WEBVTT 00:00:00.060 --> 00:00:01.780 The following content is provided 00:00:01.780 --> 00:00:04.019 under a Creative Commons license. 00:00:04.019 --> 00:00:06.870 Your support will help MIT OpenCourseWare continue 00:00:06.870 --> 00:00:10.730 to offer high-quality educational resources for free. 00:00:10.730 --> 00:00:13.330 To make a donation or view additional materials 00:00:13.330 --> 00:00:15.780 from hundreds of MIT courses, visit 00:00:15.780 --> 00:00:26.370 MIT OpenCourseWare at ocw.mit.edu 00:00:26.370 --> 00:00:27.980 PROFESSOR: Thank you. 00:00:27.980 --> 00:00:29.700 And please feel free to interrupt. 00:00:29.700 --> 00:00:33.370 I'd just as soon run this as a discussion, if you'd like. 00:00:33.370 --> 00:00:34.664 Is that permitted, do you know? 00:00:34.664 --> 00:00:35.580 MODERATOR: Absolutely. 00:00:35.580 --> 00:00:38.210 PROFESSOR: OK, so these are conflicts 00:00:38.210 --> 00:00:40.160 of interests for those of you who care, 00:00:40.160 --> 00:00:46.650 or you can get it in more detail here by going to this website. 00:00:46.650 --> 00:00:52.070 And I thought I will talk about this topic of causality. 00:00:52.070 --> 00:00:54.330 You've learned quite a bit already in this course 00:00:54.330 --> 00:01:01.120 about tools for analyzing genomes from various aspects, 00:01:01.120 --> 00:01:03.180 but what you do after you analyze it is you 00:01:03.180 --> 00:01:05.010 want to test your hypotheses. 00:01:05.010 --> 00:01:11.340 And this is a very richly enabling idea, in the sense 00:01:11.340 --> 00:01:13.600 that you can go to very small cohort sizes, 00:01:13.600 --> 00:01:16.480 as we'll see-- N of one cohort sizes-- 00:01:16.480 --> 00:01:20.370 and your false positives are less of a concern 00:01:20.370 --> 00:01:22.690 if you have a high throughput way of testing them. 00:01:22.690 --> 00:01:24.150 And so I think it's very important 00:01:24.150 --> 00:01:28.060 to know the possibilities for testing causality. 00:01:28.060 --> 00:01:30.620 And that gets us into engineering genomes-- 00:01:30.620 --> 00:01:33.520 and, in a particular, about computer-aided design. 00:01:33.520 --> 00:01:36.180 So you've talked about computer-aided analysis; 00:01:36.180 --> 00:01:38.340 now let's talk about computer-aided design 00:01:38.340 --> 00:01:43.020 of genomes, both bacterial and human. 00:01:43.020 --> 00:01:45.770 So I just want to illustrate the idea. 00:01:45.770 --> 00:01:50.060 You might say, well, why would we want to design genomes? 00:01:50.060 --> 00:01:51.790 You can test causality, typically, 00:01:51.790 --> 00:01:53.580 by changing one base pair. 00:01:53.580 --> 00:01:56.270 Why would you want to change more than one base pair? 00:01:56.270 --> 00:01:58.200 If you have a SNP, that's great. 00:01:58.200 --> 00:02:00.840 Well, sometimes you have multiple SNPs interacting 00:02:00.840 --> 00:02:05.740 in multigenic-- and we'll get to humans in a moment. 00:02:05.740 --> 00:02:07.873 But here's a radical example, something 00:02:07.873 --> 00:02:09.289 from the extreme edge, where you'd 00:02:09.289 --> 00:02:11.850 want to change almost every base pair in the genome-- 00:02:11.850 --> 00:02:14.523 not make a copy of a genome but actually design, 00:02:14.523 --> 00:02:19.660 in an intelligent way-- semi-intelligent-- 00:02:19.660 --> 00:02:22.460 combinatorial as well-- a genome that 00:02:22.460 --> 00:02:24.240 has new functions, new properties. 00:02:24.240 --> 00:02:26.760 And the four functions I submit for your consideration 00:02:26.760 --> 00:02:29.055 here is that you might want to be genetically 00:02:29.055 --> 00:02:33.040 and metabolically isolated for safety 00:02:33.040 --> 00:02:37.180 reasons or public relations reasons or both. 00:02:37.180 --> 00:02:39.150 You want to have new chemistry, new protein 00:02:39.150 --> 00:02:40.830 chemistry, new amino acids. 00:02:40.830 --> 00:02:44.590 And finally, you want to have multi-virus resistance. 00:02:44.590 --> 00:02:47.250 This is probably the most powerful of the four, 00:02:47.250 --> 00:02:50.540 where imagine that you have an organism-- whether it's 00:02:50.540 --> 00:02:53.940 industrial, agricultural, or even human-- that 00:02:53.940 --> 00:02:58.010 was resistant to all viruses, past and present-- even ones 00:02:58.010 --> 00:02:59.995 you haven't analyzed. 00:02:59.995 --> 00:03:00.870 So how do we do this? 00:03:00.870 --> 00:03:02.390 How do we get new functionality? 00:03:02.390 --> 00:03:06.330 How do we design a genome in such a way that doesn't break? 00:03:06.330 --> 00:03:09.390 Because if you change the genome enough, 00:03:09.390 --> 00:03:10.819 you get your comeuppance. 00:03:10.819 --> 00:03:13.110 You learn you don't know as much as you think you know. 00:03:13.110 --> 00:03:16.330 You have your beautiful computer simulations from your analysis, 00:03:16.330 --> 00:03:21.030 and as soon as you test them, you start getting surprises. 00:03:21.030 --> 00:03:26.530 So anyway, I'm going to focus on this process of designing 00:03:26.530 --> 00:03:29.360 and building and then testing. 00:03:29.360 --> 00:03:30.860 And then, so this part of the design 00:03:30.860 --> 00:03:32.276 has to have an analytic component. 00:03:32.276 --> 00:03:35.160 So we'll get back to your old friends in analytics. 00:03:35.160 --> 00:03:39.690 So as I go down this list, maybe just show of hands of how many 00:03:39.690 --> 00:03:44.670 have been exposed to these computational tools already. 00:03:44.670 --> 00:03:47.450 So Bowtie, anybody? 00:03:47.450 --> 00:03:48.150 OK, good. 00:03:48.150 --> 00:03:51.400 See, you covered that, so I don't need to cover that. 00:03:51.400 --> 00:03:54.150 Number two-- no? 00:03:54.150 --> 00:03:54.910 Some? 00:03:54.910 --> 00:03:57.300 SnpEff? 00:03:57.300 --> 00:03:59.410 JBrowse-- SQL, you've all heard of SQL, right? 00:03:59.410 --> 00:04:01.455 OK, good. 00:04:01.455 --> 00:04:02.940 Let's see. 00:04:02.940 --> 00:04:05.200 So the point is each of these things 00:04:05.200 --> 00:04:09.360 is integrated into this system we call "Millstone," which 00:04:09.360 --> 00:04:12.130 is all about design and analysis. 00:04:12.130 --> 00:04:14.630 So it's this loop that goes around and around, as you'll 00:04:14.630 --> 00:04:17.440 see in just a moment-- actually, may have seen already 00:04:17.440 --> 00:04:19.209 back here. 00:04:19.209 --> 00:04:20.760 So we design it. 00:04:20.760 --> 00:04:21.260 We build it. 00:04:21.260 --> 00:04:22.110 We test it. 00:04:22.110 --> 00:04:22.930 And we analyze it. 00:04:22.930 --> 00:04:28.080 And the analysis-- sometimes when you build it, 00:04:28.080 --> 00:04:29.540 you build a large number. 00:04:29.540 --> 00:04:31.560 You build a combinatorial set. 00:04:31.560 --> 00:04:35.530 So this is something that's fairly unique 00:04:35.530 --> 00:04:37.550 to biological engineering-- or even 00:04:37.550 --> 00:04:39.710 to certain branches of biological engineering-- 00:04:39.710 --> 00:04:43.720 that you don't see every day in civil engineering 00:04:43.720 --> 00:04:45.340 or aeronautics. 00:04:45.340 --> 00:04:49.970 You don't build a trillion different 787s 00:04:49.970 --> 00:04:53.720 and see which one works the best. 00:04:53.720 --> 00:04:54.840 But you can in biology. 00:04:54.840 --> 00:04:56.507 And I'll give you some examples of that. 00:04:56.507 --> 00:04:58.131 And part of the reason we could do this 00:04:58.131 --> 00:05:00.170 is just as there's next-generation sequencing, 00:05:00.170 --> 00:05:04.080 which you've heard about in this course-- 00:05:04.080 --> 00:05:07.430 and we were also involved in next-generation synthesis 00:05:07.430 --> 00:05:11.040 and next-generation inserting synthetic DNA into genomes. 00:05:11.040 --> 00:05:12.357 And you'll see all about that. 00:05:12.357 --> 00:05:13.940 There are four different ways of doing 00:05:13.940 --> 00:05:20.120 next-generation synthesis, and it's not 00:05:20.120 --> 00:05:22.602 important for this particular class. 00:05:22.602 --> 00:05:24.810 And there are various ways of doing error correction. 00:05:24.810 --> 00:05:27.645 And these are kind of analogous to the kind of error correction 00:05:27.645 --> 00:05:31.980 that you have in electronics and computational systems, 00:05:31.980 --> 00:05:35.100 but we won't stress that analogy too much. 00:05:35.100 --> 00:05:37.360 Here's an example, just practically, 00:05:37.360 --> 00:05:40.460 what you get when you build these oligonucleotides 00:05:40.460 --> 00:05:41.430 on chips. 00:05:41.430 --> 00:05:45.520 You might get oligos up to 300 nucleotides long. 00:05:45.520 --> 00:05:49.660 As they get longer, they tend to accumulate errors a little bit 00:05:49.660 --> 00:05:52.570 more towards the end. 00:05:52.570 --> 00:05:55.360 And so you can see that with the length, the number of errors 00:05:55.360 --> 00:06:01.310 goes up from 1 in 1,300 raw error rate to 1 00:06:01.310 --> 00:06:04.450 in 250 raw error rate. 00:06:04.450 --> 00:06:08.350 And then we can get rid of some of those errors 00:06:08.350 --> 00:06:11.650 with a enzymatic system called ErASE-- it doesn't really 00:06:11.650 --> 00:06:12.800 matter in this case. 00:06:12.800 --> 00:06:14.942 We can get to 1 in 6,000 without sequencing. 00:06:14.942 --> 00:06:16.400 And then with sequencing, if you're 00:06:16.400 --> 00:06:18.620 willing to clone in sequence, you 00:06:18.620 --> 00:06:20.660 can get error rates even lower. 00:06:20.660 --> 00:06:24.440 And it's important to know that fundamental limitation. 00:06:24.440 --> 00:06:29.910 You always need to think about background error in computing 00:06:29.910 --> 00:06:32.770 as well as synthesis. 00:06:32.770 --> 00:06:36.900 You can now do combined synthesis and sequencing 00:06:36.900 --> 00:06:40.120 very closely by making cis-regulatory elements, which 00:06:40.120 --> 00:06:47.290 we did in this paper that's published-- Sri Kosuri and Dan 00:06:47.290 --> 00:06:50.250 Goodman, in particular-- where you could basically synthesize 00:06:50.250 --> 00:06:54.940 cis-regulatory elements in the genome or in a plasmid. 00:06:54.940 --> 00:06:57.680 And then you could read out the RNA simply by RNA sequencing. 00:06:57.680 --> 00:06:59.896 The number of times you see this bar code in the RNA 00:06:59.896 --> 00:07:02.270 tells you how many times that particular construct, which 00:07:02.270 --> 00:07:05.910 could be heavily engineered-- it isn't like 00:07:05.910 --> 00:07:09.801 randomers-- you're making interesting, cis-regulatory 00:07:09.801 --> 00:07:10.300 elements. 00:07:10.300 --> 00:07:11.590 And you can make 10s of thousands 00:07:11.590 --> 00:07:13.256 of these-- millions of these constructs. 00:07:13.256 --> 00:07:14.380 We did 10s of thousands. 00:07:17.060 --> 00:07:18.930 Then you can measure protein levels 00:07:18.930 --> 00:07:20.180 as a result of cis-regulatory. 00:07:20.180 --> 00:07:22.450 So you can have promoter elements, 00:07:22.450 --> 00:07:25.750 ribosome binding sites, and coding region mutations 00:07:25.750 --> 00:07:30.460 that you think might influence RNA and protein. 00:07:30.460 --> 00:07:33.230 And here we do proteins by having 00:07:33.230 --> 00:07:35.990 two fluorescent proteins-- a red and a green. 00:07:35.990 --> 00:07:38.950 The red is the control, and it has a very tight distribution, 00:07:38.950 --> 00:07:41.000 as you can see here. 00:07:41.000 --> 00:07:44.610 And then the green is subject to this cis-regulatory mutations 00:07:44.610 --> 00:07:46.100 made on chips. 00:07:46.100 --> 00:07:47.570 And it has a big distribution. 00:07:47.570 --> 00:07:50.410 And you divide it up in a fluorescence-activated sorter. 00:07:50.410 --> 00:07:51.650 And you can read it out. 00:07:51.650 --> 00:07:55.590 So here, every pixel on these two plots for RNA and protein 00:07:55.590 --> 00:07:58.050 is a separate experiment. 00:07:58.050 --> 00:08:01.132 And you can drill down and get some more information 00:08:01.132 --> 00:08:01.840 on each of these. 00:08:01.840 --> 00:08:04.940 But the basic idea is each of these was individually 00:08:04.940 --> 00:08:10.520 synthesized on the chip and individually sequenced later 00:08:10.520 --> 00:08:11.950 to determine. 00:08:11.950 --> 00:08:16.010 And the bar codes can be read out of proportion 00:08:16.010 --> 00:08:18.302 to the RNA and protein expression. 00:08:18.302 --> 00:08:20.510 And here's an example of some surprises that come out 00:08:20.510 --> 00:08:27.480 of such studies-- and we're not just doing this for our health. 00:08:27.480 --> 00:08:29.490 So, for example, when we went into this, 00:08:29.490 --> 00:08:32.390 it was well known that codon usage effect 00:08:32.390 --> 00:08:37.990 was correlated with, and could even causally influence-- 00:08:37.990 --> 00:08:39.990 so here's an example of causality-- 00:08:39.990 --> 00:08:42.390 the expression of a protein. 00:08:42.390 --> 00:08:47.230 If you have very commonly used codons, which typically 00:08:47.230 --> 00:08:49.920 have high levels of the corresponding transfer 00:08:49.920 --> 00:08:54.180 RNA in the cell, that the observation-- and it makes 00:08:54.180 --> 00:08:56.770 sense-- is that those proteins would 00:08:56.770 --> 00:08:58.950 be expressed at higher levels. 00:08:58.950 --> 00:09:00.810 The thing that was new was we discovered 00:09:00.810 --> 00:09:04.370 that at the end terminus, close to the cis-regulatory elements, 00:09:04.370 --> 00:09:05.040 it flips. 00:09:05.040 --> 00:09:06.200 It's the opposite. 00:09:06.200 --> 00:09:11.190 There's almost no correlation with abundant codons, 00:09:11.190 --> 00:09:13.310 and there's essentially a negative correlation 00:09:13.310 --> 00:09:18.900 here with an r squared of 0.73, right here, 00:09:18.900 --> 00:09:27.740 that shows that there's a higher expression with very 00:09:27.740 --> 00:09:28.890 rare codons. 00:09:28.890 --> 00:09:30.500 This was published in Science. 00:09:30.500 --> 00:09:35.340 And so a lot of them tend to be AT-rich, 00:09:35.340 --> 00:09:38.580 but we can separate out that component. 00:09:38.580 --> 00:09:40.840 We can separate out things like ribosome binding 00:09:40.840 --> 00:09:43.690 sites, which are AG-rich. 00:09:43.690 --> 00:09:48.540 And there's just a general trend where rare codons help 00:09:48.540 --> 00:09:51.086 expression if they're at the beginning of the gene. 00:09:51.086 --> 00:09:53.460 And you could find that out from this kind of experiment. 00:09:57.190 --> 00:10:05.430 So now we want, if we're going to build the genome that's 00:10:05.430 --> 00:10:08.360 radically different-- let's say "radically different," here, 00:10:08.360 --> 00:10:14.000 defined as 7 to 13 codons, chains, genome-wide freed up-- 00:10:14.000 --> 00:10:19.310 liberated-- meaning that we use the synonyms 00:10:19.310 --> 00:10:21.360 in the genetic code. 00:10:24.480 --> 00:10:27.340 So there's anywhere from one to six codons 00:10:27.340 --> 00:10:31.230 for each amino acid-- three codons for stop codons. 00:10:31.230 --> 00:10:35.910 We can use that synonymous substitution table 00:10:35.910 --> 00:10:37.590 to move things around and completely 00:10:37.590 --> 00:10:39.755 free up-- get rid of every instance of a UAG 00:10:39.755 --> 00:10:41.620 and turn it into UAA. 00:10:41.620 --> 00:10:43.180 That's the first example. 00:10:43.180 --> 00:10:46.300 And we did that genome-wide and thereby derisked it. 00:10:46.300 --> 00:10:48.750 We can now build on top of that, because we 00:10:48.750 --> 00:10:52.660 can get genomes that grow well under a variety of conditions. 00:10:52.660 --> 00:10:54.640 They're still genetically engineerable. 00:10:54.640 --> 00:10:57.710 And everywhere there's a bar there, 00:10:57.710 --> 00:11:03.740 this refers to a successful mutation 00:11:03.740 --> 00:11:05.310 in the height of the bar as refers 00:11:05.310 --> 00:11:07.615 to the efficiency of introducing those mutations. 00:11:10.470 --> 00:11:16.500 Now we wanted to derisk another special category-- remember, 00:11:16.500 --> 00:11:20.710 I said AGA and AGG are special, in that they're 00:11:20.710 --> 00:11:24.480 the rarest coding codons. 00:11:24.480 --> 00:11:26.360 So UGA is a stop codon. 00:11:26.360 --> 00:11:31.432 AGA and AGG are arginine-encoding codons. 00:11:31.432 --> 00:11:32.390 And they're the rarest. 00:11:32.390 --> 00:11:34.590 And they also are complicated, because they 00:11:34.590 --> 00:11:36.660 tend to represent Shine-Dalgarno sites, which 00:11:36.660 --> 00:11:40.765 tend to be AG-rich regions that are involved in initiation 00:11:40.765 --> 00:11:42.960 of protein synthesis. 00:11:42.960 --> 00:11:46.910 Anyway, so there, the number was a little large 00:11:46.910 --> 00:11:50.480 to do genome-wide, so we focused on essential genes. 00:11:50.480 --> 00:11:55.000 And so you can computationally find all the essential genes 00:11:55.000 --> 00:12:01.770 and design strategies for getting all the AGG and AGAs. 00:12:01.770 --> 00:12:04.925 And then when you synthesize those genomes, 00:12:04.925 --> 00:12:07.050 you can do them one at a time with a process called 00:12:07.050 --> 00:12:10.330 [? MAIDS, ?] which we won't go into-- too experimental. 00:12:10.330 --> 00:12:12.130 But basically, you can essentially just 00:12:12.130 --> 00:12:14.280 go straight from oligos into the genome, 00:12:14.280 --> 00:12:16.834 and you can do multiple ones simultaneously. 00:12:16.834 --> 00:12:18.625 And you can see which ones are hard to make 00:12:18.625 --> 00:12:21.350 and which ones are easy-- again, that's the sort of efficiency 00:12:21.350 --> 00:12:22.352 number there. 00:12:22.352 --> 00:12:24.560 You can see which ones-- if they're selected against. 00:12:24.560 --> 00:12:26.559 And some of them were actually selected against. 00:12:26.559 --> 00:12:28.510 We could not find them. 00:12:28.510 --> 00:12:30.550 And so these are discoveries. 00:12:30.550 --> 00:12:35.480 These are examples where synonymous is not synonymous. 00:12:35.480 --> 00:12:39.740 It could mean that there's some other function, hidden, 00:12:39.740 --> 00:12:43.850 layered on top of the synonyms-- might 00:12:43.850 --> 00:12:45.300 be a ribosome binding site. 00:12:45.300 --> 00:12:49.810 And so what we find is that we can try other, 00:12:49.810 --> 00:12:51.900 let's say other arginine codons, rather than 00:12:51.900 --> 00:12:53.440 the one we targeted. 00:12:53.440 --> 00:12:55.700 Or you sometimes can try out other codons 00:12:55.700 --> 00:12:58.440 that are not even synonymous. 00:12:58.440 --> 00:13:01.460 And eventually we found every single one of them. 00:13:01.460 --> 00:13:03.510 So there were about a dozen. 00:13:03.510 --> 00:13:05.220 They were hard at first, and then 00:13:05.220 --> 00:13:08.930 we eventually found an engineering workaround. 00:13:08.930 --> 00:13:12.400 And that illustrates a number of interesting points. 00:13:12.400 --> 00:13:14.640 So those were all successful in essential genes. 00:13:14.640 --> 00:13:17.432 And it's our observation that if you 00:13:17.432 --> 00:13:18.890 get it to work for essential genes, 00:13:18.890 --> 00:13:20.765 getting it to work for the nonessential genes 00:13:20.765 --> 00:13:21.900 is even easier. 00:13:21.900 --> 00:13:25.520 So then we went on, and so that's one codon at a time, 00:13:25.520 --> 00:13:26.470 two more at time. 00:13:26.470 --> 00:13:29.130 So we've derisked three codons at this point. 00:13:29.130 --> 00:13:35.770 So we went on to derisk all 13 codons-- or 13 of the 64. 00:13:35.770 --> 00:13:39.280 And we did that in even smaller set of genes. 00:13:39.280 --> 00:13:42.400 So there are 290 essential genes in E. coli. 00:13:42.400 --> 00:13:44.610 We did 42. 00:13:44.610 --> 00:13:50.200 And in that case, there were 400. 00:13:50.200 --> 00:13:53.080 And some examples of those-- and every one of them 00:13:53.080 --> 00:13:54.490 worked except for one. 00:13:54.490 --> 00:13:58.620 And just like the arginine codons-- that one, 00:13:58.620 --> 00:14:00.210 we tried a number of different codons, 00:14:00.210 --> 00:14:03.760 and they worked-- including non-synonymous codons. 00:14:03.760 --> 00:14:08.970 So in almost every case, you can find something that works. 00:14:08.970 --> 00:14:13.045 And then we do biological assays that the four functions 00:14:13.045 --> 00:14:17.440 that we felt should be changed were actually changed. 00:14:17.440 --> 00:14:22.290 And here's two slides on the virus resistance. 00:14:22.290 --> 00:14:24.340 You can do, in a variety of ways, 00:14:24.340 --> 00:14:29.800 of determining how effective the virus resistance is. 00:14:29.800 --> 00:14:32.260 Here you have about a factor of 1,000 00:14:32.260 --> 00:14:36.705 for phage lambda, which has been mutated 00:14:36.705 --> 00:14:39.000 to be highly virulent in E. coli. 00:14:39.000 --> 00:14:42.310 This is a very pathogenic version of phage lambda. 00:14:42.310 --> 00:14:46.010 This is T7, which is naturally quite lytic. 00:14:46.010 --> 00:14:50.717 And you can show that this is resistant to two of the three 00:14:50.717 --> 00:14:52.200 viruses that we tested. 00:14:52.200 --> 00:14:55.260 And our hypothesis is if we change more codons 00:14:55.260 --> 00:14:57.620 than just-- that was just one codon. 00:14:57.620 --> 00:15:02.090 If we change seven or so, which is what we're doing now, 00:15:02.090 --> 00:15:03.840 then it will be resistant to all viruses-- 00:15:03.840 --> 00:15:05.730 and very heavily resistant-- so resistant 00:15:05.730 --> 00:15:10.710 that the population of viruses can't 00:15:10.710 --> 00:15:13.432 mutate enough to become resistant. 00:15:13.432 --> 00:15:14.890 So all of you should be questioning 00:15:14.890 --> 00:15:18.500 that-- do I really believe that? 00:15:18.500 --> 00:15:21.200 And we can talk about that in the discussion. 00:15:21.200 --> 00:15:23.680 So now the other big functionality 00:15:23.680 --> 00:15:28.870 is-- can we genetically, metabolically isolate these? 00:15:28.870 --> 00:15:32.650 And to do this, we took advantage 00:15:32.650 --> 00:15:34.765 of its new genetic code. 00:15:34.765 --> 00:15:37.690 Not only we've freed up a codon, we 00:15:37.690 --> 00:15:41.862 can now make that codon code for a new amino acid 00:15:41.862 --> 00:15:44.260 by another set of biochemistry. 00:15:44.260 --> 00:15:47.720 And here's some examples. 00:15:47.720 --> 00:15:51.030 The amino acids look kind of like tyrosine or phenylalanine. 00:15:51.030 --> 00:15:54.510 Here's one that's a biphenylalanine, 00:15:54.510 --> 00:15:57.330 so it's got two benzene rings instead of one. 00:15:57.330 --> 00:15:58.470 And so it's bulkier. 00:15:58.470 --> 00:16:00.650 It's bulkier than any other amino acid, any 00:16:00.650 --> 00:16:01.910 naturally occurring one. 00:16:01.910 --> 00:16:04.487 And we wanted to ask-- can we make those essential genes 00:16:04.487 --> 00:16:06.070 that we've been talking about-- can we 00:16:06.070 --> 00:16:09.080 make them addicted to this amino acid? 00:16:09.080 --> 00:16:13.660 And so we did by this computational protein design 00:16:13.660 --> 00:16:14.340 strategy. 00:16:14.340 --> 00:16:18.840 And the idea is we looked through every crystal structure 00:16:18.840 --> 00:16:24.120 of every essential protein in E. coli-- there's 129 or something 00:16:24.120 --> 00:16:29.950 like that, 120 crystal structures-- 00:16:29.950 --> 00:16:32.490 and systematically ask, were there 00:16:32.490 --> 00:16:37.030 any places where we could fit in a larger amino acid 00:16:37.030 --> 00:16:41.300 by carving away adjacent amino acids, 00:16:41.300 --> 00:16:46.560 such that when we then replace that larger one with a smaller 00:16:46.560 --> 00:16:50.270 one-- still keeping its surroundings mutated, 00:16:50.270 --> 00:16:55.210 so we could mutate it two, three, four, eight times-- 00:16:55.210 --> 00:16:56.730 however many amino acids nearby you 00:16:56.730 --> 00:16:59.090 need to accommodate the big amino acid-- if it 00:16:59.090 --> 00:17:01.244 no longer accommodates the small amino acids? 00:17:01.244 --> 00:17:02.660 So you basically systematically go 00:17:02.660 --> 00:17:08.069 through every amino acid for every crystal structure 00:17:08.069 --> 00:17:11.380 and found a short list of a half dozen or so 00:17:11.380 --> 00:17:12.880 that looked promising. 00:17:12.880 --> 00:17:16.420 And so the idea is, you put in these 2-phenol groups-- 00:17:16.420 --> 00:17:20.210 and now, when you accommodate it and shrink it down, 00:17:20.210 --> 00:17:22.203 it won't work. 00:17:22.203 --> 00:17:24.020 OK, that's the basic idea. 00:17:27.400 --> 00:17:28.920 And in context, we wanted to have 00:17:28.920 --> 00:17:30.394 a really tough test for this. 00:17:30.394 --> 00:17:33.060 We wanted to say, not only do we want it to be addicted to this, 00:17:33.060 --> 00:17:37.515 but we don't want it to be able to escape-- either by mutation 00:17:37.515 --> 00:17:39.609 and evolution, we don't want it to escape. 00:17:39.609 --> 00:17:41.150 We don't want it to be able to escape 00:17:41.150 --> 00:17:48.960 by eating it's fellow-- its classmates-- its other E. coli. 00:17:48.960 --> 00:17:52.050 And so the test we do is we do a-- 00:17:52.050 --> 00:17:55.100 did you have a question, anybody? 00:17:55.100 --> 00:18:01.165 We would lyse the cells-- lyse cells of a wild-type E. 00:18:01.165 --> 00:18:05.500 coli or certain mutant strains that 00:18:05.500 --> 00:18:07.410 would produce large amounts of these. 00:18:07.410 --> 00:18:11.550 And one of the more classic ways of making an organism that's 00:18:11.550 --> 00:18:13.990 metabolically isolated so it can't survive in the wild-- 00:18:13.990 --> 00:18:16.680 it can only survive in an industrial plant 00:18:16.680 --> 00:18:19.020 or in a laboratory-- and we did this 00:18:19.020 --> 00:18:22.340 with the classic ones, which people have avoided 00:18:22.340 --> 00:18:24.640 using lysates, because it gives them bad news, which 00:18:24.640 --> 00:18:30.116 is if you grow them on lysates, you get a lot of survivors. 00:18:30.116 --> 00:18:31.240 These are the classic ones. 00:18:31.240 --> 00:18:37.870 The deletions of these two genes makes them-- 00:18:37.870 --> 00:18:39.760 they will still grow. 00:18:39.760 --> 00:18:45.330 But this is an example of one of our designed, nonstandard 00:18:45.330 --> 00:18:47.080 amino acid strains. 00:18:47.080 --> 00:18:51.310 And we get much lower escape rates. 00:18:51.310 --> 00:18:53.090 And you'll say, even this low number here, 00:18:53.090 --> 00:18:54.680 we want to get that down to zero. 00:18:54.680 --> 00:18:56.670 And you'll see how we do that a moment. 00:18:56.670 --> 00:18:59.050 This is Mike Mee as a graduate student. 00:18:59.050 --> 00:19:02.720 So here's a close-up of-- this is not of the active site. 00:19:02.720 --> 00:19:05.030 This just could be any place in the protein 00:19:05.030 --> 00:19:08.560 where putting in a big amino acid is going to be disruptive. 00:19:08.560 --> 00:19:12.890 So we change this leucine, innocent leucine, 00:19:12.890 --> 00:19:15.480 that's packed all around with other amino acids. 00:19:15.480 --> 00:19:19.295 Have you guys done protein design in this class at all? 00:19:19.295 --> 00:19:19.795 Yeah? 00:19:19.795 --> 00:19:21.570 OK, so you know what I'm talking about. 00:19:21.570 --> 00:19:22.910 Rosetta, right? 00:19:22.910 --> 00:19:23.597 OK. 00:19:23.597 --> 00:19:24.930 So that's what we're using here. 00:19:24.930 --> 00:19:27.970 We had to modify it to use nonstandard amino acids, 00:19:27.970 --> 00:19:31.180 because normally people design proteins with 20 amino acids. 00:19:31.180 --> 00:19:34.705 So we took this leucine-- we made it into this bipA. 00:19:34.705 --> 00:19:36.330 And you can see now, it's got all kinds 00:19:36.330 --> 00:19:38.390 of clashes-- three initial clashes. 00:19:38.390 --> 00:19:40.120 That's not good. 00:19:40.120 --> 00:19:44.270 So we identify those clashes and we make them smaller-- 00:19:44.270 --> 00:19:45.135 no clashes anymore. 00:19:45.135 --> 00:19:46.510 This is all done in the computer. 00:19:46.510 --> 00:19:47.670 This is all theoretical. 00:19:47.670 --> 00:19:50.775 Can you believe that? 00:19:50.775 --> 00:19:51.275 We'll see. 00:19:53.850 --> 00:19:58.842 So then-- this is putting back in a small amino acid. 00:19:58.842 --> 00:20:00.550 These are some of the people that did it. 00:20:00.550 --> 00:20:05.010 So Marc and Dan are post-docs in the lab, 00:20:05.010 --> 00:20:09.420 and Ryo and Barry did the crystallography. 00:20:09.420 --> 00:20:10.880 I'm a crystallographer by training, 00:20:10.880 --> 00:20:13.750 but I'm a little out of practice. 00:20:13.750 --> 00:20:18.760 So here is the design again, and there's the electron density. 00:20:18.760 --> 00:20:20.160 So now you can believe it, right? 00:20:20.160 --> 00:20:21.784 Because it's not just a computer model. 00:20:21.784 --> 00:20:24.900 Well, it's still a computer model, but it's based on data. 00:20:24.900 --> 00:20:27.670 And here's a comparison of the design with the X-ray 00:20:27.670 --> 00:20:29.640 structure-- not too shabby. 00:20:29.640 --> 00:20:30.920 OK. 00:20:30.920 --> 00:20:37.330 But the question is, how well does this work in living cells? 00:20:37.330 --> 00:20:41.440 So these are cells where we've gone-- changed the whole genome 00:20:41.440 --> 00:20:45.260 so that now the stop codon, UAG, is free. 00:20:45.260 --> 00:20:50.340 It's never used, which means we can delete the release 00:20:50.340 --> 00:20:52.970 factor that normally recognizes a stop codon, which otherwise 00:20:52.970 --> 00:20:54.070 would have been lethal. 00:20:54.070 --> 00:20:55.970 And we can replace it with a transfer RNA 00:20:55.970 --> 00:21:00.220 in a tRNA synthetase that brings in this [INAUDIBLE] amino acid. 00:21:00.220 --> 00:21:03.270 And now-- this is the one we were just looking at, 00:21:03.270 --> 00:21:05.600 the crystal structure in bold here. 00:21:05.600 --> 00:21:08.480 And it has an escape frequency which 00:21:08.480 --> 00:21:14.240 is higher-- we can crank up mutagenesis 00:21:14.240 --> 00:21:16.497 by putting it in a mutS minus background. 00:21:16.497 --> 00:21:18.580 Basically, one of the mismatched repair proteins-- 00:21:18.580 --> 00:21:21.690 we can knock it out, which increases, sort of accelerates, 00:21:21.690 --> 00:21:23.080 evolution. 00:21:23.080 --> 00:21:27.230 And it has a noticeable escape frequency. 00:21:27.230 --> 00:21:32.654 So a more realistic scenario would be this mutS plus. 00:21:32.654 --> 00:21:34.570 And we can get escape frequencies as low as 10 00:21:34.570 --> 00:21:35.540 to the minus 8th. 00:21:35.540 --> 00:21:38.520 These are for other mutations in that same protein. 00:21:38.520 --> 00:21:41.540 And here are mutations in another protein. 00:21:41.540 --> 00:21:44.330 So then we said, OK, but none of these are perfect. 00:21:44.330 --> 00:21:48.580 We want something that's undetectable levels of escape. 00:21:48.580 --> 00:21:53.510 So how would we, how would you, fix this? 00:21:53.510 --> 00:21:55.857 Anybody? 00:21:55.857 --> 00:21:57.690 I'm trying to encourage you to interrupt me, 00:21:57.690 --> 00:22:00.480 so I'm interrupting you. 00:22:00.480 --> 00:22:01.980 Anybody? 00:22:01.980 --> 00:22:08.000 You've got these things that are escaping 00:22:08.000 --> 00:22:09.350 at very low frequencies. 00:22:09.350 --> 00:22:10.590 We should be proud of that. 00:22:10.590 --> 00:22:12.131 But we want to drive it even more. 00:22:12.131 --> 00:22:13.630 Rather than 10 to the minus 8th, you 00:22:13.630 --> 00:22:16.050 want get down to 10 to minus 10th, or something like that. 00:22:16.050 --> 00:22:16.760 Suggestions? 00:22:16.760 --> 00:22:18.731 AUDIENCE: So this is reversion of the mutations 00:22:18.731 --> 00:22:20.270 of the [INAUDIBLE]. 00:22:20.270 --> 00:22:25.440 PROFESSOR: Well, so this means that you can take the bipA, 00:22:25.440 --> 00:22:27.935 and you mutate the codon so it doesn't encode bipA anymore. 00:22:27.935 --> 00:22:29.060 It encodes something else. 00:22:29.060 --> 00:22:32.710 So it doesn't need bipA from the media. 00:22:32.710 --> 00:22:35.320 And it puts in another amino acid, and it somehow survives. 00:22:35.320 --> 00:22:37.810 So even though it's not a perfect fit, 00:22:37.810 --> 00:22:43.190 it does well enough that the enzyme is made. 00:22:43.190 --> 00:22:46.070 AUDIENCE: So then modified multiple essential genes? 00:22:46.070 --> 00:22:48.320 PROFESSOR: Multiple essential genes-- wow. 00:22:48.320 --> 00:22:50.100 Couldn't have said it better myself. 00:22:50.100 --> 00:22:52.200 That's what we did. 00:22:52.200 --> 00:22:57.980 So before we could choose which two we wanted 00:22:57.980 --> 00:23:01.080 to use-- or three-- we wanted to know what the spectrum was. 00:23:01.080 --> 00:23:04.860 So we forced in all 20 standard amino acids 00:23:04.860 --> 00:23:06.750 to replace the bipA. 00:23:06.750 --> 00:23:10.690 So we said, let's mutate them intentionally-- synthetically-- 00:23:10.690 --> 00:23:12.800 and see what the spectrum is. 00:23:12.800 --> 00:23:15.620 Now this is not going to be the natural spectrum, the sort 00:23:15.620 --> 00:23:20.000 of mutagenic spectrum-- this is our intentional-- 00:23:20.000 --> 00:23:22.190 so what we do is, we put in each of the 20. 00:23:22.190 --> 00:23:26.340 And then we do a quick selection at 20 doublings. 00:23:26.340 --> 00:23:29.910 It's a very fast evolution, not three billion years. 00:23:29.910 --> 00:23:32.470 My students didn't want to wait. 00:23:32.470 --> 00:23:34.610 So in 20 doublings, you get a spectrum 00:23:34.610 --> 00:23:37.147 of which amino acids will substitute for bipA. 00:23:37.147 --> 00:23:38.730 In an ideal world, none of them would. 00:23:38.730 --> 00:23:41.960 But we forced them to, and these are the survivors. 00:23:41.960 --> 00:23:45.210 And so the ones we've been talking about here, 00:23:45.210 --> 00:23:47.892 W, tryptofan, is what we'll substitute for bipA. 00:23:47.892 --> 00:23:49.100 And that kind of makes sense. 00:23:49.100 --> 00:23:51.390 It's the biggest amino acid. 00:23:51.390 --> 00:23:53.335 And that works for the [? tyrS ?], 00:23:53.335 --> 00:23:56.440 which happens to be the tRNA sythetase. 00:23:56.440 --> 00:23:57.880 And then we picked this other one 00:23:57.880 --> 00:24:06.130 under this big red arrow for AdK-- adenosine kinase-- 00:24:06.130 --> 00:24:08.540 [INAUDIBLE] kinase-- where there's 00:24:08.540 --> 00:24:10.950 very little tryptophan that will work in that one. 00:24:10.950 --> 00:24:12.990 But you get some escapees if you force 00:24:12.990 --> 00:24:16.140 it to take these hydrophobic aliphatics like leucine. 00:24:18.710 --> 00:24:24.620 So we made the double mutant of the-- we don't have it here-- 00:24:24.620 --> 00:24:29.690 but we've made the double mutant of the AdK and the tyrS, 00:24:29.690 --> 00:24:33.230 and it's vanishingly small. 00:24:33.230 --> 00:24:34.230 We're probably not done. 00:24:34.230 --> 00:24:35.400 We'll keep doing this. 00:24:35.400 --> 00:24:40.249 But this is the way that you do a radical recoding 00:24:40.249 --> 00:24:41.165 and get new functions. 00:24:47.705 --> 00:24:48.830 Any questions on that part? 00:24:48.830 --> 00:24:51.468 We're going to move onto human genome engineering. 00:24:51.468 --> 00:24:51.968 Yeah. 00:24:51.968 --> 00:24:54.129 AUDIENCE: [INAUDIBLE] and recognize 00:24:54.129 --> 00:24:55.170 the different amino acid. 00:24:55.170 --> 00:24:57.041 PROFESSOR: Yeah, I skipped over that because that's 00:24:57.041 --> 00:24:58.957 a little more on the biological, a little less 00:24:58.957 --> 00:25:00.840 on the computational side. 00:25:00.840 --> 00:25:05.200 So this was a work from Peter Shultz' lab and other groups. 00:25:05.200 --> 00:25:10.235 And what you do is you take a synthetase that's orthogonal, 00:25:10.235 --> 00:25:12.360 meaning it's from a completely different organism-- 00:25:12.360 --> 00:25:14.920 in this case, Methanococcus jannaschii, 00:25:14.920 --> 00:25:17.290 which is a hyperthermalphile. 00:25:17.290 --> 00:25:21.620 You take that synthetase-- it's about as far as you 00:25:21.620 --> 00:25:24.235 can get on the evolutionary phylogenetic tree-- you bring 00:25:24.235 --> 00:25:26.420 it into E. coli. 00:25:26.420 --> 00:25:29.220 You bring in its cognate, tRNA. 00:25:29.220 --> 00:25:30.870 You change the anticodon so that it 00:25:30.870 --> 00:25:35.910 will recognize UAG, which is not what typically 00:25:35.910 --> 00:25:39.976 any tRNA normally recognizes. 00:25:39.976 --> 00:25:41.850 And that only works with certain synthetases. 00:25:41.850 --> 00:25:46.780 So only certain synthetases are blind to the anticodon-- mainly 00:25:46.780 --> 00:25:50.350 serine and leucine synthetase is in E. coli. 00:25:50.350 --> 00:25:58.170 Anyway, so you can now evolve the active site 00:25:58.170 --> 00:26:02.400 that binds to the amino acid and the ATP. 00:26:02.400 --> 00:26:06.280 So the amino acid and ATP cause the amino acid 00:26:06.280 --> 00:26:08.910 to be [INAUDIBLE] the transfer RNA. 00:26:08.910 --> 00:26:10.730 Anyway, you can change the active site 00:26:10.730 --> 00:26:12.540 so that now it recognizes any amino acid 00:26:12.540 --> 00:26:14.649 you want to a first approximation. 00:26:14.649 --> 00:26:16.440 And you could do that through a combination 00:26:16.440 --> 00:26:22.600 of intelligent design and random mutagenesis, 00:26:22.600 --> 00:26:25.750 and there are selections for that as well. 00:26:25.750 --> 00:26:27.490 So in general, if you're going to be 00:26:27.490 --> 00:26:30.690 doing random or semirandom mutagenesis, 00:26:30.690 --> 00:26:32.322 it's always great to have a selection 00:26:32.322 --> 00:26:34.030 so there are selections for these things. 00:26:34.030 --> 00:26:36.840 And there now are dozens of amino acids 00:26:36.840 --> 00:26:38.675 that work fairly well in that scenario. 00:26:41.454 --> 00:26:43.120 The main thing that was limiting was not 00:26:43.120 --> 00:26:45.610 the synthetase-- I mean, you could get synthetases. 00:26:45.610 --> 00:26:47.910 It's the tRNA then had to compete with the release 00:26:47.910 --> 00:26:51.060 factor in the stop codon or had to compete with another tRNA 00:26:51.060 --> 00:26:53.240 if you use a different anticodon. 00:26:53.240 --> 00:26:58.680 And so freeing up this codon means there's no competition. 00:26:58.680 --> 00:27:03.120 And now it works about as well as a regular amino acid. 00:27:03.120 --> 00:27:06.770 But when it has to compete, it's at a great disadvantage. 00:27:06.770 --> 00:27:07.527 Yeah. 00:27:07.527 --> 00:27:10.866 AUDIENCE: Can you explain why changing the genetic code 00:27:10.866 --> 00:27:14.450 will cause all virus resistance? 00:27:14.450 --> 00:27:16.450 PROFESSOR: I planted that, but thank you anyway. 00:27:23.940 --> 00:27:26.320 So there's a genetic code up there in circular form-- 00:27:26.320 --> 00:27:29.200 probably you're more used to seeing it in rectangular. 00:27:29.200 --> 00:27:37.900 But imagine that we've now derisked this UAG stop codon 00:27:37.900 --> 00:27:43.580 and these AGA and AGG codons here-- R for arginine. 00:27:43.580 --> 00:27:47.150 And we're in the process of putting all those three 00:27:47.150 --> 00:27:51.309 codons together with another four for serine and leucine. 00:27:51.309 --> 00:27:53.600 And remember, I said serine and leucine is interesting, 00:27:53.600 --> 00:27:56.570 because you could swap out the anticodon-- 00:27:56.570 --> 00:27:58.310 the synthetase doesn't care. 00:27:58.310 --> 00:28:02.750 So that's why we picked those ones-- the three rarest ones, 00:28:02.750 --> 00:28:05.480 plus four where you can swap out the anticodon. 00:28:05.480 --> 00:28:09.600 So we could swap serine and leucine, for example. 00:28:09.600 --> 00:28:13.110 So serine and leucine also are examples of tRNAs 00:28:13.110 --> 00:28:15.980 that bind to six different codons. 00:28:15.980 --> 00:28:18.920 So moving two of them is not a big deal. 00:28:18.920 --> 00:28:20.750 So you still got four left. 00:28:20.750 --> 00:28:24.290 So anyway, imagine that we remove them or swap them 00:28:24.290 --> 00:28:26.320 and do weird stuff with them. 00:28:26.320 --> 00:28:27.770 Every time the phage comes in, it 00:28:27.770 --> 00:28:31.370 has lots of serines and leucines that are using these, 00:28:31.370 --> 00:28:32.950 and arginines and stops. 00:28:32.950 --> 00:28:37.200 And every time it wants to put in a leucine, 00:28:37.200 --> 00:28:40.580 the ribosome puts in a serine. 00:28:40.580 --> 00:28:44.570 Well, you can note, leucine and serine aren't that similar, 00:28:44.570 --> 00:28:47.400 and that's going to cause a mess for every single protein it 00:28:47.400 --> 00:28:48.370 makes. 00:28:48.370 --> 00:28:52.570 And there might be dozens-- maybe even hundreds 00:28:52.570 --> 00:28:55.700 for big phage-- of those codons. 00:28:55.700 --> 00:28:59.700 And so you can do the math-- that the chance of mutating 00:28:59.700 --> 00:29:03.730 one of those codons to something that will work is fairly high. 00:29:03.730 --> 00:29:08.180 Two is squared, three to the n power, where 00:29:08.180 --> 00:29:10.100 n is the number of changes it has to make. 00:29:10.100 --> 00:29:15.610 And so if you make enough changes, 00:29:15.610 --> 00:29:18.100 population sizes have to become astronomical in order 00:29:18.100 --> 00:29:23.710 to contain one member that has changed all of its codons 00:29:23.710 --> 00:29:25.500 the right way and hasn't changed a bunch 00:29:25.500 --> 00:29:27.726 of codons that would be lethal. 00:29:27.726 --> 00:29:30.141 AUDIENCE: So the ones that you chose, were they 00:29:30.141 --> 00:29:32.080 the rarest of the codons-- 00:29:32.080 --> 00:29:34.412 PROFESSOR: So the first three were the rarest. 00:29:34.412 --> 00:29:35.870 And part of that is because we felt 00:29:35.870 --> 00:29:38.665 we would run into the most trouble there. 00:29:38.665 --> 00:29:40.260 They may be rare for a reason. 00:29:40.260 --> 00:29:42.210 And we wanted to discover those reasons, 00:29:42.210 --> 00:29:44.450 both for biological curiosity, but also 00:29:44.450 --> 00:29:49.070 to derisk the subsequent engineering. 00:29:49.070 --> 00:29:51.320 But the leucine and serine ones are normal. 00:29:51.320 --> 00:29:53.930 They're not that rare. 00:29:53.930 --> 00:29:54.960 But we derisked them. 00:29:54.960 --> 00:29:59.780 And remember that one where we did 13 codons 00:29:59.780 --> 00:30:01.200 on 42 essential genes? 00:30:01.200 --> 00:30:03.910 That's how we showed that, in general, it's 00:30:03.910 --> 00:30:06.114 not toxic to individual genes. 00:30:06.114 --> 00:30:08.280 But there are examples of things where you derisk it 00:30:08.280 --> 00:30:11.434 on individual genes and you start making lots of them, 00:30:11.434 --> 00:30:13.350 and then you get so-called "synthetic lethals" 00:30:13.350 --> 00:30:17.780 where various pairs of genes conspire. 00:30:17.780 --> 00:30:21.780 But so far, most of the deleterious nature 00:30:21.780 --> 00:30:24.360 of the genomes-- where the genomes are a little bit slower 00:30:24.360 --> 00:30:27.020 growing-- it's usually due to hitchhiker mutations, 00:30:27.020 --> 00:30:31.274 not due to our design-- except in cases where it's completely 00:30:31.274 --> 00:30:32.690 not working, in which case we have 00:30:32.690 --> 00:30:36.000 to find an alternative codon. 00:30:36.000 --> 00:30:38.160 But we have to deal with all these things-- 00:30:38.160 --> 00:30:42.180 design errors, biological discovery, and hitchhikers. 00:30:45.550 --> 00:30:46.130 Yeah. 00:30:46.130 --> 00:30:47.505 AUDIENCE: If you've already found 00:30:47.505 --> 00:30:50.549 that multiple, simultaneous mutations is unlikely, 00:30:50.549 --> 00:30:52.927 works, if they all had happened at the same time, 00:30:52.927 --> 00:30:54.843 but if you have this engineered system, if you 00:30:54.843 --> 00:30:58.405 have some way of migrating code to other-- you 00:30:58.405 --> 00:31:01.964 could end up with the spreading of your non-secret codes 00:31:01.964 --> 00:31:04.250 so that you can mutate things, one of them at a time, 00:31:04.250 --> 00:31:06.530 and accumulate. 00:31:06.530 --> 00:31:09.170 PROFESSOR: Well, so, first of all, a phage 00:31:09.170 --> 00:31:13.120 doesn't carry along its own code. 00:31:13.120 --> 00:31:21.060 If it did, we could preempt that by making lethal genes-- 00:31:21.060 --> 00:31:24.540 that if you bring in the tRNA that has the old code, 00:31:24.540 --> 00:31:27.040 you activate the lethal gene. 00:31:27.040 --> 00:31:30.140 But I think you were talking about more 00:31:30.140 --> 00:31:31.910 a Darwinian perspective, where you 00:31:31.910 --> 00:31:34.990 have incremental changes that allow you to slog along 00:31:34.990 --> 00:31:36.930 well enough that you can get more mutations. 00:31:36.930 --> 00:31:40.780 The problem is, this collection of mutations-- 00:31:40.780 --> 00:31:43.340 there is no growth. 00:31:43.340 --> 00:31:46.880 Every protein is majorly messed up. 00:31:46.880 --> 00:31:50.940 And so you're not talking about, say, antibiotic resistance, 00:31:50.940 --> 00:31:54.070 where there will be kind of a gradient of antibiotics. 00:31:54.070 --> 00:31:55.820 And somewhere on the edge of the gradient, 00:31:55.820 --> 00:31:59.320 there will be just enough antibiotic to be selective, 00:31:59.320 --> 00:32:00.720 but not enough to kill it. 00:32:00.720 --> 00:32:03.610 This is something where, the instant they get into the cell, 00:32:03.610 --> 00:32:04.450 there's no gradient. 00:32:04.450 --> 00:32:08.445 They only have one code choice, and that code is something-- 00:32:08.445 --> 00:32:10.820 I think the difference between this and regular evolution 00:32:10.820 --> 00:32:14.680 is, regular evolution-- if the bacteria tried this strategy, 00:32:14.680 --> 00:32:16.560 it would be changing a little bit at a time 00:32:16.560 --> 00:32:18.380 and the phage be keeping up with it. 00:32:18.380 --> 00:32:22.900 But we took it offline, so to speak, did major code revision, 00:32:22.900 --> 00:32:24.190 and moved it back. 00:32:24.190 --> 00:32:27.880 And the phage was not watching. 00:32:27.880 --> 00:32:31.750 And the phage isn't as intelligent as hackers are. 00:32:31.750 --> 00:32:35.564 OK, any other questions? 00:32:35.564 --> 00:32:36.730 We could stay on this topic. 00:32:36.730 --> 00:32:39.540 We don't have to go on to humans. 00:32:39.540 --> 00:32:43.170 OK, just for fun let's go on to human genome. 00:32:43.170 --> 00:32:46.860 How many people here want to have their genome edited? 00:32:46.860 --> 00:32:47.805 All right. 00:32:47.805 --> 00:32:50.180 We'll ask in just a moment what you want to have changed. 00:32:53.170 --> 00:32:56.170 So these are some of the tools that my colleagues and I 00:32:56.170 --> 00:32:57.350 have worked on. 00:32:57.350 --> 00:32:59.480 I've been doing this most of my career, 00:32:59.480 --> 00:33:01.860 is coming up with new tools for engineering genomes 00:33:01.860 --> 00:33:03.430 and sequencing genomes. 00:33:03.430 --> 00:33:05.620 And the one I've been talking about so far 00:33:05.620 --> 00:33:09.795 is down here at the bottom-- is Rec A and Red Beta. 00:33:12.810 --> 00:33:17.760 And the star for going forward is this Cas9 protein. 00:33:17.760 --> 00:33:22.410 But we color-coded them here so that the recognition-- 00:33:22.410 --> 00:33:24.100 the critical thing about genome editing 00:33:24.100 --> 00:33:25.750 is finding the needle in the haystack. 00:33:25.750 --> 00:33:27.124 You want to change one base pair. 00:33:27.124 --> 00:33:29.510 You don't want to change anything else. 00:33:29.510 --> 00:33:31.430 And so something has to do that recognition. 00:33:31.430 --> 00:33:33.380 That recognition can be Watson-Crick, 00:33:33.380 --> 00:33:36.670 so you can have DNA-DNA-- searching 00:33:36.670 --> 00:33:40.070 through the entire genome with DNA-DNA interactions, 00:33:40.070 --> 00:33:42.670 or RNA-DNA interactions, or Watson-Crick, 00:33:42.670 --> 00:33:44.620 or protein-DNA interactions, which 00:33:44.620 --> 00:33:46.370 I'm sure you've learned about quite a bit. 00:33:46.370 --> 00:33:49.150 And so we have examples of each of these-- two examples 00:33:49.150 --> 00:33:53.950 are RNA, in blue; two examples of DNA, down in the box; 00:33:53.950 --> 00:33:55.470 and then all the rest are protein, 00:33:55.470 --> 00:33:58.570 where the protein-- the amino acid side chains 00:33:58.570 --> 00:34:00.790 are recognizing, typically, some kind of alpha 00:34:00.790 --> 00:34:02.690 helix in the major groove. 00:34:06.670 --> 00:34:11.010 OK, so Cas9 was something that was 00:34:11.010 --> 00:34:13.750 a nice case of computational biology, in my opinion. 00:34:13.750 --> 00:34:20.500 It was found in 1987 in E. coli by Ishino and colleagues. 00:34:20.500 --> 00:34:23.730 And it was essentially junk DNA. 00:34:23.730 --> 00:34:25.389 It was not conserved. 00:34:25.389 --> 00:34:28.210 It was repetitive, which were two 00:34:28.210 --> 00:34:30.690 of the hallmarks of junk DNA, which 00:34:30.690 --> 00:34:32.371 were very popular talk about in 1987. 00:34:32.371 --> 00:34:34.370 They were trying to shut down the Genome Project 00:34:34.370 --> 00:34:37.310 before it started, three years before it started-- 00:34:37.310 --> 00:34:40.120 before the NIH part of it started-- because they didn't 00:34:40.120 --> 00:34:44.120 want to sequence anything in the human genome that 00:34:44.120 --> 00:34:46.124 wasn't coding for proteins. 00:34:46.124 --> 00:34:46.624 I'm serious. 00:34:50.239 --> 00:34:53.570 So anyway, this languished as junk DNA for many years. 00:34:53.570 --> 00:34:57.820 It eventually became clear to the cognoscenti 00:34:57.820 --> 00:35:01.560 bacteriologists that it might be an interesting, adaptive 00:35:01.560 --> 00:35:03.920 immunity-- kind of like antibodies-- 00:35:03.920 --> 00:35:06.729 rather than the fixed or native immunity, which 00:35:06.729 --> 00:35:07.770 were restriction enzymes. 00:35:07.770 --> 00:35:10.542 So this is kind of the adaptive version of restriction enzymes. 00:35:10.542 --> 00:35:12.000 But it still didn't really catch on 00:35:12.000 --> 00:35:17.570 until 2013, when a couple of my post-docs 00:35:17.570 --> 00:35:23.150 and ex-post-doc and graduate students in January 00:35:23.150 --> 00:35:24.950 got it to work in humans-- so moved it 00:35:24.950 --> 00:35:28.070 from bacteria to humans-- kind of a big jump. 00:35:28.070 --> 00:35:31.660 And then it became surprisingly easy, 00:35:31.660 --> 00:35:34.950 once it made that jump, to get it to work in every organism 00:35:34.950 --> 00:35:37.270 that we and others have tried. 00:35:37.270 --> 00:35:39.830 So now 20 different organisms, at least, 00:35:39.830 --> 00:35:43.750 that this works in-- fungi, plants, and even elephants. 00:35:47.220 --> 00:35:48.850 We haven't published the elephant yet, 00:35:48.850 --> 00:35:53.150 but we have our reasons for doing that. 00:35:53.150 --> 00:35:56.200 And the most frequently asked question-- and this, of course, 00:35:56.200 --> 00:36:00.320 should appeal to computational biologists trying 00:36:00.320 --> 00:36:03.780 to do design-- is, what about off-target? 00:36:03.780 --> 00:36:06.590 And it turns out now there are many ways of dealing 00:36:06.590 --> 00:36:10.090 with off-target-- so much so that I would be so bold-- 00:36:10.090 --> 00:36:12.560 and this is a slight speculation-- 00:36:12.560 --> 00:36:14.680 but I would say we're currently at the point 00:36:14.680 --> 00:36:18.410 where it's almost not measurable, the off-target. 00:36:18.410 --> 00:36:20.540 And these are the different ways you can do it. 00:36:20.540 --> 00:36:24.700 So we started out, in our January 2013, 00:36:24.700 --> 00:36:26.780 with theoretical, where you would basically 00:36:26.780 --> 00:36:29.980 look for-- anybody in this room would know immediately 00:36:29.980 --> 00:36:33.490 how to do this-- would look for potential off-targets that 00:36:33.490 --> 00:36:35.840 are off by one or two nucleotides 00:36:35.840 --> 00:36:38.590 and ban those from consideration. 00:36:38.590 --> 00:36:41.920 And then you take a shorter list and do an empirical search, 00:36:41.920 --> 00:36:43.490 because this is so inexpensive. 00:36:43.490 --> 00:36:49.070 Basically, you have this guide RNA 00:36:49.070 --> 00:36:50.400 which is making a triple helix. 00:36:50.400 --> 00:36:52.025 It's binding the one strand of the DNA. 00:36:52.025 --> 00:36:53.608 It's so easy to make those guide RNAs. 00:36:53.608 --> 00:36:55.440 It's just 20 nucleotides you have to make. 00:36:55.440 --> 00:36:57.360 You pop it into a vector where everything else 00:36:57.360 --> 00:36:58.460 is taken care of. 00:36:58.460 --> 00:37:01.369 It's so easy to do that that you can make a lot of them, 00:37:01.369 --> 00:37:02.660 and you do an empirical search. 00:37:02.660 --> 00:37:04.430 You find places that are particularly 00:37:04.430 --> 00:37:09.060 hot for the right sites and very cold for the wrong off-targets. 00:37:09.060 --> 00:37:10.930 So those are the first two methods. 00:37:10.930 --> 00:37:16.210 Then paired nickases-- they don't make 00:37:16.210 --> 00:37:17.890 a double-strand break, which is what 00:37:17.890 --> 00:37:19.600 it does out of the box from nature. 00:37:19.600 --> 00:37:21.020 It makes a double-strand break. 00:37:21.020 --> 00:37:22.730 You have it make a single-strand nick. 00:37:22.730 --> 00:37:25.190 Then you require two of these to be coincident and near one 00:37:25.190 --> 00:37:25.690 another. 00:37:25.690 --> 00:37:27.550 It's like the concept of PCR. 00:37:27.550 --> 00:37:30.144 You have to have two primers that are near one another. 00:37:30.144 --> 00:37:31.060 So it's a coincidence. 00:37:31.060 --> 00:37:35.290 So it's like a p squared-- if the probability is 00:37:35.290 --> 00:37:41.182 one is off by one or two or however many it takes, 00:37:41.182 --> 00:37:43.390 the chances of getting two such sites near each other 00:37:43.390 --> 00:37:47.050 is roughly p squared. 00:37:47.050 --> 00:37:48.970 Truncated guide RNA is not something 00:37:48.970 --> 00:37:51.880 that you would necessarily guess that, if you make the guide RNA 00:37:51.880 --> 00:37:53.660 smaller, it's going to be better. 00:37:53.660 --> 00:37:55.160 But there's obviously some optimum. 00:37:55.160 --> 00:38:00.790 If you make it too long, then it can bind by any subset-- 00:38:00.790 --> 00:38:03.390 any kind of mismatched subset. 00:38:03.390 --> 00:38:08.750 If you make it too short, then from informatics standpoint, 00:38:08.750 --> 00:38:11.380 it doesn't have enough bits to recognize 00:38:11.380 --> 00:38:12.490 a place in the genome. 00:38:12.490 --> 00:38:14.660 So it turned out that the optimal length 00:38:14.660 --> 00:38:16.784 was a little bit different from the natural length. 00:38:16.784 --> 00:38:18.100 It was about two shorter. 00:38:18.100 --> 00:38:20.810 And finally-- and this just came out. 00:38:20.810 --> 00:38:27.080 And this is from Keith Joung and David Liu's lab, 00:38:27.080 --> 00:38:33.190 where you get rid of the beautiful, double-strand break 00:38:33.190 --> 00:38:34.330 capacity. 00:38:34.330 --> 00:38:36.490 You can turn into a nickase, or you 00:38:36.490 --> 00:38:39.410 can make it completely nonfunctinal as a nucleus 00:38:39.410 --> 00:38:41.260 and then add nucleus domains back. 00:38:41.260 --> 00:38:43.567 And you say, well, it seems kind of bizarre 00:38:43.567 --> 00:38:45.650 that you're doing all that work-- that you get rid 00:38:45.650 --> 00:38:47.150 of the nucleus and you add it back-- 00:38:47.150 --> 00:38:51.030 add in a different one, the FokI bacterial restriction 00:38:51.030 --> 00:38:52.080 in the nucleus. 00:38:52.080 --> 00:38:55.561 But it turns out this is the way that people 00:38:55.561 --> 00:38:57.560 have taken other DNA-binding proteins-- the zinc 00:38:57.560 --> 00:38:59.890 fingers and then the tau proteins. 00:38:59.890 --> 00:39:04.792 And so it had to be tried, and it works extremely well. 00:39:04.792 --> 00:39:06.250 And it's like the paired nickases-- 00:39:06.250 --> 00:39:10.260 you need two of these sites in order to get cleavage. 00:39:10.260 --> 00:39:11.040 And stay tuned. 00:39:11.040 --> 00:39:13.240 I'm sure there's more. 00:39:13.240 --> 00:39:16.690 So I just want to close on this idea of causality again. 00:39:16.690 --> 00:39:17.510 I opened on it. 00:39:17.510 --> 00:39:19.030 I'll close on it. 00:39:19.030 --> 00:39:24.200 Here's an example of a double null-- myostatin 00:39:24.200 --> 00:39:28.420 double null, as the both maternal and paternal copies 00:39:28.420 --> 00:39:29.167 are missing. 00:39:29.167 --> 00:39:31.000 There are a lot of examples of double nulls. 00:39:31.000 --> 00:39:35.240 We could talk about some later. 00:39:35.240 --> 00:39:36.460 And they're often rare. 00:39:36.460 --> 00:39:38.454 So at one point, there was only one person 00:39:38.454 --> 00:39:40.370 in the world that was characterized with this. 00:39:40.370 --> 00:39:47.580 And it's hard to do a large cohort study on this. 00:39:47.580 --> 00:39:49.150 And they weren't really sick. 00:39:49.150 --> 00:39:54.570 The phenotype-- this little baby had heavy musculature, 00:39:54.570 --> 00:39:58.490 as if he was working out next to Arnold Schwarzenegger. 00:39:58.490 --> 00:40:03.050 But he came out this way, and he stayed that way. 00:40:03.050 --> 00:40:04.472 But it's striking. 00:40:04.472 --> 00:40:05.930 You look at the genome and you say, 00:40:05.930 --> 00:40:08.940 wow-- a double null and a highly conserved protein. 00:40:08.940 --> 00:40:10.634 That's got to mean something. 00:40:10.634 --> 00:40:12.050 And then you can have a hypothesis 00:40:12.050 --> 00:40:15.950 of what it means based on what was known about that pathway. 00:40:15.950 --> 00:40:17.490 And it coincides with the phenotype. 00:40:17.490 --> 00:40:18.990 And so you have a strong hypothesis, 00:40:18.990 --> 00:40:20.490 and you can test it in animals. 00:40:20.490 --> 00:40:23.490 And so here, you don't normally test it 00:40:23.490 --> 00:40:24.920 in three different animal species. 00:40:24.920 --> 00:40:30.040 But this one, there happened to be either preexisting or easy 00:40:30.040 --> 00:40:33.190 tests in cows, dogs, and mice. 00:40:36.750 --> 00:40:39.030 So that's one thing you can do to get a causality. 00:40:39.030 --> 00:40:40.404 And the other thing is, there are 00:40:40.404 --> 00:40:42.385 cases where the animal models don't work. 00:40:42.385 --> 00:40:44.010 Either you knew in advance they weren't 00:40:44.010 --> 00:40:47.860 going to work because they don't have that brain structure. 00:40:47.860 --> 00:40:49.522 There's nothing other than humans 00:40:49.522 --> 00:40:51.480 that have a particular kind of brain structure, 00:40:51.480 --> 00:40:55.030 so it's hard to make mutants, because you're already 00:40:55.030 --> 00:40:57.400 a mutant. 00:40:57.400 --> 00:41:03.490 And so another option is organs on chips or organoids, 00:41:03.490 --> 00:41:06.690 because they're not really fully physiologically faithful. 00:41:06.690 --> 00:41:11.590 And this, at least, is human, but just like animal models 00:41:11.590 --> 00:41:13.860 can have artifacts, human organoids 00:41:13.860 --> 00:41:15.372 can have artifacts as well. 00:41:15.372 --> 00:41:16.830 Here's an example of something that 00:41:16.830 --> 00:41:21.490 will be coming out in a few days that we did together 00:41:21.490 --> 00:41:25.780 with Keith Parker's lab and Bill Pu's lab. 00:41:25.780 --> 00:41:28.250 And I think this is a nice example 00:41:28.250 --> 00:41:30.870 of where you can take a hypothesis, where one base here 00:41:30.870 --> 00:41:37.300 is changed-- this G right here, is deleted-- 00:41:37.300 --> 00:41:40.950 and that's putatively what causes this cardiomyopathy that 00:41:40.950 --> 00:41:42.920 affects mitochondrial function. 00:41:42.920 --> 00:41:47.260 And you can mutate that using the CRISPR technology 00:41:47.260 --> 00:41:49.510 I was talking about, where you use homologous remedies 00:41:49.510 --> 00:41:51.710 to go in, find that one base, change it. 00:41:51.710 --> 00:41:53.850 Or you can just make a mess near there. 00:41:53.850 --> 00:41:56.190 So one control is to not change it, 00:41:56.190 --> 00:41:59.670 and the other control is to put a little insertion, deletion 00:41:59.670 --> 00:42:01.020 in there. 00:42:01.020 --> 00:42:04.250 And of course that messes it up as well. 00:42:04.250 --> 00:42:11.390 And so you've now constructed three isogenic strains. 00:42:11.390 --> 00:42:12.940 These are actually my cells. 00:42:12.940 --> 00:42:17.480 In the Personal Genome Project, we take volunteers like myself 00:42:17.480 --> 00:42:18.974 and establish stem cell lines. 00:42:18.974 --> 00:42:20.390 And then from the stem cell lines, 00:42:20.390 --> 00:42:23.620 we can establish, in this case, very well-ordered 00:42:23.620 --> 00:42:26.680 cardiac tissue we'll see in the next slide. 00:42:26.680 --> 00:42:31.860 And that cardiac tissue, you can test for lipid biochemistry, 00:42:31.860 --> 00:42:35.990 for other physiological parameters, 00:42:35.990 --> 00:42:39.160 for the morphology and the contractility-- so 00:42:39.160 --> 00:42:43.609 diastole and systole that you get in the cardiac muscle. 00:42:43.609 --> 00:42:45.650 So you basically make something where you've only 00:42:45.650 --> 00:42:47.470 changed one base pair in my genome, 00:42:47.470 --> 00:42:51.210 and we've made, essentially, a version of me that's mutant. 00:42:51.210 --> 00:42:55.120 Unfortunately, I don't think I had the picture of that. 00:42:55.120 --> 00:42:56.160 I thought I did. 00:43:03.390 --> 00:43:04.480 Oh, there it is. 00:43:07.070 --> 00:43:08.950 So here's an example-- how you get 00:43:08.950 --> 00:43:11.790 this beautiful, ribbon-like striated pattern 00:43:11.790 --> 00:43:14.620 that you expect of cardiac muscle. 00:43:14.620 --> 00:43:17.540 This is programmed from my fibroblast turned 00:43:17.540 --> 00:43:19.795 into stem cells into muscle. 00:43:19.795 --> 00:43:21.670 And then if you introduce the two mutations-- 00:43:21.670 --> 00:43:25.120 either the one that corresponds to a patient or one that's just 00:43:25.120 --> 00:43:28.920 a mess-- you get a morphological mess. 00:43:28.920 --> 00:43:32.020 And then you can restore those by putting in the messenger 00:43:32.020 --> 00:43:36.580 RNA that will cover for the mutation. 00:43:36.580 --> 00:43:41.330 So I'm going to open it up for questions at that point. 00:43:41.330 --> 00:43:45.576 That's causality-- I think. 00:43:45.576 --> 00:43:46.075 Questions? 00:43:48.870 --> 00:43:50.556 While we're waiting, anybody wants 00:43:50.556 --> 00:43:51.930 to volunteer what they would like 00:43:51.930 --> 00:43:54.860 to change about themselves? 00:43:54.860 --> 00:43:56.970 You can mention a specific base pair or kind 00:43:56.970 --> 00:44:02.122 of a general idea of what you'd like to change, 00:44:02.122 --> 00:44:04.580 whether you think there's any safety considerations that we 00:44:04.580 --> 00:44:05.720 should keep in mind. 00:44:09.224 --> 00:44:10.890 AUDIENCE: The problem's delivery, right? 00:44:10.890 --> 00:44:11.370 That's the-- 00:44:11.370 --> 00:44:11.825 PROFESSOR: Delivery. 00:44:11.825 --> 00:44:12.757 AUDIENCE: Yeah. 00:44:12.757 --> 00:44:14.190 PROFESSOR: Yeah, fair enough. 00:44:14.190 --> 00:44:21.190 So gene therapy had a crack. 00:44:21.190 --> 00:44:22.565 People were a little overanxious, 00:44:22.565 --> 00:44:27.280 a little overambitious about over a decade ago. 00:44:27.280 --> 00:44:34.000 And a small number of patients died from cancers, 00:44:34.000 --> 00:44:36.000 because there was random integration. 00:44:36.000 --> 00:44:37.902 Rather than this precise genome manipulation 00:44:37.902 --> 00:44:39.360 we're talking about here, there was 00:44:39.360 --> 00:44:41.370 kind of random lentiviral integration 00:44:41.370 --> 00:44:43.580 of extra copies of genes. 00:44:43.580 --> 00:44:45.150 And if you land in the wrong place, 00:44:45.150 --> 00:44:48.500 then your lentiviral or retroviral promoter 00:44:48.500 --> 00:44:51.920 will go off into oncogenes, like LMO2. 00:44:51.920 --> 00:44:54.060 So that delivery was viral delivery, 00:44:54.060 --> 00:44:55.990 and it was random integration. 00:44:55.990 --> 00:44:57.710 We now have delivery mechanisms that 00:44:57.710 --> 00:45:01.180 are nonintegrative or integrative in a specific place 00:45:01.180 --> 00:45:05.642 or, in this case, can make precise base pair changes. 00:45:05.642 --> 00:45:07.100 So there's two levels of delivery-- 00:45:07.100 --> 00:45:08.440 one is to get it to the right tissue, 00:45:08.440 --> 00:45:10.580 and the other is to get it to the right base pair. 00:45:10.580 --> 00:45:14.580 I think both are semisolved problems. 00:45:14.580 --> 00:45:17.970 So you can do ex vivo delivery. 00:45:17.970 --> 00:45:20.100 So you can take T cells out of a body. 00:45:20.100 --> 00:45:23.370 You can use a previous generation-- the zinc finger 00:45:23.370 --> 00:45:26.700 nucleus-- to cleave both copies of the CCR5 gene. 00:45:26.700 --> 00:45:31.120 And now people that had full-blown AIDS, 00:45:31.120 --> 00:45:33.220 you put these T cells back in their body, 00:45:33.220 --> 00:45:35.800 and then now they're AIDS resistant. 00:45:35.800 --> 00:45:38.990 Those T cells that have both copies of the CCR5 gene 00:45:38.990 --> 00:45:46.810 missing, which is the AIDS coreceptor, are now resistant. 00:45:46.810 --> 00:45:48.040 So that's ex vivo. 00:45:48.040 --> 00:45:49.980 That's one way to do it. 00:45:49.980 --> 00:45:51.970 Delivery to the liver is quite easy. 00:45:51.970 --> 00:45:54.780 You can do that with nonviral vectors, 00:45:54.780 --> 00:45:57.290 and a [INAUDIBLE] virus is one that's very popular. 00:45:57.290 --> 00:45:59.970 You can get it to go to almost every cell in the body, 00:45:59.970 --> 00:46:02.826 either selectively or generally. 00:46:02.826 --> 00:46:05.075 So you just want to make sure that once it goes there, 00:46:05.075 --> 00:46:08.626 it doesn't cause any damage other than the base 00:46:08.626 --> 00:46:09.625 pair you want to change. 00:46:12.930 --> 00:46:16.740 So there are now 2,000 gene therapy trials 00:46:16.740 --> 00:46:19.396 in phase one, two, and three. 00:46:19.396 --> 00:46:21.520 It's a big change from a decade ago, 00:46:21.520 --> 00:46:25.645 where I think people had pretty much given up on gene therapy. 00:46:25.645 --> 00:46:27.520 There's now 2,000 clinical trials. 00:46:27.520 --> 00:46:31.920 And one has emerged all the way out of phase three 00:46:31.920 --> 00:46:35.690 into full approval in Europe. 00:46:35.690 --> 00:46:40.580 Ironically, they now have genetically engineered humans 00:46:40.580 --> 00:46:43.950 in a land where they don't eat genetically modified foods. 00:46:47.010 --> 00:46:50.550 But I think they're better for it So far, it's 00:46:50.550 --> 00:46:52.910 curing diseases. 00:46:52.910 --> 00:46:53.410 Yeah. 00:46:53.410 --> 00:46:56.812 AUDIENCE: For your noncanonical amino acids, 00:46:56.812 --> 00:46:59.890 does this open up enzymatic reactions that 00:46:59.890 --> 00:47:02.158 would be, say, impossible, do you think, 00:47:02.158 --> 00:47:07.020 with if you add a new amino acid that can [INAUDIBLE]? 00:47:07.020 --> 00:47:10.270 PROFESSOR: So I'll just repeat the question for our viewing 00:47:10.270 --> 00:47:12.820 audience. 00:47:12.820 --> 00:47:16.480 Do nonstandard amino acids open up new enzymatic reactions? 00:47:16.480 --> 00:47:20.160 And there's already a couple of examples in the literature. 00:47:20.160 --> 00:47:23.352 This was done prior to this wonderful strain, where 00:47:23.352 --> 00:47:24.310 there's no competition. 00:47:24.310 --> 00:47:26.162 It was done at low efficiency. 00:47:26.162 --> 00:47:28.370 But putting in one amino acid at low efficiency-- you 00:47:28.370 --> 00:47:30.190 could still get an enzyme. 00:47:30.190 --> 00:47:33.870 So even if it's, like, 10% efficiency, 00:47:33.870 --> 00:47:36.350 you produce 10 times as much enzyme, and it works. 00:47:36.350 --> 00:47:39.220 So there were some redox-coumarin derivatives 00:47:39.220 --> 00:47:40.920 of amino acids. 00:47:40.920 --> 00:47:43.420 So coumarin-redox capabilities is not 00:47:43.420 --> 00:47:46.040 present in any of the other amino acids. 00:47:46.040 --> 00:47:51.730 And they took a protein that was very well studied-- where 00:47:51.730 --> 00:47:55.980 they had by protein design, and by random mutagenesis, 00:47:55.980 --> 00:47:58.610 and they threw the book at it-- and they could not 00:47:58.610 --> 00:48:03.220 budge the activity beyond the apparently optimal, 00:48:03.220 --> 00:48:05.900 naturally occurring activity. 00:48:05.900 --> 00:48:08.410 They put in this amino acid, which was not randomly 00:48:08.410 --> 00:48:11.600 chosen-- it was a redox-coumarin derivative-- they put it 00:48:11.600 --> 00:48:15.380 in the active site. 00:48:15.380 --> 00:48:18.562 I think they tried out a few different things that 00:48:18.562 --> 00:48:20.020 made a small combinatorial library. 00:48:20.020 --> 00:48:23.370 But the point is, they got a tenfold improvement 00:48:23.370 --> 00:48:25.740 in the catalytic rate constants. 00:48:25.740 --> 00:48:27.170 So that's an example. 00:48:27.170 --> 00:48:29.370 Another example, which isn't really catalytic, 00:48:29.370 --> 00:48:33.400 but it's very popular, is that you can put in polyethylene 00:48:33.400 --> 00:48:40.290 glycol-modified amino acids wherever you want 00:48:40.290 --> 00:48:41.510 rather than kind of randomly. 00:48:41.510 --> 00:48:42.760 You can put it in precisely. 00:48:42.760 --> 00:48:47.480 And this will greatly extend the serum half-life, 00:48:47.480 --> 00:48:51.910 so that normal proteins like human growth hormone, which 00:48:51.910 --> 00:48:55.655 is a approved pharmaceutical for certain uses-- 00:48:55.655 --> 00:48:58.920 not all the uses that you find on the internet, but other 00:48:58.920 --> 00:49:03.010 uses-- but it turns over very quickly in the serum. 00:49:03.010 --> 00:49:05.540 And so if you put a polyethylene glycol in the right place 00:49:05.540 --> 00:49:08.190 on human growth hormone-- or other human protein 00:49:08.190 --> 00:49:11.040 pharmaceuticals-- they last longer. 00:49:11.040 --> 00:49:14.580 Those are two examples-- one of them definitely active site. 00:49:17.050 --> 00:49:17.550 Yeah. 00:49:17.550 --> 00:49:19.258 AUDIENCE: This is actually a small detail 00:49:19.258 --> 00:49:22.144 from your [INAUDIBLE] study, where 00:49:22.144 --> 00:49:26.369 you looked at the structure and mutated one of the amino acids 00:49:26.369 --> 00:49:28.348 to this phenyl thing, and then you 00:49:28.348 --> 00:49:29.848 changed a bunch of other amino acids 00:49:29.848 --> 00:49:31.836 to compensate for that size. 00:49:31.836 --> 00:49:36.425 So I noticed most of the changes were to either [INAUDIBLE], 00:49:36.425 --> 00:49:37.800 but one of them was a tryptophan. 00:49:37.800 --> 00:49:39.241 So why was that? 00:49:39.241 --> 00:49:40.615 PROFESSOR: Let's go back to that, 00:49:40.615 --> 00:49:43.267 and see if we can find that. 00:49:43.267 --> 00:49:44.758 AUDIENCE: It was a previous slide. 00:49:44.758 --> 00:49:45.752 Yeah, this one. 00:49:45.752 --> 00:49:50.730 So it was amino acid 271. 00:49:50.730 --> 00:49:51.580 PROFESSOR: Yeah, OK. 00:49:51.580 --> 00:49:54.666 So in each of these lines-- I didn't spend much time 00:49:54.666 --> 00:49:56.040 on this-- in each of these lines, 00:49:56.040 --> 00:49:59.410 there's one amino acid we've changed to bipA. 00:49:59.410 --> 00:50:02.670 So these three are all the same protein, 00:50:02.670 --> 00:50:06.890 and it's all the same mutation-- leucine 303 to bipA. 00:50:06.890 --> 00:50:09.420 And then all the other ones are compensating. 00:50:09.420 --> 00:50:14.010 And then here, you can see it's a different leucine 00:50:14.010 --> 00:50:16.570 and a different protein. 00:50:16.570 --> 00:50:18.600 They're all leucines-- different proteins. 00:50:18.600 --> 00:50:20.595 Now what's your question about? 00:50:20.595 --> 00:50:22.920 AUDIENCE: My question was the compensating 00:50:22.920 --> 00:50:25.710 mutations are generally all the smaller amino acids, right? 00:50:25.710 --> 00:50:26.623 PROFESSOR: Oh, I see. 00:50:26.623 --> 00:50:28.122 So why phenylalanine and tryptophan? 00:50:28.122 --> 00:50:29.358 AUDIENCE: Yeah. 00:50:29.358 --> 00:50:32.990 PROFESSOR: Well, those are pretty close. 00:50:32.990 --> 00:50:36.600 So these are done by energy, not by eyeball. 00:50:36.600 --> 00:50:40.130 They're done all by COMP ROSETTA, where 00:50:40.130 --> 00:50:43.990 we combinatorially go through lots of side chains. 00:50:43.990 --> 00:50:45.750 So we combinatorially went through lots 00:50:45.750 --> 00:50:49.650 of proteins, lots of positions to substitute amino acid, 00:50:49.650 --> 00:50:53.200 then lots of accommodating mutations-- which 00:50:53.200 --> 00:50:56.840 is not necessarily the typical way you use this software. 00:50:56.840 --> 00:50:59.410 Anyway, that probably is some stacking 00:50:59.410 --> 00:51:05.656 of one of the two aromatic rings onto the tryptophan. 00:51:05.656 --> 00:51:06.156 Yeah. 00:51:10.490 --> 00:51:12.360 And we tried many combinations. 00:51:12.360 --> 00:51:14.780 No doubt, we tried the phenylalanine 00:51:14.780 --> 00:51:16.750 and the tryptophan in various combinations 00:51:16.750 --> 00:51:18.410 with the other ones, and the tryptophan empirically 00:51:18.410 --> 00:51:19.010 works better. 00:51:22.310 --> 00:51:24.160 MODERATOR: Are there any more questions?