Richard Losick (Harvard) Part 3: Stochasticity and Cell Fate

Hello, my name is Richard Losick
and this is the third part and last part of my presentation on
developmental biology of a simple organism.
This last part is devoted to the topic of stochasticity and cell fate.
I grew up in an era in which it was believed that development, biological development
is orchestrated by highly deterministic processes.
And indeed, that's largely the case. That's true in most cases.
But, increasingly, we're seeing that there are examples, especially in the microbial world
of cell fate decisions that are stochastic.
I'm going to tell you about a series of such examples from the spore forming
bacterium Bacillus subtilis. Let me begin with a famous quote
from Albert Einstein, the father of modern physics.
Einstein famously said that "I, at any rate, am convinced
that He does not play dice."
Thereby rejecting Heisenberg and his Uncertainty Principle.
Well, in biology, as I've said, most decisions are, indeed, highly deterministic.
But, it’s also the case that some decisions, some cell fate decisions,
are, in fact, done by a role of the dice, as I'll explain.
So, I'm going to give you four examples from Bacillus subtilis
which, so far, appears to be the champion of stochasticity in the microbial world.
The four examples are: growth and competence, swimming versus chaining,
eating versus being eaten, and community versus individuality.
What do I mean by growth versus competence?
Well, B. subtilis can switch into an alternative state,
the state of competence, in which it stops growing and instead, acquires
the ability to take up DNA from its environment
which it can recombine into the chromosome.
Why would B. subtilis do such a thing if this means stopping to grow?
Well, what one imagines is that producing cells
that are able...that are on the prowl for
new genetic sequences improves the fitness of B. subtilis by allowing
it to uptake new genetic information that may help it cope
with changed circumstances in the future. So there must be a fitness benefit
to the bacterium for temporarily entering this non-growing state
so that it can be always on the look out for
potential new genetic sequences that can be useful to it.
Entry into this state of competence is controlled by a transcription factor
called ComK and as you'll see, the synthesis of ComK
is governed by a noise-driven stochastic switch. So in other words,
when cells are under conditions in which they're capable of entering the
competent state, only some of them do and the ones that decide
to do so, do so in a stochastic fashion.
Let me illustrate this to you with a beautiful experiment
from David Dubnau, who, along with others in the field are
responsible for our understanding of stochasticity in the competent state.
So what I'm going to show you is a field of cells in which all of the cells
harbor a fusion of the gene for the green fluorescence protein
to a promoter under the control of ComK.
And what you can immediately see is that only a subset of the cells
are brightly green. That is, most cells are off for ComK
and some cells are on for ComK.
These cells were grown and held in a homogeneous environment.
All of them, in principle, are capable of becoming competent,
of activating ComK but only some of them do so
and they do so in a random fashion, independently of
what the neighboring cells are doing.
How does this work?
So at the heart of this system is the following circuitry.
The comK gene, of course, encodes the ComK regulatory protein
which can bind to and activate the transcription of about 100 target genes
that define the state of competence.
But ComK also bind to the promoter for its own gene
in which case it sets up a positive feedback loop
which can stimulate transcription of its own genes.
So when ComK binds there, that leads to more transcription of comK
that results in yet more transcription which in turn leads to yet more ComK molecules
that lead to this on state in which large levels of ComK accumulate in the cell.
The key point is that this positive feedback loop has a threshold.
You can think of it as being poised on a knife edge.
And under the right conditions the cells have just less than a threshold
amount of ComK molecules in them and if, due to noise,
there are fluctuations in the amount of ComK from cell to cell then
some cells will have a bit more ComK than other cells.
Those cells that have a bit more have reached the threshold
and get the positive feedback loop going. Those that are below
the threshold can't get the positive feedback going.
And what makes this switch a bi-stable switch
is that multiple ComK molecules bind to the promoter in a cooperative fashion
by interacting with each other. This makes the switch highly sensitive
to small fluctuations in the level of ComK molecules.
So, when the amount of ComK in a cell is just below the threshold,
most cells will not activate ComK, but a few cells will have,
by noise driven processes, accumulated enough ComK molecules
to activate the positive feedback loop and get it going
and go into the competence on state.
Why does B. subtilis do this?
Well, we don't know for sure but, obviously, entering a state in which you're not
growing puts you at a disadvantage. But by deploying, stochastically, some cells
that are on the prowl for new genetic information, then B. subtilis is always
preparing itself for unexpected changes in its environment
when new kinds of genetic information may be important.
So, we can think of this as an example of bet hedging.
That is, B. subtilis is hedging its bets by deploying, stochastically,
a small proportion of cells that enter a non-growing state temporarily
so that should circumstances change
and should the right genetic sequences appear
then those cells will be at an advantage.
Remember, that evolution selects for the genome and not the individual.
So, deploying two kinds of cells in the population
can be advantageous to the genome
even if it’s not advantageous to the individual.
Let me come to my second example, motility versus chaining.
This is a phase contrast micrograph that depicts B. subtilis cells as
we've traditionally seen them over many decades of research with this organism.
And if you look closely you can see there are two kinds of cells here.
There are long chains of cells that have completed cell division
but haven't separated from each other and there are also singlets and doublets.
The singlets and doublets, it turns out, are motile cells,
where as the long chains are non-motile cells. They're sessile cells, if you will.
Well, we saw this image for many, many years but didn't pay much attention to it.
But over time it emerged that a single transcription factor
called sigmaD is responsible for the production of enzymes that
degrade the cell wall material between newly divided cells,
enabling them to separate and also for the production of the machinery
that's responsible for motility.
So, with that in mind we revisited this field of exponential phase cells
but this time using cells that were tagged with green fluorescence protein reporter
gene fused to a promoter under the control sigmaD.
And at the same time we stained the cells with a red membrane dye
so that we could see the division septa.
And now, all of a sudden, we get a radically different view of
the field of cells that's very illuminating.
As you can see there are cells in two states.
There are sigmaD on cells that are doublets or singlets
and these are the motile cells. They undergo cell division
and then the products of cell division can separate from each other.
And then there are the sigmaD off cells which we see as long
red membrane staining cells. You can see the division septa in these cells.
The cells have divided but the daughter cells haven't separated from each other.
These cells are off for sigmaD and they're non-motile.
Why would B. subtilis do this?
Well, of course we don't know but it’s attractive to imagine
it’s another example of bet hedging.
Imagine that B. subtilis is in a particular niche where there are nutrients.
The sessile chains of cells can stay put and exploit the existing niche.
But the motile cells, the sigmaD on cells, we can think of those
as nomadic cells that wander off to look few niches.
B. subtilis doesn't know what the future holds in store and so its immediate niche
may run out of nutrients or exhibit other adverse environmental factors.
So, by deploying some cells to be motile and some cells to be sessile
the bacterium can hedge its bets. Some cells stay put
and exploit existing circumstances whereas other cells swim off
looking for new niches in anticipation of the possibility that the original
niche may become exhausted.
My third example is called eating versus being eaten.
So, as we saw in the first part of my presentation, when starved for nutrients
B. subtilis enters the pathway to sporulate.
So just as competence represents a distinct state,
sporulation represents a specialized developmental state.
Entry into sporulation is governed by a master regulatory protein called Spo0A.
Sporulation is a complex process that takes multiple hours.
It takes time and it takes energy,
and it culminates in the formation of a dormant cell type,
the spore that can remain dormant for many years.
So I think it’s easy to imagine that the decision to sporulate
is not one that B. subtilis wants to take lightly.
Entry into this pathway is governed by Spo0A
which sits at the top of this regulatory sequence.
It becomes activated under conditions in which nutrients become limiting.
Well, there is a crucial window of time near the start of sporulation
when the cells can change their minds, so to speak.
Initially, when Spo0A become active but before the hallmark process
of asymmetric division takes place, if nutrients reappear, sporulation is arrested
and the cells can even start growing again.
But once the cells cross the Rubicon, so to speak, of asymmetric division
now, they're committed to making a spore,
even if lots of nutrients appear at later stages.
So there's this window of time up until asymmetric division takes place
when the process is reversible,
and then it becomes irreversible later on.
So hold that thought in mind when I tell you what I
think at this point will not come as a surprise, that Spo0A
is itself subject to a bi-stable switch.
That is, when cells are limited for nutrients, only some of the cells become on for Spo0A
whereas others remain off for Spo0A.
And once again, we can visualize this by using a green fluorescence protein gene
fusion to a promoter under the control Spo0A.
And as you can see there are two kinds of cells:
those in which Spo0A is off and those in which Spo0A is on.
Only some of the cells are on for Spo0A.
Well, why have a bi-stable switch? Why is Spo0A subject to a bi-stable switch?
Well we believe that the answer has to do with a phenomenon
that we refer to as cannibalism.
When the cells are deprived of nutrients
and Spo0A becomes activated, the Spo0A on cells produce and export
a toxin, a peptide toxin that kills their sibling cells, the cells that are off for Spo0A.
The producing cells, the on cells are immune to the toxin
but the non-sporulating cells are killed by it. They lyse and liberate their nutrients.
Now, remember, Spo0A activation is triggered by nutrient limitation in the first place.
So, if some of the cells are lysing and liberating nutrients
that will have the affect of impeding Spo0A activation
and that will arrest sporulation or perhaps even reverse it.
So, cannibalism is a process for slowing down sporulation.
Let me illustrate that for you with this single agar plate experiment.
So the left side of the slide shows a streak of wild type cells
and the right hand portion of the slide shows a cannibalism mutant.
When cells start to sporulate, the colonies become white and opaque
and so we can see that with the naked eye.
And as you can see at the early time when this photograph was taken
the wild type cells have only just begun to sporulate.
The colonies are not yet white and opaque.
But the mutant cells are filled with spores.
Now remember, cannibalism is a process for delaying sporulation.
So, therefore, in its absence sporulation is accelerated.
So we see rapid sporulation in a cannibalism mutant
and slow sporulation in the wild type.
Once again we can ask, "Why would B. subtilis do such a thing?"
And the appealing interpretation is, yet once again, it’s hedging its bets.
Consider a population of B. subtilis cells that
experiences a drop in the availability of nutrients.
How does it know whether this decrease in nutrients is a simple
fluctuation, a temporary decrease in nutrients or the beginning of a famine?
If it willy-nilly committed itself to making a spore when nutrients were depleted
and went through this multi-hour, expensive process
and nutrients actually returned after the time of
commitment, well, it would put itself
at a disadvantage relative to other bacteria that would be simple waiting out
the period of low nutrients.
On the other hand, if it’s in a period of prolonged starvation,
then going ahead to make spores makes good sense.
So, cannibalism is a way to stall for as long as possible
before crossing the Rubicon, before committing to spore formation
even at the expense of committing fratricide, killing and feeding on
genetically identical sibling cells.
Finally, I come to the example of individuality versus community.
This represents work done in collaboration with Roberto Kolter
and concerns the topic of multicellularity in biofilm formation
which was the subject of the second part of my presentation.
So, wild strains of B. subtilis can make architecturally complex communities.
In a standing culture, these communities form at the air liquid interface
in a structure known as the pellicle, that has an elaborate and distinctive architecture.
And on colonies, on solid medium, we also see an elaborate architecture
with thick veins and aerial structures.
The cells in these communities are held together
by an extracellular matrix, kind of a cement, that holds long chains
of cells together so that the architecture can be built.
This matrix consists of two components: a polysaccharide and a protein component
that are exported from the cells.
The matrix is subject to intricate regulation for its production.
And I've summarized, in a simplified form, the regulatory pathway
by which the matrix is produced.
But, most proximal to the genes for the matrix is
a repressor protein that holds them inactive.
The repressor protein is inactivated by another protein that we call an anti-repressor.
And finally, the anti-repressor is produced under the control
of our good friend Spo0A. And you will recall that Spo0A
is subject to a bi-stable switch.
Well, this predicts that if we look in cells that are about to form
a biofilm, we'll see the repressor being produced in all of the cells
but the anti-repressor, which is under the control Spo0A
will be produced in only a subset of the cells.
Let's look. First, I'll show you a field of cells that has
a green fluorescence protein gene fusion to the repressor gene.
And you can see, more or less, all of the cells are green.
All of them are producing repressor.
Now let's look at a comparable field of cells but this time
the green fluorescence protein gene is fused to the anti-repressor gene.
And now we get a radically different picture.
Only some of the cells, a minority of the cells are on for anti-repressor production.
That is, they're on for Spo0A and therefore, on for anti-repressor.
Hence, they're inactivating the repressor. Hence, these are the matrix producing cells.
And from this we conclude that some cells make matrix for the entire community.
This is a kind of altruism in which some cells are dedicated to making
matrix for the entire community of cells
and the other cells specializing in other directions.
OK, so, I've given you four examples from a bacterium,
a single bacterium in which cell choices are made in a stochastic manner.
But I don't want to leave you with the impression that stochasticity is unique to bacteria.
I'd like, in closing, to consider the case of the mouse olfactory neuron
and the eye of the fly which provide two examples of stochasticity
in complex metazoans.
So, the mouse devotes a great deal of its genetic material to the process of smell.
Fully 4% of its genes encode receptors, membrane receptors for odorants.
There are about 2000 such genes in the chromosomes of the mouse,
in the diploid mouse and...but it’s the case that any given neuron
must express only a single receptor.
Otherwise, the mouse would be confused as to what odor it was sensing.
So, how does this work?
Here, in a cartoon form, is depicted a neuron that's expressing
a particular odorant receptor depicted in red.
It’s on and all of the other 999 odorant receptor genes on one haploid set
and the homolog on the homologous chromosome are off.
This cell expresses only one out of 2000 genes.
Now, how does this work?
Well, you could imagine, I suppose, a very complicated regulatory network
that was special for every neuron that ensured that only one
out of 2000 genes was turned on.
But, that would be so complicated it’s hard even to imagine how it would work.
Instead, the mouse has evolved a very elegant strategy.
It turns on, in any given neuron a single receptor gene stochastically.
Each neuron throws a roll of the dice to decide which receptor gene to turn on
and then, by mechanisms that are not yet fully clear, all other genes in that neuron,
all other odorant receptor genes, are prevented from being expressed.
My last example, concerns the eye of the fly.
The eye of the fly is a compound eye. Flies don't have the simple eyes
that we have. They have many eyes as do other insects.
These compound eyes consist of many clusters of light sensitive cells
called ommatidia. Each of these ommatidia
can produce either of two color sensitive rhodopsins called rh5 or rh6.
So the eye is a field of many ommatidia and each of these ommatidia
switch on either the blue rh5 or the green rh6.
And they do so, they make this choice stochastically.
This is the work of Claude Desplan and I illustrate it to you
with this marvelous image from Desplan in which
you'll see a field of ommatidia in which some cells are producing the green rhodopsin
and others are producing the blue rhodopsin.
And if you stare at this image for a while you'll see that there's no consistent pattern.
It's stochastic. It’s not a simple flip of coin.
It’s not fifty-fifty. It’s a biased stochastic switch.
It’s biased in favor of the green choice in a ratio of about 70 to 30,
but any individual ommatidium is making its choice randomly, stochastically.
Such that, on average, the average decision is a ratio 70 to 30.
And if you look closely at this you'll see no consistent pattern
and if you look a the other eye of the fly you would see a different pattern.
If you looked at other flies you would see yet other patterns.
So the choice is stochastic.
So, in conclusion, we can say that nature does, indeed, know how to
make deterministic decisions, but, in contrast to Einstein's view of the universe,
she also knows how to leave certain decisions to a roll of the dice.
Thank you very much.