1 00:00:03,409 --> 00:00:01,340 well good morning or good afternoon 2 00:00:05,090 --> 00:00:03,419 depending upon what time zone you're in 3 00:00:11,000 --> 00:00:05,100 I don't think we have any good evenings 4 00:00:13,999 --> 00:00:11,010 at the moment and I would just like to 5 00:00:16,010 --> 00:00:14,009 first of all thank Estelle and Marco for 6 00:00:17,960 --> 00:00:16,020 getting this all set up Estelle 7 00:00:19,460 --> 00:00:17,970 particularly for all her past service 8 00:00:21,529 --> 00:00:19,470 and doing this in Marco for all his 9 00:00:24,050 --> 00:00:21,539 current and future service in doing this 10 00:00:27,170 --> 00:00:24,060 and I'm sure that we are going to 11 00:00:28,849 --> 00:00:27,180 continue to have a very technically 12 00:00:32,089 --> 00:00:28,859 successful as well as scientifically 13 00:00:35,209 --> 00:00:32,099 successful a series of director seminars 14 00:00:37,819 --> 00:00:35,219 I'm also really really thrilled as the 15 00:00:40,880 --> 00:00:37,829 new director to be able to kick off this 16 00:00:44,240 --> 00:00:40,890 seminar series with a great talk by a 17 00:00:47,209 --> 00:00:44,250 great speaker I'm particularly excited 18 00:00:50,209 --> 00:00:47,219 about what Julie is going to talk about 19 00:00:52,189 --> 00:00:50,219 not only because it really is an 20 00:00:56,840 --> 00:00:52,199 important advance in our understanding 21 00:01:01,880 --> 00:00:56,850 of the microbial population of the 22 00:01:05,390 --> 00:01:01,890 oceans as a whole but also because this 23 00:01:10,340 --> 00:01:05,400 piece of work links together two very 24 00:01:12,200 --> 00:01:10,350 important things one is the things the 25 00:01:14,480 --> 00:01:12,210 timescales in particular that are 26 00:01:16,760 --> 00:01:14,490 important to astrobiology that is 27 00:01:19,280 --> 00:01:16,770 evolutionary time scales and it links 28 00:01:20,840 --> 00:01:19,290 those evolutionary timescales to the 29 00:01:23,120 --> 00:01:20,850 very short time scales that are of 30 00:01:25,429 --> 00:01:23,130 interest to nasa's arts science program 31 00:01:29,719 --> 00:01:25,439 in global change and we're going to hear 32 00:01:32,630 --> 00:01:29,729 about that from Julie Julie got her PhD 33 00:01:36,319 --> 00:01:32,640 in oceanography from the University of 34 00:01:38,420 --> 00:01:36,329 Washington in 2004 that followed her 35 00:01:40,969 --> 00:01:38,430 bachelor's in 1998 in marine science 36 00:01:43,780 --> 00:01:40,979 from eckerd college and the Masters from 37 00:01:49,120 --> 00:01:43,790 the University of Washington and 2000 38 00:01:51,620 --> 00:01:49,130 her dissertation title at u-dub was 39 00:01:54,109 --> 00:01:51,630 phylogenetic and physiological diversity 40 00:01:56,420 --> 00:01:54,119 of subsea floor microbial communities at 41 00:01:58,760 --> 00:01:56,430 deep sea sea mounts and that has in fact 42 00:02:00,620 --> 00:01:58,770 continued to be her principal research 43 00:02:02,209 --> 00:02:00,630 interest the work she's going to be 44 00:02:06,020 --> 00:02:02,219 talking about today was published 45 00:02:08,990 --> 00:02:06,030 recently in pnas in a paper by Mitch 46 00:02:10,109 --> 00:02:09,000 silgan and several other authors her 47 00:02:13,559 --> 00:02:10,119 talk today 48 00:02:16,140 --> 00:02:13,569 is co-authored by Mitch Hillary Morrison 49 00:02:18,330 --> 00:02:16,150 David Mark Welch sue Hughes and I 50 00:02:20,940 --> 00:02:18,340 apologize if I got the pronunciation of 51 00:02:22,949 --> 00:02:20,950 Sue's name wrong and Phil Neal and her 52 00:02:25,860 --> 00:02:22,959 title is microbial diversity in the deep 53 00:02:27,780 --> 00:02:25,870 sea and the underexplored rare biosphere 54 00:02:33,119 --> 00:02:27,790 and without further ado I turn it over 55 00:02:34,770 --> 00:02:33,129 to Julie all right thanks Carl I also 56 00:02:36,660 --> 00:02:34,780 want to thank you for the invitation to 57 00:02:39,780 --> 00:02:36,670 give this talk today and I especially 58 00:02:41,670 --> 00:02:39,790 want to thank Estelle and Marco the IT 59 00:02:44,339 --> 00:02:41,680 people here in Woods Hole it's been a 60 00:02:46,589 --> 00:02:44,349 pretty traumatic last couple of days 61 00:02:48,330 --> 00:02:46,599 without internet or email and I really 62 00:02:49,860 --> 00:02:48,340 appreciate all the efforts and a special 63 00:02:51,330 --> 00:02:49,870 the National Academy where I have a 64 00:02:53,970 --> 00:02:51,340 beautiful view of the ocean while I'm 65 00:02:56,460 --> 00:02:53,980 giving this talk I also want to thank my 66 00:02:58,589 --> 00:02:56,470 co-authors here at the NBL as Carl just 67 00:03:03,030 --> 00:02:58,599 mentioned and I think I'll just jump 68 00:03:04,440 --> 00:03:03,040 right in from there see so this is a 69 00:03:06,300 --> 00:03:04,450 brief outline of what I'm going to be 70 00:03:09,030 --> 00:03:06,310 talking about today I'm going to give 71 00:03:11,069 --> 00:03:09,040 you an overview of our motivation for 72 00:03:12,780 --> 00:03:11,079 doing this work and then spend a good 73 00:03:15,420 --> 00:03:12,790 bit of time talking about the approach 74 00:03:17,640 --> 00:03:15,430 through use this is a 454 tag sequencing 75 00:03:19,530 --> 00:03:17,650 approach which I'll talk about I'll also 76 00:03:21,420 --> 00:03:19,540 discuss sort of how we crunch our data 77 00:03:24,120 --> 00:03:21,430 since it's really integral part of 78 00:03:26,250 --> 00:03:24,130 understanding the system I'll go through 79 00:03:28,440 --> 00:03:26,260 some results discussion and implications 80 00:03:30,900 --> 00:03:28,450 and as Carl mentioned I'll talk about 81 00:03:34,650 --> 00:03:30,910 some future applications especially as 82 00:03:35,849 --> 00:03:34,660 they relate to earth sciences at NASA so 83 00:03:37,830 --> 00:03:35,859 we all know that we live on planet 84 00:03:40,589 --> 00:03:37,840 microbe and that they're the primary 85 00:03:43,439 --> 00:03:40,599 engines of Earth's biosphere that 86 00:03:45,960 --> 00:03:43,449 microbes mediate biogeochemical cycles 87 00:03:47,460 --> 00:03:45,970 that shape planetary habitability we 88 00:03:49,409 --> 00:03:47,470 know that microbes were likely the only 89 00:03:52,470 --> 00:03:49,419 form of life for most of our biological 90 00:03:54,900 --> 00:03:52,480 history and we know that microbial 91 00:03:56,849 --> 00:03:54,910 communities of untold diversity continue 92 00:03:59,879 --> 00:03:56,859 to dominate nearly every corner of our 93 00:04:02,009 --> 00:03:59,889 biosphere so understanding microbial 94 00:04:04,229 --> 00:04:02,019 diversity and community structure than 95 00:04:08,789 --> 00:04:04,239 our key to understanding our biosphere 96 00:04:10,379 --> 00:04:08,799 our habitability and our history I'm an 97 00:04:13,500 --> 00:04:10,389 oceanographer so today I'm going to be 98 00:04:15,479 --> 00:04:13,510 focusing on microbes in the sea and if 99 00:04:18,300 --> 00:04:15,489 we look at the relative abundance and 100 00:04:20,780 --> 00:04:18,310 productivity of marine life we can see 101 00:04:23,450 --> 00:04:20,790 that the smallest class 102 00:04:25,280 --> 00:04:23,460 organisms in the ocean the prokaryotes 103 00:04:27,860 --> 00:04:25,290 which are less than three micrometers in 104 00:04:30,470 --> 00:04:27,870 size make up almost eighty-two percent 105 00:04:32,390 --> 00:04:30,480 of the biomass in the ocean and in fact 106 00:04:35,570 --> 00:04:32,400 they account for over ninety percent of 107 00:04:38,440 --> 00:04:35,580 the primary productivity in the sea so 108 00:04:41,060 --> 00:04:38,450 if we actually take a look at what a 109 00:04:43,160 --> 00:04:41,070 some sea water looks like if you filter 110 00:04:45,650 --> 00:04:43,170 it and you stain it with a nucleic acid 111 00:04:48,500 --> 00:04:45,660 stain and take a look us is sort of what 112 00:04:51,710 --> 00:04:48,510 you sort of what you see and in fact 113 00:04:54,740 --> 00:04:51,720 these larger objects here are protists 114 00:04:56,780 --> 00:04:54,750 these of the bigger of the small green 115 00:04:59,600 --> 00:04:56,790 dots or bacteria and these very small 116 00:05:02,120 --> 00:04:59,610 ones in the background are viruses and 117 00:05:03,770 --> 00:05:02,130 in fact almost any place you look in the 118 00:05:07,280 --> 00:05:03,780 ocean you're going to get a picture like 119 00:05:09,380 --> 00:05:07,290 this and the amount of biomass in the 120 00:05:11,810 --> 00:05:09,390 ocean that is microbial is absolutely 121 00:05:15,110 --> 00:05:11,820 tremendous and Earth's by some estimates 122 00:05:19,610 --> 00:05:15,120 on an order of 10 to the 29th microbial 123 00:05:22,640 --> 00:05:19,620 cells in the ocean so I'm good you know 124 00:05:25,250 --> 00:05:22,650 right person somebody has right yeah 125 00:05:28,040 --> 00:05:25,260 you're getting close please all the 126 00:05:30,350 --> 00:05:28,050 polycom sites mute your microphones and 127 00:05:33,800 --> 00:05:30,360 Mike Fitzgerald if you would mute the 128 00:05:35,630 --> 00:05:33,810 conference I'll take care of it we have 129 00:05:44,300 --> 00:05:35,640 some open mics Berkeley I think your 130 00:05:47,960 --> 00:05:44,310 bike is open Thanks okay and Mike you 131 00:05:51,440 --> 00:05:47,970 okay Julie you should be ok but we I 132 00:05:53,000 --> 00:05:51,450 hear I hear someone coming in so if 133 00:05:57,649 --> 00:05:53,010 everyone can double-check we make sure 134 00:06:06,110 --> 00:06:01,159 go ahead Jill is not you look like 135 00:06:14,600 --> 00:06:12,119 okay yeah I keep I keep seeing Berkeley 136 00:06:19,589 --> 00:06:14,610 on my end I don't know why it may be a 137 00:06:21,749 --> 00:06:19,599 software problem okay so as I was saying 138 00:06:23,129 --> 00:06:21,759 the amount of cells in the ocean is 139 00:06:25,320 --> 00:06:23,139 absolutely enormous and it's on the 140 00:06:28,309 --> 00:06:25,330 order of somewhere around 10 to the 29th 141 00:06:30,779 --> 00:06:28,319 microbial cells and if we take a look at 142 00:06:33,450 --> 00:06:30,789 microbial interactions the ocean this is 143 00:06:35,879 --> 00:06:33,460 just a schematic showing some of the 144 00:06:38,490 --> 00:06:35,889 sources or ways of primary productivity 145 00:06:40,589 --> 00:06:38,500 in the sea from heterotroph e2 photo 146 00:06:42,779 --> 00:06:40,599 auto trophy and chemo autotrophs and 147 00:06:44,129 --> 00:06:42,789 while we have a pretty good sense of 148 00:06:47,010 --> 00:06:44,139 what's happening in the upper water 149 00:06:48,480 --> 00:06:47,020 column where photosynthesis dominates we 150 00:06:50,730 --> 00:06:48,490 have much less knowledge about what's 151 00:06:52,649 --> 00:06:50,740 happening in the deep sea for example in 152 00:06:54,480 --> 00:06:52,659 the bath people ajik in other zones and 153 00:06:56,790 --> 00:06:54,490 in fact when it comes to really 154 00:06:58,679 --> 00:06:56,800 microbial diversity community structure 155 00:07:00,719 --> 00:06:58,689 and how these populations change over 156 00:07:04,140 --> 00:07:00,729 time everywhere in the ocean we're 157 00:07:06,119 --> 00:07:04,150 lacking a great deal of knowledge so 158 00:07:08,219 --> 00:07:06,129 this lack of knowledge is really what 159 00:07:09,749 --> 00:07:08,229 motivated the formation of icon which is 160 00:07:12,749 --> 00:07:09,759 the international census of marine 161 00:07:15,209 --> 00:07:12,759 microbes and the goal of icon is to 162 00:07:17,730 --> 00:07:15,219 report what is known what is unknowable 163 00:07:19,050 --> 00:07:17,740 but knowable and what may be unknowable 164 00:07:22,740 --> 00:07:19,060 about the diversity of marine 165 00:07:25,439 --> 00:07:22,750 microorganisms by the year 2010 and the 166 00:07:27,450 --> 00:07:25,449 goal of our particular icon project is 167 00:07:29,399 --> 00:07:27,460 to determine the range of genetic 168 00:07:31,050 --> 00:07:29,409 diversity and relative numbers of 169 00:07:33,059 --> 00:07:31,060 different microbial organisms at 170 00:07:35,459 --> 00:07:33,069 sampling sites throughout the world's 171 00:07:37,649 --> 00:07:35,469 ocean so this project is headed up by 172 00:07:39,600 --> 00:07:37,659 Mitch here at the MBL and yonder Lou 173 00:07:41,010 --> 00:07:39,610 who's in the Netherlands and it's 174 00:07:42,929 --> 00:07:41,020 supported through the census of marine 175 00:07:46,050 --> 00:07:42,939 life program which is funded by the 176 00:07:47,879 --> 00:07:46,060 sloan foundation so over the past couple 177 00:07:51,659 --> 00:07:47,889 of years a number of working groups have 178 00:07:53,040 --> 00:07:51,669 gotten together for icon and have put 179 00:07:54,749 --> 00:07:53,050 together okay if we're going to do the 180 00:07:57,390 --> 00:07:54,759 census how are we going to do it and 181 00:08:00,029 --> 00:07:57,400 they've determined that determining the 182 00:08:02,579 --> 00:08:00,039 number of different types of organisms 183 00:08:04,310 --> 00:08:02,589 and their community composition is an 184 00:08:06,740 --> 00:08:04,320 essential first step in 185 00:08:08,570 --> 00:08:06,750 and they also decided that the metric 186 00:08:10,790 --> 00:08:08,580 for the census needs to be molecular 187 00:08:13,190 --> 00:08:10,800 this is not to say there isn't a place 188 00:08:14,840 --> 00:08:13,200 for culturing in the census but simply 189 00:08:16,850 --> 00:08:14,850 that it's unrealistic to think that we 190 00:08:20,210 --> 00:08:16,860 can catalog all of microbial life in the 191 00:08:22,610 --> 00:08:20,220 ocean using culturing alone and this is 192 00:08:24,560 --> 00:08:22,620 just exemplified by if we look simply at 193 00:08:27,530 --> 00:08:24,570 the bacterial known phylogenetic 194 00:08:29,840 --> 00:08:27,540 divisions over the last 15 years this is 195 00:08:33,100 --> 00:08:29,850 a figure from Noren paces lab you can 196 00:08:35,510 --> 00:08:33,110 see that in 2004 over half of the known 197 00:08:38,029 --> 00:08:35,520 bacterial phylogenetic divisions are 198 00:08:39,770 --> 00:08:38,039 represented only by sequences and no 199 00:08:43,880 --> 00:08:39,780 cultures and so it's very important that 200 00:08:45,920 --> 00:08:43,890 we use a molecular metric obviously the 201 00:08:48,460 --> 00:08:45,930 ocean is big there's a lot of cells and 202 00:08:51,410 --> 00:08:48,470 the census is a big job even if its most 203 00:08:53,330 --> 00:08:51,420 simple form conventional technology will 204 00:08:55,250 --> 00:08:53,340 be insufficient and it will clearly 205 00:08:56,870 --> 00:08:55,260 require the development of new 206 00:09:00,320 --> 00:08:56,880 computational tools which I'll be 207 00:09:02,540 --> 00:09:00,330 talking about today so we discussed some 208 00:09:04,520 --> 00:09:02,550 possible solutions and of course the 209 00:09:06,350 --> 00:09:04,530 first one we talked about was the one 210 00:09:09,430 --> 00:09:06,360 many of us are familiar with and that is 211 00:09:12,200 --> 00:09:09,440 DNA sequencing of either pcr-amplified 212 00:09:14,300 --> 00:09:12,210 and cloning or foz midst of some sort 213 00:09:16,670 --> 00:09:14,310 but really this technology is still 214 00:09:19,850 --> 00:09:16,680 pretty expensive depending on what your 215 00:09:22,160 --> 00:09:19,860 lab can put out and that that cost 216 00:09:24,140 --> 00:09:22,170 constraint also then constrains the size 217 00:09:25,910 --> 00:09:24,150 of our current surveys to less than 218 00:09:27,440 --> 00:09:25,920 about a thousand sequences from an 219 00:09:29,510 --> 00:09:27,450 environment and that really only 220 00:09:31,790 --> 00:09:29,520 captures a fraction of the community 221 00:09:33,560 --> 00:09:31,800 structure in addition because you're 222 00:09:35,480 --> 00:09:33,570 only looking at a thousand clones it can 223 00:09:38,120 --> 00:09:35,490 be difficult to detect underrepresented 224 00:09:40,310 --> 00:09:38,130 members of the community and it's also 225 00:09:41,840 --> 00:09:40,320 still pretty labor-intensive even if you 226 00:09:44,600 --> 00:09:41,850 have robots doing a lot of work for you 227 00:09:47,240 --> 00:09:44,610 it can still be pretty tough we also 228 00:09:49,190 --> 00:09:47,250 discussed using DNA microarrays but 229 00:09:51,650 --> 00:09:49,200 really you're limited by what is spotted 230 00:09:53,420 --> 00:09:51,660 or printed on your array we discussed a 231 00:09:54,770 --> 00:09:53,430 lot of other solutions but what we came 232 00:09:56,780 --> 00:09:54,780 up with and what I'll be talking about 233 00:09:58,400 --> 00:09:56,790 today is a tag sequencing strategy 234 00:10:02,060 --> 00:09:58,410 because we believe it has greater 235 00:10:04,820 --> 00:10:02,070 throughput and reduced costs so a number 236 00:10:07,670 --> 00:10:04,830 of investigators have examined using the 237 00:10:09,890 --> 00:10:07,680 small subunit ribosomal RNA for tag 238 00:10:11,930 --> 00:10:09,900 sequencing strategies and they focus 239 00:10:13,750 --> 00:10:11,940 mainly on hyper variable regions of the 240 00:10:16,330 --> 00:10:13,760 ribosomal structure and 241 00:10:18,160 --> 00:10:16,340 are shown here in red so this is the 242 00:10:20,620 --> 00:10:18,170 secondary structure and in it we have 243 00:10:22,990 --> 00:10:20,630 both conserved regions and variable 244 00:10:25,660 --> 00:10:23,000 regions and based on our own experience 245 00:10:28,510 --> 00:10:25,670 in our lab we chose to target this v6 246 00:10:30,520 --> 00:10:28,520 region of the ribosomal gene and so this 247 00:10:33,430 --> 00:10:30,530 by looking at one part of the gene we 248 00:10:35,350 --> 00:10:33,440 can infer taxonomic identity so we 249 00:10:37,390 --> 00:10:35,360 design primers which are shown here in 250 00:10:39,970 --> 00:10:37,400 yellow that are just to the highly 251 00:10:42,160 --> 00:10:39,980 conserved bacterial group we didn't even 252 00:10:46,060 --> 00:10:42,170 look at archaea in our initial our 253 00:10:47,200 --> 00:10:46,070 initial experiments we then applied what 254 00:10:49,780 --> 00:10:47,210 I'm going to be explaining which is 255 00:10:51,730 --> 00:10:49,790 called a 454 tag sequencing approach and 256 00:10:54,640 --> 00:10:51,740 this is just a basic environmental DNA 257 00:10:56,350 --> 00:10:54,650 pcr reaction where you take your DNA 258 00:10:58,540 --> 00:10:56,360 from seawater or whatever sample you're 259 00:11:01,180 --> 00:10:58,550 interested in and you amplify it with 260 00:11:03,490 --> 00:11:01,190 these v6 specific primers but 261 00:11:05,350 --> 00:11:03,500 synthesized onto these primers are these 262 00:11:06,970 --> 00:11:05,360 life science adapters and I'm going to 263 00:11:10,060 --> 00:11:06,980 explain why are those and why those are 264 00:11:13,270 --> 00:11:10,070 important in just a moment so we perform 265 00:11:16,000 --> 00:11:13,280 a PCR with these primers and we what we 266 00:11:18,820 --> 00:11:16,010 end up with is a v6 amplicon library and 267 00:11:21,610 --> 00:11:18,830 we sent that off to 454 life sciences 268 00:11:23,080 --> 00:11:21,620 which is located in Connecticut and what 269 00:11:25,770 --> 00:11:23,090 they give back to you is a number of 270 00:11:27,790 --> 00:11:25,780 tags from your different samples and 271 00:11:30,010 --> 00:11:27,800 because this is a relatively new 272 00:11:32,350 --> 00:11:30,020 technology and I know that quite a few 273 00:11:34,660 --> 00:11:32,360 people in the astrobiological community 274 00:11:36,250 --> 00:11:34,670 are actually using it I thought I would 275 00:11:38,740 --> 00:11:36,260 just briefly review how this process 276 00:11:41,110 --> 00:11:38,750 works it's also very different from 277 00:11:43,480 --> 00:11:41,120 traditional capillary DNA sequencing 278 00:11:46,000 --> 00:11:43,490 methods so I also thought I would 279 00:11:47,980 --> 00:11:46,010 explain it so these first two steps most 280 00:11:50,380 --> 00:11:47,990 of us are familiar with where we extract 281 00:11:52,870 --> 00:11:50,390 environmental DNA we amplify them with 282 00:11:55,840 --> 00:11:52,880 our specific primers of interest and in 283 00:11:57,460 --> 00:11:55,850 a normal microbial ecology lab from this 284 00:12:00,130 --> 00:11:57,470 stuff you would go on to cloning and 285 00:12:01,930 --> 00:12:00,140 sanger sequencing the 454 sequencing 286 00:12:04,240 --> 00:12:01,940 approach basically eliminates that 287 00:12:06,970 --> 00:12:04,250 cloning step so what you have is your 288 00:12:09,250 --> 00:12:06,980 pool of DNA amplicons your pcr amplified 289 00:12:11,140 --> 00:12:09,260 and these products are then denatured 290 00:12:13,030 --> 00:12:11,150 and a single strand is recovered and 291 00:12:15,490 --> 00:12:13,040 it's immobilized onto these very small 292 00:12:17,560 --> 00:12:15,500 beads that have a complementary primer 293 00:12:20,170 --> 00:12:17,570 to that a and B adapter that are 294 00:12:22,240 --> 00:12:20,180 synthesized to our own primers and these 295 00:12:25,600 --> 00:12:22,250 beads are then deposited and put into an 296 00:12:26,910 --> 00:12:25,610 oil-water emulsification pcr and that 297 00:12:29,220 --> 00:12:26,920 generates millions 298 00:12:32,610 --> 00:12:29,230 copies of that single DNA strand on each 299 00:12:34,440 --> 00:12:32,620 feed these this reaction is then 300 00:12:36,150 --> 00:12:34,450 denatured and they're putting to 301 00:12:38,490 --> 00:12:36,160 something called a picot tighter plate 302 00:12:40,620 --> 00:12:38,500 and these plates were developed just for 303 00:12:44,009 --> 00:12:40,630 this technology they have about I think 304 00:12:46,139 --> 00:12:44,019 1.2 million wells in the picot tighter 305 00:12:48,480 --> 00:12:46,149 plate and each one is just big enough to 306 00:12:51,030 --> 00:12:48,490 fit one bead and your sequencing 307 00:12:53,400 --> 00:12:51,040 reaction reagents then solid-phase 308 00:12:54,960 --> 00:12:53,410 pyrosequencing occurs I'm not going to 309 00:12:56,790 --> 00:12:54,970 go through that in a lot of detail but 310 00:12:59,370 --> 00:12:56,800 basically when a base is incorporated a 311 00:13:01,710 --> 00:12:59,380 flash of light occurs and in the end you 312 00:13:03,990 --> 00:13:01,720 get about 200 thousand reads per 313 00:13:06,269 --> 00:13:04,000 sequencing run and their average length 314 00:13:08,220 --> 00:13:06,279 is about a hundred base pairs and the 315 00:13:11,370 --> 00:13:08,230 cost is somewhere around two or five 316 00:13:13,530 --> 00:13:11,380 cents per read so remember the goal of 317 00:13:15,300 --> 00:13:13,540 this is really to identify organisms in 318 00:13:18,120 --> 00:13:15,310 an environmental sample we could so we 319 00:13:20,160 --> 00:13:18,130 can do a census so the key really is to 320 00:13:22,050 --> 00:13:20,170 identify the known universe and we've 321 00:13:23,939 --> 00:13:22,060 spent a lot of time building a reference 322 00:13:26,310 --> 00:13:23,949 data set so we can understand what we're 323 00:13:28,050 --> 00:13:26,320 studying so to do this we collected 324 00:13:30,540 --> 00:13:28,060 full-length ribosomal sequences that 325 00:13:32,220 --> 00:13:30,550 contain this v6 region we collected them 326 00:13:33,870 --> 00:13:32,230 from four major sources that are 327 00:13:36,449 --> 00:13:33,880 available freely on the internet and 328 00:13:39,360 --> 00:13:36,459 then we put them through the RDP the 329 00:13:41,009 --> 00:13:39,370 ribosomal database classifier to get 330 00:13:43,230 --> 00:13:41,019 taxonomy many of them are from 331 00:13:46,620 --> 00:13:43,240 uncultured bacteria have no affiliation 332 00:13:49,650 --> 00:13:46,630 no taxonomy and the identifiers and from 333 00:13:51,960 --> 00:13:49,660 those no one full-length sequences we 334 00:13:53,880 --> 00:13:51,970 extracted just the v6 so what we were 335 00:13:57,420 --> 00:13:53,890 able to do is build a database that has 336 00:14:00,120 --> 00:13:57,430 a full-length ribosomal sequence with an 337 00:14:02,310 --> 00:14:00,130 Associated v6 and a knowing taxonomy and 338 00:14:06,449 --> 00:14:02,320 those are included in both a blast able 339 00:14:08,280 --> 00:14:06,459 format and a searchable SQL database we 340 00:14:10,590 --> 00:14:08,290 then take our data through a number of 341 00:14:12,210 --> 00:14:10,600 different steps in the first that I just 342 00:14:15,059 --> 00:14:12,220 want to focus on right now is this 343 00:14:18,120 --> 00:14:15,069 quality control this is a very new 344 00:14:21,210 --> 00:14:18,130 technology and we've spent a lot of time 345 00:14:24,000 --> 00:14:21,220 trying to understand exactly how to 346 00:14:27,389 --> 00:14:24,010 evaluate it in terms of being able to 347 00:14:29,699 --> 00:14:27,399 remove low-quality reads it's important 348 00:14:31,860 --> 00:14:29,709 to note that this is very 349 00:14:33,389 --> 00:14:31,870 computationally expensive and so we've 350 00:14:35,730 --> 00:14:33,399 had to develop a lot of new tools to 351 00:14:37,590 --> 00:14:35,740 properly evaluate this and because of 352 00:14:37,910 --> 00:14:37,600 the general audience of my talk we're 353 00:14:41,090 --> 00:14:37,920 not 354 00:14:42,530 --> 00:14:41,100 talking about the details but we 355 00:14:44,210 --> 00:14:42,540 actually believe the error sequencing 356 00:14:46,790 --> 00:14:44,220 rate is quite low and if anybody has any 357 00:14:48,379 --> 00:14:46,800 questions about that at the end I'd be 358 00:14:51,019 --> 00:14:48,389 happy to answer them we've done a number 359 00:14:52,519 --> 00:14:51,029 of control experiments remember that 360 00:14:53,600 --> 00:14:52,529 we're going into an environment where we 361 00:14:56,389 --> 00:14:53,610 don't really know what we're going to 362 00:14:58,610 --> 00:14:56,399 find we can't benefit from like when you 363 00:15:00,860 --> 00:14:58,620 sequence the genome and you get 10 20 30 364 00:15:04,540 --> 00:15:00,870 X coverage so we're really trying to 365 00:15:09,199 --> 00:15:04,550 determine how well we can trust our data 366 00:15:10,759 --> 00:15:09,209 so i guess i'll go through these two 367 00:15:12,439 --> 00:15:10,769 different pipelines when we get to the 368 00:15:14,509 --> 00:15:12,449 data but first i want to talk to you 369 00:15:18,170 --> 00:15:14,519 about the pilot study that we performed 370 00:15:20,389 --> 00:15:18,180 for icon and we collected samples from 371 00:15:21,889 --> 00:15:20,399 two sets of collaborators and the first 372 00:15:24,530 --> 00:15:21,899 were our colleagues in the netherlands 373 00:15:26,629 --> 00:15:24,540 and these are transat samples from 374 00:15:28,220 --> 00:15:26,639 cruises in the North Atlantic the goal 375 00:15:30,110 --> 00:15:28,230 of these cruises is to track the 376 00:15:33,139 --> 00:15:30,120 formation of North Atlantic deep water 377 00:15:35,360 --> 00:15:33,149 from its source through through the 378 00:15:37,579 --> 00:15:35,370 oceanic conveyor belt and these yellow 379 00:15:40,129 --> 00:15:37,589 and red dots are two truce cruise tracks 380 00:15:42,019 --> 00:15:40,139 they've had and we chose just three 381 00:15:44,750 --> 00:15:42,029 sampling sites shown here in white and 382 00:15:46,970 --> 00:15:44,760 from each of those sites we chose both a 383 00:15:50,300 --> 00:15:46,980 sample from the oxygen minimum zone and 384 00:15:52,310 --> 00:15:50,310 also at greater depths in the ocean for 385 00:15:54,079 --> 00:15:52,320 our second study site which has a very 386 00:15:55,910 --> 00:15:54,089 different deep-sea setting and that is 387 00:15:59,120 --> 00:15:55,920 my own interest deep sea hydrothermal 388 00:16:01,280 --> 00:15:59,130 vents in this case axial seamount which 389 00:16:04,280 --> 00:16:01,290 is located about 300 miles off the coast 390 00:16:07,579 --> 00:16:04,290 of oregon on the Juan de Fuca mid-ocean 391 00:16:11,000 --> 00:16:07,589 ridge spreading Center and from axial we 392 00:16:12,470 --> 00:16:11,010 chose to particular diffuse flow events 393 00:16:13,819 --> 00:16:12,480 and i'm just showing one of them here 394 00:16:15,470 --> 00:16:13,829 which I'll be mentioning quite a bit 395 00:16:17,600 --> 00:16:15,480 because it's our largest data set and 396 00:16:20,060 --> 00:16:17,610 what you're looking at this is a diffuse 397 00:16:22,340 --> 00:16:20,070 flovent so there's warm fluids about 25 398 00:16:24,829 --> 00:16:22,350 degrees C leaking out at a sea floor you 399 00:16:27,199 --> 00:16:24,839 can see our sampler inside the vent 400 00:16:29,809 --> 00:16:27,209 trying to catch that flow and a bunch of 401 00:16:31,970 --> 00:16:29,819 charismatic macro phone around you can 402 00:16:33,590 --> 00:16:31,980 see two worms and limpets this is some 403 00:16:35,900 --> 00:16:33,600 stream ciliate that we don't really 404 00:16:39,680 --> 00:16:35,910 understand and this white matter is 405 00:16:42,620 --> 00:16:39,690 microbial mats so this is just a summary 406 00:16:44,930 --> 00:16:42,630 of those of those samples you can see 407 00:16:46,850 --> 00:16:44,940 the Payard transat samples from the 408 00:16:48,800 --> 00:16:46,860 north atlantic deep water each one from 409 00:16:51,199 --> 00:16:48,810 a different depth and then the to 410 00:16:55,249 --> 00:16:51,209 diffuse flow samples which are between 411 00:16:57,889 --> 00:16:55,259 25 and 30 degrees Celsius so the first 412 00:16:59,960 --> 00:16:57,899 part of our data processing when we got 413 00:17:01,879 --> 00:16:59,970 this data back I guess it was very very 414 00:17:03,279 --> 00:17:01,889 early this year I think it was two days 415 00:17:05,899 --> 00:17:03,289 after Christmas or something like that 416 00:17:07,579 --> 00:17:05,909 was we took all of our data and we 417 00:17:10,279 --> 00:17:07,589 blasted it against our reference 418 00:17:12,889 --> 00:17:10,289 database and you can use this side of 419 00:17:15,139 --> 00:17:12,899 the pipeline to get to the taxonomy so 420 00:17:17,449 --> 00:17:15,149 you you blast all of your sequences 421 00:17:19,549 --> 00:17:17,459 against the reference database and then 422 00:17:22,370 --> 00:17:19,559 you take your query and you align it 423 00:17:24,710 --> 00:17:22,380 with the top 250 best scoring sequences 424 00:17:27,049 --> 00:17:24,720 using a program called muscle and from 425 00:17:29,510 --> 00:17:27,059 that you can calculate the distances and 426 00:17:30,769 --> 00:17:29,520 identify the minimum the minimum 427 00:17:32,779 --> 00:17:30,779 distance and that helps you get the 428 00:17:35,960 --> 00:17:32,789 closest identify identity in your 429 00:17:39,019 --> 00:17:35,970 reference database you can also take 430 00:17:41,690 --> 00:17:39,029 that top last hit and use that as sort 431 00:17:43,549 --> 00:17:41,700 of an estimate of diversity in your 432 00:17:45,560 --> 00:17:43,559 sample so if you have a hundred 433 00:17:47,990 --> 00:17:45,570 sequences and eighty of them blast to 434 00:17:50,120 --> 00:17:48,000 one sequence and 22 the other you can 435 00:17:51,769 --> 00:17:50,130 try to get a rough estimate of diversity 436 00:17:53,360 --> 00:17:51,779 and that was the first thing we did 437 00:17:55,760 --> 00:17:53,370 simply because we didn't really know 438 00:17:58,190 --> 00:17:55,770 what else to do and so this is the first 439 00:18:00,500 --> 00:17:58,200 result that we got back when we did did 440 00:18:02,750 --> 00:18:00,510 that experiment so these are rare 441 00:18:04,730 --> 00:18:02,760 faction curves for those of you who 442 00:18:06,590 --> 00:18:04,740 aren't familiar with them they basically 443 00:18:09,760 --> 00:18:06,600 allow you to compare richness among 444 00:18:12,649 --> 00:18:09,770 samples that have been sampled at 445 00:18:14,930 --> 00:18:12,659 unequally and the Kurds result from 446 00:18:17,299 --> 00:18:14,940 basically averaging randomizations of 447 00:18:18,830 --> 00:18:17,309 the observed accumulation curve and so 448 00:18:20,659 --> 00:18:18,840 these are not an actual measure of 449 00:18:22,639 --> 00:18:20,669 confidence about diversity in a sample 450 00:18:24,950 --> 00:18:22,649 that they simply help you visualize how 451 00:18:26,860 --> 00:18:24,960 much more sampling needs to occur and if 452 00:18:29,240 --> 00:18:26,870 we had actually sampled these 453 00:18:31,100 --> 00:18:29,250 environments to extinction you would see 454 00:18:32,990 --> 00:18:31,110 these curves flattening out like this 455 00:18:34,789 --> 00:18:33,000 but in fact you can see they're still 456 00:18:37,519 --> 00:18:34,799 going way up and we were very surprised 457 00:18:39,049 --> 00:18:37,529 right by this result simply because no 458 00:18:41,029 --> 00:18:39,059 one had really seen this in the marine 459 00:18:43,789 --> 00:18:41,039 environment that's been discussed pretty 460 00:18:45,440 --> 00:18:43,799 thoroughly in the soil environment but 461 00:18:47,210 --> 00:18:45,450 we are we are quite surprised and 462 00:18:49,909 --> 00:18:47,220 especially since we knew this was an 463 00:18:52,130 --> 00:18:49,919 under estimate of diversity because two 464 00:18:55,399 --> 00:18:52,140 very different sequences can blast to 465 00:18:56,659 --> 00:18:55,409 the same to the same query and so we 466 00:18:58,250 --> 00:18:56,669 decided we are going to have to do 467 00:18:58,690 --> 00:18:58,260 something a little bit more rigorous and 468 00:19:01,270 --> 00:18:58,700 not 469 00:19:03,040 --> 00:19:01,280 so naive really and in fact that was 470 00:19:04,710 --> 00:19:03,050 backed up when we looked at how many 471 00:19:07,240 --> 00:19:04,720 blast hits we got for each sample 472 00:19:09,040 --> 00:19:07,250 compared to how many unique tags were 473 00:19:10,750 --> 00:19:09,050 actually in each sample and you can see 474 00:19:13,150 --> 00:19:10,760 that there are many more unique kept 475 00:19:14,140 --> 00:19:13,160 tags than there are blast hits and so we 476 00:19:17,260 --> 00:19:14,150 are new we are under estimating 477 00:19:19,510 --> 00:19:17,270 diversity using this approach so the 478 00:19:20,530 --> 00:19:19,520 second the second time around when we 479 00:19:22,870 --> 00:19:20,540 decide to do this a little more 480 00:19:24,910 --> 00:19:22,880 rigorously we used a program called odor 481 00:19:26,620 --> 00:19:24,920 which I'm going to explain so we take 482 00:19:28,480 --> 00:19:26,630 all of our sequences we align and we 483 00:19:30,910 --> 00:19:28,490 calculate distances and we pop them into 484 00:19:33,130 --> 00:19:30,920 this program called dotor and this was 485 00:19:34,930 --> 00:19:33,140 developed by patrick's laws and jo 486 00:19:37,540 --> 00:19:34,940 Handelsman slab he's now at UMass 487 00:19:40,180 --> 00:19:37,550 Amherst and what dotor does it's really 488 00:19:41,800 --> 00:19:40,190 a great program for all sorts of people 489 00:19:44,440 --> 00:19:41,810 who are working in different microbial 490 00:19:46,240 --> 00:19:44,450 systems it takes all your sequences and 491 00:19:48,700 --> 00:19:46,250 assigns them to operational taxonomic 492 00:19:50,890 --> 00:19:48,710 units based on the genetic distances 493 00:19:52,630 --> 00:19:50,900 between the sequences and then it looks 494 00:19:54,490 --> 00:19:52,640 at the frequency at which each of those 495 00:19:56,620 --> 00:19:54,500 OT use our observed and it constructs 496 00:19:58,120 --> 00:19:56,630 rarefaction and collectors curves for 497 00:20:01,570 --> 00:19:58,130 various measures of richness and 498 00:20:03,370 --> 00:20:01,580 diversity an in Patrick's paper he uses 499 00:20:05,260 --> 00:20:03,380 some example data sets and one of them 500 00:20:07,750 --> 00:20:05,270 that he looked at was craig Venter 501 00:20:10,660 --> 00:20:07,760 Sargasso Sea dataset which was generated 502 00:20:12,250 --> 00:20:10,670 by shotgun sequencing and what he did 503 00:20:14,800 --> 00:20:12,260 was you can see here at the three 504 00:20:17,020 --> 00:20:14,810 percent level after looking at about 700 505 00:20:21,070 --> 00:20:17,030 clones they estimate there's going to be 506 00:20:23,560 --> 00:20:21,080 about 150 maybe 200 species in in the 507 00:20:27,400 --> 00:20:23,570 Sargasso Sea so this is a nice point of 508 00:20:30,190 --> 00:20:27,410 comparison for our own samples and in 509 00:20:33,430 --> 00:20:30,200 fact when we're and odor on our on our 510 00:20:34,990 --> 00:20:33,440 samples this is just FS 396 you can see 511 00:20:37,150 --> 00:20:35,000 that these curves are still climbing 512 00:20:40,090 --> 00:20:37,160 rapidly even at this five percent level 513 00:20:43,330 --> 00:20:40,100 and if we look at some of the diversity 514 00:20:45,430 --> 00:20:43,340 estimates that donor gives out these are 515 00:20:47,290 --> 00:20:45,440 non parametric estimators that again 516 00:20:49,420 --> 00:20:47,300 have become a very common tool in 517 00:20:52,240 --> 00:20:49,430 microbial ecology they were actually 518 00:20:54,550 --> 00:20:52,250 adapted from mark release and recapture 519 00:20:57,430 --> 00:20:54,560 statistics that were used in you know 520 00:20:59,350 --> 00:20:57,440 counting rabbits and things like that so 521 00:21:00,700 --> 00:20:59,360 they consider the proportion of species 522 00:21:02,710 --> 00:21:00,710 that have already been observed or 523 00:21:04,390 --> 00:21:02,720 recaptured compared to those that are 524 00:21:07,180 --> 00:21:04,400 observed only once and they appear to be 525 00:21:09,160 --> 00:21:07,190 quite rigorous for for microbial 526 00:21:11,350 --> 00:21:09,170 estimates and you can see that at this 527 00:21:11,769 --> 00:21:11,360 three percent level for example just in 528 00:21:13,690 --> 00:21:11,779 this 529 00:21:15,940 --> 00:21:13,700 use vent they estimate that in a 530 00:21:19,269 --> 00:21:15,950 singular single leader event fluid 531 00:21:21,399 --> 00:21:19,279 there's probably over 20,000 species and 532 00:21:23,379 --> 00:21:21,409 I I use that term loosely because we're 533 00:21:26,529 --> 00:21:23,389 simply defining species by genetic 534 00:21:28,209 --> 00:21:26,539 distance in this case and so what we're 535 00:21:30,070 --> 00:21:28,219 doing here is we're able to sample very 536 00:21:33,700 --> 00:21:30,080 very deeply into the microbial world 537 00:21:34,659 --> 00:21:33,710 using this 454 sequencing strategy and 538 00:21:37,419 --> 00:21:34,669 we're getting a much more complete 539 00:21:39,999 --> 00:21:37,429 picture of what organisms might be there 540 00:21:41,560 --> 00:21:40,009 but really numbers are one thing and 541 00:21:43,810 --> 00:21:41,570 that's mostly what we reported in our 542 00:21:46,239 --> 00:21:43,820 pnas paper but we really want to know 543 00:21:48,009 --> 00:21:46,249 who the players in this ecosystem are so 544 00:21:50,289 --> 00:21:48,019 we can take a look at the taxonomic 545 00:21:52,329 --> 00:21:50,299 breakdown and again I'm just going to 546 00:21:53,950 --> 00:21:52,339 focus on the de to diffuse vent sites 547 00:21:55,810 --> 00:21:53,960 since that's what I study and we have 548 00:21:57,669 --> 00:21:55,820 the most data from and this is a 549 00:21:59,739 --> 00:21:57,679 breakdown in the taxonomy just from 550 00:22:01,659 --> 00:21:59,749 those to diffuse events this is at the 551 00:22:05,079 --> 00:22:01,669 three percent level and sort of that 552 00:22:06,940 --> 00:22:05,089 class level and you can see in fact that 553 00:22:09,159 --> 00:22:06,950 these two samples are quite different 554 00:22:10,810 --> 00:22:09,169 they have a lot of the same colors but 555 00:22:12,999 --> 00:22:10,820 the relative abundance of the colors are 556 00:22:14,379 --> 00:22:13,009 different and in fact this sample is 557 00:22:16,749 --> 00:22:14,389 dominated by these epsilon 558 00:22:19,269 --> 00:22:16,759 proteobacteria whereas this one has more 559 00:22:21,129 --> 00:22:19,279 gamma Proteobacteria and when I first 560 00:22:23,019 --> 00:22:21,139 saw this I immediately went and looked 561 00:22:25,509 --> 00:22:23,029 at the chemistry from these two vents 562 00:22:27,099 --> 00:22:25,519 and in fact it's quite different there 563 00:22:29,469 --> 00:22:27,109 are a number of indicators here that 564 00:22:32,739 --> 00:22:29,479 suggests that this vent on over here FS 565 00:22:35,200 --> 00:22:32,749 396 has a much higher carbon dioxide 566 00:22:37,539 --> 00:22:35,210 content and in fact this vent was 567 00:22:40,029 --> 00:22:37,549 effervescent or bubbling when these 568 00:22:42,579 --> 00:22:40,039 samples were taken and that's shown with 569 00:22:46,690 --> 00:22:42,589 this elevated hydrogen sulfide to temper 570 00:22:49,779 --> 00:22:46,700 ratio the GCV suppressed pH the higher 571 00:22:52,119 --> 00:22:49,789 alkalinity and also the higher iron and 572 00:22:53,889 --> 00:22:52,129 this is a characteristic of a lot events 573 00:22:56,979 --> 00:22:53,899 that we see at axial that have a high 574 00:22:59,139 --> 00:22:56,989 co2 content a low pH and an elevated 575 00:23:01,239 --> 00:22:59,149 alkalinity so what we're seeing here is 576 00:23:04,719 --> 00:23:01,249 a possible link between the different 577 00:23:07,839 --> 00:23:04,729 microbial composition and the 454 578 00:23:10,299 --> 00:23:07,849 sequences that we're getting back we can 579 00:23:12,519 --> 00:23:10,309 also just look at one aspect one group 580 00:23:14,200 --> 00:23:12,529 and look at the diversity within and so 581 00:23:17,560 --> 00:23:14,210 in this example we're looking at the 582 00:23:19,629 --> 00:23:17,570 epsilon proteobacteria and this is just 583 00:23:20,950 --> 00:23:19,639 again at that three percent level and 584 00:23:22,779 --> 00:23:20,960 what you can see is there's a huge 585 00:23:24,190 --> 00:23:22,789 amount of diversity just within this one 586 00:23:27,220 --> 00:23:24,200 group of organisms 587 00:23:29,379 --> 00:23:27,230 now epsilon Proteobacteria known to be 588 00:23:31,120 --> 00:23:29,389 key players at hydrothermal vents they 589 00:23:33,279 --> 00:23:31,130 were only recently cultured really in 590 00:23:35,500 --> 00:23:33,289 the last three or four years they're 591 00:23:37,779 --> 00:23:35,510 very phylogenetically physiologically 592 00:23:39,759 --> 00:23:37,789 diverse and they can be both mesophilic 593 00:23:41,560 --> 00:23:39,769 and thermophilic they can use a variety 594 00:23:43,899 --> 00:23:41,570 of electronic scepters and donors from 595 00:23:46,120 --> 00:23:43,909 both seawater and vent fluids allowing 596 00:23:48,879 --> 00:23:46,130 them to exploit both of those gradients 597 00:23:50,769 --> 00:23:48,889 and many of them are autotrophic in 598 00:23:53,350 --> 00:23:50,779 fixed carbon dioxide using the reverse 599 00:23:56,200 --> 00:23:53,360 TCA cycle but what's especially 600 00:23:59,049 --> 00:23:56,210 interesting about just this sort of data 601 00:24:02,230 --> 00:23:59,059 set is this pattern here we're a few 602 00:24:04,000 --> 00:24:02,240 tags dominate the samples but in fact 603 00:24:06,730 --> 00:24:04,010 the bulk of the diversity is made up by 604 00:24:08,350 --> 00:24:06,740 these very low abundant tags and I'd 605 00:24:11,680 --> 00:24:08,360 like to illustrate that point in our 606 00:24:12,940 --> 00:24:11,690 next few slides so this plot is a little 607 00:24:15,370 --> 00:24:12,950 bit confusing and so I'm going to walk 608 00:24:17,710 --> 00:24:15,380 you through it so this clutch basically 609 00:24:20,230 --> 00:24:17,720 shows the similarity of all of our 454 610 00:24:22,899 --> 00:24:20,240 tag sequences from all of the data to 611 00:24:25,180 --> 00:24:22,909 the v6 reference database so this red 612 00:24:27,009 --> 00:24:25,190 curve shows the distribution of all the 613 00:24:28,389 --> 00:24:27,019 tags to the percent difference from 614 00:24:31,120 --> 00:24:28,399 their best match in the reference 615 00:24:33,879 --> 00:24:31,130 database this blue line is cumulative 616 00:24:35,259 --> 00:24:33,889 and shows the percent of our tags and 617 00:24:37,629 --> 00:24:35,269 how different they are from the best 618 00:24:39,909 --> 00:24:37,639 match in our database and finally this 619 00:24:42,389 --> 00:24:39,919 green tag which shows the percent the 620 00:24:45,669 --> 00:24:42,399 unique reads and their distance from 621 00:24:47,110 --> 00:24:45,679 their best match in the database so if 622 00:24:50,279 --> 00:24:47,120 we just take a look at this blue line 623 00:24:53,440 --> 00:24:50,289 the cumulative curve you can see that 624 00:24:55,750 --> 00:24:53,450 about twenty-five percent of our tags 625 00:24:59,529 --> 00:24:55,760 are dead-on hits to something in in the 626 00:25:01,629 --> 00:24:59,539 database and if we keep climbing up that 627 00:25:03,370 --> 00:25:01,639 curve about forty percent of them are 628 00:25:05,649 --> 00:25:03,380 within three percent of something in our 629 00:25:07,750 --> 00:25:05,659 reference database and in fact over 630 00:25:09,549 --> 00:25:07,760 seventy-five percent of them are within 631 00:25:11,830 --> 00:25:09,559 ten percent of something in the database 632 00:25:14,110 --> 00:25:11,840 so this basically means we're covering 633 00:25:16,750 --> 00:25:14,120 the known universe very well using the 634 00:25:19,539 --> 00:25:16,760 sequencing strategy but if we take a 635 00:25:22,600 --> 00:25:19,549 look at this this green line the percent 636 00:25:24,909 --> 00:25:22,610 of unique reads we see that only twenty 637 00:25:26,889 --> 00:25:24,919 percent of our tags are within ten 638 00:25:29,529 --> 00:25:26,899 percent of something in the database and 639 00:25:31,629 --> 00:25:29,539 in fact the bulk of these unique tags 640 00:25:32,980 --> 00:25:31,639 over eighty percent of them are very 641 00:25:35,470 --> 00:25:32,990 different from anything we've seen 642 00:25:36,610 --> 00:25:35,480 before and are quite divergent and it's 643 00:25:39,220 --> 00:25:36,620 really this 644 00:25:41,650 --> 00:25:39,230 this these very divergent groups that 645 00:25:44,080 --> 00:25:41,660 also occur at low abundance as indicated 646 00:25:47,049 --> 00:25:44,090 by this red line where you can see that 647 00:25:49,180 --> 00:25:47,059 these very different sequences occur at 648 00:25:52,060 --> 00:25:49,190 low abundance this is what we're terming 649 00:25:53,710 --> 00:25:52,070 the rare biosphere in the deep sea we 650 00:25:56,080 --> 00:25:53,720 can look at this another way by looking 651 00:26:01,299 --> 00:25:56,090 again at rarefaction curves just from 652 00:26:03,700 --> 00:26:01,309 sample FS 396 and if we compare base 653 00:26:05,440 --> 00:26:03,710 sequences based on how distant they are 654 00:26:07,600 --> 00:26:05,450 from the reference database you get 655 00:26:09,460 --> 00:26:07,610 forward very different curves and this 656 00:26:11,140 --> 00:26:09,470 is sort of what the Sargasso Sea data 657 00:26:12,790 --> 00:26:11,150 set looks like right where it's 658 00:26:14,830 --> 00:26:12,800 flattening out at a relatively low 659 00:26:16,630 --> 00:26:14,840 number and these sequences are very 660 00:26:18,880 --> 00:26:16,640 close to things we already know about in 661 00:26:21,700 --> 00:26:18,890 the database but as you get increasingly 662 00:26:24,150 --> 00:26:21,710 divergent sequences you get increasing 663 00:26:26,890 --> 00:26:24,160 estimates of diversity in the deep sea 664 00:26:28,840 --> 00:26:26,900 so this concept of a rare biosphere 665 00:26:30,970 --> 00:26:28,850 isn't a new one but we now feel like we 666 00:26:34,210 --> 00:26:30,980 have a tool to regularly measure it in 667 00:26:35,980 --> 00:26:34,220 the environment and we're trying really 668 00:26:38,770 --> 00:26:35,990 to understand its significance and now 669 00:26:40,360 --> 00:26:38,780 we now possibly will be able to so the 670 00:26:42,730 --> 00:26:40,370 rare biosphere may simply reflect 671 00:26:45,070 --> 00:26:42,740 biogeography and many yet to be 672 00:26:46,900 --> 00:26:45,080 discovered habitats so what's rare and 673 00:26:50,020 --> 00:26:46,910 one habitat might be quite common in 674 00:26:52,150 --> 00:26:50,030 another and these rare organisms-- might 675 00:26:54,220 --> 00:26:52,160 also be a source of genomic innovation 676 00:26:56,140 --> 00:26:54,230 and this can help us understand how 677 00:26:57,940 --> 00:26:56,150 microbial communities seemed to recover 678 00:27:00,310 --> 00:26:57,950 from all sorts of environmental 679 00:27:02,680 --> 00:27:00,320 catastrophes and also it might help us 680 00:27:04,630 --> 00:27:02,690 explain the genetic novelty that we find 681 00:27:07,380 --> 00:27:04,640 in almost every genome and meta genome 682 00:27:09,430 --> 00:27:07,390 sequence to date the extreme 683 00:27:11,620 --> 00:27:09,440 phylogenetic diversity of the rare 684 00:27:14,230 --> 00:27:11,630 biosphere suggests that it's been around 685 00:27:16,450 --> 00:27:14,240 for a long time possibly over geological 686 00:27:18,820 --> 00:27:16,460 timescales and might have had a very 687 00:27:21,790 --> 00:27:18,830 important role in shaping planetary 688 00:27:23,830 --> 00:27:21,800 processes and finally low abundant 689 00:27:25,330 --> 00:27:23,840 populations at one site might become 690 00:27:28,419 --> 00:27:25,340 dominant in response to environmental 691 00:27:30,520 --> 00:27:28,429 change and in that regard the rare 692 00:27:34,630 --> 00:27:30,530 biosphere may serve a sentinel for 693 00:27:36,970 --> 00:27:34,640 global change so a recent paper in oh I 694 00:27:39,220 --> 00:27:36,980 don't think that came out on anybody's 695 00:27:41,470 --> 00:27:39,230 slide it didn't come out of mine but a 696 00:27:43,610 --> 00:27:41,480 recent paper and trends in microbiology 697 00:27:47,270 --> 00:27:43,620 tried to illustrate this and 698 00:27:50,630 --> 00:27:47,280 what you can see this on the x y axis 699 00:27:53,180 --> 00:27:50,640 here is the number and this is the tax 700 00:27:55,820 --> 00:27:53,190 on rank and this red part of the curve 701 00:27:58,130 --> 00:27:55,830 are those abundant organisms and this 702 00:28:03,980 --> 00:27:58,140 long tail here are the rare organisms-- 703 00:28:05,870 --> 00:28:03,990 and in this paper at pedros a leo says 704 00:28:08,090 --> 00:28:05,880 that we've really only been able to 705 00:28:10,400 --> 00:28:08,100 detect these abundant organisms with 706 00:28:12,740 --> 00:28:10,410 current molecular methods and every once 707 00:28:14,840 --> 00:28:12,750 in a while with culturing we pick up 708 00:28:17,090 --> 00:28:14,850 some of these rare organisms-- and we 709 00:28:19,220 --> 00:28:17,100 believe with 454 sequencing we're now 710 00:28:22,580 --> 00:28:19,230 able to get the whole look at all of 711 00:28:24,230 --> 00:28:22,590 this biodiversity and so what I'd like 712 00:28:25,520 --> 00:28:24,240 to do really for the rest of my talk is 713 00:28:28,130 --> 00:28:25,530 what Carl would it's you in the 714 00:28:30,440 --> 00:28:28,140 beginning and I'd like to go through 715 00:28:32,240 --> 00:28:30,450 some examples of sites we might want to 716 00:28:34,580 --> 00:28:32,250 look at or situations we might want to 717 00:28:36,500 --> 00:28:34,590 look at and apply this technology not 718 00:28:38,330 --> 00:28:36,510 only in regard to the rare biosphere but 719 00:28:39,980 --> 00:28:38,340 also in microbial populations that are 720 00:28:42,560 --> 00:28:39,990 gonna be changing or responding to 721 00:28:44,090 --> 00:28:42,570 different environmental shifts and want 722 00:28:45,560 --> 00:28:44,100 to make this especially applicable to 723 00:28:47,600 --> 00:28:45,570 the science going on not only in 724 00:28:50,630 --> 00:28:47,610 astrobiology community but also NASA in 725 00:28:52,370 --> 00:28:50,640 general so we can take a very brief look 726 00:28:55,220 --> 00:28:52,380 at the history of life on our own planet 727 00:28:56,780 --> 00:28:55,230 as shown here and you can imagine major 728 00:28:59,330 --> 00:28:56,790 events in our history that really 729 00:29:00,919 --> 00:28:59,340 impacted life and I've obviously just 730 00:29:02,480 --> 00:29:00,929 highlighted a couple of them that we can 731 00:29:05,450 --> 00:29:02,490 think about including the build-up of 732 00:29:08,060 --> 00:29:05,460 atmospheric atmospheric oxygen and the 733 00:29:09,440 --> 00:29:08,070 origins and multicellularity but 734 00:29:11,299 --> 00:29:09,450 obviously there are more specific 735 00:29:14,720 --> 00:29:11,309 examples and one of these include 736 00:29:17,299 --> 00:29:14,730 snowball earth as we know snowball earth 737 00:29:19,880 --> 00:29:17,309 hypothesis is based on geological 738 00:29:22,610 --> 00:29:19,890 evidence from multiple glaciations at 739 00:29:25,430 --> 00:29:22,620 sea levels of sea level at low latitudes 740 00:29:27,650 --> 00:29:25,440 and really the leading explanation for 741 00:29:29,810 --> 00:29:27,660 these snowballs is a runaway ice albedo 742 00:29:31,760 --> 00:29:29,820 effect and basically we're putting so 743 00:29:34,760 --> 00:29:31,770 much solar energy back into space that 744 00:29:37,010 --> 00:29:34,770 we freeze over obviously life was not 745 00:29:39,680 --> 00:29:37,020 destroyed by these processes it was able 746 00:29:41,570 --> 00:29:39,690 to continue just fine but we really have 747 00:29:44,380 --> 00:29:41,580 to think about how it might have changed 748 00:29:46,790 --> 00:29:44,390 how evolution happens how organisms 749 00:29:50,299 --> 00:29:46,800 interact and respond to such a dramatic 750 00:29:52,970 --> 00:29:50,309 change globally and this is some work 751 00:29:55,820 --> 00:29:52,980 biker stink at all where they looked at 752 00:29:57,060 --> 00:29:55,830 sort of a post snowball earth setting 753 00:29:59,639 --> 00:29:57,070 and what might have been happy 754 00:30:02,789 --> 00:29:59,649 and it's just illustrated here so upon 755 00:30:04,889 --> 00:30:02,799 initial melting a cyanobacterial bloom 756 00:30:07,680 --> 00:30:04,899 may occur and then you have all this 757 00:30:10,529 --> 00:30:07,690 oxygen getting back into the ocean what 758 00:30:13,019 --> 00:30:10,539 used to be an anoxic metalliferous ocean 759 00:30:14,610 --> 00:30:13,029 is now reacting with this oxygen and you 760 00:30:16,799 --> 00:30:14,620 can imagine that they're both manganese 761 00:30:18,779 --> 00:30:16,809 oxidizing bacteria iron oxidizing 762 00:30:21,720 --> 00:30:18,789 bacteria other processes that are going 763 00:30:23,909 --> 00:30:21,730 to be mediated by micro microbes and so 764 00:30:26,639 --> 00:30:23,919 you can imagine that both at the onset 765 00:30:28,259 --> 00:30:26,649 during and after a snowball earth there 766 00:30:29,999 --> 00:30:28,269 are particular responses in the 767 00:30:33,119 --> 00:30:30,009 microbial communities to these different 768 00:30:35,909 --> 00:30:33,129 shifts of course we have modern-day 769 00:30:37,680 --> 00:30:35,919 phytoplankton blooms which are of great 770 00:30:40,619 --> 00:30:37,690 interest to a lot of people especially 771 00:30:42,810 --> 00:30:40,629 in an oceanography this is a picture of 772 00:30:45,919 --> 00:30:42,820 a phytoplankton bloom in the Arabian Sea 773 00:30:48,360 --> 00:30:45,929 this is the with the ocean chlorophyll 774 00:30:50,490 --> 00:30:48,370 concentrations the Arabian Sea has a 775 00:30:53,100 --> 00:30:50,500 very small window of time when 776 00:30:54,930 --> 00:30:53,110 phytoplankton can occur and it has to do 777 00:30:57,119 --> 00:30:54,940 with the monsoon seasons and the wind's 778 00:30:59,610 --> 00:30:57,129 coming in from the southwest which 779 00:31:01,289 --> 00:30:59,620 allows water to up well from the cold 780 00:31:03,539 --> 00:31:01,299 nutrient-rich water to up well from the 781 00:31:05,879 --> 00:31:03,549 deep and it also brings in nutrients 782 00:31:09,360 --> 00:31:05,889 from lamb and so these out in nutrients 783 00:31:12,600 --> 00:31:09,370 fee of the phytoplankton bloom and in 784 00:31:17,220 --> 00:31:12,610 response to phytoplankton blooms we also 785 00:31:19,110 --> 00:31:17,230 often see a response like this and here 786 00:31:20,580 --> 00:31:19,120 on the y-axis are just the number of 787 00:31:23,369 --> 00:31:20,590 bacterial cells and here we have 788 00:31:25,590 --> 00:31:23,379 chlorophyll and this is presumably due 789 00:31:28,440 --> 00:31:25,600 to heterotrophic bacteria which bloom in 790 00:31:30,060 --> 00:31:28,450 response to the increase of do C or 791 00:31:32,279 --> 00:31:30,070 dissolved organic carbon which is being 792 00:31:34,019 --> 00:31:32,289 leaked out of algal cells but his 793 00:31:36,240 --> 00:31:34,029 actions don't know that much about how 794 00:31:38,610 --> 00:31:36,250 diversity changes in response to 795 00:31:40,470 --> 00:31:38,620 phytoplankton blooms and we think this 796 00:31:44,580 --> 00:31:40,480 is now a great environment to fly our 797 00:31:46,139 --> 00:31:44,590 454 technology to in addition just this 798 00:31:48,930 --> 00:31:46,149 last summer you know we have things like 799 00:31:50,430 --> 00:31:48,940 dead zones and toxic algae blooms and so 800 00:31:52,680 --> 00:31:50,440 you can imagine we're just beginning to 801 00:31:55,289 --> 00:31:52,690 understand what causes those toxic algae 802 00:31:56,940 --> 00:31:55,299 blooms another human sort of impacted 803 00:32:00,509 --> 00:31:56,950 environments and we can begin to follow 804 00:32:03,060 --> 00:32:00,519 the microbial response here's another 805 00:32:05,759 --> 00:32:03,070 example of a marine environment that 806 00:32:07,799 --> 00:32:05,769 experienced both dramatic and spatial 807 00:32:09,659 --> 00:32:07,809 and temporal shifts and that is deep sea 808 00:32:11,669 --> 00:32:09,669 hydrothermal vents this is an 809 00:32:13,740 --> 00:32:11,679 illustration of a dyking eruptive event 810 00:32:16,320 --> 00:32:13,750 occurring at a mid-ocean ridge where you 811 00:32:18,450 --> 00:32:16,330 have a dike extrusion that results in 812 00:32:21,060 --> 00:32:18,460 eruption a lava eruption on the seafloor 813 00:32:25,409 --> 00:32:21,070 and this causes very extreme changes in 814 00:32:27,960 --> 00:32:25,419 heat flux in chemistry and in porosity 815 00:32:29,430 --> 00:32:27,970 and permeability of the crust and there 816 00:32:31,799 --> 00:32:29,440 have been some studies that will have 817 00:32:34,320 --> 00:32:31,809 looked at the response of the microbial 818 00:32:36,869 --> 00:32:34,330 system to this type of event and that's 819 00:32:38,759 --> 00:32:36,879 shown in the schematic here and you can 820 00:32:42,049 --> 00:32:38,769 imagine sort of a steady state system 821 00:32:45,210 --> 00:32:42,059 that sets up over longer periods of time 822 00:32:47,009 --> 00:32:45,220 with perhaps some deeper organisms that 823 00:32:49,740 --> 00:32:47,019 are living in the cross kind of just 824 00:32:51,990 --> 00:32:49,750 barely eking it out on those flew few 825 00:32:54,299 --> 00:32:52,000 hydrothermal nutrients that are around 826 00:32:55,889 --> 00:32:54,309 but in fact there's organisms that are 827 00:32:57,330 --> 00:32:55,899 living off of seawater maybe some of 828 00:32:59,789 --> 00:32:57,340 those epsilon that are making a good 829 00:33:02,100 --> 00:32:59,799 living but when you have this influx of 830 00:33:04,470 --> 00:33:02,110 hydrothermal nutrients and heat these 831 00:33:05,700 --> 00:33:04,480 isotherms get pushed up and organisms 832 00:33:08,100 --> 00:33:05,710 that can take advantage of that 833 00:33:09,840 --> 00:33:08,110 situation are going to bloom and that's 834 00:33:11,789 --> 00:33:09,850 what's shown here and in fact you might 835 00:33:13,980 --> 00:33:11,799 even have a secondary bloom then perhaps 836 00:33:16,289 --> 00:33:13,990 even see water organisms heterotrophic 837 00:33:18,330 --> 00:33:16,299 aerobes that can then feed on that 838 00:33:19,860 --> 00:33:18,340 primary gloom and eventually you're 839 00:33:21,840 --> 00:33:19,870 going to settle down to some new study 840 00:33:23,970 --> 00:33:21,850 state maybe those organisms that were 841 00:33:29,220 --> 00:33:23,980 just barely making it by now or dominant 842 00:33:31,830 --> 00:33:29,230 or vice versa we can also think about 843 00:33:33,480 --> 00:33:31,840 well I don't know if this was supposed 844 00:33:35,820 --> 00:33:33,490 to be a picture of Hurricane Katrina 845 00:33:37,649 --> 00:33:35,830 it's not on my slide but we can also 846 00:33:41,190 --> 00:33:37,659 think about natural disasters such as 847 00:33:43,470 --> 00:33:41,200 Hurricane Katrina and its aftermath and 848 00:33:45,690 --> 00:33:43,480 in response to Hurricane Katrina a 849 00:33:48,450 --> 00:33:45,700 number of microbial ecologist traveled 850 00:33:51,240 --> 00:33:48,460 to New Orleans to look at Lake 851 00:33:53,609 --> 00:33:51,250 Pontchartrain look at the canals and try 852 00:33:56,009 --> 00:33:53,619 to evaluate whether or not this water 853 00:33:59,149 --> 00:33:56,019 was really toxic as was being put out in 854 00:34:01,619 --> 00:33:59,159 the news media and here we can see 855 00:34:02,820 --> 00:34:01,629 before the hurricane and after the 856 00:34:05,490 --> 00:34:02,830 hurricane and where the flooding 857 00:34:07,230 --> 00:34:05,500 occurred Linda emerald Zettler here at 858 00:34:09,389 --> 00:34:07,240 the MDL was one of those scientists who 859 00:34:11,040 --> 00:34:09,399 went to New Orleans New Orleans to get 860 00:34:12,480 --> 00:34:11,050 follow-up samples 861 00:34:15,210 --> 00:34:12,490 she's particularly interested in looking 862 00:34:17,190 --> 00:34:15,220 for potentially pathogenic microbes and 863 00:34:19,500 --> 00:34:17,200 some of her data is shown here on the 864 00:34:21,230 --> 00:34:19,510 right these big blue chunks are 865 00:34:24,180 --> 00:34:21,240 basically a marine or freshwater 866 00:34:25,950 --> 00:34:24,190 bacteria and the small yellow of slices 867 00:34:27,960 --> 00:34:25,960 are microbes that she believed are 868 00:34:30,210 --> 00:34:27,970 potentially pathogenic or related to 869 00:34:32,370 --> 00:34:30,220 pathogenic microbes and so from this 870 00:34:35,790 --> 00:34:32,380 data set these look like low abundant 871 00:34:37,380 --> 00:34:35,800 organisms that maybe did bloom out right 872 00:34:39,030 --> 00:34:37,390 after the hurricane but it doesn't look 873 00:34:40,980 --> 00:34:39,040 like they are now but the fact that 874 00:34:43,080 --> 00:34:40,990 they're there suggest that under the 875 00:34:45,300 --> 00:34:43,090 right conditions perhaps something 876 00:34:47,490 --> 00:34:45,310 something more dangerous could happen 877 00:34:50,310 --> 00:34:47,500 and I think it emphasizes the need to 878 00:34:52,440 --> 00:34:50,320 monitor these types of systems so we 879 00:34:55,230 --> 00:34:52,450 don't just respond when something really 880 00:34:57,300 --> 00:34:55,240 terrible happens of course we have a 881 00:35:00,660 --> 00:34:57,310 very big human disaster looking on the 882 00:35:02,790 --> 00:35:00,670 horizon well actively occurring with 883 00:35:04,980 --> 00:35:02,800 increasing co2 levels and temperatures 884 00:35:06,720 --> 00:35:04,990 on our planet and we don't really know 885 00:35:09,360 --> 00:35:06,730 what the microbial response is going to 886 00:35:11,340 --> 00:35:09,370 be we know that this will impact co2 887 00:35:13,770 --> 00:35:11,350 solubility in the ocean it's certainly 888 00:35:16,320 --> 00:35:13,780 going to affect the biological pump but 889 00:35:17,880 --> 00:35:16,330 we don't know how microbial communities 890 00:35:20,100 --> 00:35:17,890 are going to respond and so it's 891 00:35:22,650 --> 00:35:20,110 essential that we begin some sort of 892 00:35:25,980 --> 00:35:22,660 census so we can track and follow these 893 00:35:28,910 --> 00:35:25,990 populations over time we can also think 894 00:35:31,110 --> 00:35:28,920 of another human impacted area where 895 00:35:33,450 --> 00:35:31,120 excuse me where we need to be able to 896 00:35:35,730 --> 00:35:33,460 assess and track microbial communities 897 00:35:37,620 --> 00:35:35,740 and where low abundant organisms might 898 00:35:39,930 --> 00:35:37,630 in fact be very important and that's 899 00:35:41,460 --> 00:35:39,940 with planetary protection so you can 900 00:35:43,590 --> 00:35:41,470 imagine applying this type of technology 901 00:35:45,590 --> 00:35:43,600 to make sure you know exactly what's 902 00:35:47,760 --> 00:35:45,600 going into space and then what comes out 903 00:35:50,220 --> 00:35:47,770 and we are in fact looking into 904 00:35:52,950 --> 00:35:50,230 collaborations with folks at both JPL 905 00:35:55,230 --> 00:35:52,960 and NASA to apply this technology for 906 00:35:58,410 --> 00:35:55,240 screening space materia for potential 907 00:36:01,980 --> 00:35:58,420 contaminants we can also think about 908 00:36:03,900 --> 00:36:01,990 life outside earth such as Mars i sat in 909 00:36:05,610 --> 00:36:03,910 on a workshop last fall for the 910 00:36:07,280 --> 00:36:05,620 microbial scientist exploration 911 00:36:10,380 --> 00:36:07,290 initiative and we talked a lot about 912 00:36:12,030 --> 00:36:10,390 sort of changes on Mars and Europa and 913 00:36:14,490 --> 00:36:12,040 how this might impact microbial 914 00:36:16,020 --> 00:36:14,500 populations and so I took from some of 915 00:36:17,370 --> 00:36:16,030 my notes some of the ideas that people 916 00:36:19,860 --> 00:36:17,380 had their which I thought were really 917 00:36:21,460 --> 00:36:19,870 interesting we know that Mars has what's 918 00:36:23,530 --> 00:36:21,470 called a chaotic liquidy 919 00:36:25,660 --> 00:36:23,540 that's showing here where it moves from 920 00:36:27,730 --> 00:36:25,670 a low mean obliquity period through a 921 00:36:30,099 --> 00:36:27,740 transition to a high mean of liquidy 922 00:36:31,690 --> 00:36:30,109 period and what this means is heating 923 00:36:34,290 --> 00:36:31,700 there's differential heating in the 924 00:36:37,060 --> 00:36:34,300 polar regions and this can cause a total 925 00:36:39,220 --> 00:36:37,070 redistribution of ice and water vapor on 926 00:36:41,200 --> 00:36:39,230 the planet and so we can see there might 927 00:36:43,420 --> 00:36:41,210 be important linkages between the solar 928 00:36:45,849 --> 00:36:43,430 forcing how the climate responds and 929 00:36:47,109 --> 00:36:45,859 geological consequences on Mars and we 930 00:36:50,140 --> 00:36:47,119 might want to think about for example 931 00:36:52,870 --> 00:36:50,150 how microbial populations could respond 932 00:36:54,820 --> 00:36:52,880 to those changes as well this is just an 933 00:36:57,250 --> 00:36:54,830 illustration of the ice evolution on 934 00:36:59,500 --> 00:36:57,260 Mars and this angle between the white 935 00:37:01,720 --> 00:36:59,510 arrow and the dotted line denotes that 936 00:37:04,150 --> 00:37:01,730 Martian of liquid e and so you could 937 00:37:06,400 --> 00:37:04,160 think about well is the martian biota in 938 00:37:08,830 --> 00:37:06,410 some sort of dormant stage when this 939 00:37:11,080 --> 00:37:08,840 obliquity winter and what might happen 940 00:37:13,930 --> 00:37:11,090 when the obliquity changes and there's 941 00:37:16,660 --> 00:37:13,940 much less ice would they hold on to some 942 00:37:18,400 --> 00:37:16,670 sort of genomic information what are the 943 00:37:21,400 --> 00:37:18,410 time constraints some of on that 944 00:37:24,190 --> 00:37:21,410 persistence we also know that your robo 945 00:37:26,109 --> 00:37:24,200 is a very dynamic system it has an 946 00:37:28,660 --> 00:37:26,119 extremely eccentric orbit which is shown 947 00:37:30,820 --> 00:37:28,670 here and it also has a complex geology 948 00:37:32,680 --> 00:37:30,830 and it's believed that the eccentric 949 00:37:35,140 --> 00:37:32,690 orbit the geology and these tidal 950 00:37:37,150 --> 00:37:35,150 heating are all tied together and in 951 00:37:39,880 --> 00:37:37,160 fact this is a model looking at Europa's 952 00:37:42,420 --> 00:37:39,890 ice thickness as it varies based on 953 00:37:45,310 --> 00:37:42,430 changes in both orbit and tidal heating 954 00:37:47,589 --> 00:37:45,320 at the workshop i was at we also talked 955 00:37:50,530 --> 00:37:47,599 about how the possibility of the 956 00:37:53,109 --> 00:37:50,540 convection occurring in the ice shell of 957 00:37:55,450 --> 00:37:53,119 Europa and also what I thought was 958 00:37:57,910 --> 00:37:55,460 really fascinating this idea that in the 959 00:38:00,220 --> 00:37:57,920 rope was very early stages it could have 960 00:38:02,170 --> 00:38:00,230 had a completely open ocean and so again 961 00:38:04,000 --> 00:38:02,180 this is another dynamic environment when 962 00:38:05,800 --> 00:38:04,010 we think about life on Europa what we 963 00:38:08,380 --> 00:38:05,810 might want to try to detect we have to 964 00:38:10,240 --> 00:38:08,390 think about all the stages that this 965 00:38:14,620 --> 00:38:10,250 planet or this moon might have gone 966 00:38:17,230 --> 00:38:14,630 through so in conclusion we believe that 967 00:38:19,599 --> 00:38:17,240 this fort by for tag sequencing approach 968 00:38:21,609 --> 00:38:19,609 provides an in-depth initial view on 969 00:38:24,040 --> 00:38:21,619 total diversity of microbes in an 970 00:38:25,599 --> 00:38:24,050 environment we think it's very efficient 971 00:38:27,940 --> 00:38:25,609 and it's high throughput which allows 972 00:38:30,460 --> 00:38:27,950 for intensive sampling of all sites of 973 00:38:33,220 --> 00:38:30,470 interest it can detect both major and 974 00:38:34,060 --> 00:38:33,230 minor population members and it offers a 975 00:38:35,830 --> 00:38:34,070 really neat 976 00:38:38,410 --> 00:38:35,840 tool to fingerprint microbial 977 00:38:40,120 --> 00:38:38,420 communities over time and space for 978 00:38:42,130 --> 00:38:40,130 correlations with biogeochemical 979 00:38:44,920 --> 00:38:42,140 activity to imagine doing a biogeography 980 00:38:47,020 --> 00:38:44,930 experiment as well we believe it's an 981 00:38:49,090 --> 00:38:47,030 important complement to metagenomic 982 00:38:51,940 --> 00:38:49,100 culturing and institute hybridization 983 00:38:53,980 --> 00:38:51,950 investigations and we're going to expand 984 00:38:56,140 --> 00:38:53,990 it expand it to include the archaea and 985 00:38:57,190 --> 00:38:56,150 the Eukarya we're in discussion about 986 00:39:00,040 --> 00:38:57,200 whether or not it's going to work for 987 00:39:02,620 --> 00:39:00,050 viruses and we recently received funding 988 00:39:04,660 --> 00:39:02,630 from the Keck foundation to begin our 989 00:39:08,740 --> 00:39:04,670 census and apply this in a variety of 990 00:39:11,170 --> 00:39:08,750 oceanic environments our GS 20 from 454 991 00:39:12,310 --> 00:39:11,180 was delivered about three weeks ago so 992 00:39:14,500 --> 00:39:12,320 we're just getting things up and running 993 00:39:16,660 --> 00:39:14,510 here but we already have samples in the 994 00:39:18,730 --> 00:39:16,670 pipeline with collaborators all over and 995 00:39:20,470 --> 00:39:18,740 that includes more samples from these 996 00:39:23,260 --> 00:39:20,480 complete crews track lines from the 997 00:39:24,850 --> 00:39:23,270 North Atlantic we're collaborating the 998 00:39:26,740 --> 00:39:24,860 people at the University of Hawaii to 999 00:39:28,450 --> 00:39:26,750 look at the Hawaii ocean time series at 1000 00:39:30,990 --> 00:39:28,460 station Aloha this is one of the best 1001 00:39:33,880 --> 00:39:31,000 long-term data sets we have in the ocean 1002 00:39:35,530 --> 00:39:33,890 we're going to be working more on human 1003 00:39:37,330 --> 00:39:35,540 impacted areas through the woods hole 1004 00:39:39,670 --> 00:39:37,340 center for oceans in human health that 1005 00:39:42,850 --> 00:39:39,680 includes collaborators in Woods Hole as 1006 00:39:44,470 --> 00:39:42,860 well as an MIT and we're also working 1007 00:39:46,480 --> 00:39:44,480 with colleagues through the ocean 1008 00:39:48,400 --> 00:39:46,490 drilling program and the ridge 2000 1009 00:39:51,180 --> 00:39:48,410 program to look at both subsea floor 1010 00:39:53,290 --> 00:39:51,190 basalts and subsea floor sediments and 1011 00:39:55,180 --> 00:39:53,300 of course we're going to be looking at 1012 00:39:57,430 --> 00:39:55,190 and in my favorite environment and 1013 00:39:59,460 --> 00:39:57,440 that's deep sea hydrothermal vents in 1014 00:40:01,840 --> 00:39:59,470 this case of the pacific ocean and 1015 00:40:04,150 --> 00:40:01,850 because i think i have a little bit of 1016 00:40:06,400 --> 00:40:04,160 time i'm going to show you a video from 1017 00:40:08,410 --> 00:40:06,410 a very dynamic environment that you can 1018 00:40:10,930 --> 00:40:08,420 imagine organisms responding to quite 1019 00:40:13,210 --> 00:40:10,940 rapidly and that is from this site here 1020 00:40:15,730 --> 00:40:13,220 in the western Pacific this is cruz i 1021 00:40:18,280 --> 00:40:15,740 participated in with noah ocean Explorer 1022 00:40:20,290 --> 00:40:18,290 this last spring and i'm going to show 1023 00:40:21,970 --> 00:40:20,300 you a video of this active eruption 1024 00:40:24,640 --> 00:40:21,980 taking place on the sea floor at about 1025 00:40:26,800 --> 00:40:24,650 500 meters depth and until last spring 1026 00:40:29,350 --> 00:40:26,810 this is something that had never been 1027 00:40:33,760 --> 00:40:29,360 seen before let me see if I can do this 1028 00:40:36,160 --> 00:40:33,770 right so while you're watching you 1029 00:40:38,500 --> 00:40:36,170 should look for gas bubbling you should 1030 00:40:39,880 --> 00:40:38,510 work look for sulfur you should look for 1031 00:40:43,509 --> 00:40:39,890 a lava bombs 1032 00:40:45,069 --> 00:40:43,519 and especially look for a red flash and 1033 00:40:47,440 --> 00:40:45,079 that's actually a magma extrusion and 1034 00:40:49,809 --> 00:40:47,450 that also is the first time it's been 1035 00:40:53,259 --> 00:40:49,819 seen on the seafloor so I'm just going 1036 00:40:56,769 --> 00:40:53,269 to wait a few more seconds and let this 1037 00:40:58,240 --> 00:40:56,779 finish loading up I should mention that 1038 00:41:01,359 --> 00:40:58,250 know people were harmed in the filming 1039 00:41:03,370 --> 00:41:01,369 of this eruption this was a remotely 1040 00:41:13,850 --> 00:41:03,380 operated vehicle Jason to which is 1041 00:41:19,190 --> 00:41:16,340 and if you could hear this we actually 1042 00:41:25,100 --> 00:41:19,200 have hydrophone data there's the red 1043 00:41:29,540 --> 00:41:25,110 flash we we actually have hydrophone 1044 00:41:33,380 --> 00:41:29,550 data that's tracking that listening as 1045 00:41:36,170 --> 00:41:33,390 we're viewing this eruption and we 1046 00:41:40,630 --> 00:41:36,180 actually got samples in 2004 from this 1047 00:41:44,870 --> 00:41:40,640 site we're not gonna see it take us off 1048 00:41:49,640 --> 00:41:44,880 um yeah we can request for her she can 1049 00:41:57,620 --> 00:41:49,650 email it it's about 150 it's your 1050 00:42:00,080 --> 00:41:57,630 microphone and so we actually got 1051 00:42:02,060 --> 00:42:00,090 samples from this site in 2004 beside 1052 00:42:05,000 --> 00:42:02,070 before it started going crazy and then 1053 00:42:07,220 --> 00:42:05,010 in 2006 we spent about a week watching 1054 00:42:09,170 --> 00:42:07,230 this volcano go through it's full of 1055 00:42:11,450 --> 00:42:09,180 rupted cycle so we have samples from 1056 00:42:14,690 --> 00:42:11,460 when it was just a wisp of 20 degree 1057 00:42:18,500 --> 00:42:14,700 fluid up to when it was full bang about 1058 00:42:21,260 --> 00:42:18,510 150 degree Celsius eruption occurring so 1059 00:42:22,850 --> 00:42:21,270 this is just an extreme example of the 1060 00:42:24,680 --> 00:42:22,860 type of environment that we're going to 1061 00:42:27,080 --> 00:42:24,690 be studying with the 454 sequencing 1062 00:42:28,610 --> 00:42:27,090 technology and this collection of 1063 00:42:30,770 --> 00:42:28,620 samples from all over the ocean is 1064 00:42:33,470 --> 00:42:30,780 really going to allow us to explore some 1065 00:42:35,660 --> 00:42:33,480 exciting things I think in the microbial 1066 00:42:39,350 --> 00:42:35,670 world in the sea and potentially in 1067 00:42:41,810 --> 00:42:39,360 other environments as well so just in 1068 00:42:43,700 --> 00:42:41,820 conclusion I want to acknowledge our 1069 00:42:45,380 --> 00:42:43,710 collaborators in the Netherlands Gerhard 1070 00:42:48,290 --> 00:42:45,390 handle and hey-zeus Arrieta for 1071 00:42:49,490 --> 00:42:48,300 providing those transaxles we have a lot 1072 00:42:52,370 --> 00:42:49,500 more of them in our freezer to get 1073 00:42:54,110 --> 00:42:52,380 working on Alfred P sloan Foundation 1074 00:42:56,780 --> 00:42:54,120 which has funded all of our pilot 1075 00:43:00,530 --> 00:42:56,790 studies the nasa astrobiology institute 1076 00:43:02,960 --> 00:43:00,540 that funds me the Keck foundation which 1077 00:43:04,820 --> 00:43:02,970 is helping us with our purchase of our 1078 00:43:08,030 --> 00:43:04,830 454 sequencing machine in the beginning 1079 00:43:09,740 --> 00:43:08,040 of our icom initiative and if you want 1080 00:43:12,170 --> 00:43:09,750 to learn more you can check out the 1081 00:43:15,380 --> 00:43:12,180 paper carl refer to or you can just drop 1082 00:43:25,150 --> 00:43:15,390 me an email so with that I'll take any 1083 00:43:31,820 --> 00:43:29,960 okay so just to review again because we 1084 00:43:36,110 --> 00:43:31,830 have a lot of sites connected please 1085 00:43:37,670 --> 00:43:36,120 raise your hand in the chat area or 1086 00:43:39,770 --> 00:43:37,680 please raise your hand in the 1087 00:43:41,840 --> 00:43:39,780 participant list or put a note in the 1088 00:43:44,930 --> 00:43:41,850 chat area to Marco bolt which is 1089 00:43:50,360 --> 00:43:44,940 actually Marco Bolton myself today and 1090 00:43:52,310 --> 00:43:50,370 and we will call on your site so I don't 1091 00:43:55,340 --> 00:43:52,320 see any time right now that are so now 1092 00:43:57,860 --> 00:43:55,350 just to open it up any questions oh wait 1093 00:43:59,630 --> 00:43:57,870 hold on sorry university of rhode island 1094 00:44:06,170 --> 00:43:59,640 you can now open your mic and talk 1095 00:44:16,310 --> 00:44:13,850 we're not hearing you okay great you're 1096 00:44:19,190 --> 00:44:16,320 dark though picking trance night yeah 1097 00:44:22,490 --> 00:44:19,200 we're just good working here you gonna 1098 00:44:25,760 --> 00:44:22,500 know how only one gets into this so yeah 1099 00:44:29,690 --> 00:44:25,770 on the 454 technology can you tell me 1100 00:44:31,430 --> 00:44:29,700 how only one one strand of DNA gets 1101 00:44:38,080 --> 00:44:31,440 incorporated on the bead that actually 1102 00:44:41,210 --> 00:44:38,090 has multiple receptors on it right so 1103 00:44:43,970 --> 00:44:41,220 you you have four possibilities that 1104 00:44:46,130 --> 00:44:43,980 could happen and they select just for 1105 00:44:48,890 --> 00:44:46,140 there's two steps they select just for 1106 00:44:51,530 --> 00:44:48,900 beads that only have one adapter picked 1107 00:44:54,110 --> 00:44:51,540 up on them but they they go through a 1108 00:44:57,560 --> 00:44:54,120 selection process to try to wean it down 1109 00:45:00,440 --> 00:44:57,570 but in fact you can get false reads that 1110 00:45:03,260 --> 00:45:00,450 have more than one amplicon on them and 1111 00:45:05,240 --> 00:45:03,270 the software is built to recognize that 1112 00:45:07,250 --> 00:45:05,250 your sequencing looks really really 1113 00:45:09,920 --> 00:45:07,260 messy because you can see multiple 1114 00:45:12,380 --> 00:45:09,930 incorporations in one flow gram and so 1115 00:45:14,270 --> 00:45:12,390 that it really happens a little bit in 1116 00:45:17,570 --> 00:45:14,280 the lab work part but mostly in the 1117 00:45:20,390 --> 00:45:17,580 bioinformatics and then you try and 1118 00:45:23,360 --> 00:45:20,400 control that by how much DNA you a deal 1119 00:45:25,130 --> 00:45:23,370 the ratio yeah so thursday things we do 1120 00:45:26,740 --> 00:45:25,140 a dilution series with your beads and 1121 00:45:32,200 --> 00:45:26,750 your amplicons and those sorts of things 1122 00:45:43,780 --> 00:45:36,250 hey thanks David uh University of 1123 00:45:48,210 --> 00:45:43,790 Arizona go ahead let wolf here very nice 1124 00:45:52,450 --> 00:45:48,220 talk Julie um coming at this from from 1125 00:45:56,380 --> 00:45:52,460 outside of biology I'm puzzled by the 1126 00:46:00,339 --> 00:45:56,390 following question since we know that 1127 00:46:04,780 --> 00:46:00,349 the same ribosomal RNA can be associated 1128 00:46:07,060 --> 00:46:04,790 with different metabolic processes how 1129 00:46:09,849 --> 00:46:07,070 do we know that the reverse can't happen 1130 00:46:12,790 --> 00:46:09,859 that what you'll see are essentially the 1131 00:46:18,460 --> 00:46:12,800 same Mike microbes with slightly 1132 00:46:22,180 --> 00:46:18,470 different ribosomal RNA that's a very 1133 00:46:23,589 --> 00:46:22,190 good question and the best tool we have 1134 00:46:26,680 --> 00:46:23,599 to look at that right now is our 1135 00:46:28,329 --> 00:46:26,690 reference database and so if we go 1136 00:46:30,370 --> 00:46:28,339 through our entire reference database 1137 00:46:33,099 --> 00:46:30,380 and have a hundred and twenty thousand 1138 00:46:36,190 --> 00:46:33,109 unique sequences we can only pull out 1139 00:46:38,680 --> 00:46:36,200 forty four thousand unique v6 regions 1140 00:46:40,750 --> 00:46:38,690 and so we already see a little bit of a 1141 00:46:43,300 --> 00:46:40,760 disparity there and so that what we're 1142 00:46:45,280 --> 00:46:43,310 trying to do is look at the diversity at 1143 00:46:47,140 --> 00:46:45,290 a level that we think is meaningful and 1144 00:46:48,910 --> 00:46:47,150 that's why we're not just looking at 1145 00:46:51,220 --> 00:46:48,920 individual sequences and we're using 1146 00:46:53,530 --> 00:46:51,230 this this distance approach where we 1147 00:46:55,540 --> 00:46:53,540 collapse thing into three percent five 1148 00:46:57,730 --> 00:46:55,550 percent levels so that we can hopefully 1149 00:46:59,950 --> 00:46:57,740 get a bit of lip rid of a little bit of 1150 00:47:02,470 --> 00:46:59,960 that wiggle room but actually this tells 1151 00:47:04,839 --> 00:47:02,480 us nothing about function right we have 1152 00:47:06,370 --> 00:47:04,849 we have no idea if these 20,000 1153 00:47:07,660 --> 00:47:06,380 estimated microbes are all doing 1154 00:47:09,730 --> 00:47:07,670 different things if they're all doing 1155 00:47:12,640 --> 00:47:09,740 the exact same thing this is a first 1156 00:47:14,230 --> 00:47:12,650 step in getting closer to a census and 1157 00:47:16,329 --> 00:47:14,240 what we're trying to come up with our 1158 00:47:18,310 --> 00:47:16,339 creative ways then to go back into the 1159 00:47:19,930 --> 00:47:18,320 environment and study those organisms to 1160 00:47:22,859 --> 00:47:19,940 find out what they're actually doing in 1161 00:47:36,609 --> 00:47:29,410 thank you thanks Nick ok MSU are you 1162 00:47:38,769 --> 00:47:36,619 there so my question is this tail end 1163 00:47:41,620 --> 00:47:38,779 this is very popular and we have seen it 1164 00:47:43,239 --> 00:47:41,630 in soil a lot tailing of microbial 1165 00:47:47,229 --> 00:47:43,249 communities where you see a lot of very 1166 00:47:49,989 --> 00:47:47,239 unique OT use how do you get a better 1167 00:47:53,499 --> 00:47:49,999 characterization of that now that 454 is 1168 00:47:57,489 --> 00:47:53,509 available right so the first thing we 1169 00:47:59,559 --> 00:47:57,499 want to do is our hope well one at one 1170 00:48:01,150 --> 00:47:59,569 of our hopes is that by doing a bunch of 1171 00:48:03,219 --> 00:48:01,160 different samples from in dump a bunch 1172 00:48:05,019 --> 00:48:03,229 of different environments we might be 1173 00:48:07,539 --> 00:48:05,029 able to see some patterns or at least 1174 00:48:10,630 --> 00:48:07,549 find places where those rare in one 1175 00:48:13,089 --> 00:48:10,640 sample occur in another sample so begin 1176 00:48:15,130 --> 00:48:13,099 to build a picture of these divergent Oh 1177 00:48:17,529 --> 00:48:15,140 to use instead of just one researcher 1178 00:48:19,689 --> 00:48:17,539 seeing them with their particular pcr 1179 00:48:21,699 --> 00:48:19,699 approach and then no one ever seeing 1180 00:48:23,259 --> 00:48:21,709 them again so the idea is if we treat 1181 00:48:25,779 --> 00:48:23,269 all the samples equally maybe we'll be 1182 00:48:27,939 --> 00:48:25,789 able to find them the second step and 1183 00:48:31,749 --> 00:48:27,949 something we're trying to show and a 1184 00:48:34,239 --> 00:48:31,759 proof of concept is to design a primer 1185 00:48:37,809 --> 00:48:34,249 based on that unique v6 sequences and 1186 00:48:42,039 --> 00:48:37,819 put it with a universal bacterial 1187 00:48:44,259 --> 00:48:42,049 sequence go back into into your sample 1188 00:48:49,359 --> 00:48:44,269 try to amplify out more of the genomic 1189 00:48:51,699 --> 00:48:49,369 DNA the 16s DNA in this case possibly 1190 00:48:54,219 --> 00:48:51,709 scream fosmon libraries for this gene 1191 00:48:57,249 --> 00:48:54,229 try to visualize it we're basically 1192 00:48:59,199 --> 00:48:57,259 we're trying to get to that point but 1193 00:49:01,209 --> 00:48:59,209 the first step is really to see how rare 1194 00:49:06,339 --> 00:49:01,219 is it actually or is it just that we 1195 00:49:08,199 --> 00:49:06,349 haven't been looking enough thank you 1196 00:49:10,449 --> 00:49:08,209 very much and if I could ask another 1197 00:49:12,910 --> 00:49:10,459 question it will be what if you actually 1198 00:49:14,739 --> 00:49:12,920 want to apply to specific functions 1199 00:49:18,189 --> 00:49:14,749 genes related to a specific functions 1200 00:49:20,739 --> 00:49:18,199 how would you change your approach well 1201 00:49:23,499 --> 00:49:20,749 right now 454 is really limited to about 1202 00:49:25,419 --> 00:49:23,509 a hundred base pairs so any primer 1203 00:49:27,069 --> 00:49:25,429 design you could think of where you're 1204 00:49:29,829 --> 00:49:27,079 going to get enough information out of 1205 00:49:31,059 --> 00:49:29,839 about a hundred base pairs that you go 1206 00:49:34,179 --> 00:49:31,069 for it you know you should give it a 1207 00:49:35,380 --> 00:49:34,189 shot the new generation of machines they 1208 00:49:37,450 --> 00:49:35,390 say are going to get about 1209 00:49:40,000 --> 00:49:37,460 200 base pairs so maybe that gives us a 1210 00:49:42,069 --> 00:49:40,010 little more lead way in the types of 1211 00:49:43,480 --> 00:49:42,079 genes we can design primers for but 1212 00:49:50,410 --> 00:49:43,490 that's sort of that's the constraint 1213 00:49:54,759 --> 00:49:50,420 right now thank you very much okay 1214 00:49:58,029 --> 00:49:54,769 University of Washington guys there hi 1215 00:50:00,430 --> 00:49:58,039 head saw Tom Quinn here um actually my 1216 00:50:02,349 --> 00:50:00,440 question was very similar to Nick's man 1217 00:50:04,299 --> 00:50:02,359 that is you know how can you tell 1218 00:50:06,910 --> 00:50:04,309 whether you've actually got diversity 1219 00:50:10,269 --> 00:50:06,920 and phenotype since you you're just 1220 00:50:12,970 --> 00:50:10,279 measuring diversity in genotype ah so 1221 00:50:16,960 --> 00:50:12,980 just to push you a little further on 1222 00:50:18,759 --> 00:50:16,970 that you know what ideas do have to try 1223 00:50:22,299 --> 00:50:18,769 to figure out the diversity of function 1224 00:50:24,309 --> 00:50:22,309 is it possible to get a hint of protein 1225 00:50:27,069 --> 00:50:24,319 structure from your sequences or are you 1226 00:50:31,630 --> 00:50:27,079 going to have to do more sort of sort of 1227 00:50:37,059 --> 00:50:31,640 whole cell culturing and things like 1228 00:50:39,220 --> 00:50:37,069 that yes using this technology we're not 1229 00:50:41,170 --> 00:50:39,230 going to get any sense of function we're 1230 00:50:43,660 --> 00:50:41,180 just going to get binning into taxonomic 1231 00:50:45,849 --> 00:50:43,670 identities we are though going to get 1232 00:50:47,650 --> 00:50:45,859 fingerprints for microbial communities 1233 00:50:49,299 --> 00:50:47,660 in all sorts of different environments 1234 00:50:54,279 --> 00:50:49,309 maybe we'll be able to pick up patterns 1235 00:50:56,470 --> 00:50:54,289 we only find certain talk tabs in oxygen 1236 00:50:58,599 --> 00:50:56,480 deplete domes or in high temperature 1237 00:51:00,549 --> 00:50:58,609 zones for example and that's how 1238 00:51:02,680 --> 00:51:00,559 microbial ecology has been going for the 1239 00:51:04,690 --> 00:51:02,690 last ten years and in the last few years 1240 00:51:07,420 --> 00:51:04,700 people have started using very creative 1241 00:51:09,009 --> 00:51:07,430 culturing techniques to get a sense to 1242 00:51:11,049 --> 00:51:09,019 find out what those organisms are 1243 00:51:12,970 --> 00:51:11,059 actually doing but really I think the 1244 00:51:15,069 --> 00:51:12,980 key is is going to be a combination of 1245 00:51:17,470 --> 00:51:15,079 culturing and doing some more functional 1246 00:51:19,359 --> 00:51:17,480 genomic methods to get a big a true 1247 00:51:21,490 --> 00:51:19,369 handle on that this is just a 1248 00:51:25,510 --> 00:51:21,500 cataloguing of diversity that's all it 1249 00:51:25,520 --> 00:51:30,430 thanks Tom anything else from Washington 1250 00:51:35,660 --> 00:51:32,810 that's it here thank you thank you julie 1251 00:51:39,680 --> 00:51:35,670 thanks yep ok Goddard Space Flight 1252 00:51:42,410 --> 00:51:39,690 Center you have a question there yeah I 1253 00:51:45,020 --> 00:51:42,420 think it's mike Mumma I'm interested in 1254 00:51:47,560 --> 00:51:45,030 the potential applicability to systems 1255 00:51:50,690 --> 00:51:47,570 that might be related to Mars or 1256 00:51:53,120 --> 00:51:50,700 subterranean life on Mars I wondered if 1257 00:51:57,110 --> 00:51:53,130 you had any plans to go to the lost city 1258 00:52:01,850 --> 00:51:57,120 formation it's not satisfied what unit 1259 00:52:05,120 --> 00:52:01,860 so forth uh well we we don't have any 1260 00:52:06,650 --> 00:52:05,130 plans to but we certainly know that a 1261 00:52:09,380 --> 00:52:06,660 lot of the data that's being generated 1262 00:52:11,990 --> 00:52:09,390 from lost city and other ecosystems 1263 00:52:13,520 --> 00:52:12,000 driven by serpent anization we know that 1264 00:52:16,640 --> 00:52:13,530 at Lost City actually the archaeal 1265 00:52:19,040 --> 00:52:16,650 community is very simple we made up only 1266 00:52:20,900 --> 00:52:19,050 a couple different organisms but the 1267 00:52:22,610 --> 00:52:20,910 bacterial story looks a little bit more 1268 00:52:24,440 --> 00:52:22,620 complicated and actually i think the 1269 00:52:29,700 --> 00:52:24,450 application of 454 to that environment 1270 00:52:37,320 --> 00:52:31,829 right here so if anyone has samples send 1271 00:52:39,630 --> 00:52:37,330 them along Ruby crusher thanks Mike okay 1272 00:52:41,490 --> 00:52:39,640 I don't see any more hands raised i just 1273 00:52:43,890 --> 00:52:41,500 want to give a chance to the people who 1274 00:52:46,859 --> 00:52:43,900 came in on the conference call number to 1275 00:52:51,400 --> 00:52:46,869 ask any questions any questions there 1276 00:53:01,510 --> 00:52:58,750 and Mike it should be unmuted good okay 1277 00:53:04,720 --> 00:53:01,520 great well thank you all very much for 1278 00:53:06,760 --> 00:53:04,730 attending and and again thanks to the 1279 00:53:09,220 --> 00:53:06,770 Johnson center for the use of their 1280 00:53:12,190 --> 00:53:09,230 video conferencing facilities I think it 1281 00:53:15,089 --> 00:53:12,200 worked wonderfully and I was very glad 1282 00:53:18,069 --> 00:53:15,099 to have ISDN connection today thanks for 1283 00:53:20,799 --> 00:53:18,079 everyone here and welcome to Carl puffer 1284 00:53:24,160 --> 00:53:20,809 Julie have a good time that C and we'll