Bacteriophages: Genes and Genomes

Part 1: Bacteriophages: What are they?

00:00:01.17 Hello. My name is Graham Hatfull. I am a professor at the University of Pittsburgh
00:00:05.27 and a Howard Hughes Medical Institute professor,
00:00:08.24 and we're going to talk today about bacteriophages, their genes and their genomes.
00:00:13.25 First of all in Part One, I'd like to just discuss what bacteriophages are,
00:00:20.10 some of their biological properties, and how they were discovered.
00:00:24.17 So bacteriophages are viruses that infect bacterial hosts.
00:00:30.21 So just as we and many of our animal cousins are infected by viruses that are specific for us,
00:00:39.29 bacteria also have viruses that infect them, and they are called bacteriophages or phages for short.
00:00:46.18 They were discovered or co-discovered by Felix D'Herelle and Frederick Twort between 1915 and 1918.
00:00:58.05 And they were discovered as agents that when added or present in a culture
00:01:03.25 of growing bacteria were capable of killing the bacteria,
00:01:09.01 and essentially having a bactericidal effect.
00:01:13.09 It was really Felix D'Herelle who developed an assay called the plaque test where he was able to take samples
00:01:24.01 of bacteriophages that were able to kill bacteria and he made dilutions,
00:01:31.03 increasingly greater series of dilutions of the bacteriophage sample
00:01:37.10 and then plated that out in the presence of the bacterial host on solid media using Petri dishes, as shown here.
00:01:46.24 And what he saw was that when he diluted out the sample sufficiently
00:01:53.01 he could see individual areas of killing as you can see here
00:01:58.05 with smaller and larger areas where the cells are dead and where the viruses have grown.
00:02:05.21 These are called plaques and each one of these individual plaques
00:02:09.07 has arisen by a single particle which was capable of infecting a cell,
00:02:17.20 and as the bacteria grew across the surface of the agar dish,
00:02:22.25 the virus propagated itself, multiplied, until... and the plaque here then
00:02:30.06 may be perhaps a million or ten million or more individual phage particles.
00:02:36.07 This plaque test was really important because they could tell
00:02:42.19 from looking at cultures of, or looking at lysates of bacteriophages,
00:02:48.10 that there was nothing to see in there.
00:02:51.00 They could filter the samples, they knew they still had the infectious property,
00:02:56.16 but the tube looked completely clear.
00:02:59.25 There was nothing to see and when they placed these particles
00:03:02.26 with this capability in the light microscope, there was nothing to be seen.
00:03:09.09 So these were mysterious entities, but Felix D'Herelle showed conclusively that they really are particulate in nature.
00:03:18.10 It was somewhat later, in the late 1930s and 1940s, that the electron microscope was developed,
00:03:27.17 which has a level of resolution way beyond the light microscope,
00:03:31.17 and was able to show for the very first time what viruses including bacteriophages actually look like,
00:03:37.25 and I'll show you some pictures in a minute.
00:03:41.17 But this just shows a stylized example of what one of those phages look like.
00:03:48.06 At the very top here you can see is a structure that is referred to as the head. Sometimes it is called the capsid.
00:03:56.00 That is attached to this longer structure here which is the tail.
00:04:02.26 And the DNA, or the genetic information that the virus carries in order to multiply itself,
00:04:09.08 the instructions for its own replication, are carried here in the virus head.
00:04:17.07 And so we can see and we now know of our understanding of what this particular virus and these types of viruses look like,
00:04:28.24 is that this structure at the very bottom which is the tip of the tail,
00:04:32.06 that is the region that recognizes and binds to the outside of the bacterial host.
00:04:39.29 During the process of infection, the structures that we see principally constructed
00:04:47.07 or made up of protein components that stuff stays on the outside of the cell,
00:04:52.22 and the DNA, or the genetic information is what passes from the head through the tail, into the cell.
00:05:02.28 and then reprograms that cell in order to make more copies of the virus.
00:05:08.24 By electron microscopy we can see and we now know that a very large proportion of these naturally found viruses
00:05:19.23 are in an order which we refer to as the Caudovirales, of which these are primarily phages that contain tails,
00:05:27.16 as I just described and shown in these examples here.
00:05:30.29 And they contain double stranded DNA.
00:05:33.14 So there are lots of different types of viruses with different shapes,
00:05:38.26 but the vast majority that you find in nature fall into this particular order.
00:05:44.14 And these have names according to the types of tails that they have.
00:05:49.00 These are the Myoviridae, and they have contractile tails so the tail actually contracts like a syringe
00:05:56.28 when the DNA is injected into the bacterial host.
00:06:01.16 These are called the Podoviridae which have little short stubby tails.
00:06:06.22 And these ones on the right here are the Siphoviridae, and these have long, non-contractile and flexible tails.
00:06:17.17 So these are the main forms of these viruses.
00:06:21.27 There are however a variety of number of different types of viruses that have different shapes
00:06:26.21 and have, indeed, different types of DNA or RNA genomes within them.
00:06:33.18 So we can think about the various steps that are involved in the propagation
00:06:39.29 of a bacteriophage during this process known as lytic growth.
00:06:44.23 The phage starts by adsorbing to the outside of the cell.
00:06:52.10 The DNA is injected inside the cell in this process of penetration.
00:06:57.29 There is a set of early proteins which are encoded by the phage, but which uses the host machinery to express them.
00:07:07.19 Viral DNA is replicated to make lots more copies of the virus DNA.
00:07:13.10 And then these late proteins are expressed that make the structures that I just showed you in the electron microscope.
00:07:22.27 The capsid structure gets assembled. The tails get assembled.
00:07:28.08 And then in the process of DNA packaging the DNA is stuffed into those heads until the heads are full.
00:07:35.15 The tails are attached. And in the very final step of this process the bacterial cell
00:07:42.01 is going to lyse with enzymes encoded by the phage genome to break open the outside of the cell
00:07:49.10 and the progeny viruses, typically 50 to 100 new progeny viruses, will be released
00:07:55.16 and will go on to repeat this cycle whenever they find another bacterial host.
00:08:01.03 So while many phages go through a lytic growth cycle,
00:08:08.00 and that is essentially simply how they reproduce themselves,
00:08:11.03 that is not the only type or form of growth cycle that phages can enjoy.
00:08:17.06 And there is a large class, perhaps even a majority of bacteriophages
00:08:20.08 that enjoy what is referred to as a temperate life state.
00:08:26.21 What that means is that when a temperate phage infects its bacterial host,
00:08:32.22 there are two possible alternative outcomes.
00:08:36.24 One of those outcomes is lytic growth in which they propagate themselves
00:08:43.02 in exactly the same way as I described to you in the previous slide.
00:08:49.07 And normally that occurs perhaps 80 or 90% of the times that the host cell is infected by the bacteriophage.
00:08:57.10 Ten to twenty percent of the time there is an alternative outcome.
00:09:02.11 And that alternative outcome is referred to as lysogeny.
00:09:06.09 In lysogeny the lytic genes, the genes that are required to propagate itself and lyse and kill the cell,
00:09:14.28 they are all switched off.
00:09:16.18 They are repressed, and the phage DNA establishes itself so that it can be propagated
00:09:23.28 in the long term within the subsequent growth cycles of the bacterial host.
00:09:29.25 And that is usually, although not always, accomplished by integration of the phage DNA into the bacterial chromosome.
00:09:38.29 So the phage genome itself becomes incorporated and becomes a part of the bacterial chromosome.
00:09:45.26 It gets replicated just like all the other of the bacterial genes do.
00:09:49.27 So we can think of lysogeny as a form of parasitism
00:09:54.02 where the phage is simply going along for the ride in what is otherwise a very healthy cell.
00:10:01.10 It is basically a free ride through many, many generations.
00:10:05.27 So lysogens tend to be stable, but they are not going to, they don't have to remain in that state forever.
00:10:20.14 They can go through many, many, many rounds of growth,
00:10:23.27 however, as a process that happens either spontaneously or can be induced by DNA damage,
00:10:31.11 such as UV light, lysogens can leave this... the comfort of that life cycle, and they can be induced into full lytic growth.
00:10:44.25 There's a relatively easy way to distinguish between phages
00:10:49.22 that are temperate and those that are lytic and can only undergo the lytic growth cycle
00:10:54.26 by the types of plaques that you see on agar plates.
00:10:58.27 Lytic phages form simply just clear plaques, as shown in the bottom right hand corner here,
00:11:05.03 where all of the cells within that infected area have been killed.
00:11:11.12 Killed by phage infection.
00:11:14.00 Over on the left on the bottom here, you can see what temperate phages look like.
00:11:18.20 They form turbid plaques.
00:11:20.15 You see plaques because there are cells that are being killed through the lytic growth, propagation of the virus.
00:11:27.11 But there is that important and key subset of cells that are...
00:11:32.07 in which lysogeny is established, and those lysogens are able to survive
00:11:39.19 and to grow. And they can grow perfectly happy even though they are bathed
00:11:44.03 within this density of bacteriophage particles.
00:11:48.28 So the fact that lysogens can actually grow quite happily even though
00:11:54.01 there's lots of viruses around to infect them is referred to as super infection immunity.
00:12:01.02 Lysogens are immune to super infection by a phage of the same or closely related type.
00:12:09.00 This slide just shows an example of how that can be studied and what that looks like in the lab.
00:12:17.09 At the top left here are plaques of a turbid phage, a temperate phage,
00:12:23.22 growing on a lawn of bacteria. Using either a toothpick or a wire you can go and pick cells from the very center
00:12:33.06 of one of those plaques. Streak it out on an agar plate, as shown on the bottom left here,
00:12:38.23 in order to generate single colonies, each grown up from a single cell,
00:12:45.04 that will have come from the turbid plaque.
00:12:48.05 And then these individual colonies can in turn be tested for their immune phenotypes as shown in the top right.
00:12:57.26 In this example we are looking at, we are comparing, a non-lysogenic strain with a lysogen
00:13:05.00 and in the top we have just spotted on dilutions of a bacteriophage sample.
00:13:11.14 And you can see that these serial dilutions from the most going down to the least number of particles
00:13:20.24 gives you the titer, i.e. the number of particles per milliliter of sample on the non-lysogen.
00:13:32.05 But when you compare it with exactly the same dilutions of that particular phage onto a lysogen of itself.
00:13:38.00 So this particular phage is called Giles.
00:13:39.29 On the right is the Giles lysogen, and you can see that there is essentially very little infection
00:13:45.25 except perhaps a little bit of clearing at the very highest concentration
00:13:49.14 of phage at the top left-hand corner of that panel.
00:13:53.18 A control phage in this case below it, showing L5, which is also a temperate phage,
00:14:00.08 but it does not share immunity with Giles,
00:14:04.03 and therefore you see essentially the same number of plaques on the lysogen of Giles and the non-lysogen.
00:14:12.03 And therefore bacteriophages can be grouped together according to their immune specificities.
00:14:19.11 And it can be done just by comparing the infectivity of a whole set of phages on lysogens and non-lysogens.
00:14:28.14 So this is an important parameter that enables us to group together phages
00:14:34.22 that may be similar by sharing these types of parameters.
00:14:38.27 I mentioned that phage DNA can integrate itself into the host chromosome.
00:14:46.10 And this is a common feature of temperate bacteriophages.
00:14:50.07 They do it by a process which is referred to as site specific recombination.
00:14:55.14 This process is catalyzed by an enzyme which is called integrase or Int for short.
00:15:03.19 The integrase is encoded by the phage genome,
00:15:07.20 and integrase catalyzes recombination between two specific sites or segments of DNA.
00:15:14.29 One is the so called phage attachment site, or attP for short,
00:15:20.06 and the other is a specific site within the bacterial chromosome
00:15:23.28 which is called the attachment site for the bacterium or attB.
00:15:29.11 Integration results in the formation of what is called a pro-phage,
00:15:34.26 an integrated phage DNA that has become part of the chromosome,
00:15:40.11 and it is now the pro-phage DNA is flanked by the left and right attachment sites
00:15:46.02 referred to as attL and attR respectively.
00:15:50.10 This reaction does use host functions quite commonly,
00:15:56.26 Integrase works together with a host protein, a bacterial protein called integration host factor or IHF for short.
00:16:05.15 And you'll recall that I told you that lysogens can undergo spontaneous or induced induction into lytic growth,
00:16:15.10 which means that there has to be a process for this to come back out again.
00:16:21.13 A biological reversal of this overall reaction.
00:16:24.17 That is called excision, and excision is again catalyzed by integrase,
00:16:30.04 and there is a second phage encoded protein called excise or Xis for short.
00:16:38.15 And Xis is a protein that essentially dictates and determines the directionality of how these reactions will occur.
00:16:48.28 In the absence of Xis you do integration.
00:16:53.09 In the presence of Xis, then the integrase is only capable of doing the excision reaction.
00:16:59.11 A few years ago people started thinking about how many bacteriophages there really were out there in the biosphere.
00:17:12.01 And what they did was that they developed a technique called epi-fluorescence
00:17:15.09 where they could take a sample, let's say of seawater, which is easy to get.
00:17:19.28 Get seawater, add a sample of a dye which binds to the nucleic acids,
00:17:26.05 place that sample under a fluorescence microscope
00:17:29.21 and simply look and count for the viruses and other components that you see in that kind of experiment.
00:17:37.11 This is an example of what they saw.
00:17:40.16 There is a very large number of what you can see as small green fluorescent dots here.
00:17:48.16 Those are all the viruses that are present.
00:17:50.02 And there is a smaller number, a fewer number, of these large brighter spots,
00:17:56.04 which are larger objects, and they are the bacteria.
00:18:00.25 And so what it was possible to do was simply to count how many virus like particles are present in these samples.
00:18:09.13 And when that was done, it was clear that the viral population is indeed absolutely vast.
00:18:15.20 When you measure there is about 10 to the power of 6 to 10 to the power of 7
00:18:19.12 viral particles per mL, and this number seems to be reasonably steady no matter where you have looked.
00:18:26.22 It's true in sea water if you look in coastal samples, if you look in oceanic samples,
00:18:33.11 or the surface or the deep.
00:18:35.27 And there's a similar abundance, or related degree of abundance in terrestrial samples as well, it is believed.
00:18:45.16 So because we can measure the number of particles present in small samples,
00:18:50.18 and we can multiply the amount of sea water
00:18:53.23 and the amount of terrestrial components and when we do that we can conclude that the biosphere
00:19:01.05 contains a total of 10 to the power of 31 virus particles, the vast majority of which are bacteriophages.
00:19:10.01 This is an incredible number. This number would suggest that there's more bacteriophage particles
00:19:18.11 in the biosphere than all other biological entities added together.
00:19:23.00 Phages are in fact the majority of all biological things in the biosphere.
00:19:30.17 They are not only abundant, but this appears to be a very dynamic population as well.
00:19:38.15 You can see from the fluorescence patterns in this slide,
00:19:45.03 but it is true in most samples that have been examined that the ratio of bacteriophage particles
00:19:52.05 to bacteria is about between 5 to 1 and 10 to 1.
00:19:58.04 And that is important because it means that the bacteria are likely to constantly be subjected
00:20:06.00 to infection by the bacteriophages in their natural environments.
00:20:12.18 And in fact there are ecological studies that estimate the number of viral infections per second
00:20:19.28 that occur on a global scale.
00:20:22.29 And that number is estimated to be between 10 to the power of 23 and 24,
00:20:27.24 which is just an incredible number of activity.
00:20:33.24 A dynamic population. Indeed these numbers would suggest that the entire phage population turns over every 4 or 5 days.
00:20:42.20 It is a large and a stunningly dynamic set of biological items.
00:20:50.01 Not surprisingly perhaps the viral population is extremely diverse when we look at the genetic level.
00:20:59.16 Currently a number far short of 10 to the power of 31 phage particles have been subjected to DNA sequencing,
00:21:09.06 perhaps about 650 or so. And from these genomes we can look
00:21:15.09 and we can see how similar or different they are to each other.
00:21:19.05 And what we have learned from this is that indeed there's many, many different types of sequences.
00:21:25.05 And these genomes appear to harbor and to contain large numbers of genes,
00:21:30.15 which are unlike any other genes that we have seen before.
00:21:34.11 So, we can conclude then that bacteriophages represent the majority of all biological entities in the biosphere.
00:21:42.15 They're a dynamic population. They are constantly infecting bacterial hosts and generating more copies of themselves.
00:21:51.12 The bacteria must be struggling to maintain their survival through resistance to these infections.
00:21:59.10 And I think a compelling argument can be made that this population has probably also been evolving for a very long time.
00:22:08.04 Perhaps 2-3, perhaps even 4, billion years, extending right back to the very early days of when life evolved.
00:22:17.10 And finally, phages appear to represent the largest unexplored reservoir of new genetic information in the biosphere.
00:22:29.19 If you want to discover new genes, perhaps with new functions, perhaps with new structures,
00:22:35.20 the bacteriophage population I think we would argue is exactly where you should start to look.
00:22:43.11 In Part Two, we'll look in some more detail at the genetic structures of bacteriophage genomes
00:22:51.26 and see how that's given us some insights into how these genomes have evolved.

Part 2: Bacteriophages: Genomic insights.

00:00:01.02 Hi. My name is Graham Hatfull. I am a professor at the University of Pittsburgh
00:00:04.15 and a Howard Hughes Medical Institute professor.
00:00:07.03 In part two we are going to talk about some of the insights that we can gain from comparing
00:00:14.02 the genomes of bacteriophages and perhaps learn something about how they are constructed and how they have evolved.
00:00:22.03 In part one we saw some morphologies of bacteriophages, what they look like in the electron microscope,
00:00:32.29 and I showed you some different types of structures
00:00:37.23 that could arguably reflect different ways in which those bacteriophages have evolved.
00:00:44.27 But we have to be very careful about interpreting differences in virion morphology, what the viruses look like,
00:00:53.09 and their evolutionary relationships and how the genomes compare to each other.
00:00:58.09 I've illustrated that in this particular slide
00:01:01.00 where I have shown five examples of bacteriophages.
00:01:05.06 These would all be classified according to their long flexible tails
00:01:10.14 as being members of the Siphoviridae, the Sipho viruses,
00:01:15.03 each with their heads and their tails attached.
00:01:18.18 It might be tempting to look at these and say well they all look very similar to each other,
00:01:25.06 almost indistinguishable, perhaps they all are genetically similar.
00:01:30.11 In fact this is an example where these five share essentially little or no sequence similarity at the genomic level whatsoever.
00:01:40.01 So if we want to understand how genomes have evolved
00:01:44.00 and how they are related to each other from a phylogenetic perspective,
00:01:48.23 we need to go in, isolate the DNA, and sequence those genomes and then compare them.
00:01:54.24 There are various ways in which we can compare the genomic sequences.
00:02:00.22 We can compare them by looking at the similarities of the nucleotide sequences,
00:02:07.26 essentially sequencing the DNA or if it's RNA, the RNA,
00:02:13.05 and then comparing them one to another and seeing what is shared.
00:02:16.08 A second way of doing that would be to look at the genes
00:02:21.09 and comparing them through their predicted amino acid sequence similarities of the proteins that are encoded by those genes.
00:02:28.22 Right here I am showing you an example of what it looks like if we take two bacteriophages
00:02:34.09 and compare their nucleotide sequences.
00:02:37.20 And this particular representation is referred to as a dot plot.
00:02:42.05 And what we have done is to take two bacteriophage genomes, in this case Fruitloop and Boomer,
00:02:49.10 and we have aligned the two sequences, and we are going to slide one next to the other computationally,
00:02:58.03 and ask if there are segments that are similar to each other within a particular window of comparison.
00:03:06.07 And every time we see sequence similarity, a dot is presented on this dot plot.
00:03:12.11 And what you can see here is that
00:03:15.12 there's a rather complex series of relationships reflecting a quasi diagonal line
00:03:25.08 from the top left to the bottom right of this representation.
00:03:29.21 So where you can see a relatively solid line that means that there is a segment of DNA
00:03:35.24 which is substantially similar between the two.
00:03:38.23 Where you fail to see a line, such as in the top left hand corner, is a region where the two genomes
00:03:47.14 appear to be substantially dissimilar.
00:03:50.10 They don't have shared nucleotide sequences.
00:03:52.15 And then there's all sorts of complicated interruptions and shifts in the diagonal line as you look between these.
00:04:02.13 And this tells us an important aspect, a component, of what we see when we compare these types of genomes.
00:04:11.09 And that is, they are not simply completely similar from end to end or completely dissimilar from end to end.
00:04:18.20 But quite commonly we see these interrupted portions
00:04:22.22 where different segments of the genomes are related to each other in different ways
00:04:27.20 as though different parts of the genome have different evolutionary histories,
00:04:33.13 different ways of arriving in the genomes as we see them in Fruitloop and Boomer today.
00:04:40.14 So from this type of analysis and looking at a number of bacteriophage genomes,
00:04:47.08 we can see the following general conclusions.
00:04:51.15 First of all, the DNA that is isolated from these particular virions, these double stranded DNA virus types,
00:05:01.12 that the genomes are linear. So they have a left end and a right end.
00:05:08.23 They tend to form predominantly two types of groups that we can see when we look at the linear genomes.
00:05:17.06 There are those that have defined ends.
00:05:20.03 That means that if you isolate the molecules from a million particles
00:05:25.10 of a particular phage type, each of the million DNA molecules that you get out
00:05:30.08 have the same left and right ends. In other cases that is not true.
00:05:35.26 The DNAs have the same overall genetic constitution,
00:05:41.14 but the specific physical ends of the left and the right can be positioned in different places.
00:05:47.27 And therefore they are referred to as being circularly permuted.
00:05:52.18 They are not circular. They are linear,
00:05:54.21 but they represent different positions of the ends relative to the genetic information.
00:06:01.28 Often these viruses also contain terminal redundancies,
00:06:06.22 which means that one segment of the genome is duplicated at both ends.
00:06:11.19 And so these two major types of genomes that you see either have defined ends
00:06:17.27 or terminally redundant and circularly permuted ends
00:06:20.24 and there are other viruses that have different variations on these themes.
00:06:25.22 The sizes of bacteriophage genomes varies enormously.
00:06:31.05 There are those that are as small as perhaps 5000 bases, and there are those that are as large as 500 kilobases,
00:06:39.02 which is quite amazing when you think that 500 kilobases
00:06:44.22 is about the same size of the smaller of the free living bacterial genomes.
00:06:50.14 And so there are examples of viruses that are the same size genomically
00:06:55.00 and have the same or more genes as small bacterial genomes.
00:07:00.15 The phage genomes tend to be densely packed with genes.
00:07:05.19 And so most of the DNA is encoding genes.
00:07:10.16 And as I mentioned before in this section, the phages infecting bacteria from different genera
00:07:17.05 tend to be unrelated at the DNA level.
00:07:21.13 So this slide shows an example of what we see when we take a DNA sequence of a particular phage,
00:07:34.00 in this case it is a phage called Giles,
00:07:36.26 And we use computational approaches and a bioinformatic strategy to identify
00:07:44.07 the protein coding genes that are present within the virus.
00:07:49.02 And so the genome is largely filled with protein coding genes,
00:07:54.22 and they are shown here by these boxes, either colored or in white.
00:08:00.21 The genome is represented by what looks like this railroad track here
00:08:05.24 which has markers every kilobase and every 100 bases.
00:08:10.21 And the genome for Giles is linear with defined ends,
00:08:14.27 and so in this representation it begins in top left hand corner and goes to the bottom right hand corner,
00:08:22.14 and each of the genes are shown in these boxes represented either above or below the DNA.
00:08:30.00 Genes that are shown above the DNA are transcribed in the rightwards direction,
00:08:36.03 coming this way, and those that are shown below such as a couple or three genes in the top left hand corner
00:08:44.23 are transcribed in the leftwards direction.
00:08:47.19 So those are the standards that we use for presenting the genes
00:08:52.25 and illustrating the direction that they are transcribed
00:08:56.17 relative to the overall genome structure.
00:08:59.19 You can see here from these genes that they are densely packed into this particular genome.
00:09:06.10 There's few non-coding spaces between the genes.
00:09:10.19 They essentially represent 95% or more of the genetic information that's available.
00:09:19.12 In this particular representation we have colored the genes in such a way
00:09:26.12 as to reflect the relationships that some of these genes share with genes that you find in other bacteriophages.
00:09:34.27 The genes that are shown in white, and you can see some across the top here,
00:09:40.02 are simply genes for which we don't have any other close relatives in any of the databases.
00:09:46.03 And this illustrates the point that phages such as this can be replete with genes
00:09:52.11 that are not closely related to known genes
00:09:55.24 and for which we have rather little idea as to what they do.
00:09:59.04 I mentioned that when we compare the nucleotide sequences of phages we can see that it looks as though the parts
00:10:12.01 have evolved differently to each other.
00:10:15.00 And this leads to the idea that phage genomes are characteristically mosaic.
00:10:21.09 They are constructed architecturally from segments which have been put together in a particular way.
00:10:29.01 Modules if you like. And that each of these modules is in effect mobile
00:10:35.08 or can move around the population of bacteriophages
00:10:38.28 such that you can find it in more than one or perhaps several different genomic contexts.
00:10:45.27 And this slide illustrates how this might look when you see mosaicism
00:10:52.15 at the level of nucleotide sequence comparisons.
00:10:56.00 So this is showing a small segment of three phage genomes.
00:11:01.19 The one at the top, PG1. Rosebush in the middle, and Qyrzula towards the bottom.
00:11:08.17 You can see the genome represented by the markers in the railroad tracks for each of these.
00:11:14.19 The genes that are encoded are shown by the color boxes with their gene names inside the boxes,
00:11:19.11 and where these genomes contain and share nucleotide sequence similarity
00:11:25.04 there is a color coded area shading between the two such as you can see here.
00:11:32.01 Now Rosebush and Qyrzula have very evident and strong nucleotide sequence similarity
00:11:39.22 both in the left part here and over here in the right part as well.
00:11:44.09 PG1 and Rosebush and Qyrzula have no sequence similarity that is evident by this comparison,
00:11:53.22 in this example, because there is no color shading over on this left part.
00:11:58.29 Nonetheless, in this middle segment things are different.
00:12:04.23 There appears to be very little sequence similarity between Rosebush and Qyrzula
00:12:10.16 because there is no shading in that area,
00:12:15.22 however, when we compare PG1 and Rosebush we can see that in this central segment right here
00:12:22.22 that there is indeed a purple color shading that reflects strong sequence similarity
00:12:29.17 between these two genomes, PG1 and Rosebush, in this center portion.
00:12:35.04 So this is really important because it illustrates an example where the different segments of these genomes
00:12:42.09 particularly Rosebush appear to have had different evolutionary histories.
00:12:47.09 They've come from different places.
00:12:49.14 This segment that's in the middle of Rosebush clearly did not come from the same place as Qyrzula
00:12:55.02 It appears to have come from a common ancestor which had more in common in this region with PG1.
00:13:02.15 So this is a good example of mosaicism, a key architectural feature of bacteriophage genomes.
00:13:08.26 When you look at the nucleotide sequence level you can see precisely where these types of events occur-
00:13:18.27 at the boundaries that must reflect where recombination occurred to give you this exchange of information.
00:13:26.08 And in this particular slide I am showing the detailed information of two genomes.
00:13:33.28 The one at the top here you can see the sequences,
00:13:36.27 and in blue the amino acid sequences of the predicted genes in that region.
00:13:41.18 In the bottom you can see a second genome that we are comparing.
00:13:46.00 And this red shading over on the right hand side is
00:13:50.28 simply reflecting a segment where these genomes are closely related.
00:13:54.22 The nucleotide sequences, the DNAs are extremely similar if not identical in this red part,
00:14:03.09 but over here, they are completely different. They are completely dissimilar.
00:14:08.07 And so the key point that you can see from this type of comparison that this module boundary, this junction
00:14:15.20 between the red and the white parts where recombination must have happened,
00:14:21.13 this module boundary, corresponds precisely to the boundaries of the genes.
00:14:27.16 It is this boundary which is where this gene starts up here, and its comparable gene begins down here.
00:14:38.04 These genes to the left are very different, and to the right they are identical.
00:14:42.23 So the module boundary, or the recombinant joint which must have brought these together
00:14:48.15 coincides with the gene boundaries themselves.
00:14:52.25 And this is a common and important observation and it helps us to think about how mosaicism can be generated.
00:15:01.14 And there are two fundamental models.
00:15:03.05 The first is that recombination happens at targeted, short, conserved boundary sequences.
00:15:13.06 The idea that there are some short conserved segments of sequences,
00:15:17.00 perhaps a dozen or a couple of dozen nucleotides in length
00:15:19.23 which corresponds to those boundary regions.
00:15:23.05 And that homologous recombination perhaps encoded by host enzymes
00:15:26.26 catalyzes exchange at that region in order to promote recombination
00:15:32.15 at places where genes themselves in their entirety get exchanged.
00:15:36.22 There are some examples of that that have been reported in the literature.
00:15:41.29 So this is certainly an event that can happen.
00:15:45.05 We think however that it is more likely that much of the mosaicism that you see
00:15:51.02 because it is this pervasive feature throughout phage genomes
00:15:55.07 can occur by an alternative mechanism which is by illegitimate recombination
00:15:59.19 at what are essentially randomly chosen sequences.
00:16:04.04 In other words, that even though we see a close correspondence
00:16:08.08 between the point of recombination and the gene boundaries,
00:16:12.03 this does not result by this model from targeted exchange at that point.
00:16:19.03 Rather that the exchange positions are random
00:16:22.28 and the reason why that correspondence occurs is because of
00:16:26.12 selection for gene function for those genes that can actually work.
00:16:32.03 And so this just illustrates the different types of examples of recombination.
00:16:39.08 In the top panel one could imagine the targeted recombination, targeted homologous recombination,
00:16:45.03 could occur at these short black segments. Short segments of DNA that are conserved at gene boundaries
00:16:53.28 in order to give you these exchange events in these recombinants.
00:16:57.08 This middle panel here shows an example of illegitimate recombination
00:17:02.06 where recombination has essentially happened anywhere.
00:17:05.20 It has happened between sequences that are not related to each other.
00:17:09.02 And you get whatever gobbledygook may arise from just a random exchange in the process.
00:17:15.15 And at the bottom here I want to emphasize that we do expect recombination to occur between shared sequences
00:17:25.04 such as whole genes that are shared.
00:17:27.01 Homologous recombination of this sort always happens,
00:17:31.19 and it gives you new combinations of flanking genes,
00:17:34.28 such as A now joined together with C.
00:17:38.03 Ok. So homologous recombination is always going to play a role in reassorting
00:17:43.07 the types of genes that can be present in the modules.
00:17:47.03 But homologous recombination of this general type does not generate new recombinant boundaries,
00:17:55.18 new module boundaries unless it is in this targeted approach.
00:18:00.27 So as I mentioned we think that whereas there are a small number of examples
00:18:06.26 that would support the exchange of boundary sequences
00:18:11.02 By far the majority of the boundaries that we see when we compare phage genomes
00:18:16.13 show no evidence of such boundary sequences, lending support to the idea that illegitimate recombination
00:18:23.27 is playing a key role. But there are some really important consequences
00:18:28.26 that we have to think about as a model for illegitimate recombination in this process.
00:18:33.16 First of all, illegitimate recombination, recombination between sequences
00:18:38.15 that don't share anything or very little in common,
00:18:41.11 is likely to happen at rather low frequencies.
00:18:45.02 It is going to occur at random positions, for the most part,
00:18:49.26 and that when you put together two pieces of DNA randomly,
00:18:55.00 for the most part it is just going to generate genomic garbage.
00:18:59.17 Material which may not have a genome of the appropriate length,
00:19:04.24 and will have lost some genes and is liable to be non-functional.
00:19:11.03 So in its essence we can think of it as a rather disruptive or destructive type of process.
00:19:19.21 And one can imagine that if this was going to play an important role,
00:19:24.24 that you would probably need multiple low frequency events in order to actually generate survivors,
00:19:34.07 the phoenix that can rise from the ashes with a full complement
00:19:37.04 of functional sequences that can function as a virus.
00:19:41.27 If sequences are going to recombine randomly with each other
00:19:49.07 then there is no necessity to think of these events as being predominantly involving two phage genomes.
00:19:58.12 The bacterial chromosome is about a hundred times the size of an average bacteriophage genome,
00:20:03.22 and therefore there is going to be a strong propensity or at least an opportunity
00:20:08.05 for the phage genome to recombine with the bacterial chromosome.
00:20:13.21 The process we can think of as being one that is infrequent and yet extremely creative.
00:20:22.08 This is the way in which you can take pieces of DNA
00:20:26.08 and put them together in a way in which has perhaps never been seen before in nature.
00:20:32.10 That's a way of making new genes, or perhaps putting domains together in novel combinations,
00:20:40.06 and generating new types of functions which perhaps have not been seen in nature before.
00:20:47.08 And so this fits in very much with our model as described by Darwin for the process of the origin of species,
00:20:58.09 where we can think of the variation being generated by these illegitimate recombination events
00:21:05.26 and then natural selection working on what is essentially this garbage
00:21:11.14 in order to select from that those components that work.
00:21:16.15 Even though we would think of this as being a very low frequency event,
00:21:21.25 requiring infrequent recombination events and multiple numbers of them, it is nonetheless it is creative.
00:21:30.27 And as we saw previously, that phages have likely to have been evolving for many, many years
00:21:40.19 in a very dynamic population very successfully.
00:21:44.26 So this will give us these recombinant joints.
00:21:49.09 These recombinant joints once they are formed are likely to be stably maintained.
00:21:53.07 There's no mechanism necessarily for undoing them and therefore
00:21:57.14 these survive as we see today as the fossilized relics of recombination events
00:22:02.29 that probably happened many of hundreds of millions or even billions of years ago.
00:22:08.27 And thinking about the mechanisms by which this might happen,
00:22:12.00 it's been shown that many bacteriophages encode recombinase enzymes
00:22:19.25 which have the capability to recombine genomes at least at very short sequences
00:22:26.23 that don't have to be completely identical to themselves.
00:22:32.00 raising the interesting possibility that bacteriophages actually encode their own machinery
00:22:36.20 that can facilitate this type of recombination,
00:22:40.27 and indeed the generation of the mosaic genomes as we see them.
00:22:44.10 Looking at bacteriophages that are very different in their sequences
00:22:53.03 is quite limited to each other and this shows us that if we really want to learn more
00:23:00.14 about the details about how mosaicism is created and how it works,
00:23:04.29 we really have to think about, and very carefully, about what types of genomes we want to compare with each other.
00:23:12.04 And we will see an example of that in part three.
00:23:15.09 So we can conclude then from this genomic comparison of phages
00:23:24.14 we can conclude that phage genomes are architecturally mosaic.
00:23:28.15 That mosaicism is fueled by this process of illegitimate recombination.
00:23:32.27 And that genome segments can eventually be reassorted by homologous recombination
00:23:39.13 once new joints between new genes are generated to form that mosaicism.
00:23:45.09 In part three, we'll look at a rather particular case
00:23:49.14 of the detailed analysis of bacteriophages that infect one particular common host
00:23:54.27 where all those bacteriophages can be argued
00:23:58.00 to be potentially in genetic communication with each other,
00:24:01.21 and we can therefore explore what they look like
00:24:04.06 and the insights that they can give us in bacteriophage evolution.

Part 3: Mycobacteriophage genomics

00:00:01.00 Hello. My name is Graham Hatfull.
00:00:03.08 I'm a professor at the University of Pittsburgh
00:00:05.23 and a Howard Hughes Medical Institute professor.
00:00:08.21 Today we are talking about bacteriophages, their genes and their genomes,
00:00:12.23 and in part three we are going to focus in on a comparative analysis
00:00:17.00 of a particular type of bacteriophages. These are the mycobacteriophages, phages that infect mycobacterial hosts.
00:00:26.09 And so I should explain why we would want to choose phages of a particular host.
00:00:37.00 And indeed, why we would want to focus on this particular group.
00:00:40.27 So, perhaps one of the most important aspects is that phages
00:00:46.10 that infect very different bacteria tend to be very unrelated to each other.
00:00:51.04 And therefore there is not much to be learned about the detailed mechanisms
00:00:56.02 of phage evolution by comparing them.
00:00:59.13 They are so different there is little to be learned.
00:01:02.23 On the other hand if we were to focus on the phages that infect a common bacterial host,
00:01:08.01 then we would argue that they must all be in some way
00:01:12.17 in genetic, at least potentially, in genetic communication with each other.
00:01:18.22 And then comes the question as to well which bacteria host should we use
00:01:22.13 in order to isolate and characterize these viruses?
00:01:27.06 And there's many of course bacteria to choose from.
00:01:32.05 If we had to think of them as ones that would be the most useful, the most interesting,
00:01:36.20 we might want to think about focusing on some bacterial pathogens.
00:01:40.17 Or alternatively bacteria that are important for other criteria.
00:01:46.15 Environmentally important, or other key aspects of their biology.
00:01:53.17 So we focused on the mycobacteriophages.
00:01:58.07 And in part because we think that the mycobacterial hosts are of sufficient importance
00:02:06.00 that they really warrant taking advantage of the viral systems that we could develop.
00:02:13.10 Not just for understanding the viruses, but for understanding the hosts that they infect.
00:02:18.17 And so I'll mention two bacterial species within this genus.
00:02:26.22 One is Mycobacterium tuberculosis, which is the causative agent of human TB.
00:02:34.19 And I'll mention a relative of Mycobacterium tuberculosis, which is called Mycobacterium smegmatis,
00:02:41.10 and this is important because it is a very helpful surrogate for us to use in the lab.
00:02:47.07 Mycobacterium tuberculosis we can grow in the lab,
00:02:51.18 but we have to be very cautious and careful with it for two reasons.
00:02:55.25 Primarily because it is a rather nasty bacterial pathogen,
00:03:01.22 and we certainly don't want any of us working in the lab to be infected with that organism.
00:03:08.19 But is has another feature that somewhat complicates its growth and manipulation in the lab.
00:03:13.22 And that is that it grows extremely slowly. It has a doubling time of about 24 hours.
00:03:18.19 So it takes a day to go from one cell to two cells with Mycobacterium tuberculosis.
00:03:24.28 That makes research pretty slow going on M. tb.,
00:03:31.23 but you also have to be very careful about sterility and your aseptic technique
00:03:36.18 because almost everything out there grows faster than Mycobacterium tuberculosis,
00:03:41.24 and if you are not careful, you will end up growing that rather than M. tb.
00:03:46.09 Mycobacterium smegmatis, in contrast, is a non-pathogen.
00:03:51.20 It does not cause disease in healthy adult human beings,
00:03:56.20 and it grows relatively quickly. It has a doubling time of about three hours,
00:04:01.29 which means that we can grow a lawn, a smooth lawn, of Mycobacterium smegmatis
00:04:05.29 on Petri dishes in about 24 hours, and we can grown individual colonies in three to four days.
00:04:14.02 Mycobacterium tuberculosis is actually a very serious and important human pathogen.
00:04:21.13 About two million people a year die from Mycobacterium tuberculosis infections, from TB.
00:04:31.02 And it is estimated that Mycobacterium tuberculosis kills more people
00:04:35.27 in the world than any other single, infectious agent.
00:04:39.05 Many people that are infected with the organism actually don't get disease
00:04:45.09 because the bacterium establishes a latent infection and doesn't cause health problems.
00:04:53.20 Although, it can do either with old age,
00:04:58.15 or with a compromise of your immune system, such as for example with HIV infection.
00:05:04.29 These are not only a very prevalent... it's a very prevalent disease is tuberculosis,
00:05:10.24 but there is a growing and widespread concern
00:05:13.22 about drug resistance strains of Mycobacterium tuberculosis,
00:05:17.27 that are either difficult to treat or effectively untreatable.
00:05:23.18 There's clearly a need for new strategies for diagnosis, prevention, and cure of tuberculosis.
00:05:32.02 And so we think these are good reasons to focus on the phages
00:05:36.20 that infect these organisms in the hope that they could contribute towards that specific cause.
00:05:43.25 And so this is an important point. The mycobacteriophages can really lead us in two directions.
00:05:51.10 They can tell us about viral diversity and the evolution of bacteriophages,
00:05:56.09 and at the same time they can provide tools for controlling TB
00:06:01.01 and in fact can provide elements that we need to manipulate TB to understand it and to work with it.
00:06:09.05 I am not going to focus here too much on the specific applications of the mycobacteriophages.
00:06:16.27 I thought that I would just mention one in passing,
00:06:19.28 which is the use of mycobacteriophages as a novel type of diagnostic system
00:06:24.28 in order to test whether a person is infected with TB
00:06:30.21 and indeed whether it is a drug resistant or a drug sensitive strain.
00:06:35.19 This is a strategy which was first described by my colleagues Bill Jacobs and Barry Bloom.
00:06:41.07 The idea is to make so called reporter mycobacteriophages,
00:06:45.26 recombinant phages that carry a gene that can report
00:06:50.27 and tell us about the metabolism of the mycobacterial cell.
00:06:57.04 So you can construct reporter phages that carry a gene
00:07:01.11 such as firefly luciferase that will make the bacteria emit light.
00:07:06.13 Or we can make reporter phages that carry green fluorescent protein from jellyfish
00:07:13.06 that when that is introduced by infection of the host, it makes the cell fluoresce.
00:07:18.12 And we can use these properties, fluorescence or light emission,
00:07:23.10 in order to then monitor what type of bacteria a particular patient is infected with.
00:07:30.18 And so this is an idea that I think shows considerable promise
00:07:33.05 and is currently undergoing further research and development.
00:07:37.20 If we want to compare the genomes of mycobacteriophages
00:07:48.13 in order to understand how they are related to each other,
00:07:52.06 how they've evolved, what their diversity is, well,
00:07:55.23 we need to have the mycobacteriophages in order to characterize.
00:08:00.08 And so we have gone out over the past few years
00:08:03.21 to isolate new mycobacteriophages and to genomically characterize them.
00:08:10.04 And whilst this has been a major focus in my laboratory,
00:08:14.17 this has also proven a very successful approach for both high school students
00:08:22.23 and undergraduate students to become involved in research endeavors
00:08:28.28 by going out and isolating new mycobacteriophages and sequencing them.
00:08:33.03 And now with the Howard Hughes Medical Institute science education alliance,
00:08:38.27 there are hundreds of students who are contributing to this cause,
00:08:42.16 and because of this we now have many new mycobacteriophages to characterize and to compare.
00:08:51.02 The process is relatively simple.
00:08:53.11 We start with a sample of soil or compost or wherever you might think to go
00:09:01.20 and look and to find out if there are some bacteriophages present,
00:09:04.18 The sample is mixed up with some liquid. The particulate matter is removed.
00:09:12.02 And we simply incubate some of that in the presence of our permissive bacterial host,
00:09:18.12 which is Mycobacterium smegmatis.
00:09:20.07 We lay those out on a Petri dish, as shown here, and we look for plaques,
00:09:26.02 for areas where a phage that was present in our original sample
00:09:32.09 has now infected these cells to form a plaque.
00:09:34.25 We can then pick an individual plaque, purify it, remove all of the other contaminants,
00:09:43.15 and we can propagate it in the laboratory until we have a high titer or a concentrated stock.
00:09:49.12 From that we can make DNA.
00:09:51.08 The DNA can be sequenced to give us tens of thousands of nucleotide sequence information,
00:10:00.20 and then we use computational approaches and bioinformatics
00:10:04.20 to predict where all the genes are in these genomes, and then we can compare them.
00:10:11.01 So we are using Mycobacterium smegmatis as our host,
00:10:16.24 fast growing, non-pathogen, and our samples predominantly come from soil and compost.
00:10:23.06 We have usually just simply plated out the sample with our permissive host,
00:10:28.24 but because the specific phages that we're after can be present at relatively low concentrations,
00:10:37.05 there is an approach that can be used with enrichment, where you simply take your soil or your compost sample,
00:10:43.00 you mix it and incubate it with some permissive host cells,
00:10:47.21 in this case Mycobacterium smegmatis, that allows even the small number of particles
00:10:53.23 that may be present to infect, to reproduce themselves,
00:10:57.27 and so that when it comes to the plating and the identification of
00:11:03.06 plaques they're present at higher concentrations.
00:11:05.20 There's a couple of different approaches,
00:11:08.03 but this is a relatively reproducible and simple process for discovering new phages.
00:11:16.02 So by this point thousands of mycobacteriophages have been isolated
00:11:19.06 using Mycobacterium smegmatis as a host.
00:11:22.16 I should state I think that some of these infect smegmatis,
00:11:27.14 but don't infect Mycobacterium tuberculosis, whereas others do.
00:11:32.19 And so we use a surrogate strain, Mycobacterium smegmatis, as a host,
00:11:37.23 but it is likely that the host range, the cell preferences of the phages that we isolate
00:11:43.28 are going to be all over the place and at this stage are not well defined.
00:11:47.13 We've... the most recent publication that describes the characterization of these
00:11:56.07 appeared earlier this year in 2010, and described a comparative analysis of 60 of these.
00:12:04.14 But because of the impact of the science education alliance program
00:12:10.21 as well as the ongoing studies of Pittsburgh,
00:12:12.17 the number of new phages and sequenced genomes, it is positively exploding.
00:12:18.27 And at this point in the middle of October in 2010,
00:12:23.28 154 completed genome sequences and much analysis awaiting to be done.
00:12:33.11 All of these phages, it turns out, even though they don't have to be,
00:12:38.20 are double stranded DNA tailed phages.
00:12:42.14 We haven't isolated any RNA phages or any single stranded DNA phages.
00:12:48.02 They are all double stranded DNA, tailed phages.
00:12:51.12 Now in part one of this lecture we saw that perhaps the most common order of bacteriophages
00:12:59.06 are the Caudovirales, the double stranded DNA, tailed viruses.
00:13:02.21 Just like these that I showed you. I also told you that there's three common types.
00:13:09.06 The so-called Siphoviridae with the long flexible tails, the Myoviridae with the contractile tails,
00:13:14.24 and the Podoviridae that have short stubby tails.
00:13:17.21 If we just compare the morphotypes of these 60 genomes,
00:13:23.13 which have been analyzed and published,
00:13:27.09 53 of them are of this Siphovirus type, 7 of them are of the Myovirus type.
00:13:33.08 We have no Podoviruses at all.
00:13:37.21 And so these numbers appear to hold true for the larger collection
00:13:42.05 of mycobacteriophages, and therefore we have growing confidence in the idea
00:13:47.15 that there really are no Podoviruses amongst the mycobacteriophages.
00:13:53.00 We don't know whether this is because phages with the short stubby tails
00:13:58.20 are physically incapable of infecting bacteria like the mycobacteria
00:14:04.25 that have thick and chemically complex cell walls,
00:14:08.06 or whether it's just a reflection of a restriction
00:14:12.12 of evolutionary opportunities to generate those types of phages.
00:14:19.04 So that's a little bit of a mystery as we don't have any Podoviruses,
00:14:24.18 but we have lots of examples of these other two morphotypes.
00:14:28.28 When we look at the genomes there are some basic parameters
00:14:34.21 that we can see that are helpful in thinking about what these genomes are like.
00:14:38.11 First of all, the average length of all them is 72,588 base pairs.
00:14:47.01 We don't really understand why mycobacteriophages would have that particular length.
00:14:51.07 Phages of other bacterial hosts often have very different average lengths
00:14:58.02 including those that are only half as long as the average mycobacteriophage genome.
00:15:03.09 And so we don't really know what determines this parameter,
00:15:08.02 either for the mycobacteriophages or indeed for any other phages.
00:15:12.28 There's also a large range in size from a little under 42,000 base pairs
00:15:20.23 up to about 164 and a half thousand base pairs.
00:15:25.06 So there is a lot of diversity in terms of size range.
00:15:29.25 The GC content on average for all of these 60 genomes is about 63 and a half percent.
00:15:37.00 A number which closely mirrors the GC content of the bacterial host Mycobacterium smegmatis.
00:15:44.08 And that's not a surprise because it has been seen from the analysis of phages of other bacterial hosts
00:15:52.27 that the GC content of the phages often mirrors that of the hosts.
00:15:57.09 What's perhaps more surprising, however, is that the range of GC content
00:16:03.10 amongst these phages is actually really amazingly broad
00:16:07.09 spanning from 56.3% at the lower end up to 69% at the upper end.
00:16:14.28 And we've been trying to think for some time as to what this span of GC content reflects.
00:16:23.16 One attractive idea although it remains to be fully tested is that these particular mycobacteriophages
00:16:33.13 whilst they have a common host in Mycobacterium smegmatis
00:16:37.12 may not necessarily have been infecting Mycobacterium smegmatis
00:16:43.17 as their preferred bacterial host in the environment from which we recovered the phages
00:16:49.13 in their recent ecological and evolutionary times.
00:16:54.11 In other words, they may have preferences for infecting some other bacterial host
00:17:00.20 that we have yet to figure out what that is.
00:17:03.24 But that might account for the range of GC content that we would see.
00:17:09.06 And so one of the things that we would like to do to test this idea
00:17:11.28 is to actually determine the specific host range
00:17:15.18 on a whole range of bacteria that are related to the Mycobacteria
00:17:21.24 to see if we can discern a pattern or a correlation between GC content and the host preferences.
00:17:27.17 And finally if we look at the number of genes that are present,
00:17:31.21 of these 60 genomes there is a total of 6858 open reading frames or putative protein coding genes, ORFs,
00:17:40.19 about a hundred and fourteen ORFs on average per genome.
00:17:45.27 And interestingly the average ORF size, the average size of an open reading frame, is only 616 base pairs.
00:17:55.14 That's about two thirds of the average size of a bacterial gene.
00:18:02.22 And this appears to be a parameter which is true not just for the mycobacteriophages,
00:18:08.08 but for other bacteriophages that people have looked at.
00:18:11.01 And we've been interested as to why this number
00:18:13.13 should be quite so different from that of the bacterial host.
00:18:17.14 It fits, however, I think, with the idea that illegitimate recombination
00:18:23.10 is playing a key role in how these genomes evolve.
00:18:29.06 And in fact we can see that many of the segments of DNA that appear to have come in
00:18:34.26 relatively recently from other genomes tend to be on the small side.
00:18:39.07 And therefore we can think of this process of evolution, as we talked about in part two,
00:18:46.29 may actually contribute to driving the average gene size down.
00:18:52.20 So we can take our 60 genomes, and we can ask the question:
00:18:58.25 "how are they related to each other at the nucleotide sequence level?"
00:19:03.27 And we can use an approach that we saw in part two,
00:19:10.17 which is where we can compare the nucleotide sequences in a dot plot analysis.
00:19:16.20 And one way of doing this is illustrated here.
00:19:21.29 Now what we've done is to take our 60 genomes,
00:19:24.29 and we've simply joined them together end to end to make a long concatamer,
00:19:30.26 and we've done that in random order.
00:19:33.01 We've just taken our sixty sequences joined them together
00:19:36.14 to get a long span and then simply compared them with each other.
00:19:40.02 Not surprisingly there is a diagonal line from the top left to the bottom right
00:19:45.22 because that simply tells us that every phage genome is identical to itself.
00:19:51.22 That is a good thing.
00:19:53.00 And then there's a number of diagonal lines you can see
00:19:57.05 where a particular phage in this part of the array
00:20:01.24 is similar to a second phage that is sitting in a different part of the array.
00:20:09.08 And because the genomes are in a random order in this concatamer,
00:20:13.10 these various types of relationships are scattered over this dot plot.
00:20:21.06 And we can see though, I think, that we have phage genomes that are similar to each other,
00:20:27.16 but there must be many that are completely dissimilar to each other at the nucleotide sequence level.
00:20:33.06 So having done this and identified, generally speaking, who is most closely related to who else,
00:20:40.13 what we can do is we can take each of the genomes
00:20:44.03 and we can change the order in which we've arrayed them in this concatamer,
00:20:50.10 and then repeat this computational comparison.
00:20:54.09 So when we do that, this is what the plot looks like.
00:20:57.14 And so all we've done is simply to group the genomes together that are similar to each other.
00:21:04.26 So for example if you look in the top right hand corner all of those genomes that are similar to each other are positioned
00:21:10.10 next to them in the top left hand part of the plot.
00:21:13.03 We can take this gross nucleotide sequence similarity
00:21:18.02 to put the genomes together into what we refer to as clusters.
00:21:24.04 Such as Cluster A, Cluster B, C, D, E, etc.
00:21:27.10 And so those clusters go up to cluster I,
00:21:30.13 and on the right hand side where it says Sin,
00:21:36.05 this corresponds to what we refer to as singleton genomes.
00:21:41.02 And out of these 60 genomes, there are 5 that are singletons,
00:21:45.11 which means that each of those has no close relatives
00:21:49.10 either here or anywhere through the biological world.
00:21:56.00 There is some important texture to this grouping and these clusterings,
00:22:01.25 and we can readily identify some clusters as being, having more than one closely related type.
00:22:11.14 And we therefore subdivide the cluster into sub-clusters.
00:22:16.09 You can see here for the cluster C that there are many of these genomes,
00:22:21.13 in fact almost all of them are very similar to each other,
00:22:25.08 and constitute sub-cluster C1, and then there is a single genome over here
00:22:31.10 which is related to the other C cluster genomes, but less so, so that constitutes sub-cluster C2.
00:22:41.10 So we have a large number of different types of genomes,
00:22:43.27 more than twenty substantially different types of genomes,
00:22:47.02 just within this group of 60 that we are looking at.
00:22:51.21 And so each of these genomes, and you can see them identified by name here,
00:22:57.05 as we zoom in on the different clusters and sub clusters.
00:23:00.18 Here we are looking at clusters A through to E.
00:23:03.15 Sub cluster C as I indicated can be divided into sub-cluster C1
00:23:10.25 with Bxz1, Cali, Catera, Rizal, ScottMcG, and Spud.
00:23:16.26 And then Myrna is the sole member of cluster C2.
00:23:20.22 And these are the remaining clusters, F, G, H, and I.
00:23:27.24 And then here are the singletons over on the right hand side here:
00:23:31.14 Corndog, Giles, TM4, Wildcat, and Omega.
00:23:37.00 And so we can take each of these genomes that we've assorted with each other
00:23:47.17 according to their nucleotide sequence similarity,
00:23:50.18 or if they are singletons, they're one of a type.
00:23:53.14 We can generate the genome maps,
00:23:55.26 and we can see what features they have and what they look like.
00:23:58.18 This is showing Giles, which I introduced previously in part 2 of the lecture,
00:24:03.29 and you can see its densely packed genes with the rightwards transcribed genes above the DNA,
00:24:12.22 and the leftwards transcribed genes below the DNA.
00:24:15.20 It is densely packed and we've color coordinated these genes according to their relatives.
00:24:22.03 And so we now have these genome maps for all of these phage genomes
00:24:28.11 and these maps then can be compared,
00:24:31.01 and in fact the genes and the predicted proteins can be compared as well.
00:24:35.23 So we look at these 60 mycobacteriophages,
00:24:39.20 and we see that the genes are tightly packed with few non-coding regions.
00:24:42.29 There's many, many genes, but there appears to be few operons.
00:24:49.11 Meaning that we think that there may be a hundred genes, but there may be only 2, 3, or 4 sites
00:24:56.09 for transcription initiation or promoters that are used to express these genes.
00:25:02.19 We actually know very little about the patterns of gene expression of any of these phages,
00:25:07.05 but the bioinformatic predictions are that there will be blocks of genes that are transcribed together.
00:25:15.16 The virion genes, those are the genes that encode the structural components, the heads and the tails,
00:25:24.23 those genes typically tend to be grouped together in the genome,
00:25:28.19 and they have a common order or synteny
00:25:32.09 which is conserved even though the genomic sequences may be extremely different to each other.
00:25:40.23 Especially once we examine the parts of the genomes outside of these virion genes,
00:25:46.14 we find vast numbers of genes, many of them relatively small,
00:25:51.03 which have a completely unknown function.
00:25:54.25 And we have failed to predict what they can do simply from comparing them with other genomes.
00:26:01.22 And so what we've done is to create a computer program.
00:26:08.06 This was a program call Phamerator, and it was written by a colleague of mine, Dr. Steve Cresawn,
00:26:13.27 which can then begin to analyze all of the genes and how they are related to each other
00:26:19.05 by comparing them at the amino acid sequence level.
00:26:22.24 This is really important because so far I have shown you how
00:26:26.29 we can compare genomes at the nucleotide sequence level,
00:26:30.05 I also showed you that we have lots of examples because we have many different types of genomes.
00:26:36.08 that appear to not share nucleotide sequence similarity
00:26:41.10 even though they are in genetic communication with each other, at least in principle,
00:26:46.10 because of the use of the common host.
00:26:49.05 Just because they don't have nucleotide sequence similarity
00:26:53.00 doesn't mean that they are completely unrelated.
00:26:56.00 And in fact, once we start to look at the gene relationships
00:27:01.16 by comparing the amino acid sequences
00:27:04.22 we can begin to see the patterns that reflect the common origins of the phages,
00:27:10.15 even though they no longer share nucleotide sequence similarity.
00:27:14.06 And so this program that Steve Cresawn wrote
00:27:19.07 called Phamerator facilitates this in a very important process.
00:27:25.07 What it does is it takes each of these open reading frames out of 60 genomes,
00:27:30.06 we have these 6,854 genes.
00:27:33.14 It takes each of the predicted proteins
00:27:37.10 and compares them with everything else
00:27:39.23 using alignment programs such as BLASTp and Clustal.
00:27:45.23 Genes which are related to each other because
00:27:49.21 they meet a particular threshold of similarity we group together.
00:27:55.13 And we put them in groups, and those groups are called phamilies or phams.
00:27:59.20 And of these 6,858 genes we have a total of 1,523 distinct phamilies or sequences.
00:28:12.04 A large proportion of those are what we refer to as "orphams".
00:28:18.13 They are phamilies but they only contain a single member.
00:28:21.25 Not because we believe that other members don't exist
00:28:26.14 but because this population of phages appears to be very diverse
00:28:31.12 and presumably quite large,
00:28:33.10 and we simply haven't yet identified the relatives
00:28:36.29 of these orphams that constitute these phamilies.
00:28:40.03 And so this is about 45% of all of our phamilies only have a single member.
00:28:45.16 This Phamerator program is extremely helpful for generating the maps
00:28:51.25 and displaying the relationships that help us understand
00:28:54.19 the mosaic components by which these are put together.
00:28:59.10 And so here I am showing segments of four genomes,
00:29:01.22 that you can see, just parts that are aligned
00:29:07.03 showing the boxes here and the numbers above the boxes such as here at the top in the middle, 1406,
00:29:16.13 refers to a particular phamily. That's a phamily number for which that gene is a member.
00:29:22.12 And then in this display we can color coordinate the degree
00:29:27.14 of sequence similarity at the nucleotide level between the various genomes.
00:29:32.26 And this is actually reflecting a part that I showed you... a part of these genomes
00:29:36.22 that we talked about in part two.
00:29:39.18 Now we can do this type of representation with large numbers of these genomes.
00:29:47.05 When we look at particular clusters, any particular cluster
00:29:51.03 can have genomes that are very similar to each other,
00:29:55.28 or they can be actually quite diverse, depending on the particular cluster
00:30:00.25 that you look at and the degree of sequence similarity.
00:30:04.03 I am just illustrating this with the clustered G phages,
00:30:09.06 for which in our expanded set we actually have 4 members now,
00:30:13.29 and the color coordination, the purple between these four genomes illustrates how very closely related they are.
00:30:21.25 And when we compare the colors of the genes at the protein levels,
00:30:25.21 you can see that these are also very similar.
00:30:30.13 This method is very powerful in part because it is a very easy way
00:30:35.18 of seeing rather smaller differences
00:30:37.27 that nonetheless have played a key role in how these genomes have evolved.
00:30:42.20 For example, down in the right hand end you can see these convolutions
00:30:46.22 here of segments that have been lost from one genome or gained by another.
00:30:51.13 And in fact this illustrates the finding of a new mobile genetic element,
00:30:57.10 a new ultra small transposon that appears to play a role in these particular...
00:31:02.08 in the evolution of these particular genomes.
00:31:05.09 So in part two we saw a lot about how mosaicism is the key architectural feature
00:31:14.16 of bacteriophage genomes. Because now we are looking at this group of mycobacteriophages
00:31:22.12 infecting a common host,
00:31:24.15 we have lots of examples where even though there is no nucleotide sequence similarity,
00:31:29.23 we can see that the genes are shared through common amino acid sequence similarity.
00:31:37.15 And therefore we can look at patterns that are contributing in generating the process of genome mosaicism
00:31:46.09 even in the absence of substantial sequence similarity.
00:31:51.23 And what we find is a massive amount of mosaicism
00:31:56.11 where the modules that contribute to the structure of the genome often correspond to simply to single genes.
00:32:08.14 So modules correspond to single genes when we conduct this type of analysis.
00:32:15.03 And we've developed a particular tool for representing this, representing the phylogenies if you like,
00:32:24.08 where we can take individual Phams- here is one Pham 233 and here is another Pham, Pham 471-
00:32:31.27 and in these representations, we've simply drawn as points around the circle
00:32:38.24 all of the genomes that we have available to us,
00:32:41.16 and for that particular sequence family we've drawn an arc between those genomes
00:32:48.28 that have a member of that Pham.
00:32:52.29 And therefore it essentially represents or reflects the phylogeny
00:32:57.26 or the evolutionary history of this particular family of sequences.
00:33:02.09 In the top part of the figure I've just shown a small segment of phage Omega from genes 125 to 128.
00:33:11.27 Gene 126 in Omega has a relative that we can see through amino acid sequence similarity
00:33:19.23 to a gene in this genome called Cjw1.
00:33:24.23 Gene 127 in Omega has a relative in a genome called KBG.
00:33:32.11 In that case, gene 84. But, and this is important, the context, the flanking sequences in each case is different.
00:33:46.15 Ok, the sequences to the left of Omega 126, which corresponds to Omega gene 125,
00:33:53.06 are completely unrelated to Cjw1 gene 72,
00:33:58.17 which is at the left part of that gene in Cjw1.
00:34:03.10 And the same goes for the KBG comparison.
00:34:08.26 So in this case we can see that we don't have any nucleotide sequence similarity between these,
00:34:13.15 but we can dissect these evolutionary relationships that show
00:34:21.09 that these two adjacent genes in this example in Omega
00:34:24.16 have clear and distinct evolutionary histories. They have different phylogenies.
00:34:30.02 And this is one example, but we have clearly thousands of examples of Phams
00:34:36.00 which share and exhibit these types of relationships.
00:34:40.26 And this has considerable importance when you start to think
00:34:46.02 about questions of phylogeny of whole phage genomes.
00:34:50.23 Why not just take whole phage genomes and construct a phylogenetic tree
00:34:54.16 so you can see how they are all related to each other?
00:34:58.16 The problem is that all of the bits of the genomes because they are mosaic,
00:35:03.10 built from modules and pieces, and all those bits and those pieces have distinct evolutionary histories.
00:35:10.04 They have different phylogenies. There is arguably no single, clear, evident
00:35:16.17 phylogeny for a genome as a whole.
00:35:18.27 The genome represents an individual phage,
00:35:23.04 and its evolutionary history is reflected in a multiplicity of events
00:35:28.17 that have put those pieces together in that particular combination, in that order, in that particular virus.
00:35:36.02 I am just showing some other genome maps here of
00:35:43.19 some of these genomes illustrating again that for some of these genes
00:35:52.05 that are encoding the structural proteins, we know what they do.
00:35:55.20 But most of these other abundance of genes with large numbers of genes,
00:36:00.02 we really have absolutely no idea what they do.
00:36:03.01 And we would certainly like to know what their functions are and indeed what their structures are.
00:36:08.00 And so now if we expand our analysis to include the unpublished information,
00:36:13.12 and this was done for a 153 genomes that are completely sequenced,
00:36:19.09 over 17,000 open reading frames, almost 3000 Phamilies of distinct and different protein sequences.
00:36:26.12 The number of Orphams has come down slightly.
00:36:30.26 It is about 41% as we have started to find some of the relatives of genes that were previously Orphams.
00:36:38.22 And amazingly, if we take these almost 3000 phamilies,
00:36:43.11 and we compare them against the sequence databases,
00:36:46.13 we find that about 80% of them are novel genes. They are novel sequences.
00:36:52.21 There are no relatives of either other phages or anything else that has been sequenced in the database.
00:36:59.18 Even of the 20% of Phams that do match, so you know there is a related protein out there in the databases,
00:37:08.14 about half of those are for genes for which people don't know what they do anyway.
00:37:14.09 So database searching is an interesting exercise with these bacteriophage genes.
00:37:23.17 It provides rather little information as to what the functions of the genes are.
00:37:27.06 It is obviously very helpful when they do,
00:37:29.24 but the amazing thing is we just don't know what most of these genes do, and we would like to.
00:37:34.27 In this particular system we've made some headway
00:37:40.05 in developing a tool that can now help us address this question.
00:37:46.03 It is called BRED, or bacteriophage recombineering of electroporated DNA,
00:37:50.26 and it provides a simple, reproducible technique
00:37:56.19 for constructing mutants in mycobacteriophage genomes, either deletions, insertions, point mutations.
00:38:04.26 This method is published, and I won't go through its details here,
00:38:09.02 but it really just requires a simple electroporation step,
00:38:13.24 an ability to put phage DNA and a synthetic substrate together
00:38:18.04 inside a cell, and those techniques are well established
00:38:20.23 for doing so, followed by nothing more complicated than simply doing
00:38:27.27 a polymerase chain reaction or PCR screens
00:38:30.27 amongst a dozen or so of the progeny plaques that are recovered
00:38:37.04 in order to find those that have the mutation that you need.
00:38:40.11 And this is all accomplished through the establishment of a so-called mycobacterial recombineering system
00:38:47.01 that enables this to happen
00:38:49.12 at much higher frequency than you would normally see it.
00:38:52.20 And so this is very powerful because we can use that type of approach
00:38:57.05 to now go and ask what those genes do.
00:39:00.05 And indeed we can use it to try to develop applications for some of what we've found and what we are learning
00:39:07.23 that might be useful for the genetics of mycobacteria or specifically control of tuberculosis.
00:39:13.17 And I will give one brief example of that which is a couple of genes called Lysin A and Lysin B.
00:39:21.04 In this case I am again showing Giles as an example.
00:39:27.06 In part one we talked about an important step that happens at the conclusion of lytic growth,
00:39:34.01 and that is that in order for the phage particles that have been generated by infection to get out
00:39:41.06 then the cell wall needs to be compromised. It needs to be broken open. The cell needs to be lysed.
00:39:49.03 And the phage encodes the enzymes that enable that to happen.
00:39:52.09 We know very little about the process in mycobacteriophages,
00:39:58.16 but we were surprised in looking at the genomes that there are two candidate genes that are involved,
00:40:04.14 lysin A and lysin B, and we were able to use this engineering technique
00:40:12.14 to construct, to find mutations where we've removed either one of these genes
00:40:17.19 and examined what the behaviors of the phages were.
00:40:20.26 That way would enable us to figure out exactly what roles these genes are playing in lysis.
00:40:27.20 And I won't show you all the detailed experiments that gave us the conclusions as to what these do,
00:40:35.29 except I think the results are very clear.
00:40:39.19 And that is that in this portrayal of what the mycobacterial cell wall looks like,
00:40:44.13 where you have an inner membrane.
00:40:45.20 You have the peptidylglycan of the cell wall.
00:40:49.14 There is a sugar layer called arabinogalactan,
00:40:53.20 and covalently attached to this is the so called mycobacterial outer membrane,
00:40:59.14 which is composed of an interesting type of lipids called the mycolic acids.
00:41:04.23 And this is found in the mycobacteria and it is found in tuberculosis,
00:41:08.11 but most bacteria don't have this type of outer wall structure.
00:41:16.15 So not surprisingly we find that genomically the Lysin B enzyme
00:41:24.18 is found predominantly only encoded by mycobacteriophages.
00:41:28.28 And that was part of what clued us in to Lysin B playing a role in perhaps degrading this cell wall structure.
00:41:37.00 What we now know is that the Lysin A is the enzyme that degrades the peptidylglycan.
00:41:43.11 And Lysin B is this novel enzyme that actually cleaves the mycobacterial outer membrane
00:41:50.27 from this arabinogalactan layer and therefore facilitates complete lysis
00:41:56.28 of the cell during the process of release of the progeny viruses at the conclusion of the lytic cycle.
00:42:06.16 And we are obviously very interested in these enzymes
00:42:09.05 because they are enzymes that degrade the cell walls of mycobacteria.
00:42:14.27 And therefore we like the idea that these enzymes could perhaps
00:42:19.19 play potentially useful roles either in the lab to try to break open and to destroy mycobacteria.
00:42:26.29 And perhaps even in a clinical setting perhaps to either help to inactivate mycobacteria
00:42:34.05 or perhaps to act synergistically with antibiotics
00:42:37.14 to make them work better and quicker in killing
00:42:40.11 Mycobacterium tuberculosis in an infected patient.
00:42:44.21 So I gave you just one example there of how we can begin to identify what these genes do
00:42:51.21 and how some of them may be useful.
00:42:53.07 We have seen that mycobacteriophages are highly diverse.
00:42:56.14 They have these architecturally mosaic genomes,
00:42:59.17 and we can dissect this mosaicism not just by looking at the nucleotide sequence similarities,
00:43:05.10 but by comparing amino acid sequence similarities, a feature that is really greatly enhancing
00:43:12.26 and aided by the fact that we have now this large number of phages
00:43:19.01 and phage genomes that infect a common host.
00:43:22.06 And I think that that raises the idea that there is probably a lot to be learned
00:43:27.12 from generating similar collections of bacteriophages that infect other bacterial hosts.
00:43:34.26 And the larger these collections grow, the greater the insights
00:43:39.04 and the resolution of the information that we can gain
00:43:42.12 from how similar they are, how related to each other, and the specific mechanisms by which they have evolved.
00:43:48.14 80% of the genes are of unknown function, and we and others have our work cut out
00:43:56.01 to try to find out what these are, what they do,
00:43:59.17 what they look like structurally, and why they are there.
00:44:04.09 We are beginning to learn about how they got to be there in these genomes.
00:44:08.12 Now we need to know what they do.
00:44:10.04 I've told you that the techniques have now been established
00:44:14.02 that we can begin to readily manipulate these genomes.
00:44:17.00 Tools that one again could imagine applying to other bacterial hosts
00:44:22.14 and other viruses in order to address these questions.
00:44:26.10 And I think that we have now a very powerful tool box
00:44:30.14 in this large set of phages, in this large number of genomes,
00:44:36.07 that can be used to understand what makes
00:44:41.06 Mycobacterium tuberculosis, a major human pathogen, tick.
00:44:46.26 And how we can exploit and use those genes and those genomes
00:44:51.21 for contributing towards the diagnosis, the prevention, and cure of human TB.
00:45:00.10 I would like to finish by acknowledging those who have helped to support this research,
00:45:07.19 both the National Institutes of Health and the Howard Hughes Medical Institute.
00:45:11.23 All the work that I have talked about was performed by a truly stunning set of colleagues,
00:45:18.28 and I've listed many of their names there.
00:45:25.01 As I mentioned throughout that the genomic work has in part been done
00:45:30.26 by a large number of undergraduate students and high school students,
00:45:35.23 both in Pittsburgh and beyond. I don't have you all listed here,
00:45:39.13 but the contributions I think are really massive,
00:45:42.20 and I acknowledge that contribution, and thank you for that.
00:45:46.24 And so thank you for your attention to this iBioSeminars lecture.

Videos in this Talk

Part 1: Bacteriophages: What are they?
Audience:
- Student
- Researcher
- Educators of H. School / Intro Undergrad
- Educators of Adv. Undergrad / Grad
Part 2: Bacteriophages: Genomic insights.
Audience:
- Researcher
- Educators of Adv. Undergrad / Grad
Part 3: Mycobacteriophage genomics
Audience:
- Researcher
- Educators of Adv. Undergrad / Grad

Speaker: Graham Hatfull

Total Duration: 1:33:35

Recorded: October 2010

All Talks in Microbiology

Talk Overview

Bacteriophages, viruses that specifically infect bacteria, are, by far, the majority of all biological entities in the biosphere. The viral population, including bacteriophage, is very diverse yet relatively few viral genomes have been sequenced. In this series of lectures, Hatfull argues that viral genomes provide a great source of new genes, potentially with new functions and structures.

In Part 1, Hatfull describes what bacteriophage are, some of their biological properties and how they were discovered. In Part 2, he discusses the organization of bacteriophage genomes and explains how genomic sequence comparison can provide information about viral evolution. And in part 3, Hatfull describes work from his lab on mycobacteriophages, phage that infect mycobacteria including M. tuberculosis, the causative agent of the human disease tuberculosis. By studying mycobacteriophage, Hatfull hopes to better understand tuberculosis and perhaps develop methods for its control.

Speaker Bio

Graham Hatfull

Graham Hatfull is Professor of Biotechnology and Chair of the Department of Biological Sciences at the University of Pittsburgh and a Howard Hughes Medical Institute Professor. He received his PhD from Edinburgh University and was a post-doctoral fellow with Nigel Grindley at Yale University and Fred Sanger at the MRC. His lab focuses on studying… Continue Reading