WEBVTT 00:00:00.000 --> 00:00:04.000 So what I want to do today is recap a little bit what we talked about 00:00:04.000 --> 00:00:08.000 last time, reiterate some of the important points, 00:00:08.000 --> 00:00:13.000 and then show you how we can learn something about microorganisms in 00:00:13.000 --> 00:00:17.000 the environment by talking about in-situ identification of 00:00:17.000 --> 00:00:21.000 microorganisms as well as genomics. We'll first talk about genomics and 00:00:21.000 --> 00:00:26.000 general and then talk about some applications of genomics to 00:00:26.000 --> 00:00:30.000 environmental microbiology because I think there is some of the most 00:00:30.000 --> 00:00:35.000 exciting new developments are in the area, actually. 00:00:35.000 --> 00:00:44.000 So last time we talked about molecular evolution and ecology. 00:00:44.000 --> 00:00:53.000 And just to recap, some of the main points were that we can actually use 00:00:53.000 --> 00:01:02.000 genes or gene sequences for a couple of very important questions that we 00:01:02.000 --> 00:01:15.000 want to explore. The first one was gene sequences act 00:01:15.000 --> 00:01:33.000 as evolutionary chronometers. 00:01:33.000 --> 00:01:40.000 Now, what do I mean by that? Basically what we said last time 00:01:40.000 --> 00:01:48.000 was that each gene, each sequence in the genome 00:01:48.000 --> 00:01:55.000 accumulates mutations with a certain probability. So what we mean is 00:01:55.000 --> 00:02:03.000 that all genes accumulate mutations over time. 00:02:03.000 --> 00:02:07.000 Now, these of course are the mutations that do not kill the 00:02:07.000 --> 00:02:12.000 organisms, so not the deleterious mutations, but these are mutations 00:02:12.000 --> 00:02:16.000 that are either slightly deleterious, or don't matter, 00:02:16.000 --> 00:02:21.000 or are beneficial mutations. OK, and what this entails is that 00:02:21.000 --> 00:02:25.000 each gene accumulates mutation with a certain probability over time. 00:02:25.000 --> 00:02:30.000 It basically means that two organisms that come from species 00:02:30.000 --> 00:02:34.000 that are relatively closely related to each other have gene sequences 00:02:34.000 --> 00:02:39.000 that will be much more similar to each other than genes from an 00:02:39.000 --> 00:02:44.000 organism that comes from a species that's much more distantly related. 00:02:44.000 --> 00:02:48.000 So, in practical terms what this means is your genes are much, 00:02:48.000 --> 00:02:52.000 much more similar to those of a monkey than they are to a crocodile, 00:02:52.000 --> 00:02:56.000 for example. And we can take advantage of that by applying some 00:02:56.000 --> 00:03:00.000 algorithms, some mathematical modeling essentially, 00:03:00.000 --> 00:03:04.000 to constrain these relationships in those phylogenetic trees that we 00:03:04.000 --> 00:03:09.000 talked about last time. And I also mentioned that the 00:03:09.000 --> 00:03:14.000 ribosomal RNA genes are particularly important for that process. 00:03:14.000 --> 00:03:19.000 In principle, you could do it with any protein coding machine or any 00:03:19.000 --> 00:03:24.000 kind of gene in the genome, but we use the ribosomal RNA genes 00:03:24.000 --> 00:03:29.000 in particular because all organisms have them. They're part of a 00:03:29.000 --> 00:03:34.000 handful of genes that are what we called universally distributed 00:03:34.000 --> 00:03:38.000 last time. And what this allows us to do is 00:03:38.000 --> 00:03:42.000 then construct phylogenetic relationships for all living 00:03:42.000 --> 00:03:46.000 organisms. And I just want to remind you of the tree of life that 00:03:46.000 --> 00:03:50.000 I showed you last time where we can really explore the relationships 00:03:50.000 --> 00:03:54.000 amongst all living organisms. And some of the important points 00:03:54.000 --> 00:03:58.000 there that we made were, for example, that the tree of life 00:03:58.000 --> 00:04:02.000 supports the endosymbiont theory, that when you actually look on the 00:04:02.000 --> 00:04:06.000 tree where the mitochondria and the chloroplasts tree, they fall 00:04:06.000 --> 00:04:10.000 into the bacteria. Now, there is a question where 00:04:10.000 --> 00:04:15.000 somebody asked in the online survey, can the mitochondria and 00:04:15.000 --> 00:04:21.000 chloroplasts still live outside of the eukaryotic cell? 00:04:21.000 --> 00:04:26.000 And the answer is no, they can't anymore because over 00:04:26.000 --> 00:04:31.000 evolutionary time the two organisms have become so integrated that the 00:04:31.000 --> 00:04:37.000 mitochondria and chloroplasts both lost their ability to live outside 00:04:37.000 --> 00:04:42.000 of the eukaryotic host cell. Another important point that we made 00:04:42.000 --> 00:04:46.000 last time that I want to reiterate here is that gene sequences, 00:04:46.000 --> 00:04:51.000 when we go into the environment, and obtain them directly from the 00:04:51.000 --> 00:04:55.000 environment act as a proxy for microbial diversity in 00:04:55.000 --> 00:05:08.000 the environment. So, the number of genes recovered 00:05:08.000 --> 00:05:30.000 directly from the environment is a measure of diversity. 00:05:30.000 --> 00:05:34.000 And we said that this actually plays a very, very important role in the 00:05:34.000 --> 00:05:39.000 analysis of microbial communities, and I showed you the example here 00:05:39.000 --> 00:05:43.000 where we went and took some ocean water and basically apply this 00:05:43.000 --> 00:05:48.000 technique that outlined last time where we can actually amplify 00:05:48.000 --> 00:05:53.000 ribosomal RNA genes from environmental samples, 00:05:53.000 --> 00:05:57.000 clone them, determine the sequence, and then constructs phylogenetic 00:05:57.000 --> 00:06:01.000 trees. And what you see here is a tree 00:06:01.000 --> 00:06:05.000 where we summarize the major groups that we found in the sample have 00:06:05.000 --> 00:06:09.000 been only for two of those groups where we show the entire set of 00:06:09.000 --> 00:06:13.000 sequences that we actually obtained because there were so many of them 00:06:13.000 --> 00:06:16.000 out there. And what we basically found was that over 1500 bacterial 00:06:16.000 --> 00:06:20.000 16S ribosomal RNA gene sequences coexist in this environment. 00:06:20.000 --> 00:06:24.000 And what we said also last time was that the analyses like these have 00:06:24.000 --> 00:06:28.000 really taught us that microorganisms are the most diverse organisms 00:06:28.000 --> 00:06:32.000 on the planet. So, most diversity is amongst the 00:06:32.000 --> 00:06:36.000 microorganisms, and one of the big questions now is 00:06:36.000 --> 00:06:40.000 what are all those microorganisms doing in the environment? 00:06:40.000 --> 00:06:44.000 And so, today what I want to do with you is basically explore this 00:06:44.000 --> 00:06:48.000 question of how we can actually figure out what those microorganisms 00:06:48.000 --> 00:06:52.000 are all doing in environmental samples? 00:06:52.000 --> 00:07:09.000 So we can say we are exploring the 00:07:09.000 --> 00:07:21.000 function of microbes in the environment. At first, 00:07:21.000 --> 00:07:33.000 I want to cover how we can actually identify them in the environment. 00:07:33.000 --> 00:07:38.000 And I want to show you one specific example, and then I want to talk 00:07:38.000 --> 00:07:44.000 about genomics in general, and then basically end with an 00:07:44.000 --> 00:07:50.000 application of genomics to environmental questions. 00:07:50.000 --> 00:07:56.000 So, let's first talk about the in-situ identification 00:07:56.000 --> 00:08:14.000 of microorganisms. 00:08:14.000 --> 00:08:18.000 And the basic problem that I alluded to already before is that most 00:08:18.000 --> 00:08:23.000 microbes are only known [SOUND OFF/THEN ON] 00:08:23.000 --> 00:08:33.000 -- from 16S ribosomal 00:08:33.000 --> 00:08:48.000 RNA clone libraries. 00:08:48.000 --> 00:09:03.000 And we basically want to search and identify them in the environment. 00:09:03.000 --> 00:09:07.000 OK, and I'll show you a specific example of that later on. 00:09:07.000 --> 00:09:12.000 Now last time, we said that the ribosomal RNA sequences consist 00:09:12.000 --> 00:09:17.000 really, like all gene sequences, in fact. We identified several 00:09:17.000 --> 00:09:22.000 stretches of nucleotides, types of stretches, that can be 00:09:22.000 --> 00:09:26.000 found. We said the A type stretches and B type stretches that are very 00:09:26.000 --> 00:09:31.000 important for construction of phylogenetic relationships, 00:09:31.000 --> 00:09:36.000 because we can align them and look for changes in the nucleotide 00:09:36.000 --> 00:09:41.000 sequences because they are the same length and only differ in mutation 00:09:41.000 --> 00:09:46.000 and single nucleotide base pair changes. 00:09:46.000 --> 00:09:50.000 But then there's also those C type stretches, if you remember, 00:09:50.000 --> 00:09:55.000 and those we said vary at much faster rates because they are not 00:09:55.000 --> 00:10:00.000 functionally constrained in those genes. 00:10:00.000 --> 00:10:08.000 OK, so they can actually also accumulate length changes. 00:10:08.000 --> 00:10:17.000 And, it's these C type stretches that we can use sort of as 00:10:17.000 --> 00:10:25.000 diagnostic sequence stretches for microorganisms. 00:10:25.000 --> 00:10:34.000 So, what we can say is we identify organisms by the C type stretches, 00:10:34.000 --> 00:10:47.000 C type sequence stretches. And we call those signature 00:10:47.000 --> 00:11:03.000 sequences. OK, and they allow the differentiation 00:11:03.000 --> 00:11:16.000 of closely related organisms, -- because they vary at very fast 00:11:16.000 --> 00:11:24.000 rates between organisms. And the way we do this is that we 00:11:24.000 --> 00:11:32.000 construct so-called phylogenetic probes. I should probably 00:11:32.000 --> 00:11:38.000 write this over here. Now what are those phylogenetic 00:11:38.000 --> 00:11:42.000 probes? They're basically short pieces of DNA that have a 00:11:42.000 --> 00:11:46.000 fluorescent molecule attached to them. 00:11:46.000 --> 00:12:03.000 -- DNA molecules that are roughly 20 00:12:03.000 --> 00:12:11.000 nucleotides in length, and they carry a florescent molecule. 00:12:11.000 --> 00:12:19.000 Now what the short, single-stranded stretches of DNA 00:12:19.000 --> 00:12:27.000 basically are is they are complementary to those C type 00:12:27.000 --> 00:12:45.000 sequence stretches -- 00:12:45.000 --> 00:12:52.000 -- in the ribosomal RNA. And so basically what we can do is 00:12:52.000 --> 00:12:59.000 we can collect microbial cells from the environment -- 00:12:59.000 --> 00:13:17.000 -- make them permeable -- 00:13:17.000 --> 00:13:33.000 -- and then basically mix them with 00:13:33.000 --> 00:13:48.000 those phylogenetic probes. 00:13:48.000 --> 00:13:52.000 And these probes will then permeate into the cell and bind to their 00:13:52.000 --> 00:14:15.000 complementary sequences. 00:14:15.000 --> 00:14:17.000 Then we wash away the unbound probe -- 00:14:17.000 --> 00:14:33.000 -- and we can view it in a 00:14:33.000 --> 00:14:53.000 microscope under UV light. 00:14:53.000 --> 00:14:57.000 Let me show you an example of this. What you see here is basically a 00:14:57.000 --> 00:15:01.000 light micrograph. So this is what you see basically 00:15:01.000 --> 00:15:06.000 when you collect microbial cells from the environment under the 00:15:06.000 --> 00:15:10.000 microscope. Most bacteria look the same, so you cannot actually 00:15:10.000 --> 00:15:15.000 differentiate them all by just looking at them. 00:15:15.000 --> 00:15:19.000 But then these cells were fixed and permeabilized and then basically 00:15:19.000 --> 00:15:24.000 mixed with two different phylogenetic probes that identified 00:15:24.000 --> 00:15:28.000 two different types of organisms. One was labeled with a red Fluor, 00:15:28.000 --> 00:15:33.000 the other one with a green Fluor. 00:15:33.000 --> 00:15:42.000 And what you see is that you can now differentiate those two organisms. 00:15:42.000 --> 00:15:52.000 Now, why is this especially interesting? Well here's just a 00:15:52.000 --> 00:16:02.000 specific example where people were looking for bacteria capable of 00:16:02.000 --> 00:16:12.000 nitrogen oxidation. These are bacteria that are very 00:16:12.000 --> 00:16:22.000 important in, for example, sewage treatment. And it was known 00:16:22.000 --> 00:16:32.000 that there were two different types out there, one that oxidizes 00:16:32.000 --> 00:16:41.000 ammonia to nitrite, -- and that a second one that 00:16:41.000 --> 00:16:47.000 oxidizes nitrite to nitrate. And by doing this type of analysis 00:16:47.000 --> 00:16:53.000 what people basically learned is that those two organisms live in 00:16:53.000 --> 00:17:00.000 very, very close proximity at all times. 00:17:00.000 --> 00:17:04.000 So the organisms that oxidized ammonia to nitrite are really 00:17:04.000 --> 00:17:08.000 attached, and oftentimes even surround by the organisms that take 00:17:08.000 --> 00:17:12.000 the nitrite to nitrate. So, where you have is a very close 00:17:12.000 --> 00:17:16.000 cooperation between two different types of microorganisms, 00:17:16.000 --> 00:17:21.000 and the transfer of one of the substrates that's a product of the 00:17:21.000 --> 00:17:25.000 metabolism of one of the organisms to another one: so extremely 00:17:25.000 --> 00:17:29.000 efficient process that really is very important to take into 00:17:29.000 --> 00:17:33.000 consideration when you want to understand processes like sewer 00:17:33.000 --> 00:17:37.000 treatment, but also nitrogen biogeochemistry and 00:17:37.000 --> 00:17:45.000 the environment. Any questions? 00:17:45.000 --> 00:17:55.000 OK, so for the remainder of the lecture I want to talk 00:17:55.000 --> 00:18:03.000 about genomics, -- and then in particular also its 00:18:03.000 --> 00:18:07.000 application to questions of environmental microbiology and 00:18:07.000 --> 00:18:12.000 environmental science. So first, what I want to do is give 00:18:12.000 --> 00:18:16.000 you a little bit of the definition of genomics, and then cover how it 00:18:16.000 --> 00:18:21.000 is actually possible that we can sequence entire genomes, 00:18:21.000 --> 00:18:25.000 and I want to give you some highlights of what we have found by 00:18:25.000 --> 00:18:30.000 comparing different genomes to each other. 00:18:30.000 --> 00:18:36.000 And then I want to talk about this field about environmental genomics 00:18:36.000 --> 00:18:43.000 where we can use genomic techniques to actually learn something about 00:18:43.000 --> 00:18:49.000 the function of different uncultured microorganisms in the environment. 00:18:49.000 --> 00:18:56.000 So first, our definition, it's basically to interpret 00:18:56.000 --> 00:19:03.000 or to sequence, -- interpret, and compare whole 00:19:03.000 --> 00:19:11.000 genomes. And as you will see the comparison part actually plays an 00:19:11.000 --> 00:19:18.000 increasingly important role because we have now actually genome 00:19:18.000 --> 00:19:26.000 sequences available from almost all, or from at least some of the major 00:19:26.000 --> 00:19:32.000 groups of life. So this, again, 00:19:32.000 --> 00:19:36.000 is a different kind of representation of the tree of life. 00:19:36.000 --> 00:19:40.000 You have bacteria, archaea, and eukarya again. 00:19:40.000 --> 00:19:44.000 And as you can see, we have a lot of representatives. 00:19:44.000 --> 00:19:49.000 In fact, this doesn't even come close to the diversity that we have 00:19:49.000 --> 00:19:53.000 now sequenced as well over a hundred bacterial genome sequence now, 00:19:53.000 --> 00:19:57.000 several archeael genomes, and increasingly also in 00:19:57.000 --> 00:20:02.000 eukaryotic genomes. Now, genomes, so how is this done? 00:20:02.000 --> 00:20:08.000 How can we actually sequence genomes? Well, 00:20:08.000 --> 00:20:13.000 on the face of it we use very large facilities where you have sequencing 00:20:13.000 --> 00:20:19.000 machines present. There's one very important one at 00:20:19.000 --> 00:20:24.000 MIT, actually at the Broad Institute, and here you see all those really 00:20:24.000 --> 00:20:30.000 industrial scale production lines actually. 00:20:30.000 --> 00:20:40.000 But the basic problem is that genomes are large. 00:20:40.000 --> 00:20:50.000 E. coli, for example, has roughly 4.4 million base pairs, 00:20:50.000 --> 00:21:00.000 and the human genome is even much, much larger. 00:21:00.000 --> 00:21:08.000 It has about 3 billion base pairs. OK, so genomes are very, very large. 00:21:08.000 --> 00:21:22.000 But a single sequencing reaction-- 00:21:22.000 --> 00:21:29.000 -- gives you only roughly 500-1, 00 nucleotides or base pairs. So 00:21:29.000 --> 00:21:36.000 how is it that we can actually sequence entire genomes? 00:21:36.000 --> 00:21:43.000 I'm going to walk you through this, and there is some variation on the 00:21:43.000 --> 00:21:50.000 theme, but this is still a major approach that's still used in some 00:21:50.000 --> 00:21:57.000 of the sequencing facilities. Now, you start out by extracting 00:21:57.000 --> 00:22:04.000 genomic DNA from organisms, and then you use restriction enzymes 00:22:04.000 --> 00:22:11.000 to cut the DNA into relatively large pieces of DNA, so about 160 00:22:11.000 --> 00:22:17.000 kilobase pairs long. On average, this is shown here. 00:22:17.000 --> 00:22:22.000 Kilo means a thousand, so 160,000 base pairs long. 00:22:22.000 --> 00:22:28.000 These pieces are then cloned into specific cloning vectors that are 00:22:28.000 --> 00:22:43.000 called BAC vectors. 00:22:43.000 --> 00:23:01.000 So therefore, cloning large pieces of DNA, and BAC stands for Bacterial 00:23:01.000 --> 00:23:17.000 Artificial Chromosome. And what they basically are, 00:23:17.000 --> 00:23:31.000 are plasmids, very special plasmids that can carry large pieces of 00:23:31.000 --> 00:23:40.000 genome, or large genome fragments. So, by cloning into those BAC 00:23:40.000 --> 00:23:45.000 vectors, what you do is you basically divide up the genome, 00:23:45.000 --> 00:23:50.000 and then the step number three is mostly done for eukaryotic genomes 00:23:50.000 --> 00:23:55.000 because they are so much larger. You can actually map and analyze 00:23:55.000 --> 00:24:00.000 the fragments, and map them onto genome maps where 00:24:00.000 --> 00:24:05.000 you know the location of different restriction fragments and different 00:24:05.000 --> 00:24:10.000 genes, actually. For bacteria, this step is mostly 00:24:10.000 --> 00:24:15.000 skipped, actually. What you do with each one of those 00:24:15.000 --> 00:24:20.000 BACs, is you cut them further up into 1 kilobase per fragment, 00:24:20.000 --> 00:24:25.000 so much smaller fragments. And these are called, 00:24:25.000 --> 00:24:30.000 and these are cloned then into normal plasmid vectors. 00:24:30.000 --> 00:24:35.000 And so you generate what are called shotgun clones. 00:24:35.000 --> 00:24:40.000 So, these are then cloned into E. coli, you go through the same type 00:24:40.000 --> 00:24:45.000 of steps that we discussed before already with environmental clone 00:24:45.000 --> 00:24:50.000 libraries. And you can actually determine the sequence of each one 00:24:50.000 --> 00:24:55.000 of those pieces of DNA. And what you will then get, 00:24:55.000 --> 00:25:00.000 is small fragments of overlapping DNA sequences. 00:25:00.000 --> 00:25:04.000 That it shown here. You'll find overlaps, 00:25:04.000 --> 00:25:09.000 basically, which piece together the whole genome. And so, 00:25:09.000 --> 00:25:14.000 first to assemble, you piece together these genome fragments that 00:25:14.000 --> 00:25:19.000 are present in the BACs, and then finally you piece together 00:25:19.000 --> 00:25:24.000 the entire genome propose large sequence pieces, 00:25:24.000 --> 00:25:29.000 and you get a so-called draft genome sequence. 00:25:29.000 --> 00:25:39.000 The next step in this analysis, then, is that you do so-called 00:25:39.000 --> 00:25:49.000 genome annotation is. And the first very important step 00:25:49.000 --> 00:26:00.000 is that you translate the gene sequences into amino acids. 00:26:00.000 --> 00:26:05.000 So, the nucleotide sequences into amino acids particularly in 00:26:05.000 --> 00:26:10.000 prokaryotes. This step can be done right away -- 00:26:10.000 --> 00:26:31.000 -- and what this allows you to do, 00:26:31.000 --> 00:26:37.000 is you can look for what we call open reading frames, 00:26:37.000 --> 00:26:44.000 or ORFs. And what you look for is a start codon and a stop codon that 00:26:44.000 --> 00:26:50.000 basically branches or frames a stretch of amino acids encoded by 00:26:50.000 --> 00:26:57.000 the nucleotides. So you look for ORFs. 00:26:57.000 --> 00:27:13.000 And these are your putative genes. 00:27:13.000 --> 00:27:26.000 The next step that you can do, 00:27:26.000 --> 00:27:32.000 then, is you can go to databases and now you compare your ORFs to 00:27:32.000 --> 00:27:39.000 information that is present in the databases. So basically, 00:27:39.000 --> 00:27:45.000 you inquire the database and ask, is a gene sequence that is similar 00:27:45.000 --> 00:27:52.000 to the one that I have statistically significantly similar present that 00:27:52.000 --> 00:27:58.000 allows me to say something about the function of this particular gene? 00:27:58.000 --> 00:28:05.000 So function, can then be identified by comparison with databases. 00:28:05.000 --> 00:28:29.000 Any questions? 00:28:29.000 --> 00:28:34.000 OK, so that allows you, then, to basically say something 00:28:34.000 --> 00:28:39.000 about the different genes that you have found in the genome, 00:28:39.000 --> 00:28:44.000 but to give you an impression of how new this field really is and how 00:28:44.000 --> 00:28:49.000 little we still know about the diversity of genes and organisms, 00:28:49.000 --> 00:28:54.000 on average when we sequence a new bacterial genome we find about 30% 00:28:54.000 --> 00:28:59.000 of the genes, or a third of the genes have no known functional 00:28:59.000 --> 00:29:05.000 analog of the databases. OK, so there's a lot to learn about 00:29:05.000 --> 00:29:11.000 the diversity of life and about the functional diversity of life. 00:29:11.000 --> 00:29:18.000 In eukaryotes, there are some little twists, 00:29:18.000 --> 00:29:24.000 as you all know. And basically, that is that genes 00:29:24.000 --> 00:29:31.000 of course consist of introns and exons, right? 00:29:31.000 --> 00:29:35.000 And so it's basically relatively difficult to directly identify those 00:29:35.000 --> 00:29:40.000 open reading frames. And what you have to do is that you 00:29:40.000 --> 00:29:45.000 have to actually oftentimes, so let's write this down. 00:29:45.000 --> 00:30:01.000 And what people oftentimes do, 00:30:01.000 --> 00:30:12.000 then, is that they search for matching sequences in so-called cDNA 00:30:12.000 --> 00:30:24.000 libraries. Now what are cDNA libraries? Let me just show you 00:30:24.000 --> 00:30:32.000 this on the next slide. Skip this. Basically what you can do is you can 00:30:32.000 --> 00:30:38.000 isolate messenger RNA from cells and that translate the messenger RNA by 00:30:38.000 --> 00:30:43.000 a process called reverse transcription that the viral enzyme 00:30:43.000 --> 00:30:49.000 that translates RNA into DNA, so you can translate it into DNA 00:30:49.000 --> 00:30:54.000 fragments. And you can then clone those DNA fragments into plasmids, 00:30:54.000 --> 00:31:00.000 sequence those, and then basically see what are the pieces that are 00:31:00.000 --> 00:31:06.000 actually, what are the introns in the genes? 00:31:06.000 --> 00:31:11.000 What are the pieces that are excised when the messenger RNA is actually 00:31:11.000 --> 00:31:29.000 created from the genome? 00:31:29.000 --> 00:31:33.000 And so, let me just cover now a few of the major insights that people 00:31:33.000 --> 00:31:37.000 have come up with. Of course, it's a very growing 00:31:37.000 --> 00:31:41.000 field and a lot of excitement is coming out. 00:31:41.000 --> 00:31:58.000 And I first want to talk about 00:31:58.000 --> 00:32:09.000 bacteria and archaea -- 00:32:09.000 --> 00:32:13.000 -- and then say a few words also about eukaryotes or eukaryote. 00:32:13.000 --> 00:32:17.000 First of all, what we learned about, bacteria and archaea, 00:32:17.000 --> 00:32:21.000 is that their genomes are very compact. 00:32:21.000 --> 00:32:35.000 Whenever they have pieces of DNA 00:32:35.000 --> 00:32:43.000 that are not frequently used, they're actually lost from the 00:32:43.000 --> 00:32:51.000 genome. OK, so they lose genes, I should say, relatively easily, and 00:32:51.000 --> 00:32:59.000 we can see this that the genome size is correlated to metabolic 00:32:59.000 --> 00:33:12.000 diversity. 00:33:12.000 --> 00:33:23.000 So, for example, we have Mycoplasma genetalium and 00:33:23.000 --> 00:33:37.000 Streptomyces -- 00:33:37.000 --> 00:33:42.000 coelicor are two very different bacteria. The first one is an 00:33:42.000 --> 00:34:01.000 obligate intracellular parasite. 00:34:01.000 --> 00:34:08.000 OK, so, which means it's actually bathed in a nutrient solution in the 00:34:08.000 --> 00:34:16.000 eukaryotic cells that it invades. It doesn't have to make amino acids. 00:34:16.000 --> 00:34:23.000 It gets it just from the host cell. And it turns out it has a very 00:34:23.000 --> 00:34:31.000 small genome, so only 0. 8-based mega-base pairs, so 580, 00:34:31.000 --> 00:34:37.000 00 base pairs, and only 517 genes. And interestingly, 00:34:37.000 --> 00:34:41.000 actually people are now using this organism to try and ask, 00:34:41.000 --> 00:34:46.000 well, what's the minimum number of genes that organism can actually 00:34:46.000 --> 00:34:50.000 will live with? And so, they are deleting in a 00:34:50.000 --> 00:34:55.000 stepwise fashion the different genes in this organism, 00:34:55.000 --> 00:34:59.000 and it turns out that you need about two to 300 genes minimum in order to 00:34:59.000 --> 00:35:03.000 make the things survive. On the other hand, 00:35:03.000 --> 00:35:15.000 streptomyces is a soil bacterium -- 00:35:15.000 --> 00:35:20.000 -- has a very complex lifestyle, can degrade a lot of environmental 00:35:20.000 --> 00:35:26.000 substrates, and it has a very big genome, one of the biggest bacterial 00:35:26.000 --> 00:35:31.000 genomes. And so, those two organisms basically span 00:35:31.000 --> 00:35:37.000 pretty much the range of bacterial genome sizes. 00:35:37.000 --> 00:35:41.000 And so, it's thought that it has about 7,846 genes. 00:35:41.000 --> 00:35:57.000 Now, we also have a very large 00:35:57.000 --> 00:36:09.000 genetic diversity -- 00:36:09.000 --> 00:36:23.000 -- between species. And typically what you find is that 00:36:23.000 --> 00:36:38.000 roughly 15 to 30% of genes are unique to a specific species. 00:36:38.000 --> 00:36:44.000 And that's really because bacteria and archaea have the capability to 00:36:44.000 --> 00:36:50.000 affect a lot of chemical reactions that eukaryotes, 00:36:50.000 --> 00:36:56.000 for example, cannot. There's about 20 million known 00:36:56.000 --> 00:37:02.000 organic substances, organic chemicals, and almost all of 00:37:02.000 --> 00:37:07.000 them are biodegradable by bacteria. Even the minutest compounds if it 00:37:07.000 --> 00:37:12.000 were not biodegradable bacteria, would build up in the environment, 00:37:12.000 --> 00:37:16.000 OK? So, if it just where a cofactor that some organism produces because 00:37:16.000 --> 00:37:21.000 we have such a long period of time of evolution on this planet and 00:37:21.000 --> 00:37:26.000 evolutionary history, you probably would be able to dig it 00:37:26.000 --> 00:37:32.000 up in your backyard. One of the other very important and 00:37:32.000 --> 00:37:39.000 interesting insights that has come out with comparing genomes for 00:37:39.000 --> 00:37:46.000 microorganisms is that lateral gene transfer is a very important process 00:37:46.000 --> 00:38:07.000 amongst microorganisms. 00:38:07.000 --> 00:38:11.000 Now what do I mean by lateral gene transfer? It basically means that 00:38:11.000 --> 00:38:16.000 we find evidence among bacterial genomes that they have actually 00:38:16.000 --> 00:38:20.000 taken genes from completely unrelated organisms. 00:38:20.000 --> 00:38:25.000 And I just want to show you one example here from that of 00:38:25.000 --> 00:38:38.000 thermotoga maritima -- 00:38:38.000 --> 00:38:47.000 -- which lives in hot springs. This is a very interesting 00:38:47.000 --> 00:38:56.000 bacterium that lives in hot water of around 80∞C and thrives only in 00:38:56.000 --> 00:39:05.000 those kinds of environments. And they coexist there with many 00:39:05.000 --> 00:39:14.000 archaea. And when people sequenced the genome of thermotoga maritima 00:39:14.000 --> 00:39:23.000 what they found was that about 25% of the genes have their closest 00:39:23.000 --> 00:39:32.000 relatives in archaeal genomes. So roughly 25% of genes in 00:39:32.000 --> 00:39:39.000 thermotoga are of archaeal origin. And how can we actually figure 00:39:39.000 --> 00:39:44.000 something like that out? Well, the most important technique 00:39:44.000 --> 00:39:49.000 is, again, phylogenetic tree construction. And so when you have, 00:39:49.000 --> 00:39:54.000 for example, gene A, well let me draw this, actually, 00:39:54.000 --> 00:40:10.000 on a new board. 00:40:10.000 --> 00:40:15.000 So you're comparing, say, three organisms, 00:40:15.000 --> 00:40:21.000 organism A, B, and C and you compare gene one with gene two. 00:40:21.000 --> 00:40:27.000 And you notice that most genes adhere to this pattern, 00:40:27.000 --> 00:40:33.000 but that every now and then there's a gene that gives you this 00:40:33.000 --> 00:40:38.000 type of pattern. What you can then conclude is that 00:40:38.000 --> 00:40:43.000 this gene, C, has not coevolved with the other genes in the genome of 00:40:43.000 --> 00:40:48.000 these organisms but was actually transferred into it from another 00:40:48.000 --> 00:40:53.000 source. And I don't have time to go actually into the mechanisms. 00:40:53.000 --> 00:40:58.000 If you're interested, I teach a graduate class that undergraduates 00:40:58.000 --> 00:41:03.000 actually take in our department, environmental microbiology, where we 00:41:03.000 --> 00:41:08.000 discussed a lot of the mechanisms. It's basically a lot of viruses can 00:41:08.000 --> 00:41:13.000 affect gene transfer but also plasmids and transposons. 00:41:13.000 --> 00:41:18.000 But for bacteria, again, you should remember that often new function is 00:41:18.000 --> 00:41:23.000 actually oftentimes arises by lateral gene transfer. 00:41:23.000 --> 00:41:28.000 And one of the interesting things is that lateral gene transfer is 00:41:28.000 --> 00:41:34.000 actually very important in the evolution of pathogenic bacteria. 00:41:34.000 --> 00:41:48.000 So, the so-called virulence genes, 00:41:48.000 --> 00:41:57.000 which are the genes that basically affect pathogenesis. Do 00:41:57.000 --> 00:42:13.000 you have a question? Among pathogenic bacteria, 00:42:13.000 --> 00:42:35.000 often arise by lateral gene transfer. OK. Any questions? 00:42:35.000 --> 00:42:43.000 OK, now for eukarya, I just want to make the point that 00:42:43.000 --> 00:42:52.000 their genomes are generally orders of magnitudes larger -- 00:42:52.000 --> 00:43:16.000 OK, and that the exons, 00:43:16.000 --> 00:43:23.000 so the stretches that really encode the protein that make up the 00:43:23.000 --> 00:43:30.000 organism, the exons are only typically a few percent 00:43:30.000 --> 00:43:37.000 of the genome. That's particularly in higher 00:43:37.000 --> 00:43:44.000 eukaryotes. Yeasts, for example, have a much more 00:43:44.000 --> 00:43:50.000 compact genome also. We, for example, are full of DNA 00:43:50.000 --> 00:43:57.000 that people still have a very hard time figuring out what that actually 00:43:57.000 --> 00:44:04.000 does. But it seems that the majority of the genome, 00:44:04.000 --> 00:44:10.000 so-called repeated sequences -- -- many of which seems to be ancient 00:44:10.000 --> 00:44:15.000 retroviruses that have inserted themselves into the genome and have 00:44:15.000 --> 00:44:21.000 since then lost actually their function. OK, 00:44:21.000 --> 00:44:26.000 so the remaining time I want to just give you an example of how we can 00:44:26.000 --> 00:44:31.000 now use these techniques that I outlined before to learn something 00:44:31.000 --> 00:44:37.000 about microorganisms in the environment. 00:44:37.000 --> 00:44:47.000 It's called environmental. 00:44:47.000 --> 00:45:04.000 Basically, the way this all started 00:45:04.000 --> 00:45:08.000 was by going into the environment and extracting nuclear gases and 00:45:08.000 --> 00:45:13.000 treating them exactly the same way as if you had a single genome. 00:45:13.000 --> 00:45:18.000 But, again, remember, we have a very large mixture of microorganisms 00:45:18.000 --> 00:45:22.000 present in the environment. And where this is mostly done was 00:45:22.000 --> 00:45:27.000 in the ocean, actually. And what people did, was they 00:45:27.000 --> 00:45:32.000 constructed those BAC clones directly from the environment and 00:45:32.000 --> 00:45:36.000 then looked amongst those BAC clones for specific 16S ribosomal 00:45:36.000 --> 00:45:41.000 RNA genes. Remember, this is the marker that we 00:45:41.000 --> 00:45:45.000 have for microorganisms in the environment. We know the diversity 00:45:45.000 --> 00:45:49.000 of microorganisms through those types of genes, 00:45:49.000 --> 00:45:53.000 and we have a lot of the data available. And so, 00:45:53.000 --> 00:45:57.000 in order to link a specific function of such an organism that we only 00:45:57.000 --> 00:46:01.000 know from the 16S ribosomal RNA genes. 00:46:01.000 --> 00:46:06.000 So, to ask the question of what much of this organism might be carrying 00:46:06.000 --> 00:46:12.000 out in the environment, it's very useful to sequence BAC 00:46:12.000 --> 00:46:17.000 clones that have 16S ribosomal RNA genes on them, 00:46:17.000 --> 00:46:23.000 and determine what kinds of protein coding genes are on there that might 00:46:23.000 --> 00:46:28.000 reveal some of the function of the organism in the environment. 00:46:28.000 --> 00:46:34.000 And one example that I want to show you is that of the proteorhodopsin. 00:46:34.000 --> 00:46:45.000 So basically, the initial task was to sequence BAC clones containing 00:46:45.000 --> 00:46:57.000 ribosomal RNA genes, and look for other genes that might 00:46:57.000 --> 00:47:15.000 reveal some of the function. 00:47:15.000 --> 00:47:18.000 So, you don't want to look for all the genes that encode proteins that 00:47:18.000 --> 00:47:22.000 are important to the cell cycle and things like that, 00:47:22.000 --> 00:47:25.000 but really sort of metabolic genes that might tell you something about 00:47:25.000 --> 00:47:29.000 the type of metabolism that this organism carries out 00:47:29.000 --> 00:47:33.000 in the environment. And so, what the first example that 00:47:33.000 --> 00:47:39.000 turned out to be really, really important is that people 00:47:39.000 --> 00:47:44.000 found rhodopsin genes on one of those BAC fragments, 00:47:44.000 --> 00:47:50.000 and it turns out this rhodopsin catalyzes or these rhodopsin genes 00:47:50.000 --> 00:47:55.000 produce a protein that inserts itself into the bacterial membrane, 00:47:55.000 --> 00:48:01.000 and it's a photoreceptor that when it's hit by light, 00:48:01.000 --> 00:48:06.000 it actually becomes a proton pump. So, it expels protons from the cell 00:48:06.000 --> 00:48:11.000 interior to the outside, and you already know that this is 00:48:11.000 --> 00:48:16.000 important in energy generation in all living cells. 00:48:16.000 --> 00:48:20.000 So proton gradient across membranes basically give the cells sort of a 00:48:20.000 --> 00:48:25.000 battery status that can be exploited by ATPase molecules or ATPase 00:48:25.000 --> 00:48:30.000 proteins that equalize the proton gradient and affect ATP 00:48:30.000 --> 00:48:35.000 synthesis in doing so. Now, why is this so important? 00:48:35.000 --> 00:48:40.000 Well, it turned out that this type of protein is present in almost all 00:48:40.000 --> 00:48:45.000 microbial cells that were previously thought to be heterotrophs alone in 00:48:45.000 --> 00:48:49.000 the ocean in the parts of the ocean that receive enough life. 00:48:49.000 --> 00:48:54.000 And what this means is that our estimates of the global carbon 00:48:54.000 --> 00:48:59.000 budget of the ocean were basically wrong because most microorganisms in 00:48:59.000 --> 00:49:12.000 the ocean have this. 00:49:12.000 --> 00:49:31.000 So most prokaryotes in the ocean have a light-driven proton pump 00:49:31.000 --> 00:49:52.000 which is called proteorhodopsin. And it basically allows them to gain 00:49:52.000 --> 00:50:06.000 energy from sunlight. And there's an increasing number of 00:50:06.000 --> 00:50:12.000 such examples now where we are learning to interpret environmental 00:50:12.000 --> 00:50:18.000 communities, and the function of environmental microbial communities 00:50:18.000 --> 00:50:23.000 through those genomic approaches. And it reveals basically an 00:50:23.000 --> 00:50:29.000 enormous diversity of organisms out there. And what we also are 00:50:29.000 --> 00:50:35.000 learning to do now is to assemble entire genomes from those samples by 00:50:35.000 --> 00:50:40.000 applying genomic techniques. And this is an example here where 00:50:40.000 --> 00:50:44.000 you see, this was published last year, where people went out and 00:50:44.000 --> 00:50:49.000 basically were able to piece together from pieces of genes 00:50:49.000 --> 00:50:53.000 obtained from the environment, entire genomes or fragments of 00:50:53.000 --> 00:50:58.000 entire genomes. And that's shown here. 00:50:58.000 --> 00:51:02.000 Those are contiguous sequences. OK, so if you have any questions 00:51:02.000 --> 00:51:07.000 let me know by e-mail, or if you're interested in pursuing 00:51:07.000 --> 00:51:11.000 this further I also teach another class in civil and environmental 00:51:11.000 --> 00:51:14.000 engineering.