1 00:00:00,790 --> 00:00:07,320 [Music] 2 00:00:12,299 --> 00:00:09,259 [Applause] 3 00:00:13,799 --> 00:00:12,309 I've had problems with this Absalom's in 4 00:00:17,150 --> 00:00:13,809 the past which I was just telling Nadia 5 00:00:19,409 --> 00:00:17,160 about so in the interest of 6 00:00:22,380 --> 00:00:19,419 interdisciplinarity I tried to go with a 7 00:00:23,640 --> 00:00:22,390 simpler more general title and I'm going 8 00:00:26,609 --> 00:00:23,650 to give rather than the talk I 9 00:00:28,980 --> 00:00:26,619 originally originally planned a more of 10 00:00:30,630 --> 00:00:28,990 an overview of the research program that 11 00:00:33,330 --> 00:00:30,640 we're trying to develop at Georgia Tech 12 00:00:34,890 --> 00:00:33,340 and so my new title will be stabilizing 13 00:00:37,460 --> 00:00:34,900 the evolutionary transition to 14 00:00:40,229 --> 00:00:37,470 multicellularity against reversion and 15 00:00:43,140 --> 00:00:40,239 so the evolution of multicellularity is 16 00:00:45,000 --> 00:00:43,150 one of a very small number of events in 17 00:00:48,600 --> 00:00:45,010 the history of life in which the 18 00:00:51,149 --> 00:00:48,610 hierarchical complexity of biological 19 00:00:55,170 --> 00:00:51,159 systems has increased and so the others 20 00:00:57,689 --> 00:00:55,180 in this very simplified diagram are so 21 00:00:59,939 --> 00:00:57,699 the origin of life may be considered as 22 00:01:02,520 --> 00:00:59,949 one or the origin of chromosomes the 23 00:01:04,229 --> 00:01:02,530 origin of the prokaryotic cell eukaryote 24 00:01:07,980 --> 00:01:04,239 Genesis then the evolution of 25 00:01:11,130 --> 00:01:07,990 multicellularity and later in some cases 26 00:01:14,060 --> 00:01:11,140 the evolution of eusociality and so 27 00:01:16,289 --> 00:01:14,070 multicellularity is quite unique in that 28 00:01:19,200 --> 00:01:16,299 unlike many of the other transitions 29 00:01:21,330 --> 00:01:19,210 like eukaryote genesis which we know has 30 00:01:23,789 --> 00:01:21,340 which we only know has occurred at least 31 00:01:26,660 --> 00:01:23,799 once multicellularity has evolved 32 00:01:29,730 --> 00:01:26,670 independently across multiple lineages 33 00:01:32,249 --> 00:01:29,740 here in the eukaryotes it's thought to 34 00:01:35,310 --> 00:01:32,259 have occurred at least 25 times and I 35 00:01:38,190 --> 00:01:35,320 think the big question for us in the 36 00:01:41,039 --> 00:01:38,200 field of multicellularity research now 37 00:01:43,730 --> 00:01:41,049 that Frank rosensweig set up very nicely 38 00:01:46,800 --> 00:01:43,740 is how do you go from a unicellular 39 00:01:50,130 --> 00:01:46,810 ancestor of say metazoan all metazoans 40 00:01:52,160 --> 00:01:50,140 to something like a kangaroo and so I've 41 00:01:57,179 --> 00:01:52,170 simplified that process in this 42 00:01:59,249 --> 00:01:57,189 three-part diagram so our understanding 43 00:02:01,559 --> 00:01:59,259 of multicellularity is sort of in its 44 00:02:04,129 --> 00:02:01,569 infancy in terms of the process that 45 00:02:06,359 --> 00:02:04,139 generates something like a kangaroo from 46 00:02:09,150 --> 00:02:06,369 something that may resemble a coin of 47 00:02:10,559 --> 00:02:09,160 flageolet so first we think there are 48 00:02:13,230 --> 00:02:10,569 it's necessary that you have external 49 00:02:15,890 --> 00:02:13,240 drivers that create the benefit to you 50 00:02:19,410 --> 00:02:15,900 forming a simple social group of cells 51 00:02:22,050 --> 00:02:19,420 so that that drives the increased group 52 00:02:23,430 --> 00:02:22,060 size from one to many 53 00:02:26,130 --> 00:02:23,440 and then later there are subsequent 54 00:02:28,500 --> 00:02:26,140 social changes associated with the 55 00:02:31,080 --> 00:02:28,510 transformation of simple groups to more 56 00:02:33,030 --> 00:02:31,090 complex ones and this could be anything 57 00:02:35,490 --> 00:02:33,040 and this is the part that we really 58 00:02:38,699 --> 00:02:35,500 don't quite understand so I would argue 59 00:02:41,960 --> 00:02:38,709 that our our understanding of these 60 00:02:45,479 --> 00:02:41,970 external drivers and their role is 61 00:02:47,699 --> 00:02:45,489 fairly well-established we have good 62 00:02:49,440 --> 00:02:47,709 theoretical and empirical results to 63 00:02:51,809 --> 00:02:49,450 support things like increased stress 64 00:02:54,920 --> 00:02:51,819 tolerance protection from predation and 65 00:02:57,600 --> 00:02:54,930 improved utilization of public goods as 66 00:03:00,900 --> 00:02:57,610 good drivers that might favor the 67 00:03:02,520 --> 00:03:00,910 evolution of simple social groups but 68 00:03:06,150 --> 00:03:02,530 what we really don't understand are 69 00:03:08,280 --> 00:03:06,160 these social changes but are associated 70 00:03:12,180 --> 00:03:08,290 with increases in the complexity of 71 00:03:13,979 --> 00:03:12,190 those simple groups and so as I may not 72 00:03:16,830 --> 00:03:13,989 have mentioned I work in the field of 73 00:03:18,809 --> 00:03:16,840 experimental evolution and actually 74 00:03:21,690 --> 00:03:18,819 there have been research groups evolving 75 00:03:26,100 --> 00:03:21,700 simple multicellular organisms in the 76 00:03:29,160 --> 00:03:26,110 laboratory for about 25 years and this 77 00:03:31,350 --> 00:03:29,170 is just a small sampling maybe I've 78 00:03:33,600 --> 00:03:31,360 captured about half of the instances of 79 00:03:37,680 --> 00:03:33,610 this happening in the field there have 80 00:03:40,470 --> 00:03:37,690 been multiple works with algae both this 81 00:03:42,420 --> 00:03:40,480 chlorella vulgaris in the middle and two 82 00:03:45,410 --> 00:03:42,430 experiments with different selective 83 00:03:48,509 --> 00:03:45,420 pressures with commit aluminium bonus 84 00:03:51,630 --> 00:03:48,519 there's also been work done in bacterial 85 00:03:54,960 --> 00:03:51,640 systems as well as different yeast model 86 00:03:57,060 --> 00:03:54,970 organisms and so there's just two 87 00:04:00,780 --> 00:03:57,070 lessons I want to summarize across these 88 00:04:02,970 --> 00:04:00,790 25 years of history of this emerging 89 00:04:05,400 --> 00:04:02,980 discipline so first simple 90 00:04:08,610 --> 00:04:05,410 multicellularity seems to evolve quite 91 00:04:11,520 --> 00:04:08,620 rapidly under the right conditions so 92 00:04:13,890 --> 00:04:11,530 all the experiments that I summarized 93 00:04:16,860 --> 00:04:13,900 here in the slide saw the evolution of 94 00:04:18,990 --> 00:04:16,870 multicellular genotypes over the course 95 00:04:21,870 --> 00:04:19,000 of a few hundred generations or less and 96 00:04:24,659 --> 00:04:21,880 this suggests that the genetic barriers 97 00:04:26,520 --> 00:04:24,669 to making this transition this first 98 00:04:30,029 --> 00:04:26,530 step of the transition at least are 99 00:04:32,370 --> 00:04:30,039 quite minimal the second lesson from 100 00:04:34,440 --> 00:04:32,380 experimental evolution is that simple 101 00:04:35,640 --> 00:04:34,450 multicellularity seems to be costly in 102 00:04:38,700 --> 00:04:35,650 the absence of these 103 00:04:41,280 --> 00:04:38,710 kernel drivers so just to pull out two 104 00:04:44,520 --> 00:04:41,290 examples in the yeast in the yeast 105 00:04:47,520 --> 00:04:44,530 picture on top right there was a ten 106 00:04:50,790 --> 00:04:47,530 percent cost measured by Ratcliffe a 107 00:04:52,770 --> 00:04:50,800 Talon twenty twelve when their snowflake 108 00:04:55,020 --> 00:04:52,780 yeast system was grown in liquid medium 109 00:04:56,879 --> 00:04:55,030 without the external driver in their 110 00:04:59,219 --> 00:04:56,889 case which was selection for rapid 111 00:05:01,409 --> 00:04:59,229 sedimentation and in the bacteria 112 00:05:03,050 --> 00:05:01,419 example there was a twenty percent cost 113 00:05:06,659 --> 00:05:03,060 measured in Pseudomonas fluorescens 114 00:05:08,310 --> 00:05:06,669 wrinkly spreaders when there was colony 115 00:05:12,510 --> 00:05:08,320 colonization of the air water interface 116 00:05:15,029 --> 00:05:12,520 was not necessary and so together these 117 00:05:18,540 --> 00:05:15,039 two lessons point towards a major 118 00:05:20,490 --> 00:05:18,550 problem for the increase subsequent 119 00:05:23,159 --> 00:05:20,500 increases in complexity for new 120 00:05:24,750 --> 00:05:23,169 multicellular forms which is that if you 121 00:05:28,800 --> 00:05:24,760 have something that's genetically labile 122 00:05:30,680 --> 00:05:28,810 and has high fitness costs when the 123 00:05:34,560 --> 00:05:30,690 external drivers are not present you 124 00:05:37,110 --> 00:05:34,570 should see the the reversion of back to 125 00:05:40,230 --> 00:05:37,120 unicellular t should the environment 126 00:05:42,900 --> 00:05:40,240 change and actually maria replicated 127 00:05:44,879 --> 00:05:42,910 gomez and mike travisano had a pair of 128 00:05:47,189 --> 00:05:44,889 great papers come out in the past year 129 00:05:49,790 --> 00:05:47,199 where they measured exactly this 130 00:05:54,149 --> 00:05:49,800 so using that snowflake yeast system 131 00:05:56,399 --> 00:05:54,159 they first show in a growth in liquid 132 00:05:58,200 --> 00:05:56,409 without settling selection that ten 133 00:06:00,480 --> 00:05:58,210 percent fitness costs they measure that 134 00:06:03,240 --> 00:06:00,490 i mentioned and they also demonstrated 135 00:06:05,610 --> 00:06:03,250 that when you do your selections on agar 136 00:06:08,580 --> 00:06:05,620 plates there's an even greater fitness 137 00:06:11,490 --> 00:06:08,590 cost for the multicellular forms so what 138 00:06:15,800 --> 00:06:11,500 they found really interestingly here we 139 00:06:18,510 --> 00:06:15,810 have a simple histograms of the area 140 00:06:21,540 --> 00:06:18,520 have these multicellular types that form 141 00:06:23,670 --> 00:06:21,550 a distribution of clump sizes over the 142 00:06:26,250 --> 00:06:23,680 course of 30 days when they're doing 143 00:06:29,189 --> 00:06:26,260 selection on plates you see rapidly 144 00:06:32,730 --> 00:06:29,199 going from the black distribution to the 145 00:06:34,890 --> 00:06:32,740 lighter ones you see size decrease quite 146 00:06:37,350 --> 00:06:34,900 rapidly but when you do your selections 147 00:06:39,839 --> 00:06:37,360 in liquid that size decreases both 148 00:06:43,350 --> 00:06:39,849 slower and less pronounced 149 00:06:46,080 --> 00:06:43,360 and so the cost associated with the 150 00:06:49,380 --> 00:06:46,090 simple multicellularity actually is a 151 00:06:53,040 --> 00:06:49,390 good predictor of how rapidly multi cell 152 00:06:56,420 --> 00:06:53,050 we'll be lost as well as the extent to 153 00:06:58,830 --> 00:06:56,430 which the phenotype will change and so 154 00:07:02,160 --> 00:06:58,840 we need at least one more arrow in my 155 00:07:04,020 --> 00:07:02,170 simple diagram which is that before you 156 00:07:06,680 --> 00:07:04,030 get locked into this positive feedback 157 00:07:09,600 --> 00:07:06,690 loop that produces things like kangaroos 158 00:07:11,430 --> 00:07:09,610 you might actually experience an 159 00:07:15,570 --> 00:07:11,440 environmental change that favors you 160 00:07:18,420 --> 00:07:15,580 exiting this simple this simple system 161 00:07:19,020 --> 00:07:18,430 and so the vicious cycle can't ever 162 00:07:20,940 --> 00:07:19,030 occur 163 00:07:23,490 --> 00:07:20,950 you'll never get increases in complexity 164 00:07:26,250 --> 00:07:23,500 and so this is this is just say that 165 00:07:27,840 --> 00:07:26,260 reversion may in fact be a major problem 166 00:07:32,580 --> 00:07:27,850 that we should consider in the evolution 167 00:07:35,000 --> 00:07:32,590 of multicellularity and so in my view 168 00:07:37,110 --> 00:07:35,010 there's probably two possible routes to 169 00:07:39,480 --> 00:07:37,120 stabilizing the evolution of 170 00:07:41,820 --> 00:07:39,490 multicellularity the first being you can 171 00:07:44,130 --> 00:07:41,830 reduce the number of potential reversion 172 00:07:46,650 --> 00:07:44,140 mutations so here I've drawn just a 173 00:07:49,500 --> 00:07:46,660 simplest distribution of fitness effects 174 00:07:54,930 --> 00:07:49,510 of reversion mutations that I could 175 00:07:59,370 --> 00:07:54,940 imagine so here you have this greyed out 176 00:08:01,140 --> 00:07:59,380 dashed you know modal distribution that 177 00:08:03,330 --> 00:08:01,150 represents all the possible mutations 178 00:08:04,500 --> 00:08:03,340 that will cause a reversion and the 179 00:08:06,240 --> 00:08:04,510 arrow is saying that through 180 00:08:08,730 --> 00:08:06,250 evolutionary time while you're in this 181 00:08:10,740 --> 00:08:08,740 positive feedback loop you may lose some 182 00:08:12,480 --> 00:08:10,750 of those potential routes back to 183 00:08:15,620 --> 00:08:12,490 unicellular T that's just a question 184 00:08:18,630 --> 00:08:15,630 about the availability of mutations 185 00:08:22,500 --> 00:08:18,640 another route would be to reduce the 186 00:08:25,920 --> 00:08:22,510 selective advantage to fixing one of 187 00:08:28,170 --> 00:08:25,930 those reversion mutations so if by 188 00:08:31,110 --> 00:08:28,180 further genetic interactions and 189 00:08:33,000 --> 00:08:31,120 mutations reversion is no longer 190 00:08:35,219 --> 00:08:33,010 beneficial even when you have 191 00:08:38,360 --> 00:08:35,229 environmental change then that should 192 00:08:44,070 --> 00:08:38,370 also prevent the exiting of this 193 00:08:46,170 --> 00:08:44,080 cyclical this cycle here that I had on 194 00:08:51,630 --> 00:08:46,180 the previous slide or of course you can 195 00:08:53,610 --> 00:08:51,640 do both and so we saw we our aim was to 196 00:08:55,260 --> 00:08:53,620 look for evidence of whether either of 197 00:08:58,020 --> 00:08:55,270 these two processes were happening in 198 00:09:00,210 --> 00:08:58,030 our laboratory experiments so we also 199 00:09:02,590 --> 00:09:00,220 are working with the sacrum icy service 200 00:09:05,620 --> 00:09:02,600 see a snowflake yeast system 201 00:09:07,749 --> 00:09:05,630 and what will Ratcliffe in my crevice on 202 00:09:09,400 --> 00:09:07,759 Oh and others did in this original 203 00:09:12,910 --> 00:09:09,410 experiment is they subjected a 204 00:09:15,189 --> 00:09:12,920 unicellular strain of baker's yeast to 205 00:09:18,759 --> 00:09:15,199 selection for rapid settling in liquid 206 00:09:21,519 --> 00:09:18,769 media and over the course of 60 days or 207 00:09:24,370 --> 00:09:21,529 fewer they got things that grew in this 208 00:09:26,620 --> 00:09:24,380 beautiful fractal like pattern and being 209 00:09:29,079 --> 00:09:26,630 from Minnesota as I already spoiled they 210 00:09:35,019 --> 00:09:29,089 called it snowflake yeast because that 211 00:09:38,319 --> 00:09:35,029 was familiar so the thing that really 212 00:09:40,800 --> 00:09:38,329 enables our present study is that the 213 00:09:43,180 --> 00:09:40,810 genetics of multicellularity are 214 00:09:46,210 --> 00:09:43,190 excessively simple in this system at 215 00:09:50,590 --> 00:09:46,220 least in five of the 10 original 216 00:09:52,749 --> 00:09:50,600 isolates the mutation was a single loss 217 00:09:55,180 --> 00:09:52,759 of function in a transcription factor 218 00:09:57,340 --> 00:09:55,190 called ace 2 that produced the 219 00:09:59,259 --> 00:09:57,350 multicellular growth form and the 220 00:10:04,240 --> 00:09:59,269 restoration of a functional copy of ace 221 00:10:06,519 --> 00:10:04,250 2 can produce unicellular progeny so 222 00:10:09,340 --> 00:10:06,529 here's the original mutant here's a 223 00:10:12,040 --> 00:10:09,350 functional ace to put into that mutant 224 00:10:14,350 --> 00:10:12,050 and here if you knock out both copies of 225 00:10:17,319 --> 00:10:14,360 the ancestral type you recapitulate the 226 00:10:20,050 --> 00:10:17,329 snowflake yeast form and so we did this 227 00:10:23,860 --> 00:10:20,060 from the original 60 day evolution 228 00:10:26,889 --> 00:10:23,870 experiment and just at two-two intervals 229 00:10:29,530 --> 00:10:26,899 so and then we measured a competitive 230 00:10:31,300 --> 00:10:29,540 fitness of the unicellular riverton's 231 00:10:34,030 --> 00:10:31,310 that were derived from multicellular 232 00:10:36,340 --> 00:10:34,040 types against their ancestors and what 233 00:10:38,439 --> 00:10:36,350 we saw was really striking is that at 234 00:10:40,179 --> 00:10:38,449 the beginning their fitness is equal to 235 00:10:44,530 --> 00:10:40,189 their ancestor they haven't undergone 236 00:10:46,179 --> 00:10:44,540 any mutations but then by 30 transfers 237 00:10:47,679 --> 00:10:46,189 they were a bit lower which is the 238 00:10:51,249 --> 00:10:47,689 opposite of what you normally expect and 239 00:10:53,889 --> 00:10:51,259 then by 60 days there was about a 5% 240 00:10:56,470 --> 00:10:53,899 fitness cost associated with this 241 00:10:58,509 --> 00:10:56,480 reversion mutation so this is this is 242 00:11:02,559 --> 00:10:58,519 evidence of that second class of 243 00:11:05,679 --> 00:11:02,569 mutations potentially fixing and at 244 00:11:08,470 --> 00:11:05,689 least in the case of the transition the 245 00:11:10,960 --> 00:11:08,480 the difference between day 30 and day 60 246 00:11:13,329 --> 00:11:10,970 we had a few candidate traits that we 247 00:11:16,030 --> 00:11:13,339 thought might explain this decrease in 248 00:11:18,220 --> 00:11:16,040 unicellular Fitness so 249 00:11:20,370 --> 00:11:18,230 he's populate this particular population 250 00:11:23,130 --> 00:11:20,380 of all of dicks elevated rates of 251 00:11:26,380 --> 00:11:23,140 programmed cell death or apoptosis as 252 00:11:28,240 --> 00:11:26,390 well as increased cell size and we have 253 00:11:31,000 --> 00:11:28,250 these uh priori reasons to think that 254 00:11:33,220 --> 00:11:31,010 those traits might be costly in a 255 00:11:37,150 --> 00:11:33,230 unicellular background but beneficial 256 00:11:38,260 --> 00:11:37,160 for the multi cells so what we really 257 00:11:41,470 --> 00:11:38,270 want to know after getting this 258 00:11:43,240 --> 00:11:41,480 provocative first piece of data is what 259 00:11:46,780 --> 00:11:43,250 are the dynamics and the consequences of 260 00:11:50,110 --> 00:11:46,790 long-term selection on size and luckily 261 00:11:52,630 --> 00:11:50,120 for me and hopefully for other people we 262 00:11:55,000 --> 00:11:52,640 already had a fantastic postdoc that 263 00:11:56,350 --> 00:11:55,010 joined the lab years before me that was 264 00:11:59,110 --> 00:11:56,360 conducting a long term evolution 265 00:12:01,150 --> 00:11:59,120 experiment with settling selection and 266 00:12:03,940 --> 00:12:01,160 that is ozone buzz Doug who's here in 267 00:12:05,890 --> 00:12:03,950 the audience and later today he'll be 268 00:12:09,310 --> 00:12:05,900 giving a poster on this long term 269 00:12:11,770 --> 00:12:09,320 experiment and also talking a bit about 270 00:12:13,390 --> 00:12:11,780 the effects of high and low oxygen 271 00:12:15,730 --> 00:12:13,400 levels on the evolution of multicellular 272 00:12:19,480 --> 00:12:15,740 size so please check out his poster this 273 00:12:22,180 --> 00:12:19,490 evening but what we did is again take 274 00:12:24,190 --> 00:12:22,190 all of the isolates from this original 275 00:12:26,110 --> 00:12:24,200 experiment and perform these genetic 276 00:12:28,930 --> 00:12:26,120 reversions on them and so on the next 277 00:12:31,870 --> 00:12:28,940 slide and I promised Frank it'll 278 00:12:34,210 --> 00:12:31,880 actually happen on the next slide will 279 00:12:37,720 --> 00:12:34,220 either these dots that are representing 280 00:12:40,240 --> 00:12:37,730 day 200 day 400 and day 600 isolates 281 00:12:42,730 --> 00:12:40,250 from two parallel experiments that ozone 282 00:12:45,730 --> 00:12:42,740 had run will sit tell you whether or not 283 00:12:48,880 --> 00:12:45,740 the transform ins with a functional h2 284 00:12:50,740 --> 00:12:48,890 are unicellular or multicellular and so 285 00:12:54,190 --> 00:12:50,750 unsurprisingly based on the way I set 286 00:12:56,020 --> 00:12:54,200 this up we have some some some of the 287 00:12:57,280 --> 00:12:56,030 populations have actually lost the 288 00:12:59,500 --> 00:12:57,290 ability to revert to you know 289 00:13:02,220 --> 00:12:59,510 cellularity when we put a functional ace 290 00:13:04,870 --> 00:13:02,230 2 back into them this was particularly 291 00:13:06,760 --> 00:13:04,880 validating for us because the two 292 00:13:09,660 --> 00:13:06,770 populations are actually the three 293 00:13:13,390 --> 00:13:09,670 populations at the bottom there that 294 00:13:16,690 --> 00:13:13,400 were unable to revert also exhibited the 295 00:13:18,430 --> 00:13:16,700 most pronounced phenotypic changes over 296 00:13:20,920 --> 00:13:18,440 the course of these 600 days of 297 00:13:23,470 --> 00:13:20,930 evolution and one of those really 298 00:13:25,120 --> 00:13:23,480 dramatic changes was that the site the 299 00:13:28,270 --> 00:13:25,130 average size of the snowflake yeast 300 00:13:29,230 --> 00:13:28,280 clusters increased from 50 microns in 301 00:13:36,759 --> 00:13:29,240 diameter 302 00:13:39,999 --> 00:13:36,769 so we did some genome sequencing of 303 00:13:42,129 --> 00:13:40,009 these large 600 transfer isolates those 304 00:13:45,220 --> 00:13:42,139 suggest further genes of interest that 305 00:13:46,720 --> 00:13:45,230 might be costly in a unicellular context 306 00:13:49,299 --> 00:13:46,730 but beneficial in a multicellular 307 00:13:51,249 --> 00:13:49,309 context and many of them are associated 308 00:13:53,489 --> 00:13:51,259 with the phenotypes that we think are 309 00:13:58,869 --> 00:13:53,499 enabling this extremely large size 310 00:14:03,009 --> 00:13:58,879 namely very elongated cells and both 311 00:14:05,049 --> 00:14:03,019 polar and side budding and so if you're 312 00:14:07,389 --> 00:14:05,059 interested in some of these new 313 00:14:09,489 --> 00:14:07,399 candidate genes and the interaction 314 00:14:11,679 --> 00:14:09,499 between those genes we have another 315 00:14:14,410 --> 00:14:11,689 postdoc here also giving a poster this 316 00:14:15,970 --> 00:14:14,420 evening Toni Bernette II and his poster 317 00:14:19,660 --> 00:14:15,980 will be in the Evergreen Ballroom as 318 00:14:22,569 --> 00:14:19,670 well and so in summary our results 319 00:14:25,660 --> 00:14:22,579 suggest this third model where both the 320 00:14:28,210 --> 00:14:25,670 Fitness effects of reversion mutations 321 00:14:31,030 --> 00:14:28,220 as well as the availability of reversion 322 00:14:32,319 --> 00:14:31,040 mutations is changing over evolutionary 323 00:14:35,109 --> 00:14:32,329 time at least for some of our 324 00:14:37,329 --> 00:14:35,119 populations the failure of the 325 00:14:40,059 --> 00:14:37,339 functional ASA to to restore unicellular 326 00:14:44,019 --> 00:14:40,069 T is associated with more dramatic 327 00:14:46,499 --> 00:14:44,029 phenotypic changes and we'll continue 328 00:14:50,859 --> 00:14:46,509 working on this and be doing some direct 329 00:14:52,749 --> 00:14:50,869 pairwise competitions with these more 330 00:14:54,640 --> 00:14:52,759 derived strains in the future so thank