1 00:00:00,820 --> 00:00:08,980 [Music] 2 00:00:18,260 --> 00:00:12,799 Thank You Candace so Pedro makes the 3 00:00:21,050 --> 00:00:18,270 transition to organizers so how why the 4 00:00:25,330 --> 00:00:21,060 more complex life has evolved over time 5 00:00:28,130 --> 00:00:25,340 back in 1995 - made and thought Mary 6 00:00:30,859 --> 00:00:28,140 proposes this can be can be achieved 7 00:00:33,770 --> 00:00:30,869 through a serious major evolutionary 8 00:00:37,040 --> 00:00:33,780 transitions including the origins of 9 00:00:37,869 --> 00:00:37,050 cells genomes genetic code eukaryotes 10 00:00:40,639 --> 00:00:37,879 sex 11 00:00:42,110 --> 00:00:40,649 multicellularity social groups such as 12 00:00:45,920 --> 00:00:42,120 social insects 13 00:00:49,639 --> 00:00:45,930 among those transitions the origin of 14 00:00:53,930 --> 00:00:49,649 motifs errantly and allows opportunities 15 00:00:57,200 --> 00:00:53,940 for further complexity to evolve and led 16 00:01:02,330 --> 00:00:57,210 to more complex life we see today like 17 00:01:05,410 --> 00:01:02,340 animals and plants across the Tree of 18 00:01:08,750 --> 00:01:05,420 Life our earth multicellularity has 19 00:01:12,859 --> 00:01:08,760 originated independently a number of 20 00:01:16,460 --> 00:01:12,869 times the thing we want to know is that 21 00:01:20,929 --> 00:01:16,470 what our minimum necessary steps that 22 00:01:23,690 --> 00:01:20,939 are required at first usually people do 23 00:01:27,280 --> 00:01:23,700 this by comparing those Modi's thermal 24 00:01:29,840 --> 00:01:27,290 images with their unicellular relatives 25 00:01:31,969 --> 00:01:29,850 but the problem is that those things 26 00:01:35,719 --> 00:01:31,979 happened long time ago and many changes 27 00:01:39,560 --> 00:01:35,729 have occurred in the extant species and 28 00:01:45,289 --> 00:01:39,570 which makes a typical to infer infirmity 29 00:01:48,230 --> 00:01:45,299 happens in the beginning so one of those 30 00:01:50,210 --> 00:01:48,240 multicellular lineages it's green algae 31 00:01:54,020 --> 00:01:50,220 which includes bobasa 32 00:01:57,289 --> 00:01:54,030 LG this group of algae contains species 33 00:02:00,050 --> 00:01:57,299 with different levels of complexity from 34 00:02:01,310 --> 00:02:00,060 unicellular Chlamydomonas to fully 35 00:02:05,120 --> 00:02:01,320 differentiated 36 00:02:09,619 --> 00:02:05,130 Volvox so with this unicellular algae 37 00:02:11,500 --> 00:02:09,629 and how we can replay lights - like tape 38 00:02:12,890 --> 00:02:11,510 to see how mori celerity 39 00:02:18,830 --> 00:02:12,900 originates 40 00:02:21,890 --> 00:02:18,840 in the first place the model species of 41 00:02:26,210 --> 00:02:21,900 the unicellular algae is law 42 00:02:29,530 --> 00:02:26,220 chemita Moniz Ryan hardy I it's a model 43 00:02:33,589 --> 00:02:29,540 system for photosynthesis and motility 44 00:02:36,199 --> 00:02:33,599 their vegetative cells or asexual cells 45 00:02:40,699 --> 00:02:36,209 are haploid and their genome has been 46 00:02:42,979 --> 00:02:40,709 sequenced and annotated which make them 47 00:02:45,619 --> 00:02:42,989 a great system to do evolution 48 00:02:47,390 --> 00:02:45,629 experiment experiment ways so that we 49 00:02:52,699 --> 00:02:47,400 can see how they are phenotypes and 50 00:02:56,119 --> 00:02:52,709 genotypes change in real time among the 51 00:02:59,449 --> 00:02:56,129 possible selective pressures and might 52 00:03:02,119 --> 00:02:59,459 generate multicellularity predation has 53 00:03:05,390 --> 00:03:02,129 long been her has hypothesized as a 54 00:03:08,449 --> 00:03:05,400 major cause for motor celerity because 55 00:03:11,420 --> 00:03:08,459 it favors bigger sites such as groups of 56 00:03:12,289 --> 00:03:11,430 cells over single cells so here's a 57 00:03:16,069 --> 00:03:12,299 picture of 58 00:03:16,789 --> 00:03:16,079 Silius and algae those feed her feelings 59 00:03:19,580 --> 00:03:16,799 ciliates 60 00:03:22,670 --> 00:03:19,590 they are also unitl they can feed on 61 00:03:26,270 --> 00:03:22,680 those single-celled algae however if we 62 00:03:29,030 --> 00:03:26,280 continuously exposing those algae to 63 00:03:32,479 --> 00:03:29,040 ciliates and that will generate the 64 00:03:38,170 --> 00:03:32,489 selective pressure to make them become 65 00:03:44,890 --> 00:03:42,289 so a few years ago my advisor matt and 66 00:03:48,199 --> 00:03:44,900 jacob a graduate student in our lab 67 00:03:50,930 --> 00:03:48,209 disease evolution experiments to say 68 00:03:55,520 --> 00:03:50,940 select four multicellularity using 69 00:03:58,849 --> 00:03:55,530 predators so the experiment was started 70 00:04:01,159 --> 00:03:58,859 with out cross population by making two 71 00:04:04,039 --> 00:04:01,169 different algae strands so we have 72 00:04:06,920 --> 00:04:04,049 genetic diversity in the beginning the 73 00:04:09,229 --> 00:04:06,930 starting population was split into five 74 00:04:11,990 --> 00:04:09,239 experimental probation that were 75 00:04:14,240 --> 00:04:12,000 cultured with predators and three 76 00:04:18,050 --> 00:04:14,250 control populations that were cultured 77 00:04:20,810 --> 00:04:18,060 without predators those cultures were 78 00:04:22,159 --> 00:04:20,820 transferred into fresh media every week 79 00:04:26,570 --> 00:04:22,169 over a year 80 00:04:29,899 --> 00:04:26,580 at the end of the experiment we two of 81 00:04:33,230 --> 00:04:29,909 the experimental populations P 2 and P 82 00:04:36,529 --> 00:04:33,240 five population they involve those multi 83 00:04:41,899 --> 00:04:36,539 cellular structures not observed in any 84 00:04:46,760 --> 00:04:41,909 of the control populations so here's a 85 00:04:50,149 --> 00:04:46,770 time-lapse video over 72 hours of a 86 00:04:52,640 --> 00:04:50,159 unicellular algae strip strength so you 87 00:04:54,709 --> 00:04:52,650 can see that when cells go through cell 88 00:04:58,249 --> 00:04:54,719 division there are some little clumsy 89 00:05:06,439 --> 00:04:58,259 but soon after that the total cells will 90 00:05:09,070 --> 00:05:06,449 separate and console division here's one 91 00:05:12,019 --> 00:05:09,080 of our evolved more tea seller strengths 92 00:05:15,469 --> 00:05:12,029 and you can see that the cells stayed 93 00:05:18,980 --> 00:05:15,479 together for a while and then they start 94 00:05:25,820 --> 00:05:18,990 to pop out those smaller multicellular 95 00:05:28,640 --> 00:05:25,830 puppeteers here's another multicellular 96 00:05:32,709 --> 00:05:28,650 strands from the other extreme to 97 00:05:35,420 --> 00:05:32,719 population p5 they also stay together 98 00:05:38,689 --> 00:05:35,430 for a while and then they start to pop 99 00:05:41,149 --> 00:05:38,699 out single motile cells and then they 100 00:05:44,800 --> 00:05:41,159 start to form those multi solar coasters 101 00:05:47,179 --> 00:05:44,810 again so there are these two the 102 00:05:50,360 --> 00:05:47,189 medicinal strands from those two 103 00:05:53,149 --> 00:05:50,370 populations differ in their life cycles 104 00:05:55,490 --> 00:05:53,159 but they both from those structures by 105 00:05:59,570 --> 00:05:55,500 the failure of daughter cells to 106 00:06:01,760 --> 00:05:59,580 separate among cell division and most 107 00:06:03,980 --> 00:06:01,770 importantly they from those structures 108 00:06:05,329 --> 00:06:03,990 in even when their predators are not 109 00:06:07,689 --> 00:06:05,339 around anymore 110 00:06:12,909 --> 00:06:07,699 meaning that there's a genetic basis 111 00:06:19,189 --> 00:06:15,829 so to find out what kind of mutations 112 00:06:22,360 --> 00:06:19,199 happened in those evolved LG LG strands 113 00:06:26,119 --> 00:06:22,370 with it Illumina whole genome sequencing 114 00:06:28,790 --> 00:06:26,129 so for the 2x raindrop operation one 115 00:06:31,699 --> 00:06:28,800 control population we pick up multiple 116 00:06:34,580 --> 00:06:31,709 colonies to do whole genome sequencing 117 00:06:37,280 --> 00:06:34,590 we also sequence the 118 00:06:40,480 --> 00:06:37,290 - ancestral strength to know what 119 00:06:43,430 --> 00:06:40,490 genetic gration existed in the beginning 120 00:06:50,660 --> 00:06:43,440 so that we can know what actually 121 00:06:54,020 --> 00:06:50,670 happened in those evolved algae so what 122 00:06:56,870 --> 00:06:54,030 we found out is that appear that is two 123 00:07:00,200 --> 00:06:56,880 different genetic basis underlying two 124 00:07:03,740 --> 00:07:00,210 origins of multicellularity in your 125 00:07:07,490 --> 00:07:03,750 experiment so here are the mutations 126 00:07:11,180 --> 00:07:07,500 occurring each strand each Royces is an 127 00:07:14,020 --> 00:07:11,190 evolved isolate the first four are from 128 00:07:16,940 --> 00:07:14,030 the beetle permission that about 129 00:07:19,400 --> 00:07:16,950 medicinal properties the next four are 130 00:07:22,940 --> 00:07:19,410 from the be pipe operation that part 131 00:07:25,730 --> 00:07:22,950 about single cell properties and the 132 00:07:28,790 --> 00:07:25,740 last four are come from control 133 00:07:32,120 --> 00:07:28,800 population that are Romanian insulin and 134 00:07:35,540 --> 00:07:32,130 each prize mutation happened in their 135 00:07:39,610 --> 00:07:35,550 genomes and you can see that for the 136 00:07:42,910 --> 00:07:39,620 multicellular isolate in the b2 and b5 137 00:07:46,820 --> 00:07:42,920 populations they shared certain set of 138 00:07:49,910 --> 00:07:46,830 mutation all together suggesting that 139 00:07:56,750 --> 00:07:49,920 those are or any thousand are important 140 00:08:00,710 --> 00:07:56,760 for those medicinal phenotypes so how do 141 00:08:03,530 --> 00:08:00,720 we know that which which mutations are 142 00:08:06,820 --> 00:08:03,540 actually responsible for those most 143 00:08:11,330 --> 00:08:06,830 logical changes with it so these 144 00:08:15,470 --> 00:08:11,340 analyzers code park second analyzes the 145 00:08:19,610 --> 00:08:15,480 idea is that say let free meat mutations 146 00:08:24,290 --> 00:08:19,620 in all evolve isolate and we want to 147 00:08:27,140 --> 00:08:24,300 know whether it's a PC or a BP CAC or 148 00:08:29,930 --> 00:08:27,150 ABC together that are you responsible 149 00:08:33,230 --> 00:08:29,940 for the phenotype you can make it with 150 00:08:35,540 --> 00:08:33,240 the wild-type strength to generate a 151 00:08:38,600 --> 00:08:35,550 group of cells with different 152 00:08:41,900 --> 00:08:38,610 combinations of those mutations and then 153 00:08:44,780 --> 00:08:41,910 we can select full of phenotype we are 154 00:08:48,500 --> 00:08:44,790 interested in put summary cell phenotype 155 00:08:51,610 --> 00:08:48,510 and then those 156 00:08:57,200 --> 00:08:51,620 you know hats associate associated with 157 00:09:00,680 --> 00:08:57,210 phenotype huapi imaged this way so to 158 00:09:03,770 --> 00:09:00,690 actually do this with LG we made it our 159 00:09:07,100 --> 00:09:03,780 evolved multi server phenotypes with the 160 00:09:10,730 --> 00:09:07,110 unicellular strand to generate an actual 161 00:09:14,750 --> 00:09:10,740 population with different combination of 162 00:09:19,060 --> 00:09:14,760 the mutations so some details are multi 163 00:09:22,010 --> 00:09:19,070 seller and something on unit cell and 164 00:09:25,610 --> 00:09:22,020 then we select the multi cellular 165 00:09:27,500 --> 00:09:25,620 phenotype by centrifugation briefly so 166 00:09:31,280 --> 00:09:27,510 that's the bigger one should sink to the 167 00:09:33,650 --> 00:09:31,290 bottom more quickly and then so that the 168 00:09:36,920 --> 00:09:33,660 bottom ones go to the multi seller pool 169 00:09:40,130 --> 00:09:36,930 and the cells aren't have go to the 170 00:09:44,060 --> 00:09:40,140 single cell pool by repeating this for 171 00:09:47,930 --> 00:09:44,070 multiple times we can enrich the multi 172 00:09:51,230 --> 00:09:47,940 seller genotypes and unison what is the 173 00:09:54,770 --> 00:09:51,240 genotypes and then we sequence is two of 174 00:09:56,840 --> 00:09:54,780 pools of cells to see the allele 175 00:10:02,930 --> 00:09:56,850 frequency differences across their whole 176 00:10:05,840 --> 00:10:02,940 genomes and it turns out there are 177 00:10:08,690 --> 00:10:05,850 multiple regions in those LG's genome 178 00:10:11,960 --> 00:10:08,700 responsible for the motor severity 179 00:10:15,830 --> 00:10:11,970 evolved in your lab so it's a result 180 00:10:20,260 --> 00:10:15,840 from the multicellular isolates from the 181 00:10:23,840 --> 00:10:20,270 p2 population the y-axis means that 182 00:10:28,270 --> 00:10:23,850 given the overall allele frequency in 183 00:10:32,210 --> 00:10:28,280 both unicellular and multicellular pool 184 00:10:34,910 --> 00:10:32,220 these allele the transit alia appear in 185 00:10:38,900 --> 00:10:34,920 the MOTC report and the higher the dots 186 00:10:41,240 --> 00:10:38,910 are the laurel urchins are and the more 187 00:10:45,650 --> 00:10:41,250 likely they are associated with the 188 00:10:48,410 --> 00:10:45,660 multi cellular phenotypes so you can see 189 00:10:50,570 --> 00:10:48,420 several peaks here and three of them 190 00:10:52,280 --> 00:10:50,580 also match to the mutations we 191 00:10:57,150 --> 00:10:52,290 identified through whole genome 192 00:11:00,450 --> 00:10:57,160 sequencing approach and then there also 193 00:11:03,090 --> 00:11:00,460 other pics they're suggesting certain 194 00:11:08,190 --> 00:11:03,100 genetic background is important for 195 00:11:12,600 --> 00:11:08,200 those mute new mutations to arise here's 196 00:11:14,820 --> 00:11:12,610 the results from the b5 isolate and the 197 00:11:17,490 --> 00:11:14,830 other population that you bought 198 00:11:20,580 --> 00:11:17,500 multicellularity and we also see a 199 00:11:23,820 --> 00:11:20,590 similar trend so while the pigs match to 200 00:11:27,110 --> 00:11:23,830 one of the mutations we identified well 201 00:11:29,670 --> 00:11:27,120 there are several other pics that 202 00:11:32,480 --> 00:11:29,680 suggesting its ancestral genetic 203 00:11:37,830 --> 00:11:32,490 background plot the mute mutations 204 00:11:40,260 --> 00:11:37,840 responsible for those phenotypes both 205 00:11:44,340 --> 00:11:40,270 these mutations and the why I showed in 206 00:11:47,070 --> 00:11:44,350 the last slide now then they are either 207 00:11:50,730 --> 00:11:47,080 in introns or intergenic regions or none 208 00:11:53,580 --> 00:11:50,740 of them change amino acid sequences so I 209 00:11:58,670 --> 00:11:53,590 still working on what they actually do 210 00:12:02,670 --> 00:11:58,680 to change those phenotypes to summarize 211 00:12:05,490 --> 00:12:02,680 we evolved multicellularity black with 212 00:12:09,240 --> 00:12:05,500 predators we have identified mutations 213 00:12:13,590 --> 00:12:09,250 that ever happened in those evolved 214 00:12:16,910 --> 00:12:13,600 algae appear that is two different 215 00:12:21,180 --> 00:12:16,920 genetic bases undesigned those two 216 00:12:24,120 --> 00:12:21,190 different origins of multicellularity we 217 00:12:28,560 --> 00:12:24,130 are working on the functional relevance 218 00:12:31,800 --> 00:12:28,570 of those mutations at the moment for 219 00:12:34,950 --> 00:12:31,810 future directions now that we have 220 00:12:38,100 --> 00:12:34,960 evolved those simple modular structures 221 00:12:41,940 --> 00:12:38,110 we want to know how cellular 222 00:12:44,520 --> 00:12:41,950 differentiation would evolve and another 223 00:12:46,860 --> 00:12:44,530 postdoc in your lab is working on this 224 00:12:49,170 --> 00:12:46,870 project right now she's pretending a 225 00:12:54,780 --> 00:12:49,180 poster today if you haven't talked to 226 00:12:56,550 --> 00:12:54,790 her you should so what I presented today 227 00:13:00,260 --> 00:12:56,560 is multiple people's work especially 228 00:13:03,450 --> 00:13:00,270 Matt who oversees this whole project and 229 00:13:04,140 --> 00:13:03,460 we also like to paint our funding 230 00:13:09,810 --> 00:13:04,150 sources 231 00:13:09,820 --> 00:13:13,290 [Applause] 232 00:13:13,300 --> 00:13:19,520 we have time for some questions 233 00:13:33,760 --> 00:13:31,220 [Laughter] 234 00:13:36,100 --> 00:13:33,770 [Music] 235 00:13:38,230 --> 00:13:36,110 great talk thank you much I know you're 236 00:13:40,990 --> 00:13:38,240 working on hallucinating the genetic 237 00:13:42,760 --> 00:13:41,000 basis for the drive towards 238 00:13:45,190 --> 00:13:42,770 multicellularity under these 239 00:13:48,430 --> 00:13:45,200 circumstances but do you have any sort 240 00:13:51,700 --> 00:13:48,440 of predictions or hypotheses about what 241 00:13:54,630 --> 00:13:51,710 the phenotypic you know results of these 242 00:13:56,170 --> 00:13:54,640 genetic mutations are what would drive 243 00:14:04,660 --> 00:13:56,180 the con 244 00:14:08,710 --> 00:14:04,670 you know the collective nature can be 245 00:14:12,880 --> 00:14:08,720 something evolving cell lung cell cycle 246 00:14:14,740 --> 00:14:12,890 that one day what usually when cell goes 247 00:14:17,350 --> 00:14:14,750 through division there will from those 248 00:14:21,400 --> 00:14:17,360 two four or eight cell 249 00:14:23,830 --> 00:14:21,410 cops but then something else mutations 250 00:14:28,000 --> 00:14:23,840 might affect this process so that they 251 00:14:28,180 --> 00:14:28,010 couldn't separate after that great thank 252 00:14:32,020 --> 00:14:28,190 you 253 00:14:37,890 --> 00:14:32,030 but they don't look like it's in the 254 00:14:53,470 --> 00:14:49,540 other questions I have a question Green 255 00:14:57,430 --> 00:14:53,480 now is a bicones and all know this other 256 00:15:00,160 --> 00:14:57,440 organism say unique wants so is there 257 00:15:04,180 --> 00:15:00,170 any way that you can try to do any 258 00:15:08,320 --> 00:15:04,190 Studies on some single seller unicorns 259 00:15:10,500 --> 00:15:08,330 and try to see if you can do sort of 260 00:15:15,500 --> 00:15:10,510 explore the origin of multicellularity 261 00:15:30,550 --> 00:15:18,710 it's a fool major clades of eukaryotic