Session 6: Synthetic Biology and Metabolic Engineering

00:00:06.09 My name is Kristala Prather, or Kris. I'm an associate professor
00:00:11.15 in the Department of Chemical Engineering at MIT
00:00:14.00 and today I'm going to talk to you about metabolic engineering and synthetic biology,
00:00:17.08 two fields in which I work and which have a very, very strong influence from biology.
00:00:22.01 If you think about biology in the world around you,
00:00:25.28 if you look around at nature, you'll see a lot of beauty and a lot of diversity.
00:00:30.17 You might see feathers on a peacock.
00:00:34.00 You can look at a nautilus shell
00:00:36.06 and see the structures, the symmetry, all of the details that are there.
00:00:40.08 You might look at the wing of a butterfly, for example,
00:00:42.28 and you see colors, and patterns, and lots of richness,
00:00:46.24 or even as you look at the leaf of a tree, you'll see lots of wonderful details and structures.
00:00:52.10 And all of that we see on a very large scale,
00:00:54.27 thinking about how nature gives us a lot of beauty, a lot of diversity,
00:00:59.01 a lot of function. If we actually look deeper, though, at that same leaf that came from the tree,
00:01:05.03 you'll notice that as you go closer and closer in scope,
00:01:08.02 or in scale, you'll see details that you couldn't see before.
00:01:11.20 You'll see that that leaf is actually composed of a series of individual cells,
00:01:15.15 and even within those cells, you see even smaller structures, structures we call organelles
00:01:20.25 that are all giving you this macroscopic property, or phenotype you see,
00:01:25.00 that you recognize as a leaf. And when we look at structures like this,
00:01:29.10 what we really realize is that it's really all about the DNA.
00:01:33.24 Everything that gives us what we see from nature,
00:01:37.00 that gives us the color, the structure, and the function,
00:01:40.11 the things that we think are beautiful, and the things that we think are useful,
00:01:44.08 really come back down to the DNA.
00:01:46.18 And it's really this focus on the DNA which is a hallmark of synthetic biology.
00:01:52.01 Synthetic biology has been defined in many different ways,
00:01:56.15 and it's actually interesting to think about some of the older definitions.
00:02:00.04 One of my favorite actually comes from 2000,
00:02:02.20 in a publication called Chemical and Engineering News.
00:02:05.21 And in that publication, synthetic biology was defined in the way that you can see on the screen now.
00:02:10.06 In particular, there's a focus on the use of non-natural, synthetic molecules.
00:02:15.24 That is, things that aren't really of biological origin,
00:02:19.05 and being able to use those molecules in order to give you function.
00:02:22.18 Ok, so the keys here are things being non-natural,
00:02:25.08 and the function that you would get in biological systems.
00:02:27.26 In the 10 to 15 years since this definition came out,
00:02:31.26 really synthetic biology has changed and the work that's going on in synthetic biology has become much broader.
00:02:38.13 And the definitions that are used more commonly than this first one that's given
00:02:42.17 are the following. One is that synthetic biology is the design and construction
00:02:47.15 of new biological parts, devices and systems.
00:02:52.01 And those parts, devices and systems are words that actually come from engineering.
00:02:57.07 So, one of the ways that we also think about synthetic biology, then,
00:03:00.22 is to re-design existing, natural biological systems for useful purposes,
00:03:06.06 which is really what engineering is all about.
00:03:08.12 Engineering is about design, it's about re-design, for useful purposes,
00:03:12.26 and that is, for specific applications.
00:03:14.25 So, you can see in just looking at how the definitions have changed,
00:03:18.07 from one in 2000, to the working definitions that are used more today,
00:03:22.15 we don't have to focus on unnatural pieces giving us natural or biological functions,
00:03:27.25 but rather, there's more of a focus on taking what nature has given us,
00:03:31.27 repurposing or re-using that for intentions that we as engineers
00:03:36.21 actually design.
00:03:38.02 So, for us, within what's called the Synthetic Biology Engineering Research Center,
00:03:43.24 this also a center in which I work,
00:03:45.17 we have what I describe as a practical definition of synthetic biology.
00:03:49.00 And that is synthetic biology is the effort to make biology easier to engineer.
00:03:54.09 And it's this fusion of engineering principles with biology
00:03:58.01 that really gives synthetic biology its heart and its purpose.
00:04:02.04 And here are some of the engineering principles that we think about as engineers.
00:04:05.27 Things like design; by design, what we mean in that case is saying,
00:04:09.27 I want to build a certain machine that has this specific function,
00:04:14.16 and I know how to draw out or sketch out a way in which I get there.
00:04:18.17 If I think about modeling in an engineering sense, modeling is really about mathematics.
00:04:23.07 That means I can write an equation that actually will support my design,
00:04:27.25 and it represents as well the understanding I have of the underlying principles
00:04:32.24 that allow me to have that design.
00:04:34.17 And then we have these principles of characterization and abstraction,
00:04:37.23 and that really means the practice of going through your design,
00:04:42.02 what you have actually designed, to the point where you build that and then you test it,
00:04:45.22 and in the process of testing it, you characterize the system as a whole,
00:04:49.16 as well as the individual parts.
00:04:51.20 And finally, abstraction means actually being able to take, now,
00:04:55.09 a larger view and if go back to my definitions of parts and devices,
00:04:59.18 it means not always having to look at the very specific level of detail,
00:05:03.26 but knowing that if I want some bigger function,
00:05:06.18 I could encode that in a simpler way.
00:05:09.08 So, the key technology in synthetic biology for all of this
00:05:13.08 is DNA synthesis.
00:05:14.27 And DNA synthesis is really about having biology or biological function
00:05:20.07 but taking a step where you really remove biology from that process.
00:05:24.01 Here's actually an example of how that works;
00:05:27.28 so if we think about biology and think about DNA,
00:05:30.27 I've already told you that all of biology is really about the underlying DNA,
00:05:36.02 there's a sequence of A's and G's and C's and T's
00:05:39.03 in the natural system that is the DNA and how those strings of nucleotides, as they're called,
00:05:44.20 are strung together, actually gives us the function that we're interested in.
00:05:48.14 So, I may study the biological system,
00:05:51.05 and then figure out, what is that sequence that gives me the function that I actually want,
00:05:55.22 or the function that I'm looking to be able to now design,
00:05:58.21 into a new system,
00:06:00.03 I can then go now to a computer, store that information digitally
00:06:04.25 and go through the design process that I talked about
00:06:07.27 where I can specify now my own sequence of those A's and G's and C's and T's,
00:06:13.01 to give me the function that I want and then rather than having to go back into the biological host,
00:06:17.28 I can take advantage of DNA synthesis to make the DNA that I want,
00:06:21.20 without actually having to go back into a biological host to do this,
00:06:26.06 but by rather recognizing that those sequences of A's and G's and C's and T's
00:06:31.02 are just chemicals and those chemicals can be synthesized without biology,
00:06:34.28 and they can be strung together without biology.
00:06:37.05 I can though, once I've made those, put those, put them back into a biological host,
00:06:41.26 and that gives me then the function, as far as biology is concerned, that I'm interested in.
00:06:46.24 So, this is a nice representation of this type of process,
00:06:51.27 from Seed magazine, this was drawn by Drew Endy,
00:06:54.18 who's now at Stanford University,
00:06:56.15 and it's just a cartoon representation of exactly what I described,
00:07:00.01 where you start at the beginning, with actually defining what that sequence is,
00:07:03.17 of the A's, the G's, the T's and the C's, from now a natural host,
00:07:08.04 you can then reconstruct those now as synthetic DNA,
00:07:11.19 and then this abstraction is actually the line that I'm crossing here,
00:07:15.10 where we think about now that I have that DNA, if you will,
00:07:18.28 encoding a function that I'm interested in,
00:07:20.24 and that may result in taking a certain input, converting it to a different output,
00:07:25.16 or stringing together different devices here now,
00:07:28.27 one that may have one function, one that has another function,
00:07:31.21 and having now a composite device,
00:07:34.20 as we would call it, that would give us this feature that we're interested in.
00:07:38.05 And I might be able to string these together in many different ways,
00:07:41.02 in order to get different kinds of functions now,
00:07:43.07 putting two devices together, or maybe multiple devices together that may give me a certain structure,
00:07:48.14 or feature, that has the function that I'm interested in.
00:07:50.29 So, if we now think about some examples of how we could make this work,
00:07:56.08 that is designing different pieces of DNA such that we put them together and get different functions,
00:08:01.11 you can see a movie that's playing on the screen now
00:08:04.23 where there are individual cells that are growing, they're dividing,
00:08:07.14 and you can see that they're actually blinking.
00:08:09.20 They're sometimes having light turned on and sometimes having light turned off.
00:08:13.22 This is actually an example of something that's called an oscillator,
00:08:16.27 that process of turning on and off means that the expression, in this case of this protein,
00:08:21.27 is oscillating. And that feature of being able to have a system now that blinks, if you will,
00:08:26.25 is something that can be encoded in the DNA,
00:08:29.03 by taking advantage of different parts,
00:08:31.08 for example, what's shown here is a Tet repressor,
00:08:34.05 a Lac repressor and a lambda repressor,
00:08:36.12 that all work together in a way that you have now a system that gives you sometimes the gene expression being on,
00:08:43.13 that is the light is on,
00:08:44.26 and sometimes the light being off.
00:08:46.15 So, this is an example of how a specific function, that is, oscillation, or blinking,
00:08:51.24 could be designed, there were models that were built in this system,
00:08:54.28 that is mathematical equations to describe how that had to happen,
00:08:58.16 and then you could actually build in those pieces with DNA,
00:09:01.10 these circles here are plasmid DNA, and the output, finally, is GFP.
00:09:05.22 And that's actually the protein that gives you the lightness or the darkness,
00:09:08.22 that is the blinking pattern.
00:09:11.21 Here's a different example of being able to string those pieces of DNA together
00:09:15.25 in order to get a particular type of function that we want,
00:09:18.24 and in this case, the goal was to have effectively a bacterial photography system.
00:09:23.11 And in this case, there was DNA taken from many different pieces,
00:09:26.15 there was something called phytochromes, or light sensors, from an organism called Synechocystis.
00:09:31.23 There was something called an osmoregulation system,
00:09:34.23 and this is really just a way to make proteins from E. coli
00:09:38.15 and then a protein called LacZ, which has really been around for quite a long time,
00:09:43.18 in biological standards, since the late 1970's,
00:09:46.06 which allows you to either have color or not have color.
00:09:49.14 And you can see now in the pictorial diagram here the way this is supposed to work
00:09:53.14 is that when light is present, you're going to have now activation of this osmoregulation system,
00:09:58.27 that gives you an output which in this case is going to be black.
00:10:02.11 If there's no light that's present, then you'll have an output that's going to be light.
00:10:06.07 So, we can have a table that's written here, that would be our design table that says,
00:10:10.14 the first condition that we want is a light condition,
00:10:13.07 and in that case the LacZ is going to be low,
00:10:16.07 and the result is a light color.
00:10:17.15 The second condition now would be a condition that's dark.
00:10:20.08 In that case, the LacZ output is going to be high, and that's actually going to give us a dark color.
00:10:25.00 How does this actually work?
00:10:27.21 Well, what you can do now is to create a mask where if you look on the left-hand side here,
00:10:33.16 what's shown is 'Hello World,' where everything now that's white would be white,
00:10:37.24 and you would actually be able to shine light through the words hello and world
00:10:41.29 and you can see next to that then the result of what happens.
00:10:45.02 When the light output was low,
00:10:47.10 you have no color, when it's high, you have a dark color,
00:10:50.15 and that actually gives you now, in bacteria, bacteria that are dark that say hello,
00:10:55.07 bacteria that are dark that say world, and will actually recapitulate, or give you that image,
00:11:00.05 much like a camera would.
00:11:01.26 Here's an example of this same system now, taking it a little bit further,
00:11:07.01 with images that are even more complex.
00:11:09.05 And you see in this case, now, from a paper published in Nature from the same group,
00:11:13.08 that you can actually end up with a picture of a bacteriophage
00:11:16.14 based on this same principle of having a mask and exposing light,
00:11:20.27 and in the places where the light is there, you have a dark color,
00:11:23.21 when it's not, you have a lighter color.
00:11:25.25 And you can even go even further and make a picture of Andy Ellington,
00:11:29.02 who is the professor in whose lab this was developed.
00:11:32.08 These are examples, now, of being able to put biology, or biological pieces together
00:11:38.19 for functions, but as engineers, we often want to think about how do we actually solve problems,
00:11:43.26 whether they be problems in healthcare, in energy or the environment.
00:11:47.29 And so I'd like give you a few examples of applications
00:11:51.12 that are emerging from synthetic biology
00:11:53.23 where researchers are actively working to build these biological systems
00:11:57.17 to address some of these global problems.
00:11:59.26 And the first example I'm going to give you is from the lab of professor Ron Weiss
00:12:03.18 who's in biological engineering at MIT,
00:12:05.16 and he's been looking at the issue of diabetes.
00:12:08.19 There are two types of diabetes: type I and type II.
00:12:11.18 In type I diabetes, what actually happens is that your body destroys
00:12:16.08 the cells that make the insulin that you need
00:12:18.17 to control your glucose levels in the blood.
00:12:21.24 And so, you may have seen an image like this before,
00:12:25.02 where patients who have diabetes have to check their blood glucose levels,
00:12:29.07 they actually have to prick themselves to extract blood,
00:12:32.19 expose that to a glucose meter, and then based on their glucose levels,
00:12:36.01 decide to dose themselves with insulin or not.
00:12:39.05 Well, if we say the problem is in the pancreas,
00:12:41.26 the question is, can you actually engineer an artificial pancreas
00:12:46.11 or engineer cells that will perform the function of the pancreas
00:12:49.21 so that you now no longer need to have this process of measuring blood glucose levels,
00:12:55.00 and then actually dosing yourself with insulin.
00:12:57.09 So, what Professor Weiss is doing is looking at engineering stem cells
00:13:01.01 to be able to stay in an undifferentiated state
00:13:03.19 to then sense when the presence of these insulin producing cells has gone very low,
00:13:09.00 and then to differentiate and produce new cells, only up to a point,
00:13:12.18 and then to stay quiet again, or quiescent,
00:13:14.29 such that you maintain this population of cells
00:13:17.18 that can spontaneously produce new insulin producing cells whenever your body needs them.
00:13:22.19 That's an application in health.
00:13:25.23 There are other applications, for example, in the environment,
00:13:29.05 and this is actually a significant problem in agriculture,
00:13:32.02 which is that you have to provide a lot of nitrogen
00:13:35.09 to plants in order to get them to grow.
00:13:37.18 Proteins, for example, have a lot of nitrogen in them, and so it's necessary to provide that
00:13:42.03 because it can be difficult to actually extract it in a way that's usable.
00:13:46.04 But it turns out that there are certain organisms that will actually live on the roots of plants
00:13:51.02 that have the ability to fix nitrogen, that means they can take nitrogen from the atmosphere,
00:13:56.00 and convert it into the kind of nitrogen which is useful for plants.
00:14:00.01 And so Professor Chris Voigt, who's in biological engineering at MIT
00:14:03.28 has been looking at whether or not you could take that ability to fix nitrogen, as we call it,
00:14:08.21 that is to take nitrogen out of the atmosphere and put it into a usable form for plants,
00:14:13.25 can you actually take that capacity from these microorganisms and put it directly into the plants
00:14:20.10 so that you actually have a need for much less fertilizer in the environment.
00:14:25.13 Here's a third example of a way that now a group of students
00:14:30.23 were looking at using synthetic biology to be able to really address a critical problem in both health and the environment.
00:14:37.14 And this is actually part of the iGEM program, you can see the URL for that at the bottom of the screen,
00:14:41.28 where iGEM stands for international genetically engineered machines
00:14:46.01 and the iGEM competition is an opportunity for students from all over the world to come together
00:14:51.15 and decide for themselves, here's a problem that we want biology to try to solve
00:14:55.21 and then to go through this process of designing, modeling, characterizing and building these systems
00:15:00.29 to see if they can address those problems.
00:15:02.29 The University of Edinburgh iGEM team in 2006
00:15:07.15 decided to try to tackle the problem of groundwater contaminated by arsenic in Bangladesh.
00:15:13.03 They studied the problem, found that it really is significant,
00:15:15.29 in terms of a lot of the groundwater being contaminated
00:15:18.15 and there not being really any easy systems for villagers to know whether or not a source of water
00:15:24.14 was safe to drink or not.
00:15:26.03 So, they decided to take pieces from biology that naturally responded to arsenic
00:15:31.07 and to build a sensor that would tell them whether or not there was arsenic in the water or not.
00:15:35.29 And it was actually designed after something you may have seen,
00:15:38.29 which is just a sensor that tells you, for example, the chlorine level and the pH level in a pool.
00:15:43.14 The idea being that you could take a sample of water, you could add now this sample of bacteria,
00:15:48.19 E. coli in this case, that could detect the arsenic.
00:15:51.19 If the arsenic was present at a certain level, the colors would become very bright,
00:15:55.13 and you would know that that water was not safe to drink.
00:15:58.02 Now, I want to actually switch gears a little bit and talk about metabolic engineering,
00:16:04.07 which is an area that's been around for awhile,
00:16:06.10 but we're increasingly seeing a merger between principles of metabolic engineering and those of synthetic biology.
00:16:12.16 And metabolic engineering is really about the fact
00:16:15.17 that biology is very good at doing chemistry;
00:16:18.09 that is, from biological systems, you can get a wide range of chemical molecules
00:16:23.10 that have useful functions. And I've shown two of them here.
00:16:26.02 The first one is called caspofungin, and then there's another one that's shown here that's called lovastatin.
00:16:31.15 Caspofungin is actually an antifungal organism, that is, it's used to treat fungal infections,
00:16:37.00 and lovastatin is one of the first cholesterol lowering drugs.
00:16:40.11 So, you've heard about statins, perhaps, and there are lots of them now,
00:16:43.14 but lovastatin was one of the first that was discovered.
00:16:45.29 Both of these are naturally produced by biological systems
00:16:49.20 and they've been very useful as natural products, we would call them in this case,
00:16:53.21 to have therapeutic functions. And traditionally, when we think about biology being used for chemistry,
00:16:59.06 it's usually for molecules like this.
00:17:01.06 If you look at caspofungin, for example, you can see that it has complexity
00:17:04.20 both in terms of just the number of atoms that it has, it's a pretty big molecule,
00:17:09.17 and then you'll also see a lot of these hydroxyl groups, you'll also see chiral centers,
00:17:13.16 which are shown now by the bold, or the arrows that go back and forth.
00:17:18.14 And so traditionally, if you think about how synthetic chemistry works
00:17:22.18 it's not that chemistry can't make a molecule like that,
00:17:25.09 but the yields would be very low, it would take a large number of steps
00:17:29.03 to get to that compound, whereas you have a biological organism
00:17:32.19 that can make these molecules very easily.
00:17:35.01 And so it's molecules like this that traditionally have been made by biology.
00:17:39.28 Now, I want to introduce as well a couple of other molecules, one being an amino acid, glutamic acid,
00:17:45.12 and the other being an organic acid, malic acid.
00:17:48.12 And these are also molecules that biology can make efficiently
00:17:52.09 using biological means as opposed to chemistry.
00:17:55.14 And what I mean by that is they can be produced commercially through fermentation.
00:17:59.16 So, you have an organism that's capable of making these compounds,
00:18:03.12 you can grow them up in very large quantities,
00:18:05.12 and now you have a product that you can bring to market.
00:18:08.02 What's true about all of these molecules is that they are produced by organisms
00:18:13.11 that naturally make them and the goal when metabolic engineering first arose
00:18:17.25 was to figure out how do you actually get these organisms
00:18:20.28 to do what they do better.
00:18:23.11 And better, in an engineering extent means to make more of the molecule, to make it faster,
00:18:28.13 and to make it more efficiently and the efficiency part, it's typically considered as yield.
00:18:33.19 That is, how much of the starting material that goes into the system
00:18:37.06 ends up in the product that you're interested in.
00:18:39.00 So, I have a graduate student who once came up with this analogy, or this cartoon,
00:18:44.09 to describe how metabolic engineering actually works in terms of improving these natural producers.
00:18:49.14 And what you see here is a maze, where you have this poor mouse, Wemberly,
00:18:53.28 that's lost its pet rabbit Petal, and Wemberly has to figure out how to get to Petal.
00:18:58.20 And you can see, as with any maze, there are a number of different starting points
00:19:02.12 that the mouse could use in order to get to the end point.
00:19:05.00 However, we know not all of those are going to be productive.
00:19:07.22 So, with metabolic engineering, what you want to do is to remove those routes
00:19:12.05 that are going to be non-productive.
00:19:13.11 That means to actually knock out, or delete, competing pathways.
00:19:17.07 Pathways that would actually take your substrate, your intermediate or your carbon
00:19:21.29 a place that you don't want it to go.
00:19:23.23 The other thing that you might want to have in order to have this faster objective met
00:19:28.13 is a little bit of a stimulation or motivation
00:19:31.17 for the enzymes to be overexpressed.
00:19:33.13 And overexpressing those enzymes, you can increase the amount of a limiting enzyme
00:19:40.01 in order to get more of that through the system.
00:19:42.26 And now again, in our cartoon fashion, what that means is encouraging the mouse to run a little bit faster
00:19:47.10 and to get through the maze quicker than it otherwise would.
00:19:50.12 So, I finally want to introduce just as background two other molecules that are interesting
00:19:57.06 both from a metabolic engineering and a synthetic biology standpoint.
00:20:00.23 And these are 1,3-propanediol and artemisinic acid
00:20:04.03 and you can see on the slide the uses for them.
00:20:06.20 1,3-propanediol, or PDO, as it's called, is an industrial chemical that's also used for materials production,
00:20:12.09 and artemesinic acid is a precursor to an anti-malarial drug.
00:20:16.19 Now, these are also compounds that are produced by biology,
00:20:20.00 meaning that we can make them through fermentation,
00:20:22.07 that is growing up a large number of microorganims to produce the compound that we're interested in.
00:20:27.14 They're also natural products, meaning that they're naturally produced by organisms.
00:20:32.01 But the difference between these two molecules and the first four examples that I gave
00:20:36.05 is that those molecules are produced naturally by one particular host,
00:20:41.17 but it's actually a different host that's been able to be used to have them produced economically.
00:20:46.28 And this allows us now to think about that DNA that we talked about, in terms of moving that around,
00:20:52.19 to be able to move it to reconstitute natural pathways in heterologous hosts,
00:20:57.19 or in hosts that don't normally contain that pathway.
00:21:01.10 Here's actually an example of doing just this thing.
00:21:05.14 So, the artemisinic acid that I told you about is a precursor to the drug that's shown here,
00:21:09.17 which is called artemesinin. It's naturally produced in a plant that's called Artemisia annua
00:21:14.17 and the goal is to be able to have, rather than that plant, a yeast cell
00:21:19.10 make this same compound. The reason for that is that you can put yeast cells into a factory
00:21:24.11 that looks much like factories that you may have seen before,
00:21:27.00 or, if you think about yeast and fermentation, this might actually be a brewery,
00:21:31.13 or a beer manufacturing unit.
00:21:33.00 And you can't take plants and actually scale them up in that same way.
00:21:37.02 Instead, you have to plant plants in the ground,
00:21:39.13 and wait for the proper amount of sunlight and nutrition in order for them to grow.
00:21:43.11 So, if my goal is to actually have a compound that's produced in Artemisia annua,
00:21:48.27 to have that be produced in a yeast, so that I can put it into a factory,
00:21:53.06 what that really means is identifying the DNA that encodes for those enzymes
00:21:57.25 that gives me the chemical that I'm interested in. I now can go through this process that I talked about before,
00:22:03.01 of sequencing that DNA and then synthesizing the DNA to get just those pieces that I need,
00:22:08.16 and then I can move that DNA now into my unnatural, or my heterologous host,
00:22:13.25 and that host, once it's properly engineered, is able to make the compound that I'm interested in,
00:22:18.22 and I can actually grow it up now in a large factory.
00:22:21.20 And this is work that's been done by Professor Jay Keasling, in chemical engineering at UC-Berkeley.
00:22:26.20 So, the work that's done in my lab is really focused on expanding this capacity of biology
00:22:33.04 to do chemistry. And we're motivated by the diagram that's shown here,
00:22:37.01 where if we think about the materials that we get in our world today,
00:22:40.16 where they come from and what they're used for, most of it comes from crude oil as the input,
00:22:45.15 and the outputs are things that you're familiar with, which include fuels,
00:22:49.05 which I think is mostly what we think about in terms of oil being used for,
00:22:52.27 but also quite a large bit of petrol chemicals.
00:22:55.22 And these are actually the molecules, olefins and aromatics are highlighted here as examples,
00:22:59.24 that are used for polymers, for resins, for adhesives, et cetera.
00:23:03.28 That is, those are molecules where the chemicals that are being produced
00:23:08.05 are being used for their mass properties, or their properties as chemicals
00:23:11.14 and not for their energetic properties, which is what we use them for for fuels.
00:23:15.18 And we've talked a lot in this country and across the world
00:23:18.19 about replacing crude oil as the input for this process,
00:23:21.29 and instead we can think about creating what we might describe as a bio refinery,
00:23:26.05 where the input in that case, rather than being oil, is glucose or other sugars,
00:23:31.07 that might come from biomass, in the same way that we now want to be able to make biofuels,
00:23:36.01 we want to be able to make more chemicals,
00:23:38.21 that is, the same chemicals that give us the function that we're used to from petrochemicals,
00:23:42.23 we want to be able to access those from biomass as well.
00:23:46.17 In the second part of my talk, I'm actually going to give you examples from my lab,
00:23:50.14 where we focus on exactly this, that is, building new kinds of chemical molecules
00:23:55.02 from biology in different ways that really take advantage of the key principles of synthetic biology,
00:24:00.25 but also are very firmly rooted within metabolic engineering as well.
00:24:05.23 So, this is actually our vision of how that happens, this is a cartoon representation from an artist at MIT,
00:24:13.16 where really what we're looking at doing in expanding the capacity of biology to do chemistry,
00:24:18.03 is to think about these microbes as they were, this is an E. coli representation,
00:24:22.21 as little chemical factories, where we can now, inside the cell, engineer different pathways
00:24:28.12 to make different products and that same image that I showed you before
00:24:31.28 of a large factory, we can think about that on a greatly, greatly magnified scale
00:24:36.23 or a greatly miniaturized scale, I should say, in terms of having now small microbes give us this same capacity.
00:24:43.00 So, let me give you my final thoughts about the field of synthetic biology
00:24:47.10 and a little bit about metabolic engineering. Synthetic biology is a very diverse field
00:24:51.21 and it's actually composed of very diverse individuals as well,
00:24:55.06 and so people like myself, who work in metabolic engineering are in that field,
00:24:58.13 those who are trained as electrical engineers, as computer scientists, as biological engineers,
00:25:03.11 as physicists, they are a lot of different people in this area who are looking at how do you actually use DNA
00:25:08.21 in order to get important functions of interest to solve the problems that we have to solve in the world.
00:25:13.25 The problems that are being worked on are very diverse problems;
00:25:16.25 I gave you examples that come from health, from the environment, from energy,
00:25:21.02 and again, this diverse set of people are working on this diverse set of problems
00:25:24.21 and are also taking diversity of approaches towards solving those problems.
00:25:28.27 And I would say that the goal for all of us as we go through this
00:25:32.10 is to actually make biology easier to engineer,
00:25:35.02 so that we really can bring solutions to some of our most pressing global problems.
00:25:39.22 In the second half of my talk, I'll talk much more about examples from my lab,
00:25:43.10 but this is my overview for metabolic engineering and for synthetic biology.

00:00:06.07 My name is Kristala Prather and I'm an associate professor of chemical engineering at MIT.
00:00:11.18 In the first part of my presentation, I gave an overview of metabolic engineering and synthetic biology
00:00:16.23 and now I'd like to talk more specifically about work being done in my lab
00:00:20.24 towards expanding the capacity of biology for chemistry, or as I've titled it here,
00:00:25.22 teaching an old bacterium new tricks.
00:00:28.04 So, in my introduction, I gave this maze as an example of how we think about metabolic engineering,
00:00:34.23 where you have the example here of a mouse Wemberly
00:00:38.08 that's lost its pet rabbit Petal and there's a maze of possibilities
00:00:42.16 of how the mouse might get to the rabbit it's looking for.
00:00:45.22 And our goal is to be able to block off, or to obstruct,
00:00:49.24 those pathways which are not going to be productive,
00:00:52.06 or to stimulate, in this case, the mouse to run faster, or in biological terms,
00:00:57.11 to increase the rate at which material will flow through our maze
00:01:00.15 so that we get to the product that we're interested in more quickly.
00:01:03.17 Now, there's another way that we actually think about engineering pathways
00:01:07.15 that also looks at this maze analogy.
00:01:09.25 In this case, our goal is completely different. Now, we actually want to blow the maze up
00:01:16.00 so that rather than forcing the mouse to run from one to the other through all these obstacles,
00:01:21.20 we have a more direct way to get from point A to point B.
00:01:25.00 And that's actually the focus of much of the work that goes on in my lab.
00:01:28.18 When we started thinking about this problem,
00:01:31.23 that is how do we actually get biology to do more chemistry,
00:01:35.19 the question was, well, what kind of targets could we look at?
00:01:38.09 What are good molecules to look at that might be produced by biology?
00:01:42.00 And in 2004, the US Department of Energy put together a report
00:01:46.02 called "Top Value Added Chemicals from Biomass"
00:01:48.21 where they actually sought to answer that very question.
00:01:51.09 That is to say, if you're using biology, or biomass,
00:01:55.01 as the input for chemicals, what are the right molecules that you'd want to produce.
00:01:59.20 And they came up with a list which is actually called the "top 10" list in the literature
00:02:04.21 of building block molecules.
00:02:06.08 Now, I always find this interesting because it turns out the top ten list actually has 12 lines
00:02:11.24 and a few of these lines have more than one molecule,
00:02:14.09 but nevertheless, it's called the top ten list because that sounds a lot better than the top 14 or 15 list.
00:02:19.12 If we look at this list, we see things for example like glutamic acid,
00:02:23.21 and that's an amino acid. We see aspartic acid, which is also an amino acid,
00:02:28.15 and those were compounds that we weren't really interested in working on
00:02:31.28 because our challenge was to find a pathway that either didn't exist
00:02:36.00 or one that was really, really complicated that we could, again, blow up our maze,
00:02:40.07 if we use that analogy, in order to get to the compound that we're interested in.
00:02:43.27 So, when we looked at this list, we eliminated compounds like that.
00:02:47.22 We also eliminated compounds like glycerol, which it turns out is actually relatively cheap today,
00:02:53.16 but wasn't when this report was first produced.
00:02:56.16 So, once we went through this process,
00:02:58.12 of saying, well here are things that we're not intellectually interested in,
00:03:01.17 and here are compounds that we don't think really give us the value that we want,
00:03:05.08 we began to focus on a couple of different compounds,
00:03:08.16 one of which is glucaric acid, that's shown here,
00:03:10.25 and I'd like to talk to you today about our work that we've done
00:03:14.06 to be able to produce this compound in a microbe, namely, E. coli.
00:03:19.00 Glucaric acid, as it's shown again here, is a structure that has 6 carbons,
00:03:24.13 so it's actually pretty similar to glucose in how it's arranged, and it is actually a natural product
00:03:30.07 and I mentioned natural products in the first half of my talk
00:03:32.24 as being compounds that are naturally produced by nature.
00:03:35.26 It turns out this is a compound that's produced in fruits and vegetables
00:03:39.09 and also in mammals, but there's no known microbial pathway for it,
00:03:43.10 meaning that if we look at the simplest organisms,
00:03:45.22 the ones that are easiest to think about putting into a factory,
00:03:49.13 there are no microbes like that where we know that glucaric acid is produced.
00:03:53.02 This compound has been studied for therapeutic purposes
00:03:56.10 either as an agent to reduce cholesterol, or even possibly to fight cancer,
00:04:00.25 but we've actually been more interested in its properties as a monomer
00:04:04.04 for different kinds of materials, or as detergents.
00:04:07.11 And the final bullet point on this slide just emphasizes the fact
00:04:10.10 that we actually know how to make this compound chemically, from glucose,
00:04:14.19 but it turns out that that process, the way it exists now,
00:04:17.13 is pretty messy, it requires a lot of harsh materials,
00:04:20.21 and so it's both not economical and not environmentally friendly.
00:04:24.22 So, we set out to come up with a way to make glucaric acid using biology.
00:04:29.05 This is actually what the natural pathway looks like
00:04:33.10 and hopefully you can see a lot of arrows here that might make you a little bit squeamish
00:04:37.15 if you were going to graduate school and your advisor said,
00:04:40.09 you've got to get this whole thing to work in E. coli.
00:04:43.08 Just to give you a quick overview of this pathway,
00:04:46.10 the compound we're interested in, glucaric acid, is in this box at the top.
00:04:50.00 I mentioned that this is something that could come from glucose
00:04:52.18 and we actually will use glucose as our starting compound as well,
00:04:55.20 and glucose is on this figure.
00:04:57.20 I'll give you a second to look and see if you can find it,
00:04:59.27 because it turns out there are quite a bit of arrows here,
00:05:02.17 but if you look very closely along the left-hand side, then you can see glucose right here.
00:05:07.15 You can also see that all these arrows are going back and forth,
00:05:10.22 you have this interaction with the pentose phosphate pathway,
00:05:13.17 you have another sugar, galactose, which is one input,
00:05:16.13 and you actually have an additional output, which is ascorbic acid.
00:05:19.23 This is a mess,
00:05:20.27 and this is not something that we would really want to think about putting into E. coli.
00:05:25.14 So, our challenge was to figure out, is there a different way for us to get from glucose
00:05:30.26 to the molecule that we're interested in, that would be much simpler,
00:05:33.25 that would have much, much less of this maze-like effect.
00:05:36.22 One of the nice things about having a molecule, however, that is a natural product,
00:05:42.25 is that we could go to the databases and say, is glucaric acid there?
00:05:46.26 That is, in known metabolism, is there an example where glucaric acid has been found
00:05:52.04 to be associated with biology.
00:05:54.16 And in fact, what we found is that glucaric acid could be produced from a compound
00:05:57.29 called glucaronic acid and it can produced using an enzyme called uronate dehydrogenase
00:06:03.03 that's actually found in a bacterium called Pseudomonas syringae.
00:06:06.15 But that was sort of the end of the story as far as Pseudomonas was concerned.
00:06:11.00 With our glucaronic acid, now, we could go back to the databases again
00:06:14.20 and ask the same question, that is, do we see glucaronic acid being produced by nature,
00:06:18.23 and in fact what we could find is that glucaronic acid could be produced
00:06:22.00 from a compound called myo-inositol with an enzyme called myo-inositol oxygenase.
00:06:26.28 And that enzyme is found in a number of sources, a number of mammalian sources,
00:06:31.12 and fungal sources, and we actually chose the variant from mouse
00:06:35.04 because it was one that had been shown to work well when it was expressed in E. coli.
00:06:39.15 But that was really the end of the story as far as mammalian biology was concerned,
00:06:43.11 but if we said where else does myo-inositol show up in metabolism,
00:06:47.21 we could actually find a linkage directly from myo-inositol, or to myo-inositol,
00:06:52.00 from glucose, and that was work done by John Frost's lab at Michigan State,
00:06:55.18 where he showed that you could use glucose as the input,
00:06:58.15 you would go through glucose-6-phosphate,
00:07:00.14 and then you would have just a single recombinant enzyme,
00:07:03.09 that is, a yeast myo-inositol-1-phosphate synthase that would produce
00:07:07.08 myo-inositol-1-phosphate and that, in E. coli, was naturally dephosphorylated
00:07:12.06 in order to give the myo-inositol compound that we're interested in.
00:07:14.24 So, now, rather than having this very complex network
00:07:17.24 of 11 or 12 steps, we really only need 3 different enzymes
00:07:21.29 to be expressed in E. coli, although from three very different sources,
00:07:25.24 in order to get the compound that we're interested in.
00:07:27.26 And so we could take advantage of that to actually have the first gene
00:07:31.17 directly PCR-amplified because we knew that would work in E. coli from John Frost's work.
00:07:36.11 The second gene we could take advantage of this DNA synthesis
00:07:39.23 that I talked about in the first part to be able to have this version of the gene synthesized
00:07:44.21 but synthesized in a way that E. coli would be able to produce it more easily
00:07:49.11 than the natural sequence of DNA that would come from mouse,
00:07:52.06 and then we actually had to do a little bit of work to figure out what was the sequence of DNA,
00:07:57.05 or the gene, encoding from the uronate dehydrogenase in bacteria.
00:08:01.23 But once we were able to do that, we now had all three of the genes that we needed
00:08:05.18 to put into E. coli to see whether or not it could make glucaric acid.
00:08:09.19 So, when we co-expressed all three of these genes,
00:08:13.28 what we found was exactly what we hoped to find.
00:08:15.28 And that is that we got glucaric acid being produced.
00:08:19.03 And the figure that's shown here shows the titer, or the concentration, in grams per liter,
00:08:24.05 of glucaric acid that we can measure in the culture medium.
00:08:27.13 So, this is actually spit out by the cell into the surrounding medium.
00:08:31.20 And I have two different bars that are shown here, one that has 0.1 millimolar IPTG
00:08:36.10 and one that has 0.05 millimolar IPTG.
00:08:39.04 I want to take just a second and explain what that really means.
00:08:42.07 IPTG in this case is what we'd call an inducer;
00:08:45.15 that means that it's something that we add to the culture that tells the cells
00:08:48.26 you should start making the proteins, or the enzymes, that we're interested in.
00:08:52.16 And what's shown now is the result on this slide, as something that we see a lot of times,
00:08:56.27 which is that if we have a somewhat higher concentration of our inducer,
00:09:00.21 where we're making more protein, you see that we actually have less of the product
00:09:04.27 than if we have a lower concentration of our inducer.
00:09:07.00 And that's really a core principle of metabolic engineering, which is that
00:09:10.18 changes that we make to the cell have these very broad systems-wide effects
00:09:14.23 that we don't always understand.
00:09:16.08 And so every time we seek to engineer an organism to make a compound we're interested in,
00:09:21.17 we have to go through this trial and error process of trying to identify
00:09:24.24 what really are the best conditions to make the compound that we're interested in.
00:09:28.25 The second thing that I want to point out is that we see,
00:09:31.22 besides glucaric acid being produced, we also find that we have myo-inositol,
00:09:36.13 which is accumulating, meaning we can measure that in the culture medium.
00:09:39.14 And the fact that that myo-inositol is there, it lets us know that the enzyme
00:09:44.06 which is converting myo-inositol to glucaronic acid is a limitation in the system.
00:09:48.22 That is, it's not working the way its supposed to work, such that all the myo-inositol that's produced
00:09:53.24 is converted to glucaronic acid, and then onto to glucaric acid.
00:09:57.20 I always think at this point, there must be a joke in here somewhere.
00:10:01.24 We have a yeast, a mouse and a bacterium
00:10:05.07 and they all go into a bar and I'm not really sure what the end result is here,
00:10:08.27 but we know that glucaric acid comes out somewhere.
00:10:10.26 Unfortunately, it's not quite that easy and we have a lot of challenges that we have to try to address
00:10:16.12 in trying to actually get the cells to make a lot more of this product that we're interested in.
00:10:20.20 The first of those challenges actually comes into place
00:10:24.26 when we actually look at the fact that we have this myo-inositol accumulating,
00:10:28.11 as I pointed out in the first graph, that showed glucaric acid being produced.
00:10:31.25 And in this case now, if we take a closer look at this enzyme,
00:10:35.00 all we're focused on is this one reaction.
00:10:37.21 We can see that this MIOX gene, the myo-inositol oxygenase,
00:10:41.12 takes myo-inositol as its input. It also uses molecular oxygen
00:10:45.19 and the product that's produced now is glucaronic acid.
00:10:48.15 And so we know that the cells are not actually doing this reaction,
00:10:52.29 that is, converted myo-inositol to glucaronic acid, at a fast enough rate
00:10:57.08 to consume of all it. So, if we study that enzyme by itself,
00:11:00.26 the experiment we did in this case was to look at cells producing just this enzyme,
00:11:05.01 so it doesn't have the first enzyme, which gives us myo-inositol,
00:11:08.09 it doesn't have the third enzyme, which actually takes that glucaronic acid
00:11:12.05 and converts it to glucaric acid.
00:11:13.19 Instead, we're looking at this in isolation, and we looked at two different conditions:
00:11:17.20 one where we actually have myo-inositol present in the culture medium
00:11:21.23 as we're growing up the cells and making the protein,
00:11:24.06 and one where it's missing.
00:11:26.04 And the only difference now is that at a point where we measure the activity of the cells,
00:11:30.22 we actually have some cells that saw substrate, that is the myo-inositol,
00:11:34.18 and some that didn't, but at the same time, when we would go to analyze them,
00:11:38.23 we take the cells away, so now there's no myo-inositol,
00:11:42.05 we break open the cells and release the protein and we expose those cells
00:11:46.18 to the same concentration of the substrate.
00:11:48.25 And in doing that and measuring the activity,
00:11:51.10 what we find is that for the cells that were able to previously see the substrate,
00:11:55.23 the activity of that protein is about an order of magnitude higher
00:11:59.03 than the cells that only saw substrate for the first time after the protein had actually been produced.
00:12:04.20 Well, so this actually raised an interesting question for us.
00:12:08.09 And we thought about we actually solve this problem,
00:12:10.27 and I can tell you the answer is not toss in a lot of myo-inositol,
00:12:14.11 because that's actually cheating. What we want to do is start from glucose,
00:12:17.19 which is going to be a more cheaply available substrate,
00:12:20.02 and make the product that we're interested in.
00:12:22.00 But now we can think about this as engineers and say, well,
00:12:26.03 what information do we have that actually gives us some guidance
00:12:29.14 on how we might actually be able to sole this problem,
00:12:32.04 even if we don't exactly understand the underlying reasons for the phenomenon that we see.
00:12:37.07 And so the first thing that we thought is, ok, what we want then
00:12:40.15 is for that first enzyme, the INO1, to make a lot of the myo-inositol,
00:12:45.14 and then that would be really good
00:12:47.06 because that's what we need for the second enzyme to be effective.
00:12:50.07 The only problem with that is that it sounds really good to say that,
00:12:53.06 but as we've worked on that, that turned out to be a lot easier said than done.
00:12:56.26 At the same time as we were looking at this,
00:12:59.12 we actually came up with another idea.
00:13:02.28 In this case, the idea came from a collaborator, John Dueber,
00:13:07.07 in SynBERC, which is the Synthetic Biology Engineering Research Center,
00:13:10.18 and John's work was looking at something called enzyme colocalization,
00:13:15.05 where the goal here was to be able to take enzymes
00:13:17.22 that normally might be freely disbursed throughout the cell,
00:13:20.15 with no reason for them to be together,
00:13:22.15 and to cause a way for those enzymes to be physically located next to each other.
00:13:26.24 In fact, what happens in this case is that the enzymes, shown here now as MIOX and INO1,
00:13:32.19 are actually exposed, or they have covalently attached to them these tags,
00:13:37.01 and those tags fold into a certain 3-dimensional conformation
00:13:40.28 that can then be recognized by a different piece of a protein.
00:13:45.08 That piece of a protein can then be put into something that we call a scaffold,
00:13:48.16 and if you now have the scaffold in the cell,
00:13:51.08 and you have these enzymes that are tagged with pieces that will recognize that scaffold,
00:13:56.04 that actually causes two enzymes to become located close to each other within the cell.
00:14:01.22 So, our idea here was very simple, that if we couldn't actually change the activity of the enzyme
00:14:06.19 and the way that we could get the upstream enzyme to make much more product,
00:14:10.11 if we actually reduced the distance between the two enzymes,
00:14:13.26 that would give us a higher local concentration of myo-inositol,
00:14:17.18 and maybe if that local concentration was higher,
00:14:19.21 that would give us the higher activity that we had seen before,
00:14:22.13 and that would actually give us higher yields and productivities.
00:14:25.15 And the first way that we tested this was exactly as its diagrammed on this slide,
00:14:31.02 where we actually had just these two enzymes being recruited to the scaffold,
00:14:35.01 in a one to one ratio, and in doing that, we actually got an increase of about a factor of 3
00:14:40.29 in the amount of glucaric acid that we were producing.
00:14:43.17 Now, as all good scientists, we have to ask ourselves,
00:14:46.27 is this working the way that we want it work?
00:14:49.10 And I'll remind you that our theory here was that what we would get was not just more glucaric acid,
00:14:55.05 but that that would happen because we would have a higher activity of MIOX,
00:14:58.23 that is, we would have better activation, and that would result in this faster conversion
00:15:02.27 that would give us more of the product that we're interested in.
00:15:05.15 So, we actually needed to test that theory,
00:15:08.07 that is, to measure the activity of this MIOX protein and find out
00:15:12.00 whether or not it actually had higher activity, as we supposed that it might.
00:15:16.25 What's shown now in the upper left-hand corner is the data for the product, or the glucaric acid titer,
00:15:22.19 where the lighter bars here are, well, on the left hand side, I should say,
00:15:26.15 without scaffold, and then on the right hand side, with scaffold.
00:15:29.10 And you can see again, these are two different conditions in terms of how much of this IPTG we use
00:15:34.14 to induce the expression of the proteins.
00:15:36.28 And in the first case now, of these lighter bars, there's no real difference
00:15:41.01 between not having scaffold and having scaffold,
00:15:43.22 on the amount of product that's being produced,
00:15:46.03 and if we actually look at the activity of the protein,
00:15:48.17 there's also no significant difference between the protein activity here and the protein activity in this case as well.
00:15:54.25 However, in our best case, where we actually had an increase of 3-fold
00:15:58.08 in the amount of glucaric acid being produced, that's the darker bar in this case,
00:16:01.29 we can look at the specific activity of the protein and we see about a 30% improvement
00:16:07.15 in the activity of this protein relative to when the scaffolds aren't present.
00:16:11.15 And the p-value is here just to show you that that difference is actually significant.
00:16:15.15 So, now we've actually verified that we have not just higher production of the product that we're interested in,
00:16:21.08 but we're getting that higher production by the mechanism that we had supposed
00:16:25.17 would actually happen.
00:16:27.12 Now, one of the nice things about these scaffolds is that what it allows you to do
00:16:31.16 is to explore different stoichiometries.
00:16:33.22 What I mean by that is you don't just have to have one of one protein
00:16:37.19 and one of a second protein coming together, but you can actually, in that scaffold,
00:16:41.27 dial in the stoichiometry by specifying the number of binding domains that you have
00:16:47.02 for each particular protein. So, this is an example of a different scaffold,
00:16:50.20 where you can see two binding domains for one of the proteins,
00:16:53.29 four binding domains for another protein and a single binding domain for the last protein.
00:16:58.12 And if we put that together, what it actually means is that we have,
00:17:01.10 in this case, four copies of the first gene, the INO1 enzyme, that is,
00:17:06.05 two copies of the second enzyme, and only one copy of that third enzyme.
00:17:09.23 This actually allows us to look at a wide variety of different configurations
00:17:14.20 as well as look at varying the amount of the scaffold that we have
00:17:18.11 and the amount of the enzyme that we have,
00:17:20.03 to look at the effect of that on the productivity.
00:17:22.23 And the result of that exercise is shown here,
00:17:25.12 where each of those dots is the average of a triplicate experiment
00:17:29.07 where we have the same amount of enzyme being produced in all cases,
00:17:32.20 but we're looking at a wide variety of scaffold induction levels
00:17:36.07 and also looking at a very wide configuration of different scaffolds themselves,
00:17:41.10 meaning different numbers of binding domains for these enzymes that we're interested in.
00:17:44.18 What we see if that we actually are able to change
00:17:49.00 the activity of this enzyme over a factor of about 7-fold
00:17:52.21 and that actually results in a change in the amount of glucaric acid that we have
00:17:56.24 in a factor of about 5-fold. So, we really have shown that we can use,
00:18:01.04 in this case what's called a synthetic biology device,
00:18:04.02 that is, these protein-protein co-localization mechanisms,
00:18:07.08 to be able to solve a problem with an engineering approach,
00:18:11.05 even if we still don't understand exactly what is it that leads to these differences
00:18:15.21 that we see in the activity of the protein.
00:18:18.08 Now, I want to remind you again of this maze analogy that we had before
00:18:24.03 of a protein, or rather a compound, coming into a maze
00:18:28.02 and having a number of different places that it could go.
00:18:30.17 And I showed a very simple diagram before of the maze having four different entry points.
00:18:35.11 Well, the reality is that this is really what the maze looks like inside the cell,
00:18:39.28 where each of the individual dots in this figure represents a particular chemical,
00:18:44.10 and each of the lines between those dots represents an enzyme
00:18:48.02 that can convert that chemical into something else.
00:18:50.20 So, that means that the networks that we're really talking about are very, very large mazes,
00:18:55.16 not these very simplified ones that I showed you.
00:18:57.26 And if our goal is to have glucose, for example, as a starting molecule,
00:19:01.06 work its way through this maze, and end up with a final compound that we're interested in,
00:19:05.28 we can often have by-products that are being produced.
00:19:08.24 And ideally what we'd like is to, again, knock-out those unproductive routes,
00:19:13.19 which are going to lead to byproduct formation, but the question becomes,
00:19:17.05 what if your byproduct is actually growth?
00:19:19.11 And growth in this case also means the ability to make the enzymes that you need
00:19:24.19 in order to catalyze all these chemical reactions
00:19:27.12 that are going to give you conversion of your starting substrate, glucose,
00:19:30.21 down to your final product, glucaric acid.
00:19:32.28 In this case now, we don't have the option of simply knocking out or deleting growth,
00:19:38.16 because now we're not actually going to make the enzymes that we need
00:19:41.10 and this means that we have to have a different way of solving this problem,
00:19:44.23 or a different approach to dealing with the byproduct that we have.
00:19:48.08 So, what we can do in this case is again, take advantage of these principles of synthetic biology,
00:19:54.05 which are based on design, to think about a control system.
00:19:57.17 In particular what we want is dynamic control of these activities.
00:20:01.22 We like to have our initial condition be fast growth, or growth being favored,
00:20:06.02 such that we actually make not just the cells, but again the proteins that we need,
00:20:10.03 that are going to give us the enzymes that give us the chemical reactions
00:20:13.02 that we need to make the product that we're interested in.
00:20:15.07 And then we want to trigger a switch to a production phase
00:20:18.16 where we say, stop growing now, and instead of growing,
00:20:21.15 use all of that glucose to make the molecule that we want you to make.
00:20:25.00 I can represent that diagrammatically like this,
00:20:27.26 where if we have our competing activity, initially, when the input is low,
00:20:32.04 that activity will be high, and at some point, I'm going to now add an input
00:20:36.03 that causes the competing activity to be low.
00:20:38.14 You can see that now, specifically, in what we're interested in, which is growth versus production,
00:20:43.24 which is that we want growth to actually start high,
00:20:46.29 and then after awhile, we want growth to go down, and instead we want the production here
00:20:52.21 to actually start to go up. This is actually something called a genetic inverter.
00:20:57.10 It's an inverter because when the input is low, the output is high.
00:21:01.17 When the input is high, the output is low.
00:21:04.05 And there is actually a precedent for this in nature, namely in secondary metabolite production.
00:21:09.03 Now, for secondary metabolites, these are natural products
00:21:12.25 where growth first is favored, and then the cell will naturally make this switch
00:21:17.12 such that you then will have the metabolites being produced later.
00:21:21.00 So, how do we actually make this process happen
00:21:25.20 when we're talking about having a switch for growth
00:21:28.22 where ideally what we're doing is having the cells use glucose for growth initially,
00:21:32.28 and then change that in order to use glucose for product formation
00:21:36.06 at some point after which we apply our trigger.
00:21:39.02 If we look at how glucose is normally used in our cells,
00:21:42.05 it comes in in what's called the PTS system,
00:21:44.11 and that PTS system brings in glucose as glucose-6-phosphate.
00:21:48.21 And it has two different routes that it can go into;
00:21:50.27 glycolysis or the pentose-phosphate pathway
00:21:54.02 and that's actually how that glucose is used by the cells for growth.
00:21:58.06 That's how the glucose is eaten, if we want to think about it that way.
00:22:01.16 And that's the process that we want to compete against.
00:22:03.24 Well, glucose-6-phosphate is the original substrate of our glucaric acid pathway,
00:22:08.19 but we didn't really want to deal with quite this complexity to start with,
00:22:12.12 so we decided to start on a simpler scale
00:22:14.22 and see if we could just address the glucose utilization issue
00:22:17.19 and then what we're doing now is to try to work up to the increasing complexity
00:22:21.27 that's required to deal with glucose-6-phosphate specifically.
00:22:25.11 That can be addressed by the fact that there is actually another way that glucose can come into the cell.
00:22:30.00 It can come in through what's called the galP, or galactose permease,
00:22:33.29 and in this case, it comes in as free glucose.
00:22:36.11 That glucose now has to be converted to glucose-6-phosphate
00:22:39.21 with an enzyme called glucokinase that uses ATP.
00:22:43.15 And because now the glucose has to go through that route,
00:22:46.17 it gives us just a single control point for being able to regulate,
00:22:51.01 that is control, how much of the glucose goes into our endogenous metabolism, or growth,
00:22:56.09 versus what goes into the product that we're interested in.
00:22:58.18 So, we can actually have this system now where we knock-out the PTS system,
00:23:02.25 we apply what we describe as a valve to regulate Glk activity,
00:23:07.06 and in doing that, we're able to modulate how much of the glucose is available
00:23:12.00 for endogenous metabolism, that is for growth,
00:23:14.21 versus how much is available for productivity.
00:23:17.06 And I just want to remind you that when we're talking about modulating the protein,
00:23:21.10 that is, how much of the glucokinase that's available, what we're really talking about
00:23:26.03 is controlling how much of the DNA, or how that DNA is being expressed.
00:23:30.18 So, we're actually doing all of our manipulations at the level of DNA synthesis,
00:23:34.21 which comes back to how we think about synthetic biology.
00:23:37.14 So, one way that we can actually test this,
00:23:41.27 rather than immediately going to a process where we have to worry about dynamic control,
00:23:46.18 is to look at what we would call static control of the system.
00:23:49.28 And that is that we can replace the natural glucokinase operon,
00:23:54.01 or production system,
00:23:55.20 which naturally consists of two different promoters that are negatively regulated
00:24:00.01 by this protein called FruR, we can get rid of all of that regulation,
00:24:04.16 that is we can replace that DNA, and instead have a library of different promoters
00:24:09.26 where the binding site for FruR is gone,
00:24:12.19 so the only thing that's regulating how much of this protein is produced
00:24:15.25 is the kind of promoter that we use.
00:24:17.26 And by varying the strength of these promoters, by using different variations here,
00:24:22.21 then we can end up with a library of different expression states
00:24:25.16 and ask the question, does that actually affect how much of a heterologous product
00:24:30.08 we could actually produce.
00:24:31.27 Here's now a little bit of characterization of this library.
00:24:35.16 The first thing that we're looking at in this slide is whether or not we actually do have increases in the mRNA,
00:24:40.28 that is, whether or not changing the promoter strength
00:24:43.10 changed the transcription, and then if that corresponded to increases in the protein being produced.
00:24:48.09 And what's shown in this case now, along the x-axis, is the relative promoter strength,
00:24:52.29 from very low strength, or weak promoters, up to very high strength promoters,
00:24:57.15 and then what's shown on the y-axis, on the left-hand side,
00:25:00.19 is the activity of the protein that we're interested in, glucokinase,
00:25:04.00 and what's shown on the right hand side is the mRNA levels.
00:25:07.05 And you can see now, that activity, which is in the solid circles,
00:25:11.19 does actually go up as we go from low promoter strengths
00:25:15.14 up to high promoter strengths, but it only goes up to a certain point,
00:25:19.01 after which we see it start to decline.
00:25:21.01 The same thing is true for the mRNA, that it actually will go up as we go along this axis here,
00:25:25.20 and it only will go up to a certain point and then it starts to decline as well.
00:25:30.06 These measurements were all done where we use glycerol
00:25:33.20 as a carbon source instead of glucose and that's actually to allow us
00:25:37.00 to decouple growth from measuring the properties of this enzyme
00:25:40.29 just to see if the library is working.
00:25:42.26 And what we actually found when we went to glucose
00:25:45.10 is that when the expression levels were too high here,
00:25:48.07 then these cells no longer grew. So, this cell has high mRNA,
00:25:52.03 but you can see the protein levels are pretty low.
00:25:54.10 And these cells would not grow on glucose.
00:25:56.29 The ones where the protein levels were still pretty high
00:26:00.01 would grow on glucose, except that we did have this gray region here,
00:26:03.29 this stipple region, where we saw the cells could grow, but only very, very poorly.
00:26:09.17 We could then take the cells that we knew were growing well, in this region here,
00:26:13.25 and then ask, can we actually now, in glucose,
00:26:17.03 relate the growth rate to the activity of this protein, which tells us whether or not it really can control
00:26:23.16 how much of the substrate is available for endogenous growth.
00:26:26.25 The result of that experiment is shown on this slide,
00:26:30.24 where again what we have now is expressed in terms of Glk activity
00:26:34.15 where it goes from a very low activity up to our higher activity
00:26:37.29 and then what's shown on the x-axis is the growth rate of the cells.
00:26:41.15 The native promoter is shown right here in this open triangle
00:26:44.25 and the filled squares will tell you that we're able to actually increase the growth rate
00:26:49.13 of the this cell. We can also decrease the growth rate of the cell by changing the glucokinase activity.
00:26:55.08 So, that confirms for us that we actually do have a control point
00:26:58.12 or a specific protein where if we vary the activity of that protein,
00:27:02.22 that actually will tell us, or allow us to control, rather,
00:27:05.26 how the cells are growing. The next question then is,
00:27:09.02 if you can control the growth of the cells, does that actually result in more product being produced.
00:27:14.13 So, in this case we have an example molecule, or a test molecule, gluconate,
00:27:18.02 this can be produced in one single enzymatic step from glucose,
00:27:21.25 and again, the competing reaction here is glucose-6-phosphate,
00:27:24.29 which is actually going to be produced from glucokinase.
00:27:27.18 What's shown now here is 5-KG, this is 5-ketogluconate,
00:27:31.05 which is just a spontaneous product that we actually get in very, very small amounts,
00:27:35.02 but we want to account for that by making sure that we look at the sum of both of these products,
00:27:39.18 to give us a sense of how much of the flux is coming through this side
00:27:43.17 versus this side of our pathway.
00:27:45.23 And now what's shown in this slide is actually the result of that experiment,
00:27:49.23 where what's shown now is the Glk activity, that is from, lower to higher amounts of that protein,
00:27:55.17 which is controlling how much glucose goes into endogenous metabolism
00:27:59.18 and what's shown on the y-axis is the molar yield,
00:28:02.29 and this is really how much of the glucose that we start with
00:28:06.04 goes into the compound that we're interested in, versus goes into other byproducts,
00:28:10.11 or into cellular growth.
00:28:12.04 And we see this very nice relationship where, when the activity is very low,
00:28:15.23 then we can see that we have a moderate amount of the yield, in this case,
00:28:21.12 that is the product that we're interested in. As we increase the activity,
00:28:25.00 then we get a slight bump, but as the activity goes higher and higher,
00:28:28.21 what we actually find is that we are decreasing the yield,
00:28:31.29 which basically tells us that as we get, now,
00:28:34.17 to the point where we're making more and more of this glucokinase,
00:28:38.06 we have more of the glucose going into growth
00:28:40.17 and less of it going into the product that we're interested in.
00:28:43.00 So, that actually gave us the validation that we needed
00:28:46.18 that the system design that we had envisioned,
00:28:49.12 one in which we could control the activity of this enzyme,
00:28:52.20 was going to be useful. What I haven't told you so far is that these cells here,
00:28:56.27 although they had the highest yield, did not have the highest concentration.
00:29:00.27 The concentration wasn't very different from the cultures that surrounded it
00:29:04.27 as far as yield was concerned, and they also didn't grow very well.
00:29:08.05 So, that just meant that the cells overall were not happy,
00:29:11.07 and that our original design of having them have a state where they grow very well first,
00:29:15.29 would probably work better in terms of giving us the maximum yield possible.
00:29:20.03 So, the system that we wanted to design here, again, is an inverter,
00:29:24.17 and the way that this will work is that we have this protein,
00:29:27.25 now as an example, GFP,
00:29:29.15 which is being produced by a promoter which is regulated by the lacI protein,
00:29:34.02 or the lacI operator. When lacI is not present, GFP is turned on.
00:29:39.11 We then have lacI, however, under the control of something called the tet promoter,
00:29:43.27 and the tet promoter is responsive to a small molecule
00:29:46.29 such that when you add aTc, this small molecule, it would turn on
00:29:51.10 the expression of lacI and that would turn off our GFP.
00:29:53.29 Now, that you can see by looking at the graph; the first point that we actually have
00:29:58.04 is the fact that in the absence of any aTc, then we have a very high fluorescence,
00:30:03.03 which means that the whole system is on.
00:30:04.27 If we then move to a point where we add aTc,
00:30:08.13 what you'll find in that case is that you can see the GFP levels start to go down
00:30:12.16 as a function of how much aTc we add,
00:30:15.10 and at the point where we've added 100 ng/ml of aTc, we have very little GPF being produced.
00:30:20.27 We can show that the mechanism of this is working the way we intend it to work
00:30:25.04 by adding an additional protein called IPTG, and what IPTG actually does
00:30:29.23 is to interfere with this lacI binding
00:30:32.13 such that you can recover some of the GPF expression
00:30:34.28 and that's actually shown in the last two points of this graph here,
00:30:38.07 that show that GFP can go back up.
00:30:40.23 So, now we know that our system, our basic inverter is working,
00:30:44.22 and what we have to do in this case now is to integrate that
00:30:47.12 into our cell, that is to change now Glk activity so that it responds in this same way.
00:30:54.06 And what's shown now in this slide is the result of having done exactly that.
00:30:58.25 So, here's now the construct of our inverter,
00:31:01.03 where again, this is really just how the DNA is being constructed,
00:31:04.26 and we're using that to control how Glk is being produced
00:31:07.20 and we can look at the same two properties that we looked at before,
00:31:10.21 which is, is the mRNA changing, that is, is the DNA to mRNA, that transcription process,
00:31:17.05 is that being regulated the way we want it to,
00:31:19.03 and does that correspondingly result in differences in the Glk activity?
00:31:22.25 And the mRNA levels are actually shown at the bottom,
00:31:25.04 where you can see that as we increase the amount of aTc,
00:31:28.00 we actually do see that we start initially with high levels of mRNA,
00:31:31.05 and then those levels of mRNA eventually come down.
00:31:34.05 The top graph here actually shows the response of Glk,
00:31:37.02 where it also starts very high, and then it also will come down to a very, very low level.
00:31:42.09 This is again a characterization in glycerol, where we don't have glucose present,
00:31:46.29 so we're only able to see the response of the cells to Glk
00:31:51.18 when it doesn't really need Glk and that actually tells us, is the system really working.
00:31:55.21 Now, we also want to know that it's actually dynamic.
00:32:00.01 So, the way we tested our static system before was just to change the promoters
00:32:05.13 that were encoding for Glk and then to ask, does that actually give us differences?
00:32:09.13 We now want to know if we have a switch.
00:32:11.13 If we start off with it on and then add this inducer so that we turn it off,
00:32:16.07 that is, we invert the response, do we actually get what we're interested in.
00:32:19.28 And the top graph that's shown here is the response of what happens
00:32:23.02 to the cell growth as we actually add our inducer,
00:32:26.10 where the top part of this is now uninduced,
00:32:28.24 that means that we're not adding anything chemically,
00:32:31.12 and we see that the cells are continuing to grow.
00:32:33.17 If we compare that now to the second line here,
00:32:35.26 where initially they both started off at the same point,
00:32:38.18 we add our inducer, we can see that the cells where we now have turned the gene off
00:32:43.10 by activating our inverter, are growing to a lower point.
00:32:47.00 We can also see a control plot in this very bottom here, which is what happens
00:32:51.14 if we add inducer from the very beginning.
00:32:53.20 That actually means that it turns off gene expression so low
00:32:56.17 that those cells never grow. You can see that the OD stays flat
00:32:59.22 and pretty much close to zero the whole time.
00:33:02.04 So, we know that again, the response we're looking for, growth,
00:33:05.14 is changing the way we want it to,
00:33:07.05 and just very briefly, what's shown in these bottom slides
00:33:09.21 is that the growth rate again is changing,
00:33:12.00 this is now relative OD between those two.
00:33:14.17 The activity is also changing, it's decreasing,
00:33:17.10 and the mRNA levels are going down as well.
00:33:19.22 Ok, so now we know the system is working exactly the way we want it to work,
00:33:23.19 it was designed in a certain way, we seem to have the output that we're interested in
00:33:27.13 from the design perspective of growth.
00:33:29.06 The question now is, does it actually give us the productivity enhancements that we were looking for.
00:33:34.09 And we're now again looking at the same system as before,
00:33:37.23 where our goal is to make this compound gluconate,
00:33:40.16 and the only difference now on this slide is that I've added now this product acetate
00:33:45.01 which is a byproduct of metabolism,
00:33:47.04 and is a representation of how much glucose flux is actually going down into endogenous metabolism.
00:33:52.28 And if you look at now the charts here on the right hand side,
00:33:56.06 the top one gives us the titers, or the concentrations,
00:33:59.07 and it shows that more glucose is being consumed, that's what in this white bar here,
00:34:03.05 is how much glucose is consumed.
00:34:04.21 More of that is consumed when the inverter is on;
00:34:07.20 the gray bar is how much product is being produced.
00:34:10.04 We make substantially more product being produced here as well,
00:34:13.05 and then these smaller bars here, the lightest kind of dark gray and the very, very black bar,
00:34:18.29 give us an indication of some of the minor byproducts.
00:34:21.19 And that's actually represented more easily in the bottom graph here,
00:34:25.04 where again, I'm showing the yield, that is how much of what goes in as glucose
00:34:29.15 is being converted to the glucaric acid product that we're interested in,
00:34:32.18 sorry, in this case the gluconate, or the gluconic acid product that we're interested in.
00:34:36.14 And the open white bars here give us the yield measurements
00:34:40.03 and in this case we've actually increased our yield from about 0.7,
00:34:43.23 and this was actually higher than what we had seen with the other system,
00:34:47.17 which tells us the cells are happier now,
00:34:49.06 and our yield in this case goes up to about 0.8, or a little bit higher than 0.8,
00:34:54.08 so we have about a twenty percent increase in the yield.
00:34:56.17 The grey bars that are shown here is this acetate by-product,
00:34:59.28 and you can see an even larger reduction in the waste going to acetate.
00:35:04.29 So, we have again a twenty percent increase in the yield here,
00:35:08.13 but we also have almost a fifty percent decrease in waste,
00:35:12.12 that is this acetate waste.
00:35:14.03 Now, the last thing that we wanted to look at was the timing of the induction
00:35:19.05 because we do know that based on exactly when we add this inducer to turn off Glk expression,
00:35:24.26 we could have the cell growth go way, way down,
00:35:27.15 I showed you that as a control plot, or if we wait too late,
00:35:30.22 then the cell is not actually able to respond
00:35:33.05 because it's going to stop being very active.
00:35:35.19 So, what we're looking at here now is the OD, or that is the growth,
00:35:39.06 at which we induce, starting from very early induction times,
00:35:42.08 up to later induction times, and then what's shown on the y-axis is the yield
00:35:46.19 relative to an uninduced culture. And we have two different yields that we're looking at,
00:35:50.27 one is the yield of product, and that's shown in the top here,
00:35:53.15 with the squares, and the second is the acetate yield,
00:35:56.22 or, again, a measure of waste that we have here.
00:35:59.29 What we find in this case is that the yield improvements are actually best
00:36:03.13 when we induce earlier. That means give the cells a little bit of time grow,
00:36:07.03 but don't let them grow too far, and we can see in our best case
00:36:10.20 about a 70% reduction in waste and a 20% increase in product being produced.
00:36:15.29 Let me summarize the story that I've given you about glucaric acid.
00:36:21.06 I started by talking about how we could come up with a new pathway
00:36:24.18 to be able to make this compound that was still a natural product,
00:36:28.16 but whose natural pathway was too cumbersome from being, to be produced in E. coli.
00:36:33.09 What we used in this case is part selection, or bio prospecting,
00:36:36.25 to find the enzymes that we could move from one source into another source,
00:36:41.04 and we're able to do this because once we know the DNA that encodes for those enzymes,
00:36:45.07 we can synthesize that DNA, and easily move it around between organisms.
00:36:49.15 And the second thing I showed you was this example of a synthetic biology device,
00:36:54.02 that was the protein-protein colocalization study,
00:36:56.26 which gave us increases in productivity.
00:36:59.04 And those protein-protein colocalization devices, or the scaffolds,
00:37:02.28 have been shown to be useful in other projects as well,
00:37:05.18 so that we know that they are reusable
00:37:07.14 and modular in a way that makes them very useful
00:37:10.10 for thinking about how do we actually engineer the metabolism of cells
00:37:13.26 to make the products that we're interested in.
00:37:15.16 And the last part that I showed you was an example
00:37:18.00 of how we might engineer the host, or chassis in the language of synthetic biology
00:37:22.10 to give us further improvements both in the titers,
00:37:25.16 that is the concentrations that we're interested in,
00:37:27.18 and in the flux, or the yield of the product that we want,
00:37:31.00 such that we get more of the substrate that we start with
00:37:33.23 going into more of the product that we're interested in.
00:37:35.29 I'd actually like to end this whole iBio seminar by acknowledging the folks that did the work.
00:37:41.26 I won't go through all the names,
00:37:43.12 but you can see them highlighted here in red,
00:37:45.13 as students who are both currently in the group working on these projects,
00:37:48.26 as well as former students and postdocs in the groups.
00:37:51.18 I've recognized John Dueber as a collaborator, he is still at the University of California at Berkeley,
00:37:56.13 and this work was primarily funded by the National Science Foundation
00:37:59.19 through SynBERC and through the Office of Naval Research through the young investigator program
00:38:03.13 with the last part of it being funded primarily by the National Science Foundation
00:38:07.06 through the career program. I hope you've enjoyed the iBio seminar
00:38:10.18 and thank you very much.