, and test. And going back again to the original slide where I showed the synthetic biology, using synthetic biology this concept, the three concepts. And in fact, these tools are not just for antibiotics. We can use them for any high-value chemicals. All these tools, using design, build, and test concept. Thank you very much.