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Developmental Biology of a Simple Organism: Bacillus subtilis

Transcript of Part 3: Stochasticity and Cell Fate

00:00:06.06		Hello, my name is Richard Losick
00:00:08.19		and this is the third part and last part of my presentation on
00:00:14.03		developmental biology of a simple organism.
00:00:17.10		This last part is devoted to the topic of stochasticity and cell fate.
00:00:23.04		I grew up in an era in which it was believed that development, biological development
00:00:30.20		is orchestrated by highly deterministic processes.
00:00:34.23		And indeed, that's largely the case. That's true in most cases.
00:00:39.23		But, increasingly, we're seeing that there are examples, especially in the microbial world
00:00:45.01		of cell fate decisions that are stochastic.
00:00:51.18		I'm going to tell you about a series of such examples from the spore forming
00:00:56.28		bacterium Bacillus subtilis. Let me begin with a famous quote
00:01:02.00		from Albert Einstein, the father of modern physics.
00:01:05.20		Einstein famously said that "I, at any rate, am convinced
00:01:10.10		that He does not play dice."
00:01:12.08		Thereby rejecting Heisenberg and his Uncertainty Principle.
00:01:17.21		Well, in biology, as I've said, most decisions are, indeed, highly deterministic.
00:01:26.01		But, it’s also the case that some decisions, some cell fate decisions,
00:01:32.12		are, in fact, done by a role of the dice, as I'll explain.
00:01:37.21		So, I'm going to give you four examples from Bacillus subtilis
00:01:40.28		which, so far, appears to be the champion of stochasticity in the microbial world.
00:01:46.05		The four examples are: growth and competence, swimming versus chaining,
00:01:51.25		eating versus being eaten, and community versus individuality.
00:01:59.25		What do I mean by growth versus competence?
00:02:03.03		Well, B. subtilis can switch into an alternative state,
00:02:07.19		the state of competence, in which it stops growing and instead, acquires
00:02:12.29		the ability to take up DNA from its environment
00:02:15.20		which it can recombine into the chromosome.
00:02:21.13		Why would B. subtilis do such a thing if this means stopping to grow?
00:02:25.04		Well, what one imagines is that producing cells
00:02:30.13		that are able...that are on the prowl for
00:02:33.00		new genetic sequences improves the fitness of B. subtilis by allowing
00:02:38.21		it to uptake new genetic information that may help it cope
00:02:43.23		with changed circumstances in the future. So there must be a fitness benefit
00:02:48.15		to the bacterium for temporarily entering this non-growing state
00:02:52.21		so that it can be always on the look out for
00:02:55.28		potential new genetic sequences that can be useful to it.
00:03:00.16		Entry into this state of competence is controlled by a transcription factor
00:03:05.11		called ComK and as you'll see, the synthesis of ComK
00:03:10.22		is governed by a noise-driven stochastic switch. So in other words,
00:03:15.11		when cells are under conditions in which they're capable of entering the
00:03:20.08		competent state, only some of them do and the ones that decide
00:03:23.12		to do so, do so in a stochastic fashion.
00:03:26.17		Let me illustrate this to you with a beautiful experiment
00:03:31.02		from David Dubnau, who, along with others in the field are
00:03:34.06		responsible for our understanding of stochasticity in the competent state.
00:03:43.00		So what I'm going to show you is a field of cells in which all of the cells
00:03:46.10		harbor a fusion of the gene for the green fluorescence protein
00:03:50.09		to a promoter under the control of ComK.
00:03:54.09		And what you can immediately see is that only a subset of the cells
00:03:58.11		are brightly green. That is, most cells are off for ComK
00:04:03.25		and some cells are on for ComK.
00:04:07.23		These cells were grown and held in a homogeneous environment.
00:04:12.03		All of them, in principle, are capable of becoming competent,
00:04:16.25		of activating ComK but only some of them do so
00:04:20.15		and they do so in a random fashion, independently of
00:04:26.07		what the neighboring cells are doing.
00:04:28.25		How does this work?
00:04:30.07		So at the heart of this system is the following circuitry.
00:04:35.12		The comK gene, of course, encodes the ComK regulatory protein
00:04:40.04		which can bind to and activate the transcription of about 100 target genes
00:04:44.14		that define the state of competence.
00:04:48.03		But ComK also bind to the promoter for its own gene
00:04:52.18		in which case it sets up a positive feedback loop
00:04:56.18		which can stimulate transcription of its own genes.
00:04:59.26		So when ComK binds there, that leads to more transcription of comK
00:05:04.03		that results in yet more transcription which in turn leads to yet more ComK molecules
00:05:11.00		that lead to this on state in which large levels of ComK accumulate in the cell.
00:05:17.14		The key point is that this positive feedback loop has a threshold.
00:05:22.28		You can think of it as being poised on a knife edge.
00:05:26.10		And under the right conditions the cells have just less than a threshold
00:05:31.17		amount of ComK molecules in them and if, due to noise,
00:05:37.18		there are fluctuations in the amount of ComK from cell to cell then
00:05:43.21		some cells will have a bit more ComK than other cells.
00:05:47.18		Those cells that have a bit more have reached the threshold
00:05:51.13		and get the positive feedback loop going. Those that are below
00:05:55.20		the threshold can't get the positive feedback going.
00:05:59.08		And what makes this switch a bi-stable switch
00:06:03.10		is that multiple ComK molecules bind to the promoter in a cooperative fashion
00:06:11.05		by interacting with each other. This makes the switch highly sensitive
00:06:16.07		to small fluctuations in the level of ComK molecules.
00:06:20.24		So, when the amount of ComK in a cell is just below the threshold,
00:06:25.13		most cells will not activate ComK, but a few cells will have,
00:06:31.06		by noise driven processes, accumulated enough ComK molecules
00:06:36.19		to activate the positive feedback loop and get it going
00:06:39.27		and go into the competence on state.
00:06:42.12		Why does B. subtilis do this?
00:06:44.24		Well, we don't know for sure but, obviously, entering a state in which you're not
00:06:49.29		growing puts you at a disadvantage. But by deploying, stochastically, some cells
00:06:56.15		that are on the prowl for new genetic information, then B. subtilis is always
00:07:02.03		preparing itself for unexpected changes in its environment
00:07:06.07		when new kinds of genetic information may be important.
00:07:11.16		So, we can think of this as an example of bet hedging.
00:07:15.09		That is, B. subtilis is hedging its bets by deploying, stochastically,
00:07:20.02		a small proportion of cells that enter a non-growing state temporarily
00:07:25.05		so that should circumstances change
00:07:29.18		and should the right genetic sequences appear
00:07:32.14		then those cells will be at an advantage.
00:07:34.17		Remember, that evolution selects for the genome and not the individual.
00:07:39.00		So, deploying two kinds of cells in the population
00:07:42.02		can be advantageous to the genome
00:07:44.16		even if it’s not advantageous to the individual.
00:07:47.19		Let me come to my second example, motility versus chaining.
00:07:52.26		This is a phase contrast micrograph that depicts B. subtilis cells as
00:07:58.04		we've traditionally seen them over many decades of research with this organism.
00:08:05.16		And if you look closely you can see there are two kinds of cells here.
00:08:09.03		There are long chains of cells that have completed cell division
00:08:12.01		but haven't separated from each other and there are also singlets and doublets.
00:08:17.17		The singlets and doublets, it turns out, are motile cells,
00:08:20.17		where as the long chains are non-motile cells. They're sessile cells, if you will.
00:08:26.03		Well, we saw this image for many, many years but didn't pay much attention to it.
00:08:33.01		But over time it emerged that a single transcription factor
00:08:36.07		called sigmaD is responsible for the production of enzymes that
00:08:42.29		degrade the cell wall material between newly divided cells,
00:08:46.25		enabling them to separate and also for the production of the machinery
00:08:51.18		that's responsible for motility.
00:08:55.16		So, with that in mind we revisited this field of exponential phase cells
00:09:02.08		but this time using cells that were tagged with green fluorescence protein reporter
00:09:07.21		gene fused to a promoter under the control sigmaD.
00:09:12.17		And at the same time we stained the cells with a red membrane dye
00:09:16.22		so that we could see the division septa.
00:09:18.25		And now, all of a sudden, we get a radically different view of
00:09:23.11		the field of cells that's very illuminating.
00:09:26.08		As you can see there are cells in two states.
00:09:30.04		There are sigmaD on cells that are doublets or singlets
00:09:34.28		and these are the motile cells. They undergo cell division
00:09:38.26		and then the products of cell division can separate from each other.
00:09:43.02		And then there are the sigmaD off cells which we see as long
00:09:47.06		red membrane staining cells.  You can see the division septa in these cells.
00:09:51.19		The cells have divided but the daughter cells haven't separated from each other.
00:09:55.22		These cells are off for sigmaD and they're non-motile.
00:10:00.13		Why would B. subtilis do this?
00:10:02.03		Well, of course we don't know but it’s attractive to imagine
00:10:05.26		it’s another example of bet hedging.
00:10:09.04		Imagine that B. subtilis is in a particular niche where there are nutrients.
00:10:13.04		The sessile chains of cells can stay put and exploit the existing niche.
00:10:21.07		But the motile cells, the sigmaD on cells, we can think of those
00:10:25.11		as nomadic cells that wander off to look few niches.
00:10:29.28		B. subtilis doesn't know what the future holds in store and so its immediate niche
00:10:34.14		may run out of nutrients or exhibit other adverse environmental factors.
00:10:41.04		So, by deploying some cells to be motile and some cells to be sessile
00:10:46.06		the bacterium can hedge its bets. Some cells stay put
00:10:50.21		and exploit existing circumstances whereas other cells swim off
00:10:56.00		looking for new niches in anticipation of the possibility that the original
00:11:02.15		niche may become exhausted.
00:11:06.12		My third example is called eating versus being eaten.
00:11:11.13		So, as we saw in the first part of my presentation, when starved for nutrients
00:11:16.29		B. subtilis enters the pathway to sporulate.
00:11:20.26		So just as competence represents a distinct state,
00:11:24.00		sporulation represents a specialized developmental state.
00:11:28.13		Entry into sporulation is governed by a master regulatory protein called Spo0A.
00:11:35.01		Sporulation is a complex process that takes multiple hours.
00:11:40.17		It takes time and it takes energy,
00:11:43.16		and it culminates in the formation of a dormant cell type,
00:11:47.28		the spore that can remain dormant for many years.
00:11:50.22		So I think it’s easy to imagine that the decision to sporulate
00:11:54.11		is not one that B. subtilis wants to take lightly.
00:11:58.01		Entry into this pathway is governed by Spo0A
00:12:01.18		which sits at the top of this regulatory sequence.
00:12:06.04		It becomes activated under conditions in which nutrients become limiting.
00:12:12.28		Well, there is a crucial window of time near the start of sporulation
00:12:17.25		when the cells can change their minds, so to speak.
00:12:22.17		Initially, when Spo0A become active but before the hallmark process
00:12:28.16		of asymmetric division takes place, if nutrients reappear, sporulation is arrested
00:12:34.16		and the cells can even start growing again.
00:12:37.21		But once the cells cross the Rubicon, so to speak, of asymmetric division
00:12:42.25		now, they're committed to making a spore,
00:12:44.18		even if lots of nutrients appear at later stages.
00:12:48.18		So there's this window of time up until asymmetric division takes place
00:12:54.05		when the process is reversible,
00:12:56.14		and then it becomes irreversible later on.
00:12:59.15		So hold that thought in mind when I tell you what I
00:13:02.29		think at this point will not come as a surprise, that Spo0A
00:13:06.21		is itself subject to a bi-stable switch.
00:13:11.01		That is, when cells are limited for nutrients, only some of the cells become on for Spo0A
00:13:18.02		whereas others remain off for Spo0A.
00:13:22.01		And once again, we can visualize this by using a green fluorescence protein gene
00:13:27.07		fusion to a promoter under the control Spo0A.
00:13:31.10		And as you can see there are two kinds of cells:
00:13:33.19		those in which Spo0A is off and those in which Spo0A is on.
00:13:38.20		Only some of the cells are on for Spo0A.
00:13:41.24		Well, why have a bi-stable switch? Why is Spo0A subject to a bi-stable switch?
00:13:49.00		Well we believe that the answer has to do with a phenomenon
00:13:52.18		that we refer to as cannibalism.
00:13:55.22		When the cells are deprived of nutrients
00:13:59.16		and Spo0A becomes activated, the Spo0A on cells produce and export
00:14:05.15		a toxin, a peptide toxin that kills their sibling cells, the cells that are off for Spo0A.
00:14:13.07		The producing cells, the on cells are immune to the toxin
00:14:16.20		but the non-sporulating cells are killed by it. They lyse and liberate their nutrients.
00:14:22.23		Now, remember, Spo0A activation is triggered by nutrient limitation in the first place.
00:14:28.19		So, if some of the cells are lysing and liberating nutrients
00:14:30.26		that will have the affect of impeding Spo0A activation
00:14:36.08		and that will arrest sporulation or perhaps even reverse it.
00:14:39.17		So, cannibalism is a process for slowing down sporulation.
00:14:44.26		Let me illustrate that for you with this single agar plate experiment.
00:14:52.08		So the left side of the slide shows a streak of wild type cells
00:14:58.08		and the right hand portion of the slide shows a cannibalism mutant.
00:15:05.00		When cells start to sporulate, the colonies become white and opaque
00:15:09.27		and so we can see that with the naked eye.
00:15:12.28		And as you can see at the early time when this photograph was taken
00:15:16.26		the wild type cells have only just begun to sporulate.
00:15:21.13		The colonies are not yet white and opaque.
00:15:23.11		But the mutant cells are filled with spores.
00:15:28.04		Now remember, cannibalism is a process for delaying sporulation.
00:15:32.12		So, therefore, in its absence sporulation is accelerated.
00:15:37.29		So we see rapid sporulation in a cannibalism mutant
00:15:42.20		and slow sporulation in the wild type.
00:15:45.03		Once again we can ask, "Why would B. subtilis do such a thing?"
00:15:50.17		And the appealing interpretation is, yet once again, it’s hedging its bets.
00:15:55.27		Consider a population of B. subtilis cells that
00:15:59.18		experiences a drop in the availability of nutrients.
00:16:02.23		How does it know whether this decrease in nutrients is a simple
00:16:07.21		fluctuation, a temporary decrease in nutrients or the beginning of a famine?
00:16:13.07		If it willy-nilly committed itself to making a spore when nutrients were depleted
00:16:21.08		and went through this multi-hour, expensive process
00:16:24.12		and nutrients actually returned after the time of
00:16:28.21		commitment, well, it would put itself
00:16:30.25		at a disadvantage relative to other bacteria that would be simple waiting out
00:16:35.17		the period of low nutrients.
00:16:39.00		On the other hand, if it’s in a period of prolonged starvation,
00:16:42.03		then going ahead to make spores makes good sense.
00:16:46.28		So, cannibalism is a way to stall for as long as possible
00:16:51.01		before crossing the Rubicon, before committing to spore formation
00:16:56.22		even at the expense of committing fratricide, killing and feeding on
00:17:01.07		genetically identical sibling cells.
00:17:05.19		Finally, I come to the example of individuality versus community.
00:17:11.19		This represents work done in collaboration with Roberto Kolter
00:17:16.21		and concerns the topic of multicellularity in biofilm formation
00:17:21.12		which was the subject of the second part of my presentation.
00:17:26.08		So, wild strains of B. subtilis can make architecturally complex communities.
00:17:31.24		In a standing culture, these communities form at the air liquid interface
00:17:37.22		in a structure known as the pellicle, that has an elaborate and distinctive architecture.
00:17:43.04		And on colonies, on solid medium, we also see an elaborate architecture
00:17:48.08		with thick veins and aerial structures.
00:17:51.25		The cells in these communities are held together
00:17:54.15		by an extracellular matrix, kind of a cement, that holds long chains
00:18:00.15		of cells together so that the architecture can be built.
00:18:05.08		This matrix consists of two components: a polysaccharide and a protein component
00:18:11.05		that are exported from the cells.
00:18:15.01		The matrix is subject to intricate regulation for its production.
00:18:20.02		And I've summarized, in a simplified form, the regulatory pathway
00:18:25.20		by which the matrix is produced.
00:18:29.12		But, most proximal to the genes for the matrix is
00:18:32.02		a repressor protein that holds them inactive.
00:18:35.20		The repressor protein is inactivated by another protein that we call an anti-repressor.
00:18:41.12		And finally, the anti-repressor is produced under the control
00:18:45.27		of our good friend Spo0A. And you will recall that Spo0A
00:18:50.14		is subject to a bi-stable switch.
00:18:54.18		Well, this predicts that if we look in cells that are about to form
00:18:59.15		a biofilm, we'll see the repressor being produced in all of the cells
00:19:04.27		but the anti-repressor, which is under the control Spo0A
00:19:08.15		will be produced in only a subset of the cells.
00:19:12.04		Let's look. First, I'll show you a field of cells that has
00:19:16.10		a green fluorescence protein gene fusion to the repressor gene.
00:19:20.08		And you can see, more or less, all of the cells are green.
00:19:25.06		All of them are producing repressor.
00:19:27.22		Now let's look at a comparable field of cells but this time
00:19:30.15		the green fluorescence protein gene is fused to the anti-repressor gene.
00:19:35.04		And now we get a radically different picture.
00:19:38.25		Only some of the cells, a minority of the cells are on for anti-repressor production.
00:19:45.18		That is, they're on for Spo0A and therefore, on for anti-repressor.
00:19:50.03		Hence, they're inactivating the repressor. Hence, these are the matrix producing cells.
00:19:54.18		And from this we conclude that some cells make matrix for the entire community.
00:20:00.02		This is a kind of altruism in which some cells are dedicated to making
00:20:05.12		matrix for the entire community of cells
00:20:10.13		and the other cells specializing in other directions.
00:20:17.05		OK, so, I've given you four examples from a bacterium,
00:20:20.21		a single bacterium in which cell choices are made in a stochastic manner.
00:20:26.15		But I don't want to leave you with the impression that stochasticity is unique to bacteria.
00:20:32.29		I'd like, in closing, to consider the case of the mouse olfactory neuron
00:20:37.25		and the eye of the fly which provide two examples of stochasticity
00:20:43.10		in complex metazoans.
00:20:46.16		So, the mouse devotes a great deal of its genetic material to the process of smell.
00:20:55.04		Fully 4% of its genes encode receptors, membrane receptors for odorants.
00:21:03.06		There are about 2000 such genes in the chromosomes of the mouse,
00:21:08.04		in the diploid mouse and...but it’s the case that any given neuron
00:21:15.09		must express only a single receptor.
00:21:20.08		Otherwise, the mouse would be confused as to what odor it was sensing.
00:21:24.00		So, how does this work?
00:21:30.00		Here, in a cartoon form, is depicted a neuron that's expressing
00:21:36.06		a particular odorant receptor depicted in red.
00:21:40.18		It’s on and all of the other 999 odorant receptor genes on one haploid set
00:21:49.10		and the homolog on the homologous chromosome are off.
00:21:54.11		This cell expresses only one out of 2000 genes.
00:21:57.25		Now, how does this work?
00:21:59.07		Well, you could imagine, I suppose, a very complicated regulatory network
00:22:05.08		that was special for every neuron that ensured that only one
00:22:09.23		out of 2000 genes was turned on.
00:22:11.24		But, that would be so complicated it’s hard even to imagine how it would work.
00:22:16.14		Instead, the mouse has evolved a very elegant strategy.
00:22:21.17		It turns on, in any given neuron a single receptor gene stochastically.
00:22:26.10		Each neuron throws a roll of the dice to decide which receptor gene to turn on
00:22:34.09		and then, by mechanisms that are not yet fully clear, all other genes in that neuron,
00:22:40.07		all other odorant receptor genes, are prevented from being expressed.
00:22:45.26		My last example, concerns the eye of the fly.
00:22:49.26		The eye of the fly is a compound eye.  Flies don't have the simple eyes
00:22:55.02		that we have. They have many eyes as do other insects.
00:22:58.08		These compound eyes consist of many clusters of light sensitive cells
00:23:04.06		called ommatidia. Each of these ommatidia
00:23:07.12		can produce either of two color sensitive rhodopsins called rh5 or rh6.
00:23:15.13		So the eye is a field of many ommatidia and each of these ommatidia
00:23:21.07		switch on either the blue rh5 or the green rh6.
00:23:28.06		And they do so, they make this choice stochastically.
00:23:32.15		This is the work of Claude Desplan and I illustrate it to you
00:23:37.08		with this marvelous image from Desplan in which
00:23:40.24		you'll see a field of ommatidia in which some cells are producing the green rhodopsin
00:23:48.08		and others are producing the blue rhodopsin.
00:23:52.05		And if you stare at this image for a while you'll see that there's no consistent pattern.
00:23:56.25		It's stochastic. It’s not a simple flip of coin.
00:24:00.29		It’s not fifty-fifty. It’s a biased stochastic switch.
00:24:04.12		It’s biased in favor of the green choice in a ratio of about 70 to 30,
00:24:10.16		but any individual ommatidium is making its choice randomly, stochastically.
00:24:20.14		Such that, on average, the average decision is a ratio 70 to 30.
00:24:25.15		And if you look closely at this you'll see no consistent pattern
00:24:29.04		and if you look a the other eye of the fly you would see a different pattern.
00:24:32.21		If you looked at other flies you would see yet other patterns.
00:24:35.18		So the choice is stochastic.
00:24:38.21		So, in conclusion, we can say that nature does, indeed, know how to
00:24:44.20		make deterministic decisions, but, in contrast to Einstein's view of the universe,
00:24:50.07		she also knows how to leave certain decisions to a roll of the dice.
00:24:55.19		Thank you very much.

This material is based upon work supported by the National Science Foundation and the National Institute of General Medical Sciences under Grant No. 2122350 and 1 R25 GM139147. Any opinion, finding, conclusion, or recommendation expressed in these videos are solely those of the speakers and do not necessarily represent the views of the Science Communication Lab/iBiology, the National Science Foundation, the National Institutes of Health, or other Science Communication Lab funders.

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