Molecular Biology of Gene Regulation: Transcription Factors

Part 1: Gene Regulation: An Introduction

00:00:00.22 My name is Bob Tjian,
00:00:01.18 I'm a professor of MCB at the University of California, Berkeley,
00:00:05.29 and I'm also serving as the President of the Howard Hughes.
00:00:09.29 I'm going to spend the next 25 or 30 minutes telling you about some fundamentals
00:00:15.09 of one of the most important molecular processes in living cells,
00:00:21.02 which is the expression of genes through a process called transcription.
00:00:26.19 Now, first, to understand what gene expression means,
00:00:32.08 you have to have a sense of what we tend to refer to in the field as the "central dogma" of molecular biology.
00:00:39.25 Another way to think about this is the flow of biological information from DNA
00:00:46.28 (in other words, our chromosomes, which every cell has its complement)
00:00:50.21 to be transcribed into a sister molecule called RNA.
00:00:56.25 So this process of converting DNA into RNA is called "transcription,"
00:01:01.13 and that is the topic of this lecture.
00:01:05.12 This process is very complicated, as you will see by the end of my two lectures.
00:01:11.20 And it is very important for many, many fundamental processes in biology.
00:01:18.08 What I'm going to spend today's lecture on is the discovery of a large family of transcription proteins:
00:01:27.03 These are "factors," we call them, that are key molecules
00:01:31.11 that regulate the use of genetic information that has been encoded in the genome.
00:01:38.22 Now, transcription factors, or proteins, are involved in many fundamental aspects of biology,
00:01:46.04 including embryonic development, cellular differentiation, and cell fate.
00:01:51.20 In other words, pretty much what your cells are doing, how a tissue works,
00:01:56.13 and how an organism survives and reproduces is dependent on the process of gene expression,
00:02:03.17 and the first step in this process is transcription.
00:02:08.07 Now, there are many other reasons why a large group of people and scientists are interested in transcription,
00:02:16.13 and another reason is that understanding the fundamental molecular mechanisms that control transcription,
00:02:23.27 in humans or in any other organism, can inform us
00:02:29.13 and teach us about what happens when something goes wrong, for example, in diseases.
00:02:34.28 And I list here just a few diseases that we could study as a result of understanding the structure and
00:02:42.19 function of these transcription factor proteins that I'm going to be telling you about.
00:02:48.08 And of the course the hope is that in understanding the molecular underpinnings of complex diseases,
00:02:53.26 like cancer, diabetes, Parkinson's, and so forth,
00:02:58.00 that we will be able to develop and use better, more specific therapeutic drugs
00:03:05.01 and also to develop more accurate and rapid diagnostic tools.
00:03:10.04 So those are a couple of the reasons why many of us have spent,
00:03:14.01 in my case, over 30 years, studying this process of transcriptional regulation.
00:03:20.15 Now, to get the whole thing started, I have to give you a sense of what the magnitude of the problem is.
00:03:27.05 So imagine that one would really like to understand how this process of decoding the genome happens in humans.
00:03:34.24 So, as you may know, the human genome has some 3 billion base pairs
00:03:39.04 (or bits of genetic information), and that encodes roughly 22,000 genes.
00:03:45.01 These are stretches of DNA sequence that encode, ultimately,
00:03:50.08 a product that is a protein, which actually makes the cells function.
00:03:55.21 So as I already explained to you, there's this flow of biological information
00:04:00.19 where you have to extract the information buried in DNA, convert it into RNA,
00:04:05.17 and what I'm not going to tell you about today is the process of going from RNA to protein,
00:04:10.06 which is a reaction called a translational reaction.
00:04:13.27 I'm going to instead just focus on the first step of converting DNA into RNA,
00:04:18.22 which is the process of transcription.
00:04:22.02 Now, one of the most amazing results that we got over the last decade or so was,
00:04:29.11 when the human genome was entirely sequenced,
00:04:34.13 we realized that actually the number of genes in humans is not vastly different from many other organisms,
00:04:42.15 even simple organisms like little worms or fruit flies and so forth.
00:04:47.14 That is, roughly 22,000 to 25,000 genes is all the number of genes that all these different organisms have.
00:04:55.19 And yet, anybody looking at us versus a little roundworm in the soil or a fruit fly
00:05:02.13 can tell that we're a much more complex organism with a much bigger brain,
00:05:06.24 much more complex behavior, and so forth.
00:05:09.27 So how does this happen?
00:05:11.21 Part of the answer to this very interesting mystery or paradox
00:05:17.10 lies in the way genes are organized and how they're regulated.
00:05:21.22 And one of the most striking results of the genome sequencing project was to realize that
00:05:27.01 a vast, vast majority of the DNA in our chromosomes is actually not coding for specific gene products,
00:05:35.02 and that only roughly 3% of the DNA is actually encoding.
00:05:40.11 Those little arrows that I show you on this purple DNA are the gene-coding regions,
00:05:46.13 so you'll notice that there's a lot of "non-arrow" sequences,
00:05:50.04 which I'll show you in this next slide as green.
00:05:52.20 These are "non-coding" regions, so the vast majority (97% or greater) is non-coding.
00:05:58.26 So what are these other sequences doing?
00:06:02.18 And of course, it turns out that these sequences carry very important,
00:06:07.15 little fragments of DNA which we call "regulatory sequences."
00:06:12.09 And these are the sequences that actually control whether a gene gets turned on or not.
00:06:19.02 I'll be spending much of the next 20 minutes telling you about how this process all works
00:06:24.14 and what these little bits of DNA sequences actually function to control gene expression.
00:06:34.10 Now, the other thing I have to bring you up to date on
00:06:37.10 is this mysterious process we're calling "transcription,"
00:06:41.01 which reads double-stranded DNA and then makes a related molecule,
00:06:45.13 which is a single-stranded RNA molecule, which is an informational molecule.
00:06:49.25 That reaction is catalyzed by a very complex, multi-subunit enzyme called RNA polymerase II.
00:06:59.02 Now, there's the Roman numeral II at the end of this because there are actually at least three enzymes in most mammals,
00:07:07.13 that carry out different processes and different types of RNA production.
00:07:11.23 But I'm only going to tell you about the ones that make the classical messenger RNA,
00:07:16.17 which then ultimately becomes proteins.
00:07:20.03 Now, one of the things we learned early on in the study of mammalian
00:07:25.28 (or other multicellular organism) transcription processes is that,
00:07:30.17 despite the fact that this enzyme is quite complex in its structure,
00:07:35.28 it turns out to be an enzyme that nevertheless needs a lot of help to do its job.
00:07:42.16 So, on its own, this RNA polymerase II cannot tell the difference between the non-coding regions of the genome
00:07:49.29 and places where it's supposed to be coding, or reading, to make the appropriate messenger RNAs.
00:07:57.00 So this sort of leads you to think that there must be a number of other factors
00:08:02.25 that somehow directs RNA polymerase to the right place at the right time in the genome of every cell in your body,
00:08:11.08 so that the right products get made, so each cell in your body is functioning properly.
00:08:18.15 And this is where things get really interesting.
00:08:23.01 Some 25, 30 years ago, a number of laboratories took on the job of hunting for these elusive and,
00:08:31.17 as it turned out, specialized protein factors that recognize these little stretches of DNA sequences that I've been telling you about,
00:08:40.14 that make up the vast majority of the non-coding part of the genome,
00:08:45.15 and how these proteins can then recognize and, ultimately,
00:08:49.13 physically interact with these little bits of genetic information to then turn genes on or off.
00:08:57.21 Now, in this lecture,
00:09:00.02 I can't go into all the details of the types of experiments or the ranges of experiments
00:09:05.24 that many, many laboratories have done over the last two decades
00:09:09.11 to finally work out this molecular puzzle of how transcription works.
00:09:14.24 But I can tell you that there are fundamentally two major approaches
00:09:18.14 that have been taken over the last few decades to get a "parts list"
00:09:24.14 of the machinery that decodes the genome and carries out the process of transcription.
00:09:29.20 One is kind of the old style.
00:09:32.16 I'll call it "bucket biochemistry":
00:09:35.21 Take a live cell, crush it up, spread out all of its parts,
00:09:40.17 and then try to figure out how to put it back together again.
00:09:43.04 That's what I call "in vitro" biochemistry.
00:09:45.23 And the other one is "in vivo" genetics,
00:09:47.27 where you effectively use genetic tools (mutagenesis)
00:09:51.23 to go in there and selectively remove or "knock down"
00:09:55.18 or "knock out" certain genes
00:09:58.03 and gene products, and then ask what is the consequence on that cell or that organism.
00:10:03.01 Both of these technologies are very powerful and highly complementary,
00:10:10.20 and they continue to be used.
00:10:13.13 Today, I will focus primarily on the in vitro biochemical techniques
00:10:18.16 which led us to the discovery of the first few classes of transcription factors,
00:10:24.15 and in subsequent lectures, we'll go to more recent technologies that
00:10:29.22 allow us to speed up this whole process of identifying key regulatory molecules and how they work.
00:10:38.26 So, let's go back to the basic unit of gene expression, which is a gene,
00:10:45.04 here shown in the orange arrow, and the non-coding sequences surrounding it.
00:10:51.29 And you'll see that now I've added a few more elements to this purple DNA.
00:10:56.06 You see some symbols: blue square, round circle that's pink, and then a yellow triangle.
00:11:02.22 Those are just a way for me to graphically represent the little bits of
00:11:07.28 DNA sequences that I told you about that are the regulatory sequences.
00:11:11.08 So the little, round one happens to be very GC-rich,
00:11:14.22 the triangle one is a classical element that's called a TATA box
00:11:19.06 (I'll tell you about it a little bit later),
00:11:20.20 and the blue one is yet another recognition element.
00:11:23.18 So, why are we so interested in these little stretches of nucleic acid sequence
00:11:28.26 in the genome when it's buried amongst billions of other sequences?
00:11:33.10 Well, these individual little sequences turn out to be very important because of
00:11:37.25 where they sit (you'll notice they are sitting near the top of the arrow),
00:11:42.07 and they are recognized by very special proteins, which are the transcription factors.
00:11:48.11 So now I'm showing you some symbols with little cut-outs
00:11:51.24 which fit into either the square, the circle, or the triangle.
00:11:56.03 So, transcription factors, at least one major family of transcription factors,
00:12:03.07 are proteins whose three-dimensional structure is folded into a shape that
00:12:08.21 allows them to recognize these short stretches of double-stranded DNA,
00:12:13.19 in fact, largely through interactions with the major groove of DNA.
00:12:17.16 And I'll show you a structure of one in a little bit.
00:12:22.03 Now it turns out that there are probably thousands of these transcription factors,
00:12:26.23 because the number of genes that we have to control,
00:12:29.25 as I showed you, is on the order of 20,000 or 25,000 genes.
00:12:33.18 And so it turns out that you need a pretty large percentage of the genome devoted to
00:12:38.23 encoding these regulatory proteins, in order for a complex organism like ourselves to survive.
00:12:45.09 The other component of this, let's call it the "transcriptional apparatus,"
00:12:49.16 is of course the enzyme that catalyzes RNA, and I already told you that this enzyme
00:12:55.04 on its own can't tell the difference between random DNA sequence and a gene or a promoter.
00:13:01.16 These other sequence-specific DNA-binding proteins are the ones that
00:13:06.17 must recruit or otherwise direct RNA polymerase to essentially land on the right place and
00:13:14.03 at the right time in the genome, to turn on a certain subset of genes that are specifically required
00:13:20.26 in a specialized cell type, whatever cell you happen to be looking at.
00:13:26.16 So, that is kind of the first level of complexity of informational interactions between
00:13:33.05 the transcription factors and the more ubiquitous,
00:13:38.20 I would call it promiscuous, RNA polymerase II enzyme.
00:13:43.19 Well, as it turns out, it took several decades to work out most,
00:13:49.18 if not all, of the components of this so-called transcriptional machinery.
00:13:56.14 And it turns out, in this slide I'm showing you things are already starting to get more complicated,
00:14:03.05 so not only do you have RNA polymerase, but you have a bunch of other proteins that
00:14:07.04 go by names like TFIIA, B, D, E, H, F, and so forth.
00:14:13.12 So, it looks like there are going to be many, many proteins that
00:14:17.14 are necessary to form the transcriptional apparatus.
00:14:22.04 And then on top of that, you need sequence-specific DNA-binding proteins,
00:14:26.13 which I already described to you, to further inform or otherwise regulate the process of when
00:14:33.04 a particular RNA polymerase molecule should be binding to a particular gene.
00:14:37.29 So that's the sort of overview.
00:14:40.05 Now let me get into the specifics and how did we actually discover the family of proteins,
00:14:45.15 and it'll be interesting for you to see how science in this field evolved.
00:14:51.14 Now, as is often the case, when you first try to tackle a very complex problem,
00:14:56.28 and of course we didn't really know how complex it was when we began these studies,
00:15:00.24 but we assumed it might be complicated.
00:15:03.16 Certainly it would be more complicated than systems that we already had some idea about,
00:15:09.06 for example in bacteria or bacteriophages.
00:15:13.24 We took a lesson from our studies of bacteriophages and decided that,
00:15:18.11 to begin to dissect the molecular complexities of
00:15:22.09 the transcription process in animal cells, we should start with viruses,
00:15:26.28 because we knew that viruses will enter these host cells, these complex cells that
00:15:31.27 we ultimately want to study, and have to use the same
00:15:35.17 molecular machinery to transcribe their genes as the host mammalian cell would do.
00:15:41.27 So, this was kind of a trick or a way to look at a
00:15:45.28 molecular window into a complex system and try to simplify it.
00:15:49.22 And in our case, the early studies of the late 70s and early 80s
00:15:54.18 involved a very simple, one of the simplest double-stranded DNA viruses, called "Simian virus 40."
00:16:00.28 And Simian virus 40 is of course a monkey virus, which was nice because it's very close to humans,
00:16:06.15 and many things that we could learn about the way this virus uses its host, which are monkey cells,
00:16:12.12 to replicate and to express their RNAs and genes, would be applicable to our studies of humans, as you'll see.
00:16:20.11 And this virus was one of the first whose DNA,
00:16:24.07 its double-stranded DNA of about 5000 base pairs, was fully sequenced.
00:16:29.14 This was long before rapid, modern-day sequencing was available,
00:16:33.12 so this gave us a very powerful tool.
00:16:35.20 It basically allowed us to look at the entire genome of this virus,
00:16:39.03 which was tiny by comparison, only 5243 base pairs.
00:16:44.28 But just that information was already very important
00:16:47.27 because it very quickly allowed us, for example, to map where the genes are,
00:16:53.02 and one of the genes encoded a protein called the "Tumor antigen"
00:16:57.19 which turns out to be a transcription factor.
00:17:00.08 This then allowed us to get our hands (basically, to do biochemistry and genetics)
00:17:06.04 on the very first eukaryotic transcription factor.
00:17:09.14 Which in this case happens to be a repressor;
00:17:12.09 that is, a protein that, when it binds to DNA, just the same way as I showed you for the model case,
00:17:19.24 binds through specific protein-DNA interactions,
00:17:23.28 but in this case actually shuts transcription down rather than turn it up.
00:17:29.19 In the process of studying the way that this little virus, when it infects a mammalian cell,
00:17:36.15 uses proteins like T-antigen to regulate its gene expression,
00:17:42.17 it became clear that it had to use the host machinery to do the process.
00:17:47.22 And that meant that there must be monkey proteins that
00:17:53.00 are also involved in activating or repressing genes of this virus,
00:17:57.19 and this then led us to the most important step,
00:18:00.19 which is to transfer the technology we learned about viruses
00:18:04.06 and how to work with the virus transcription factor, like T-antigen,
00:18:07.26 to the cellular ones. And I'm going to give you just one example
00:18:11.05 of how the simple jump into the host cell allowed us to discover the first human transcription factor.
00:18:17.28 So, the question we then asked back in the early 1980s was:
00:18:23.17 What host molecule is regulating the expression of transcription of this virus, when the virus is in the host?
00:18:31.10 And we knew from the DNA sequence of the virus that
00:18:34.02 there were these six very GC-rich snippets of DNA that were regulatory,
00:18:41.05 because if we deleted them, the virus no longer would express the gene of interest.
00:18:45.20 So we knew that something was probably responsible for recognizing these GC boxes,
00:18:51.10 and we knew that it wasn't a virally encoded gene because we had tested
00:18:54.29 all of the viral genes, of which there were only six to begin with.
00:18:59.05 So we knew it had to be a host gene, and that led us to a whole,
00:19:04.06 I would say "family," of experiments that
00:19:06.20 led to the discovery of sequence-specific mammalian transcription factors.
00:19:10.25 And, as I said, we could've taken multiple approaches
00:19:13.25 to try to address this complicated issue.
00:19:16.26 I'll just give you one example of using in vitro biochemistry
00:19:20.29 to finally get our hands on this key, sequence-specific human transcription factor,
00:19:27.12 which of course has a homolog in the monkey.
00:19:31.29 And the way we did it was very interesting and simple in retrospect,
00:19:37.02 and that is recognizing the fact that whatever this protein was,
00:19:41.24 it had to have the property of recognizing those GC boxes that were sitting next to the viral gene.
00:19:49.22 We assumed that it must be a sequence-specific DNA-binding protein,
00:19:53.09 so all we had to do was figure out a way to extract proteins from human cells or monkey cells,
00:20:01.05 and then try to fish out those specific proteins out of the many thousands
00:20:05.26 of different proteins that were in this gemisch of cellular extract that would be responsible for
00:20:11.12 discriminating between random DNA sequences and the specific GC box.
00:20:17.15 And I'll quickly run through sort of the logic behind this.
00:20:20.19 So what I'm showing you here is a solid surface with DNA coupled to it that is
00:20:28.00 highly enriched for the recognition element, the GC box,
00:20:31.14 which should be the sequence recognized by the protein of interest.
00:20:35.14 Now, we had no idea what this protein was going to look like,
00:20:37.18 how many proteins there were going to be, and so forth,
00:20:39.20 but we knew it had to recognize the GC box.
00:20:42.05 So, we're going to try to fish this out of a pool of many thousands of other proteins.
00:20:47.18 Now, the key trick here was that,
00:20:49.28 because all cell extracts contain not only one DNA-binding protein but, as I told you,
00:20:55.25 thousands of different DNA-binding proteins, most of them, or in fact, in our case,
00:21:00.17 none of the other of several hundred to a thousand proteins that could bind DNA actually happened to recognize the GC box.
00:21:08.27 They just bind other DNA sequences.
00:21:11.07 So to kind of favor our protein being able to bind to our GC box and
00:21:16.07 not have to compete with all the other proteins, what we did was
00:21:20.22 to add nonspecific DNA in mass stoichiometric excess, so that all the other proteins that
00:21:28.21 wouldn't recognize the GC box would still have some partner to hang onto.
00:21:33.11 And this trick worked very well.
00:21:35.00 So, having the specific DNA on the solid resin and the nonspecific DNA flowing all over the place,
00:21:43.23 we could capture selectively the pink molecules here, which were the GC box recognition ones,
00:21:50.07 and the blue-green molecules of course predominantly bind to nonspecific DNA.
00:21:56.02 I show you one little blue one on the column because nothing works perfectly in real science
00:22:01.10 and it tells you that we have to go through this process iteratively to finally obtain
00:22:06.21 a preparation that's purely pink molecules with no green-blue ones.
00:22:11.18 Well, that turned out to work very, very well,
00:22:14.13 and that whole process of biochemical fractionation followed by a direct affinity, sequence-specific DNA resin
00:22:23.21 gave us the ability to perform a biochemical purification, followed by a molecular cloning,
00:22:30.12 of the transcription factor that encodes the protein Sp1.
00:22:35.03 And then we carried out a bunch of experiments, which I'll tell you next,
00:22:38.20 to show that this protein actually does activate transcription.
00:22:43.16 And of course, we went back and we proved that this protein, which turned out to be a rather large polypeptide,
00:22:49.08 can indeed recognize the GC box, and it doesn't matter if it's a GC box from the SV40 genome
00:22:55.20 or any other GC box that we could find in the human genome.
00:22:59.16 It would find that sequence and bind to it,
00:23:02.12 and then it would generally activate transcription.
00:23:05.22 So this led to the discovery of the first of a
00:23:09.03 very large family of sequence-specific DNA-binding proteins.
00:23:13.23 Now, I told you that the way these proteins tend to recognize short DNA sequences
00:23:19.08 is to interact with DNA through the major groove, and here is a perfect example.
00:23:23.12 So the stick, blue model there shows the actual three structures
00:23:28.17 that are called "zinc fingers," and the reason they're called zinc fingers is because
00:23:31.29 there are amino acids that are organized around a center that contains a zinc molecule,
00:23:37.27 which holds the three-dimensional shape of the polypeptide in a position just right for
00:23:43.23 fitting into the major groove of DNA, and the DNA here is shown in pink.
00:23:47.24 And you can see that that blue outline fits right into the major groove of the DNA, but not to the minor groove.
00:23:54.25 And one of the most important findings was not only the discovery of the first human transcription factor,
00:24:00.27 but the realization that most if not all sequence-specific DNA-binding transcription factors
00:24:06.18 have a similar structural motif. That is to say,
00:24:10.19 some structure is built to recognize sequences in the major groove of DNA,
00:24:15.24 and these three-dimensional motifs are recognizable as amino acid sequences in the genome.
00:24:23.27 So we can now much more quickly scan the entire sequence of a genome and
00:24:28.24 identify genes that are likely to be DNA-binding proteins,
00:24:31.28 as a result of understanding the structure-function relationships of these DNA-binding motifs, like zinc fingers.
00:24:40.03 So, what I'd like to show you now is that I've only introduced you to one class of transcription factors,
00:24:48.08 which are the sequence-specific DNA-binding proteins.
00:24:51.07 Well, I think I gave you a little taste of the level of complexity that's probably going to be needed to be able to
00:24:58.11 build the machine that's ultimately going to be able to allow you to transcribe every gene in every cell of the human body.
00:25:07.08 So that turns out to be a much more elaborated machine than what I just showed you.
00:25:12.05 So I want to show you now what is sort of our state-of-the-art thinking about
00:25:17.18 what is actually needed to build the machinery at a gene to allow it to be expressed and transcribed.
00:25:25.16 And the term I want to introduce you to is the "pre-initiation complex,"
00:25:30.27 and it's pretty much what it says.
00:25:33.10 It's the complex of multiple subunits that has to essentially land on the promoter of a gene,
00:25:40.28 which will be designated for later expression.
00:25:45.14 And this is a process that is probably quite orderly;
00:25:50.01 that is, there's an order of events that happens, which we,
00:25:52.28 by the way, are not entirely sure exactly what the order is or
00:25:56.18 even if the order is the same from one gene to the next.
00:25:59.12 But we can kind of see where it starts and where it ends up,
00:26:02.07 and the pathway in between, I would say, is still a little bit murky.
00:26:06.18 And the story here, again, starts with a little snippet of DNA called the TATA box,
00:26:11.13 which I already introduced you to briefly.
00:26:13.21 It's an AT-rich sequence which sits at the 5' end,
00:26:18.22 or the beginning, of many genes, but not all genes...
00:26:21.01 maybe 20% of the genes might contain this AT-rich region.
00:26:26.15 And that AT sequence is the signal or landmark, if you like,
00:26:31.16 for a particular protein to bind to it.
00:26:34.07 And that protein is called, not surprisingly,
00:26:36.26 the "TATA-binding protein," because it's the TATA sequence.
00:26:40.11 And so this represents a second class of transcription factors.
00:26:45.08 These are not the type that I just introduced you to, which are going to be different for every gene.
00:26:50.22 The TATA sequence is present in a very large number of genes, so it can't be gene-specific.
00:26:56.22 But it turns out to be very crucial for our understanding of how gene regulation works.
00:27:02.06 So, you start with a TATA-binding protein finding a TATA box.
00:27:08.01 We later found out that the TATA-binding protein rarely functions on its own
00:27:12.07 and has a bunch a of friends that we call "TAFs," for "TBP-associated factors."
00:27:17.03 And now you're talking about an assembly, a multi-subunit complex of almost a million daltons.
00:27:23.12 There are somewhere between 12 to 15 subunits in addition to the TATA-binding protein that make up this
00:27:29.05 little complex of proteins that kind of travels around together, and this is found in most cell types.
00:27:36.00 And later on I'll show you in a subsequent lecture that not every cell type might have
00:27:40.29 exactly the same complement of these subunits,
00:27:43.17 but many of them have this prototypic complex.
00:27:47.27 Is this enough for building the pre-initiation complex?
00:27:52.06 Unfortunately not. It turns out that there are a host of other,
00:27:57.21 I'll call them "ancillary factors," in addition to the multi-subunit RNA polymerase itself,
00:28:03.12 that are necessary for you to build up an assemble that is necessary to form an active,
00:28:12.06 ready-to-activate transcriptional pre-initiation complex, or the PIC.
00:28:18.27 This is kind of the picture we're getting to, and even this picture,
00:28:24.29 with many, many colors and many, many different polypeptides,
00:28:28.18 that adds up to probably greater than 85 individual proteins that all have to kind of fit together like a jigsaw puzzle...
00:28:37.00 it's probably not even the whole story.
00:28:39.17 You'll notice I still have one big, red question mark there,
00:28:42.24 because I think, as we begin to study specific cell types and specific processes,
00:28:48.09 like embryonic development or germ layer formation, additional components that are not present here
00:28:55.19 in this prototypic pre-initiation complex will come into play.
00:29:00.14 And that's a subject of a subsequent lecture.
00:29:03.15 But already you can tell that the transcriptional machinery is anything but simple.
00:29:09.23 So, can we get a better idea of what transcription might actually look like?
00:29:15.19 What's happening when a transcription process takes place?
00:29:18.20 So, let me first of all say that I'm going to finish my lecture now with a little cartoon,
00:29:24.15 which is our attempt to imagine the events that take place when you form a pre-initiation complex,
00:29:33.01 you bring regulatory proteins to the activated gene, and what happens during this process.
00:29:40.09 Now, keep in mind that this is, at this point, mostly a cartoon that is in our imagination,
00:29:47.25 and only parts, if any, of this is probably real.
00:29:52.29 But it gives you a sense of the complexity of the transactions that have to
00:29:57.26 take place just for one gene to transcribe and express itself.
00:30:02.07 So let me show you the movie, and then we'll finish
00:30:05.28 just by keeping in mind that there is much to be learned, and in my next lecture,
00:30:11.09 we'll go into the selectivity of this process in specialized cell types.
00:30:16.20 So now let's see what this cartoon of transcription looks like.
00:30:22.08 So we start off with DNA with some pre-assembled TFIID molecule,
00:30:26.27 and along comes this other green molecule, which is actually a co-factor,
00:30:30.24 which then forms this very large complex with RNA polymerase.
00:30:34.06 And then a distal activator protein came in and activated the process,
00:30:39.10 and this molecule, this bluish molecule that's moved away from the complex,
00:30:46.18 is actually the RNA polymerase.
00:30:48.03 And that little, yellow, sort of a bead-on-a-string is actually the RNA product.
00:30:53.20 So that gives you a sense of:
00:30:56.01 Things have to happen quickly, and yet it involves many, many molecules having to assemble
00:31:01.02 and then disassemble to give you this reaction to happen.
00:31:04.06 And in my next lecture, we'll go into more specific aspects of this reaction,
00:31:10.13 and particularly during embryonic development and tissue-specific gene expression.

Part 2: Gene Regulation: Why So Complex?

00:00:01.03 My name is Bob Tjian,
00:00:02.19 I'm a professor at the University of California at Berkeley,
00:00:06.19 where I've taught many years in Molecular Biology and Biochemistry,
00:00:11.02 and more recently, I've also taken on the job of being the President of the Howard Hughes Medical Institute.
00:00:16.26 And it's my pleasure today to continue with my second lecture in this series,
00:00:22.29 to describe to you some exciting ideas about how gene regulation works,
00:00:30.25 particularly in more complex organisms.
00:00:34.21 Now, in my last set of lectures, I left you with this view of the type of complexity that has to be evolving
00:00:48.26 to allow the type of gene expression patterns that we see in the many, many organisms that we know exist on this planet.
00:01:00.29 And so, there's some really intriguing questions that I'm going to address in this second lecture.
00:01:09.15 And one thing I left you with was an image of the interplay of many molecules that have to come together,
00:01:18.26 and to land on a particular site of the DNA molecule that's part of the chromosome of an organism
00:01:24.25 or within a cell of an organism, and how this process might work.
00:01:31.04 But, I think the question that's plagued us for decades,
00:01:35.20 now that we had a better idea of what this molecular machinery looks like that's involved in decoding DNA information into gene expression,
00:01:49.07 we wondered why is it so complex?
00:01:53.20 And to sort of begin to address this issue, let me just take you back to a simple concept.
00:02:01.10 And you remember that different organisms have different sizes of their genomes,
00:02:08.00 that is, the amount of DNA that is required to encode the particular organism.
00:02:14.23 And here are some examples of both bacteria, simple, single-celled prokaryotic organisms,
00:02:22.18 as well as single-cell eukaryotic organisms like the baker's yeast, and then there's the little, round soil worm C. elegans,
00:02:30.25 and then you can go up to up mammals and vertebrates.
00:02:33.29 And you'll see first of all that the amount of DNA can vary a lot from a few million base pairs
00:02:40.17 all the way up to 3 billion base pairs or more.
00:02:45.00 To go along with this sort of expanding level of DNA and chromosome length, you also have different levels of genes.
00:02:54.13 Now, you'll notice that the range of genes is a lot less than the range of DNA length,
00:03:00.20 so this partly informs us about maybe why we need the complexity that we ultimately discovered is involved
00:03:10.26 in forming this molecular machinery that's responsible for reading the genetic information.
00:03:17.22 So this is just a little table to reemphasize that these more complex genomes, which also means more complex organisms,
00:03:28.15 which really means a lot of different cell types, many different behaviors, complex interactions with their environment and so forth,
00:03:38.24 how is all this information really decoded from our genomes?
00:03:43.03 And on one side here, you see the prokaryotic core gene regulatory machinery,
00:03:50.07 or the core transcription machinery, and in almost all bacteria,
00:03:54.20 it's only a few polypeptides... 5, 6, 7 polypeptides.
00:04:00.00 Then, on this side, you'll see that the so-called eukaryotic organisms,
00:04:05.00 and particularly when you talk about multicellular metazoan organisms, now you see huge diversity and number of proteins or,
00:04:15.17 as we call, transcription factors, that are necessary to assemble into very large, multi-subunit ensembles
00:04:25.18 that are required to transcribe the 10,000 to 30,000 genes that define these more complex organisms.
00:04:33.21 So, right away you can see that there's this proliferation of the subunits and the machinery and the complexity.
00:04:43.00 So, in this lecture, I'm going to give you a little sense of maybe why this is the case,
00:04:49.11 and what's special about the more complex, multicellular organism,
00:04:55.11 and why this machinery may have to have been more elaborated through evolution, compared to simpler organisms.
00:05:05.07 Now, one of the first things that you realize when you look into the cell,
00:05:10.03 or particularly the nucleus of a higher organism, let's say our own cells, versus a bacteria,
00:05:17.10 is that the DNA, the very molecule that makes up the genetic information, is kind of packaged away in a very different way.
00:05:25.11 So, in all eukaryotes, the double-stranded DNA doesn't sit there in the form that we would call
00:05:33.29 the "naked" DNA, which is shown up at the top here. But rather,
00:05:38.17 this DNA is wrapped up with a set of proteins, very basic proteins, called "nucleosomes,"
00:05:46.00 and these are in turn further packaged all the way to highly condensed form
00:05:52.09 that ultimately forms the chromosomes that you'll be able to see under a microscope.
00:05:57.26 And the blue figures over here and green figures just give you a view of the
00:06:02.29 high-resolution structure of a nucleosome with DNA wrapped around it.
00:06:09.00 So, what is the consequence of having all of our DNA,
00:06:14.04 all our chromosomes, condensed and wrapped up in this way?
00:06:18.09 You can think of it as packaged away.
00:06:20.15 Well, one thing is that you can shove all this down into a small nucleus,
00:06:25.15 so if we strung out our DNA in every cell in our body, out from end to end
00:06:31.20 and stretched it out like a string, it's almost a meter long.
00:06:35.12 And yet, you have to cram all that into a tiny, little volume.
00:06:39.10 And part of the way that that happens is that you can compact the DNA by these structures.
00:06:46.24 Now, the consequence of that is, of course, you somehow have to negotiate
00:06:52.18 through this highly compacted form of DNA to get access to the DNA information and the genes.
00:07:00.12 So, to put it another way, you have to have a machinery,
00:07:04.24 a transcriptional apparatus whose job is to read DNA and, you remember from the first lecture,
00:07:10.27 convert that DNA information into RNA, an intermediate molecule
00:07:14.23 which ultimately then gets translated into a protein product.
00:07:18.19 Well, clearly one of the reasons we have this highly elaborated transcriptional machinery
00:07:25.02 is in part to deal with having to navigate through a chromatin template, as opposed to a naked DNA template.
00:07:33.24 And so there are various proteins and protein complexes that are called
00:07:38.29 "chromatin remodeling complexes," "chromatin modifying complexes,"
00:07:44.12 and these have to coordinate with the transcriptional machinery down here in the yellow and the orange,
00:07:50.01 in order to navigate and basically express a series of interactions
00:07:55.08 that are transactions between the protein machinery and the DNA.
00:08:00.29 So this is a very challenging problem.
00:08:04.10 So that's part of the problem, or part of the reason why we think there's such complexity.
00:08:09.11 So, how did we come to this picture?
00:08:12.05 How did we finally get to figuring out that there were over 85 proteins that all have to assemble on a chromatin template,
00:08:21.16 to give you gene expression and transcription, in the right place, in the right time?
00:08:26.25 And I want to just give you one sort of quick look into a technology that one can use to address the issue of,
00:08:37.03 how do we break down this complex machinery into understandable units?
00:08:42.29 And as I said in the first lecture, there are many tools that molecular biologists
00:08:47.18 and biochemists can use to try to tease out these complex molecular transactions.
00:08:53.18 One of them, of course, is to use genetics, which is to use genetic mutation
00:08:58.29 to either remove or alter one particular gene product and then ask what is the consequence.
00:09:05.06 The other way to do it is to actually take a cell with all of its complexity
00:09:10.02 and break it down literally into its component parts, and then try to put it back together
00:09:14.09 again in a functional form. And that's what I'm going to show you today.
00:09:18.00 And it's a technology I kind of call the "biochemical complementation assay."
00:09:23.03 And it's very simple: You ask, what are the minimal components,
00:09:27.24 for example, in the case of a human gene... what are the minimal protein components of the transcriptional apparatus
00:09:33.29 that you can extract from the nucleus of a cell that you need to put into a test tube
00:09:38.19 that will allow you to essentially reconstruct or, as we say,
00:09:42.19 reconstitute the activity that will allow you to read the gene in an accurate fashion?
00:09:48.19 And you can keep adding or taking away different proteins,
00:09:53.13 the yellow ones, the green ones, the orange ones, and so forth,
00:09:56.24 and ask, does it make any difference?
00:09:59.11 And by playing this adding and subtracting, or "biochemical complementation," assay,
00:10:04.29 you can very quickly discover what are the minimal components you need to activate a gene in a regulated fashion,
00:10:11.12 and what are other things that might be necessary to support this activity.
00:10:16.15 So, the first question that was asked was from the biochemical analysis of about
00:10:24.02 four dozen different proteins: What are really necessary and sufficient?
00:10:30.07 In other words, what's the minimal component set that you need to give you regulated transcription?
00:10:36.29 So we're now asking a more complicated question.
00:10:39.09 Not only what is necessary to just simply give you transcription, in other words the conversion of DNA into RNA,
00:10:45.29 but to do it in a regulated fashion.
00:10:47.27 Because after all, that's what's really interesting...
00:10:50.23 is why one cell does it in one way and a different cell has a different program.
00:10:55.25 And this experiment here says that our sequence-specific classical transcription factor that
00:11:02.00 binds DNA at its regulatory promoter region, together with what we will call the "core" or
00:11:09.07 "basal" machinery of transcription, is necessary but not sufficient.
00:11:14.07 So, plus or minus the activator Sp1 doesn't make any difference,
00:11:19.16 even though we know that in a living cell, Sp1 is highly activating this gene that we're looking at.
00:11:26.16 So, that means there's something missing in this reconstitution experiment.
00:11:31.16 So, how do we go find what's missing?
00:11:35.03 And this biochemical complementation really relies on our ability to take the cells that contain the necessary components
00:11:43.25 and the sufficient components, and then start to extract it
00:11:47.28 and to find which molecules are missing that we're not adding to our reaction yet.
00:11:54.05 And to do that, we basically have to take the cells, in this case, human cells,
00:11:58.28 break the cells apart, extract the nucleus, remove all the proteins from the nucleus,
00:12:04.03 and begin to separate the thousands of different proteins
00:12:08.05 that are in the nucleus into different pools, if you like.
00:12:12.08 And we separate them based on their physical and chemical properties,
00:12:17.19 and some of you probably have had some experience in running column chromatographs.
00:12:23.16 This is basically a way of separating proteins based on their positive charge, negative charge, molecular size,
00:12:32.16 hydrophobicity (in other words, how greasy they are... how well they interact with water), and so forth.
00:12:38.20 So if you do that iteratively, as is shown here in a series of different anion exchange
00:12:45.23 and cation exchange, as well as gel filtration, chromatographs,
00:12:50.12 you can eventually separate the thousands of different components of a nuclear extract into its individual parts.
00:12:59.00 And then you can test each one to see if they're the missing piece.
00:13:03.15 And when you do that, lo and behold, you find that there are a couple of missing pieces
00:13:07.22 that are necessary for you to add back, in other words, reconstitute, the reaction
00:13:13.12 so that now you have regulated transcription.
00:13:15.27 So unlike the previous data that I showed you,
00:13:19.14 now you can see that the machinery is more complex and, most importantly,
00:13:24.23 you can see also that the machinery is now responsive to the activator.
00:13:29.19 So, the signal with the activator, plus Sp1, is much darker than in the signal without Sp1.
00:13:36.13 That means that there is activated transcription that is Sp1-, a classical transcription factor, dependent.
00:13:43.11 So that allowed us to identify two very important, key components that we didn't know about before we did this experiment:
00:13:51.29 One is a multi-subunit complex called the "Transcription factor II D,"
00:13:57.25 and the other one is called the Mediator complex.
00:14:01.01 And these turn out to actually define an entirely new class of transcription factors, which are the so-called co-factors.
00:14:09.27 So I'm going to tell you a little bit more about one of these co-factors,
00:14:13.15 because they both really perform similar functions,
00:14:16.13 but we happen to know quite a bit more about one of them than the other.
00:14:20.17 So this so-called TFIID complex has roughly 15 subunits, in other words,
00:14:25.28 15 separate proteins that have to mesh together to form a complex.
00:14:31.22 And it's a very large macromolecule, so it's a million daltons...
00:14:36.23 that's a very, very large, floppy molecule, with many pieces to it.
00:14:41.12 One of its functions you already know about,
00:14:43.16 because it contains as one of its subunits the so-called "TATA-binding protein."
00:14:48.12 That's that saddle-shaped molecule that binds to double-stranded DNA,
00:14:55.02 at the AT-rich sequence called a TATA box,
00:14:58.06 which is associated with many genes in animal cells.
00:15:02.09 But what we've come to learn in the last decade or so is that this little complex
00:15:07.22 is doing much more than just simply binding to the TATA box;
00:15:11.18 it's doing a whole bunch of other things that we didn't have any idea about.
00:15:15.12 And now that we knew the existence of this activity and that it was critical not only for TATA binding,
00:15:22.08 but also for mediating or potentiating transcription activation, we then could break down
00:15:28.04 more of its functions of individual subunits, because you remember there's 15 different polypeptides here.
00:15:34.09 And this is just a little summary showing you that this complex of proteins
00:15:38.11 is doing a lot of different functions.
00:15:41.18 It's recognizing the nucleosomes, which have a basic protein called a "histone,"
00:15:49.26 and so it recognizes histones only when it's got
00:15:53.09 a certain chemical modification called an acetylation event.
00:15:59.12 This big orange complex also itself has enzymatic activity, including kinase activity,
00:16:06.06 which can put phosphate groups on other proteins and enzymes.
00:16:10.04 It has acetylase activity, and of course, it has to interact directly
00:16:15.05 with activators in order to potentiate their function in turning on transcriptional activation.
00:16:22.07 And I'm probably safe in speculating that
00:16:26.20 there are yet unknown functions of this large complex that we still have to discover,
00:16:32.23 because we've really only understood maybe half of the subunits, and even there,
00:16:37.25 only partially understood the functions of that half of the subunits that are part of this complex.
00:16:44.05 So, there's clearly much more work to be done, but I think what's clear from these experiments
00:16:48.25 is that these proteins are doing a lot more than just binding DNA.
00:16:53.08 They're what I would think of as integrators of information.
00:16:58.01 So, this integrator of information means that this structure and the function is very complex, and so,
00:17:04.27 one of the things that we've had to do...
00:17:08.00 it's been a very challenging problem that remains challenging,
00:17:11.21 because we haven't solved all the technical problems...
00:17:13.18 is that because it's a large, megadalton, floppy molecule, solving the three-dimensional
00:17:19.25 structure of such large assemblies has proven to be rather technically challenging.
00:17:26.08 And we have to use many different techniques to try to address this in:
00:17:31.28 X-ray crystallography, NMR...
00:17:34.07 but one of the techniques that's emerging, that's very, very powerful
00:17:38.09 for solving the structures of these large assemblies is something called "cryo-electron microscopy."
00:17:46.24 It's basically a way of freezing these large assemblies in place,
00:17:52.18 and then solving their structure by microscopy.
00:17:55.29 And this is just about a 25 angstrom, so relatively low-resolution structural determination,
00:18:03.17 of the human TFIID complex and, most importantly,
00:18:08.24 its relationship to two other transcription factors that are part of the assembly
00:18:14.03 that has to align itself up on the promoter to start transcription,
00:18:18.08 and that's the other two transcription factors TFIIA and B, which are shown in green and purple here.
00:18:24.13 So you can slowly start building up the entire complex in pretty accurate three-dimensional space
00:18:33.00 to figure out what its shape will inform us about its function,
00:18:37.15 and that's something that's an ongoing project in many laboratories in molecular biology.
00:18:43.01 So, this cartoon... and again I want to emphasize that
00:18:46.27 all the figures there and the colored blobs are more a part of our imagination at this point,
00:18:53.16 although, as I just showed you, we actually have real structures of some components of this pre-initiation complex.
00:19:02.25 This slide just emphasizes the point that there's a lot of information integration going on,
00:19:09.22 and that there is protein-protein and protein-nucleic acid interactions
00:19:15.06 that are critical for the regulatory functions of these large, macromolecular assemblies.
00:19:21.02 And this also reminds you that there are at least three separate classes of transcription factors
00:19:27.17 that are playing a key role in the regulation of genes:
00:19:30.28 the classical activator and repressor that are sequence-specific DNA-binding proteins,
00:19:35.26 like the Sp1 protein I talked to you about earlier, just shown here in pink;
00:19:41.11 there are the components of the core machinery, which are shown in yellow;
00:19:45.27 and then you have these things we call co-factors or co-activators,
00:19:49.12 that are integrating information between the activators and the core machinery.
00:19:56.06 So this kind of gives you a slightly better view of why there's this kind of complexity,
00:20:04.06 but it still doesn't really address all of the issues with respect to:
00:20:09.22 Why do you need 85 proteins to do this?
00:20:12.09 So, let me dig a little deeper into this.
00:20:15.00 So, first, let me just pose some of the questions that are really still largely unresolved in the field,
00:20:21.14 even though this is a pretty mature area of study;
00:20:24.10 we've been trying to address these issues for a couple of decades,
00:20:28.14 and it goes to show how difficult it is to really tease apart this complex molecular machinery.
00:20:34.23 And I should say that the complexity of this machinery is not unique
00:20:38.19 to the transcriptional apparatus. Many other biological processes are also dependent on
00:20:43.23 macromolecular machines that are very similar in complexity to this one.
00:20:49.04 So I think things that we learn about the transcriptional machinery could be applied in principle to many other machineries.
00:20:56.20 So, couple of interesting questions:
00:21:00.20 What are the transcriptional mechanisms that regulate complex cell types?
00:21:06.29 Because, after all, multicellular organisms evolved to having many, many different
00:21:13.12 cell types, so our bodies are made up of many different cell types,
00:21:18.23 which means that each cell's performing a different function.
00:21:22.02 Our hair follicle cells are producing hair, our red blood cells are
00:21:27.02 producing hemoglobin and doing something else, our skin cells are protecting us.
00:21:31.17 Each cell type is doing a different thing, so how does this happen,
00:21:36.01 how do we generate this diversity of cell types through the gene regulatory networks?
00:21:42.17 And then, knowing what we now know about the first level of complexity of the machinery
00:21:49.04 that's responsible for decoding this information, what more can we learn about the process of regulation now?
00:21:57.03 Particularly, what is the division of labor between the core machinery
00:22:03.24 (which binds to the promoter), the activators, and the co-activators?
00:22:08.22 So, what is their relationship, and what's their respective roles in defining cell type-specific gene expression?
00:22:16.19 That's really the last topic that I want to cover in this lecture.
00:22:21.06 So, let's review a few basic facts about individual cell types.
00:22:26.13 So, let's take two well-recognized cell types: fat cells and muscle cells.
00:22:33.12 Very different cells that perform very different functions,
00:22:36.27 but every cell in a particular organism has the same genetic information.
00:22:43.04 It has the same DNA, it has the same set of chromosomes.
00:22:46.08 That means that these two cells have to be using different parts of the information
00:22:52.09 from the genome to give it their distinct identities.
00:22:56.20 So, each cell must only express some subset of the genes,
00:23:03.21 and that particular subset would define the function of a fat cell versus a muscle cell.
00:23:10.11 And, so then the question becomes:
00:23:12.26 Okay, that makes sense, but how do you get there?
00:23:15.02 How do you get cell type-dependent differential gene expression patterns?
00:23:20.16 How do you turn on the right genes to make fat
00:23:22.27 versus keeping the muscle cell gene functions turned off, and vice versa?
00:23:29.06 So that is a fundamental question of trying to understand the process of cellular differentiation,
00:23:36.19 cell-specific function, and really, developmental biology.
00:23:41.09 Another set of interesting points to make is that, of the 20,000 to 30,000 genes
00:23:46.18 that a typical metazoan organism encodes, a pretty big chunk of it is devoted
00:23:54.03 to the very machinery that I'm talking about, in other words, the transcription factors.
00:23:59.04 So roughly somewhere between 5 and 10% of the entire coding capacity
00:24:04.02 of genes in a genome is devoted to encoding transcription factors.
00:24:10.11 So this is clearly a very important class of molecules.
00:24:13.02 So that means there are several thousand transcription factors.
00:24:16.28 But now if you start thinking about the many, many thousands of cell types and the behavior of different cells,
00:24:23.16 are a few thousand transcription factors, in and of themselves, enough to generate the diversity of function?
00:24:31.14 And this is where we have to start thinking about,
00:24:33.21 how do you create really large numbers of distinct transcriptional networks?
00:24:40.28 And they really are networks, as you'll see in a minute.
00:24:43.15 And one thing that became clear as we defined what genes look like and what a promoter as a transcriptional unit looks like,
00:24:51.22 we come to understand that the only way to create the kind of huge levels of diversity of distinct transcriptional
00:24:58.20 components and patterns, is to do it by combinatorial regulation.
00:25:03.12 And what do I mean by that?
00:25:04.27 So, one way to think about it is that you might only have ten cards,
00:25:09.25 but if you shuffle those ten cards and pick four at a time,
00:25:13.06 you can have many, many combinations.
00:25:15.11 So here's a perfect example of three different cell types, could be in the same organism,
00:25:20.11 and each of those symbols represents binding sites,
00:25:25.22 and then the little boxes and triangles above them represent the binding proteins.
00:25:32.18 And you can see that those three cell types might express these sets of genes in similar ways,
00:25:38.25 but they use different combinations of proteins to do it.
00:25:42.08 And this is really the notion of combinatorial mechanisms for gene regulation,
00:25:46.27 and we now know that that is indeed the way, at least in part,
00:25:51.05 that gives us the ability to create many different specific transcription patterns.
00:26:00.04 I have to now also tell you about another, I would say, defining,
00:26:04.23 unusual property of transcription in animal cells,
00:26:09.06 and this is a hard one sometimes to get your head around.
00:26:12.19 And that is that these different little units of DNA that specify the activity of a gene
00:26:18.20 don't have to be sitting, linearly and spatially, directly next to the gene that it's activating or repressing.
00:26:26.21 They can sit tens of thousands of base pairs away from the site.
00:26:32.04 So these we call long-distance enhancers or silencers, so they can both upregulate a gene...
00:26:39.01 in other words, make more of the gene or less or the gene.
00:26:41.27 And the thing that was so surprising was that the intervening DNA can be very, very long;
00:26:48.04 it can be thousands and maybe even millions of base pairs.
00:26:53.00 So how does this work?
00:26:53.28 How can something sit so far away actually influence transcription at a very remote site?
00:27:00.26 And this is one of the big conundrums that we still face in the field.
00:27:05.15 We have some models and we have some ideas that we can test,
00:27:08.09 and I'll end my lecture with a few speculations about that.
00:27:11.24 But clearly, we don't fully understand this so-called long-distance regulation,
00:27:16.27 which clearly is regulated by activators and repressors just like
00:27:21.09 the same players that we've been talking about, like the Sp1 molecule and other activators.
00:27:26.12 But yet, how they can reach across long distances of the chromosome to grab on to the core machinery to actually impart information
00:27:36.06 and to create the kind of specific regulatory events is still somewhat obscure.
00:27:43.28 So, another thing that I should say is that,
00:27:47.07 because of the combinatorial mechanisms of generating diversity was so dependent
00:27:54.18 on the distinct sets of sequence-specific DNA-binding proteins,
00:28:00.12 over the last two decades we've come to kind of a traditional model that the core machinery stays relatively invariant.
00:28:10.04 In fact, we kind of think of it as universal, because if you break open a nucleus of a very
00:28:15.11 simple organism like yeast, or you break open the nucleus of a human cell,
00:28:21.22 that machinery looks remarkably similar to each other.
00:28:24.26 And yet, their gene networks are very, very different, so we thought,
00:28:29.05 well, maybe it's all having to do with the sequence-specific DNA-binding proteins,
00:28:34.07 that will generate the diversity through combinatorial regulation.
00:28:38.25 And that's probably true; in fact, there's a lot of evidence to support that.
00:28:42.27 But it was only part of the story.
00:28:45.04 So, a kind of related question would be:
00:28:47.25 Are we really right in thinking that the core machinery is universal and invariant?
00:28:54.03 And that turns out to be an oversimplification.
00:28:57.06 So it turns out evolution didn't work that way.
00:29:02.26 And when we looked very carefully in the last few years, particularly at individual,
00:29:07.02 different, distinct cell types, let's say muscle versus fat, or neuron, or liver cell,
00:29:13.01 we certainly see differences in the activators, as we would expect, and indeed they are working in combinatorial fashion,
00:29:20.02 but they're not only working combinatorially with each other,
00:29:23.06 but they are combining in different combinations with the core machinery, which is itself variable.
00:29:29.23 And that was kind of a revelation that's really become more clear just in the last few years.
00:29:35.29 So, in addition to the sequence-specific binding proteins and their diversity,
00:29:41.10 there turns out to be a much greater degree of diversity in the core machinery,
00:29:46.09 the parts that we thought were invariant, than we ever imagined.
00:29:50.11 Now, once you realize that that's the case,
00:29:54.07 that opens up a whole other level of generating diversity that we didn't anticipate,
00:29:59.13 and that of course really allows multicellular organisms to diversify in unbelievable ways.
00:30:06.18 So, let's drill down finally a little bit at how did we find this out, and where are we going?
00:30:13.08 So now, unlike a few decades ago when we first began to study the process of transcription
00:30:20.02 and discovered all of this initial complexity, in those days we mainly worked on just a few different cell types.
00:30:28.13 But today, we have the ability technically to work with just about any cell type,
00:30:33.18 from the most complex, such as embryonic stem cells,
00:30:37.13 to perhaps the simple cell, like the skeletal muscle, and everything in between...
00:30:41.18 liver cells, neuronal cells, and so forth.
00:30:45.03 And this has really opened up our view of just how diverse, interesting,
00:30:51.17 and variable the transcriptional apparatus is, that is probably really necessary
00:30:56.18 from an evolutionary standpoint to drive the diversity of gene expression and cell types that we see.
00:31:04.10 The first hint that this core machinery that we thought was so invariant may not be so invariant,
00:31:10.14 came from studying the development of the skeletal muscle.
00:31:14.21 So when you go from a precursor cell called a myoblast, which looks like most every other mammalian cell,
00:31:21.13 with its standard, prototypic core machinery, and then when you look at it when that cell type differentiates, in other words,
00:31:29.16 specializes into a myotube (which will ultimately form skeletal muscle, which is the muscle around your large bones
00:31:37.03 that makes you be able to move),
00:31:39.16 it turns out that it not only shifts which transcriptional activators it uses,
00:31:45.05 but it also jettisons the prototypic core machinery and substitutes it with some modified versions of that core machinery,
00:31:53.21 which is shown down here in the purple and the bright blue.
00:31:57.29 So this was really a change in the paradigm of the way we're thinking about regulation,
00:32:03.08 and of course, this was just the first example.
00:32:07.08 One wanted to know if similar things were happening in other different cell types, and very quickly,
00:32:13.04 if you look at hepatocytes or liver cells, if you look at adipocytes or fat cells,
00:32:18.21 if you look at neuronal cells, and you compare what's going on in muscle,
00:32:23.07 in every case, one can find changes in the core machinery, either because a particular component
00:32:28.28 like one of the TBP-associated factors is highly upregulated (that means its concentration went way up,
00:32:35.15 when all the other ones went down), or some other permutation.
00:32:39.09 In other words, clearly, components of the so-called core machinery were variable from cell type to cell type,
00:32:46.20 and that really changed the way we thought about how regulation of multicellular organisms works.
00:32:54.28 At the same time that we were looking at these,
00:32:57.17 what we would call mature, terminally differentiated cell types,
00:33:01.20 we were also looking at perhaps one of the most interesting cell types that we could study,
00:33:06.18 particularly if we're interested in understanding the process of mammalian development,
00:33:11.23 and those are of course the embryonic stem cells.
00:33:14.14 These are those amazing cells that, when tickled with just the right chemicals or physiological signals,
00:33:21.08 can turn themselves into every cell type of an organism, maybe 10,000 different cell types.
00:33:31.06 So, this so-called pluripotency made these human and mouse embryonic stem cells very special for all kinds of reasons,
00:33:41.05 partly because they are amazing models to study this process of development and differentiation,
00:33:46.18 but partly because of biomedical possibilities for cell regeneration and therapeutics.
00:33:57.02 So we've studied this, and these are very, very new studies,
00:34:01.27 and I'll just very quickly touch on it. We really were curious,
00:34:05.23 how can these cells be so pluripotent?
00:34:09.08 That is, their capacity to turn into every other cell type seems so amazing, what is the mechanism,
00:34:15.07 what's the machinery that's going to allow these cells to be able to differentiate into every cell type in the body?
00:34:22.16 And so, we began to probe this.
00:34:24.27 In some cases, we did it by the genetic technology, which is we made
00:34:29.08 mutations in certain candidate regulatory factors and transcription factors,
00:34:34.10 and then asked, does that have a consequence on the development of different cell types?
00:34:41.10 In other cases, we used a standard biochemical complementation technology to figure out what's going on.
00:34:47.21 So, I'll finish with two quick stories.
00:34:51.06 So, using the genetic tools of knocking genes out and asking
00:34:55.02 what effect it has on differentiation and pluripotency, we discovered that
00:35:01.15 a component of the core machinery (or at least we used to think of it as being purely of the core machinery),
00:35:07.07 that is, one of the TBP-associated factors, particularly TAF3,
00:35:11.26 turns out to be extremely important for the regulation and
00:35:15.23 expression of genes that will ultimately define the so-called endoderm.
00:35:23.17 And that's true for both the so-called primitive endoderm and the definitive endoderm,
00:35:28.04 which ultimately will give rise to the placenta, the yolk sac, lungs, liver,
00:35:32.12 pancreas, intestines, and so forth.
00:35:34.17 At the same time, knocking out this TAF3 had the opposite effect on the other two major germ layers,
00:35:42.09 which are the mesoderm and the ectoderm.
00:35:44.13 So here was a really beautiful case of differential function of a transcription factor
00:35:50.28 that was not a standard sequence-specific binding protein.
00:35:55.09 This core machinery factor, which by the way, probably on its own doesn't even bind to DNA directly,
00:36:01.20 when you knock it out, you lose the ability to form endoderm,
00:36:05.22 but you elevate the probabilities of forming mesoderm and ectoderm.
00:36:09.18 In other words, the balance between these different cell types gets messed up,
00:36:13.20 and of course this will cause major difficulties for a developing embryo.
00:36:21.16 Even more interesting and intriguing, and this really goes to show the level of information that we still lack,
00:36:28.15 although TAF3 was originally defined both genetically
00:36:32.04 and biochemically as part of the TFIID core promoter recognition complex,
00:36:37.02 and it is absolutely true that that is the case,
00:36:40.04 it had another life that it led that we didn't know about.
00:36:43.17 So TAF3, it turns out, it doesn't have to strictly function as part of this large multi-subunit core promoter complex,
00:36:52.17 but it can also do other jobs, and in this case, it pairs up, or partners up,
00:36:57.06 with a different transcription factor called CTCF (doesn't really matter what the name is)
00:37:03.02 and now it does its job in a completely different way.
00:37:06.16 And in fact, the most recent experiments suggest that TAF3 and CTCF get together
00:37:12.03 to partly allow that amazing property of long-distance regulation.
00:37:17.25 So, regulators bound at thousands of base pairs away from the site of activity
00:37:24.21 can be brought together in three-dimensional space by what's known as "DNA looping,"
00:37:31.04 and it turns out that TAF3 is involved in this DNA looping, together with a whole bunch of other proteins,
00:37:38.00 whose relationship to TAF3 is still not entirely clear.
00:37:45.02 And we find it particularly intriguing and exciting that this type of long-distance function is being
00:37:50.06 carried by a TAF and in the context of embryonic stem cell differentiation potential to form endoderm.
00:37:57.19 So this is a very, very new type of way of thinking about the core transcription factors.
00:38:06.11 Likewise, when we looked at the embryonic stem cell transcriptional circuitry and asked,
00:38:13.20 what other transcriptional co-regulators, or regulators and co-factors,
00:38:17.18 are necessary to allow this so-called pluripotency program?
00:38:23.01 This amazing ability of these cells to be able to differentiate into every other cell type,
00:38:27.03 how does that happen? What is allowing that to happen
00:38:30.23 in this particular cell type, and not in other cell types?
00:38:33.23 And again, using the biochemical complementation technology,
00:38:38.00 we recently were able to identify a new co-factor complex, again a multi-subunit complex,
00:38:45.15 called the SCC, or "stem cell co-factor."
00:38:49.25 And remarkably, this SCC-B turns out to be a well-known protein that again had a different lifestyle in other cell types.
00:38:59.18 It's a protein complex that had previously been described as XPC,
00:39:03.29 which stands for "Xeroderma pigmentosum, complex C,"
00:39:08.22 which means that it's involved in DNA repair.
00:39:11.07 So up until now, we thought XPC was only functioning as a DNA repair complex,
00:39:16.14 and now we know that it's doing something quite different,
00:39:19.12 but only in the context of ES cells, which is to form a co-factor complex that will potentiate
00:39:25.27 the activity of two critical transcriptional activators, Oct4 and Sox2,
00:39:31.21 which define the pluripotent, self-renewing state of ES cells.
00:39:36.20 So these are just two examples of sort of what we're learning about,
00:39:41.21 the continuing saga of how transcriptional machinery evolved and works in animal cells.
00:39:50.26 And I'll finish with this last model slide,
00:39:54.00 which just simply reiterates what I just said:
00:39:57.00 We have to keep in mind that, in generating large sets of combinatorial, specific gene networks,
00:40:07.07 we have to use the diversity not only of sequence-specific DNA-binding proteins,
00:40:11.29 but we more and more see examples that components of the previously thought to be invariant core machinery
00:40:19.14 are an integral part of diversifying the combinatorial regulation of gene expression.
00:40:26.18 And this of course opens up many new possibilities,
00:40:30.08 and I suspect that there are many question marks yet about what exactly each of these components
00:40:36.27 is doing to drive complex regulation that gives rise to complexity like human beings,
00:40:44.12 the human brain, all the physiology that goes on.
00:40:48.03 And of course, as we understand these mechanisms in greater detail,
00:40:51.23 I think we have a much better chance of tackling the problems of human disease and diseases of other organisms.
00:40:59.10 Because ultimately, we have to understand the molecular basis of disease,
00:41:03.21 and I think a big part of that is understanding the mechanisms of gene regulation.

Videos in this Talk

Part 1: Gene Regulation: An Introduction
Audience:
- Student
- Researcher
- Educators of H. School / Intro Undergrad
- Educators of Adv. Undergrad / Grad
Part 2: Gene Regulation: Why So Complex?
Audience:
- Researcher
- Educators of Adv. Undergrad / Grad

Speaker: Robert Tjian

Total Duration: 1:12:48

Recorded: November 2011

Educator Resources for this Talk

All Talks in Genetics and Gene Regulation

Talk Overview

Transcription, the conversion of DNA to RNA, is one of the most fundamental processes in cell biology. However, only about 3% of our total DNA encodes genes to be transcribed. RNA polymerase II, the enzyme that transcribes DNA to RNA, relies on a large set of proteins known as transcription factors to recognize the coding sequences and to transcribe the correct genes, in the correct cell type, at the correct time.

In Part 1 of his lecture, Tjian gives an overview of the complex and critical role that transcription factors play in regulating gene expression. How do different cells from the same organism, such as muscle cells, neurons and red blood cells, all of which have identical DNA, have such different phenotypes? Tjian addresses this question in his second lecture, where he expands on the mechanisms of gene regulation.

Speaker Bio

Robert Tjian

Robert Tjian has been President of the Howard Hughes Medical Institute since 2009. He is also a professor of biochemistry and molecular biology at the University of California, Berkeley where he maintains a research lab focused on understanding the regulation of gene expression. Tjian received his bachelor’s degree in biochemistry from UC Berkeley and his… Continue Reading