Enseignement 2023-2024 : Régulation du volume des cellules
Séminaire du 05 février 2024 : Cell Growth under Mechanical Pressure: Effect of Macromolecular Crowding
Intervenant : Morgan Delarue, Chargé de Recherche CNRS, LAAS-CNRS, Toulouse
We will discuss the biophysical regulation of cell proliferation under spatial confinement, and the key role macromolecular crowding plays in modulating biogenesis under mechanical pressure.
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Chaire Matière molle et biophysique
Professeur : Jean-François Joanny
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Don’t mology okay okay so I’ll switch to English I guess uh so hello everyone um so I come from the last indeed so theas I mean the acronym it’s it’s very complicated it’s basically so complicated that you can do anything in that lab which is good and uh but it’s
Mainly biotechnology that we do in the lab and what I present is also based on microfuidic development that I present in a few seconds so what we are interested in is growth under confinement uh as je FR explained in the introduction uh cell proliferation is this coordinated process of growth and
Division so cells they grow they expand their volume roughly they double the volume before they divide the volume and if you take one cell and you let it proliferate then you will get a cell population what’s uh often the case when one uh look at cell proliferation is the
Fact that they actually proliferate in a specially confined environment and that’s true for a lot of different examples in the living that’s true for bacteria for plants from a million cells for fungi and I’ll come back to that in a few seconds and if cells proliferate in a confined environment a necessary
Condition for them to keep on growing is to push against the surroundings to push against the cells themselves and what will happen in that case is that they will develop what we call a growth induced pressure that’s a necessary condition to grow under confinement um recently we’ve also been interested in multicellular assemblies
Multicellular cohesive assemblies and actually if you take for instance a cell at the center of a bow of cells a SPID I mean that cell also needs to push against the surroundings and may also experience that type of mechanical stress even in 3D environment without spatial confinement um so we find examples of
That anywhere in the living we have examples for plants so here it’s the mer stem of arid uh where the cells they grow in a planer configuration and because they grow attached to the bottom they will start to exert mechanical stress in plane and that mechanical stress is also
Growth induced but if you think of plants you actually have the roots of trees that grow in the ground and in the ground you don’t have space to grow and the roots of trees actually they develop enormous amount of mechanical stress such that they can actually break
Concrete um you also have examples in the case of tumors tumors they grow in a specially limited environment and in that specially limited environment they will accumulate growth indu pressure that can be evidenced if you take a bit of that tumor and you see the relaxation
Of that of these tumor cells inside the tumor you have examples for the case of microbes Fung bacteria in the case of bacteria for instance for menitis neria menitis these cells they grow as cohesive assemblies they can olude blood vessels and when cells olude blood vessels you have blood clots and when
You have blood clots then you again have a confinement of these cells that will grow there and we measure the force that these cells develop and we see that it’s enough to actually break a blood vessel so maybe that’s also linked to the break of the blood brain
Barrier um so we study in the lab all of these organisms except for plants yet I would love to work on Plants we don’t know how to work on Plants yet but we have the tools um and the way we do it is by engineering microfic cages
Oopsie and the tools that we have are always the same all right so they consist of a microfic cage where we load the cells in the device and we have a valve that can be passively or actively closed and the Chamber is connected on the side to a
Set of narrow channels so these channels are too small for the cells to go in but they are large enough so that we can feed the cells correctly so here we work at a constant chemical environment uh we then design deformable elements or we just use the deformability of the environment to
Directly measure the force that the cells develop so here you have sakar the buing ye the bers that you find in a in a bread in wine in beer anywhere it’s the yeast of life and it proliferates in in this confined environment and when it reaches confluency it starts to push
Everywhere it pushes on that twizer here that is deformable and by exerting pressure it will deform everywhere and it will deform that twizer and then self close the device so like that it will self confine and it will build up an enormous amount of pressure that we can
Measure through the deformation of the pdms when I say enormous it’s close to the MEAP Pascal range the pdms the material that we use is the young modulus is 2 megapascal and you see a clear deformation of that of that chamber so over the years uh we have
Develop developed a set of different microfic devices to confine and study the effect of confinement of organisms ranging from bacteria eoli cier but also Mamon cells here for instance you cannot use irly the deformation of the pdms which is not deformable enough so we developed a suspended membrane that can
Be pushed by the spheroid which moves into some sort of a cuboid in that chamber and exerts that mechanical stress against that membrane that we can calibrate and then infer the pressure um before diving into um what we study and what we are interested in in terms of
The response of growth to confinement I just want to give you two examples also in terms of pathologies that we study uh we study another yeast which is called Candid albicans candid alans is a commensal of the gut it can be found normally growing inside our body and it
Is dimorphic so it has two shapes the yeast shape the ovaloid rugby so it’s to lose to lose this rugby so the ovaloid rugby shaped and it can Mo into a filament filamentos yeast which is compartmentalized so here you have different cells they just attach to one
Another and uh this highall form is the one that is responsible for the pathogenicity of the yeast because it can go in between epithelial cells and even P epithelia what we have recently found so we developed a three layer device where we have the chamber this channels so
That the cells cannot pass and here we have another set of channels which are now big enough to let the high pass through but not the yeast and what you can find what you can appreciate in that movie is that when the says which is Confluence and start to exert mechanical
Stress and a very low amount of mechanical stress in the kilopascal range which is totally compatible with the physiological kilopascal bran of our body we see that the cells morph into this high and invade and they have this sort of epigenetic switch because they don’t trt here you don’t have mechanical
Stress and but the Hy remains a filament at the end we see e so we don’t see but the filament remains a filament Until the End um another example is um antibiotic resistance uh here you have the I thought so here you have the baker
Yeast and uh these ah this bake yeast uh we grow it in the confined Chambers okay for some reason the movie doesn’t work anyways we grow the cells in confined Chambers to different amount of mechanical stress no no mechanical stress saw a lot of mechanical stress
And then we add that drug and theis in B and we see that under mechanical stress they start to become resistant of that drug we don’t know why but this is something that we actually see uh ah here the movies so here all the cells in blue that die and here we
See no cell death basically um recently we’ve also been interested in in cancer cells and in confined growth of cancer cells uh one key feature that we see in all the organisms so far that we have studied when they go in confinement is the fact that cell proliferation decreases so we have less
Dividing cells less growing cells and we thought that that could have a a major consequence in the case of chemotherapeutic treatments uh indeed chemotherapeutic agents most of them they target all the cells that do proliferate so if you add a typical chemotherapy agent for instance here we
Used gabin which is used in the case of pancreatic cancer it will kill the cells that do proliferate so if you add that agent on a SPID that is growing we see that over time the SPID is actually being killed and we see the radius of that SPID being
Reduced if we now confine this fuid into a hydrogel so we limit the growth of this fuid we do see that it at the same time it grew much less you have the same amount of time and you have a radius that is much smaller and we see that
Actually inside the Spy the cells divide much less which is why it’s smaller but then when we add the drug the drug works much less as well because you have less target cells actually we can mathematically predict the reduction of the radius just based on the additive
Effect of the growth reduction on the mechanical stress and the killing of the drug so we don’t need to call for any kind of uh drug resistance due to some specific Gene being activated it’s just the combined effect of a growth reduction and the fact that the drug is
Supposed to kill proliferating cells but you have less target cells so all of that and again there the antibotic resistance so um all of that and because we see that in all of the organisms there’s a proliferation reduction that triggered to us the question of okay what could be
The cause for this proliferation reduction so just to be clear when I talk about proliferation reduction proliferation is growth and Division and these two processes are coordinated and what we see in yeast and that’s a bit less true in bacteria uh we see that there’s an
Accumulation of cells in the G1 phase of the cell cycle that John FR talked about earlier so the cells stop at the beginning of the cell cycle and at the same time their growth rate which is the volumetric increase decreases with increased growth Inu pressure so that is probably organism specific because
The cell Cy regulation between a bacteria and a bacterium and a Mamon cell has to be somehow different but we think that the growth reduction can have some similarities between different organisms and that’s what I’m going to talk about now so um the first thing that we have found in
Yeast and that’s disappointing for what John just told you is that the cell volume does not change in our case so we have measured through 3D reconstruction the Single Cell volume so here you have the classical rugby shaped buding yeast and here you have its version when it’s
Confined so it’s polygonal it’s the the the bud is actually squashed at the bottom of the chamber it doesn’t really look like a bing East but we can measure the cell volume and we found that the cell volume distribution has not fully change uh we actually have less big
Cells and this is due to the fact that you have less proliferating cells less dividing cells and the dividing cells are the big cells but the small cell volume is the same for all the cells what’s interesting is that if we relax the mechanical stress then the cell
Volume increases and it increases to a predicted value that is associated with the amount of mechanical stress that was built up so the growth in this pressure is due to the accumulation of osmolites inside the cell and we’ll come back to that in a second and if you relax
Mechanical TR the cell they have accumulated osmolites and then they swell to the predictive predicted volume that they should have would they have the space to grow um we can measure cell density inside the cells another method that uh Shan FR did not also discuss is the did not
Discuss is the quantitative phase microscopy to measure refractive index the idea is that if you have light shining through a sample you can measure uh the phase shift and the phase change sorry is diff is due to the difference in refractive index and the height of the sample that the light goes
Through uh using that we can measure the refractive index of a buing East for instance here we found it it’s 1.38 okay it is what it is uh and here for instance you have a map of this uh Optical path difference inside the yeast
We see the vacu of the yeast we see the bud we see the Mother cell so it works quite nicely uh so we can do that on the mechanical stress and what we see is that the refractive index of the cell increases as growth induced pressure increases the refractive index is
Related to not directly proportional to but related to the mass the dry Mass inside the cell so if the refractive index increases in that case is pro probably due to the fact that the dry Mass inside the cell increases so here that’s the first signature that even
Though the volume of the cell hasn’t changeed the cell is not inactive it still produces things it produces aites that’s the growth of the chamber but it still also produces biomass and we see it through the increase of the refractive index so the working model that we have
Is um is the following how could cell combine biomass production and osoite production so as joh FR explained um the proteins all the protein complexes inside the cell they are related to the mass of the cell but there are there’s not enough of the so that they can provide a large enough
Osmotic pressure to swell the cell the osmolites conversely they are much smaller but you have them at a very high concentration so they are numerous enough so that they can exert a large OS molic pressure but as FR said as well what we measure experimentally is that
The density inside the cell is roughly constant and it’s tightly regulated it’s not very clear still how but it’s tightly regulated ated so it means in a way that the biomass and the osmolite concentration should be matched over time so for a growing cell we can imagine that the cell produces proteins
These proteins are linked to osoite production the cells produce osmolites this way and that lets water inside the cell the cell then swells a bit dilutes everything back to the nominal concentration and in some sort of a model like that you can have a constant density but in our
Case the cells are confined their volume cannot fully increase so it means that the cells produce Mass they produce osmolites but the cell volume cannot increase so both concentration should increase in time do they increase proportionally or not that’s a question but they should increase in time and
What we see indeed through the refractive index measurement is that the biomass increases and the osoite increase is linked to an osmotic pressure increase which is the growth induc pressure that we measure in our Chambers so with that in mind uh that could have a big consequence in how the
Cells then work and this is what I’m going to try to explain and we think that this is linked to the feedback that we see on uh bio on a growth rate regulation so this is related to a property of the cytoplasm that’s called macromolecular cring so the cytoplasm of
A cell is highly crudded with with mcro molecules okay I think I’m just going to do it with a hand is highly cded with macro molecules so the macro molecules they’re not large enough so that they can exert osmotic stress but they are big enough so that they can occupy a
Large fraction of the cytoplasm for instance we have measured that in yeast ribosomes just ribosomes occupy about 25% of the cytoplasm which is a lot just ribosomes and there much more than just ribosomes inside the cell so that’s a picture from the cytoplasm of a bacteria of a bacterium from David Goodell but
That illustrates the idea that the cytoplasm could be highly crowded cring is an interesting property as it has uh a large impact on biochemical reactions as je fris reminded if you increase cring you will entropically increase reaction rates by favoring the bound the bound state of two
Substrata but at the same time if cring increases to much then the two subst that are supposed to react then will diffuse much slower and in that case the reaction rate will decrease so theoretically for any kind of reaction virtually there should be an optimal cing ceding concentration this Optimum
Can be different for two different types of reactions of course it can be more more or less pronounced but theoretically they should be one for any type of biochemical reactions so we have developed a tool in order to measure how things move inside the cell to measure diffusion and not
Just the density inside the cell because you can have changes of density that reflect different states of how things move inside the cell so to us it was more informative to have how things move inside the cell than just the row value of intracellular density so what we designed are uh
Genetically encoded multimeric nanoparticles the idea is was to fuse a floresent protein onto a scaffolding protein that can self assemble into the inside the cell so here this monomer the scaffold plus the FR and protain they are expressed genetically in the cell we put them into a low expressing promoter
And then they will self assemble and their size will be guided by the stomry of the of this multim so in the case of that complex it’s 120 M period so that means that they we always have the same size and we have reconstructed the size
Through uh we have measured the size s through to a cryo electron microscopy reconstruction and we have found that there exactly 41 nanometer in that case uh I’ll come back to that uh later what we can do with these nanop particles what’s interesting is that this size is roughly the size of the
Protein complexes inside the C ribosome is roughly 30 nanometer polymerases forance RNA polymerases 25 nanometer the T complex is about 40 50 nanometer so it’s the size of the typical complexes inside the cell so we can express them in different organisms we can express them in bacteria in plants in yeast in Mamon
Cells even in embryo now and uh they look like very bright particles you have 120 person protain on one point so it’s very easy to image uh and we can track them in wow okay I’m not sure you see that movie uh but you see the trajectories there so here you have
Moving particles but you don’t see them but here you have the trajectories of them so so we can track these nanoparticles we can extract a wealth of information from this diffusion and this is something that we are working on which is understanding this motion because it’s not fairly easy it’s not
Very bronan but it’s close to being bronan but there’s there’s some subtleties that we don’t fully understand but we can anyways measure some diffusion coefficients from these trajectories and what we have found in yeast and here you can see the movies is that and that’s uh consistent
With the fact that density increases is that the motion of these nanoparticles decreases as pressure increases and here we can quantify this effective diffusion coefficient at 100 millisecond and we see that it’s decreasing as pressure increases again if you relax mechanical stress this yes the diffusion constant
Doesn’t depend on time yes it does for a short time and a long time diffusion constant in a t system like this it’s it’s actually decreasing con constantly over time um I don’t think I mean the current models that we have you measure you measure r s
Function of T we measure just the R square at so we measure at a function of T but what I plot here is the short time scale we just a over 20 millisecond because indeed it depends on time to us it’s not fully anomalous in the sense
That it’s due to some electrostatic or chemical interaction uh we think it’s sterical steric interaction so the cell is a fixed volume so you have confinement because of the boundaries of the cell and on on top of that you also have large macromolecule complexes inside the cell that diffuse much
Smaller much slower than the particles that we see so they are effective obstacles to the diffusion and if you combine the two then we kind of reproduce the motion but it’s still ongoing so here when I say effective diffusion coefficient is the short time scale tens of millisecond diffusion
Coefficient because indeed it depends on time um and if we relax mechanical stress then diffusion increases the cell vol volum increased as I said but it increases to the value it should have had would it be growing without dilu without confinement and we see that because the Dil the cytoplasm is as
Diluted as the control in that case um the decrease that we see in diffusion is exponential with mechanical stress and we can show experiment theoretically that it should be exponential exponential uh we can actually even calibrate the slope of that uh the critical pressure sorry characteristic pressure of that
Exponential for the 40 Nom particle that I showed you and that’s here the prediction in Black uh what’s interesting is that the diffusion does not change similarly as the size with the size of the particle that we see small particles that are much less impacted by an increase of
Cring than large particles here you have RNA particles are about 100 nanometer in size and they are super impacted by pressure whereas the 20 nanometer particles they are almost not impacted uh I come back to the nucleus in a second we also have measured the
Locus of uh a DNA of a gene inside the DNA and we see that the motion of DNA the Dynamics of DNA also decreases with this increase of mechanical stress I’m not showing the data but we see that actually the nuclear volume is decreased when uh pressure
Increases so just to how do you justify the fact Cas exponentially so we combine two equations and one hypothesis we combine a d equation that you may know which relates to diffusion to the packing fraction of a given polymer inside a given given solution to the vov equation
And we combine the two saying that the osmic the osoite concentration is proportional to the mass to the mass concentration if you do that it should give you an exponential dependence um we can put these nanop particles in a lot of different compartments also inside the cell so I
Don’t know if you’re familiar with NLS it means nuclear localization signal so we can fuse to this uh monomer a nuclear localization signal which means that this monomer will then be imported in a different in a given compartment here the nucleus and then the particle will self assemble inside this compartment
What’s interesting is that the NLS the end part of the protein is actually hidden buried inside the D particle so then when the particle is assembled the NLS cannot interact with the nuclear P so then you you have a nanop particle that is entrapped in a given compartment
Such as the nucleus and we can measure the diffusion inside the nucleus we also have them in the reticulum endoplasmic reticulum we also have them in mitochondria so it’s very interesting to us because it allows us to measure diffusion coefficient in different subcellular compartments and to also relate how the diffusion the cring
Inside the compartment relates to the cring of the cytoplasm or different compartment um with this for instance we can measure the diffusion of the cytoplasm and of the nucleus as a function of growth induced pressure uh or also increased hyperosmotic stress and we see that so first surprisingly
The diffusion inside the nucleus for sacar is the is the same in the nucleus and in the cytoplasm I think it’s just random because in skizo sacares pom it’s not the case so I think in that case it’s R it’s the case but I’m not sure it’s a general rule but what’s very
Interesting is the fact that the diffusion decreases the same way in both the nucleus and the cytoplasm both for growth OS growth indu pressure and an osmotic stress okay so uh density increases diffusion decreases it decreases in differently in uh it decreases in different subcellular compartments in the cytoplasm in the nucleus the
Question we then asked was can that change in diffusion impact some kinetic kinetics inside the cell so the first thing that we wanted to look at is protein production globally protein production so we looked at that to uh with Optical means it was hard for us to find a
Condition to measure properly that parameter because so we wanted to use an inducible promoter the problem is that the typical inducible promoter that you use like ton cannot fully work in microfic because toxic cycling or tetrac cycling they kind of absorb to the pdms which is complicated but the second problem is
That if you induce a promoter when the cell is still growing because of the growth induc pressure being growth induced if you don’t stop growth then pressure will keep on increasing and then it’s hard to know at which pressure you measure measure the First Signal increase so we decided to starve the
Cell because when we starve the cell then they stop growing and we there’s a promoter inside the inside the yeast that’s called adh2 for alcohol dehydrogenase so basically it switches the conception of the energy usage of glucose to ethanol in that case to produce energy uh and that’s turned on by
Glucose starvation and we hooked a m Cherry to this promoter so when we glucose St the cells the gr induc pressure is fixed so we work at fixed pressure and we look at the forence increase in the cells so that promoter is Bodel uh so some cells express it
Some others don’t but we just we are just interested in the cells that do produce that so way what you can observe that in this movies is the fact that the first intensity increases when we starve the cells but it increases much faster when the cells are not compressed than
When the cells are compressed uh these are single cell first intensi CS we can build a very very simple model of a protein production uh in the case of glucose starvation we can neglect protein degradation because it’s mainly turned off so it’s just the production of through transcription and
Translation and in that case uh the frestance intensity should increase after a certain lag which is due to the induction as the product of the transcription times the translation rate and quadratically in time these are the the fits of the experimental data and what call protein production is
Basically the product of these two rates here so we don’t know what change is here it’s decreasing but we know that one of the two or maybe the two rates decrease uh here you have the quantification and we see that this production rate does decrease and dramatically actually as a function of
Growth inded pressure so protein production is decreasing when growth IND pressure is increasing and when crowding is increasing inside the cell but where it was hard to relate fully that to the increasing cring because a lot of things can also happen so we also use an orthogonal means of changing cring which
Was osmotic stress to verify that indeed protein production would decrease when you increase cring so if you do an osmotic stress to the cell the cell will shrink cring will increase and that’s almost instantaneous uh there’s a lot of more adaptative Pathways in inside the celling the yeast has only one that’s
Fairly easy it’s hog one so if you do a knockout of that protan then you have no osmo adaptation that’s why yeasts are cool in that sense so in that case we kill osmo adaptation and the cells will remain shrunk for a very long period of
Time so they will remain at very high crowling so we used exactly the same reporter and we have found that indeed the prod production rate decreases when you increase inre concentration of the osoite yes question this Oso adaptation is supposed to produce something to make the cell yes so this shouldn’t change
The pressure inside the cell it should keep High even though it’s tically adap volume right yes so if you don’t kill hog one what happens to the so if we don’t kill hog one uh what happens is that so if it’s a let’s say large osmotic stress like these two guys
Uh hog one is a transcription Factor it goes inside the nucleus and it produces proteins that produce more glycerol in order to accelerate the production of glycerol which is the main osoite in the yeasts but if you don’t then you remain with the housekeeping production of osmolytes which is too slow to
Compensate in that case expression rate is of your report yes correct this change uh with Theos ADT does this manage to change so if we do exactly the same experiment so the difficulty here is that when you do an osmotic stress and you have hog it’s also triggering some uh
Transcription shut down for a lot of different genes and that we have measured at a single cell level the problem then is that this Gene is not turned on so we cannot fully do the experiment in that case so it’s not perfect I agree so there’s still a lot of
Assumptions um what we can then do with this data is plot this expression rate of the reporter as a function of the diffusion that we can measure in the different conditions osmotic and growth induced because then here we have two metric that we can measure independently for the two types of mechanical stress
And what we see is that they kind of follow exactly the same relation where the expression rate decreases as the diffusion decreases it’s not proportional um actually if you have diffusion limited reaction in our case where the rate should be proportional to diffusion the big question is the diffusion of
What and in our case what we can see through the change of diffusion we the size of the particle is that if we plot that as a function of the diffusion of 40 nanom it should scale as a power low of the diffusion of something of a given
Size so if we fit that that power low the exponent is actually the ratio of the size of what’s limiting to the size of the 40 nanometer and in that case we find that what’s limiting is in the range of the 100 nanometer okay then we know that but it
Doesn’t tell us a lot maybe you also have a combination of different processes that are limiting uh we okay so with all of that we wanted to build a a model of normal and confined growth um the idea of that model is so that’s basically summarizing everything
That I’ve told you the idea of that model is to say that micro molecules they produce for themselves uh think of ribosomes producing ribosomal proteins is often the model that is used to explain exponential growth and these proteins they can also produce osmolites what we enforce in the
Model is that the rate of Oso of osoite production is proportional to the rate of micromolecule production and we also enforce that they have the same feedback with crowding so that’s is something that we can debate but it’s what we enforce in the model so because you have more molecules that’s cring the
Cytoplasm and what we have seen is that this increased cring will negatively feed back onto protein production decreasing it and osmolites that will lead to water influx that will lead to cell growth which dilutes the cell and then negatively feedbacks onto crowding by diluting the cytoplasm okay so now if the cells are
Confined it’s in our case as if they are growing against an effective elastic modulus which is the combination of the elasticity of the surrounding cells and the pdms gauge that the cells are around so growth will lead to that growth induced pressure which will also elastically limit the production of
Growth so we can parameterize all of that model there were eight parameters if I remember correctly and some of us some of these parameters took us years uh but the good thing is that with that parameterization we can predict how the model works and and it works actually
Very well and here you can see the prediction of the model so this is just enforced but here you see how the growth rate to change as a function of pressure how the growth IND this pressure should change with time and how cell number should also scale with time so that’s a prediction
Of the model because this is a model that’s fully calibrated we can relax one hypothesis and the one that we wanted to relax is actually that one what happens if we kill the feedback of cring onto uh macromolecule biogenesis and if we do that then okay protein production
Doesn’t change with pressure growth rate still decreases a bit and that’s due to the fact that you limit growth in this elastic cage so that was expected but what we see is the magnitude of change so most of the grow Decay is actually explained by the feedback of cring onto
Macromolecule biogenesis and not the mechanical limitation of the uh of the cage and we see that pressure shoots up very fast and also the cell number is then not as limited and increases very very fast uh so I’m almost done I just have two more um things to say the first question
I want to ask is okay can that be Universal as I told you in the beginning what we see is that in every studied organisms we see a decrease in cell proliferation we see that in bacteria in Mamon cells and in fungi so do we also see in these
Different organisms a change in cring a change in uh growth rate a change in protein production Etc so here I don’t I’m not going to show you all the data I’m just going to show you two pieces of data for eoli what we see is that cring indeed
Increases when the sales are confined and growth rate is also decreasing roughly exponentially when the sales are confined we also see that protain production decreases roughly exponentially when the sales are confined so everything that I told you in yeast seems to be true in eoli okay the second thing is in meleon cells
So in meleon cells we are much less advanced in our data uh what we see though very recently is that what we have seen though is that the diffusion of these nanop particles also decreases with increased mechanical stress so if you confine a multicellular SPID pressure increases and the diffusion
Also decreases in that multicellular SPID just as a side note because this is uh intriguing to us um what we see is uh that inside a freely growing multicellular SPID we feel like we have the same thing happening we see that as you go from the
Border of the SPID towards the center of the there’s a decrease in diffusion intracellular diffusion so the cells become more and more cred as you go towards the center and this is related also to the fact that you have less and less dividing cells we just wonder if what we
Have seen in a confined case could also be true in a non-confined case through the similar origin I’m just raising the question I don’t know if that’s the case because there’s so much more interactions that can happen in that case um so if you look at the growth IND those
Pressure curves that we measure for all of the different organisms we see that in all the three cases uh there are concave um the fact that they’re concave is related to the fact that the growth trate decreases when with uh increased pressure so as because the growth induced pressure is growth related As
Time increases growth induced pressure increases and growth trade increases and that leads to that concave shape if we assume in all organisms that the growth trade decreases exponentially with mechanical stress then we can predict that the pressure should rise logarithmically in time with a typical time that would be
Related notably to the division time of the organism and a typical pressure that will be related notably to the typical turgo pressure of the organism so we can fit all of these curves with model extract all of these different parameters and then plot these different Curves in a normalized
Pressure versus normalized time plot and here you see that all the curves seem to uh superimpose very very nicely suggesting maybe that similar uh um regulation of growth through maybe cing increase could lead to that exponential decrease of growth leading to that logarithmic increase of pressure over
Time okay the last thing I want to tell you is um is that I’ve been roughly lying to you because in all the talk I’ve been were I’ve been talking of what’s happening in that region of this uh of this plot here assuming that all the cells they mainly sit at their maximum
Crowding concentration and if you increase pressure If pressure increases or cring increases so you will necessarily go there but what if you start with a cell that actually is less crowded than the W type in that case what we should expect is that rates should increase with
Pressure and not decrease in that case so we have found a mutant in yeast again uh it’s called sfp1 Uh for those who know cancer this is a semic orthologue so semic is a very nasty one it’s mutated in most of the tumors uh what we have measured for both
The wild type and this mutant is the fact that indeed this mutant which is imp indicated in a ribosome biogenesis it has a much lower product growth rate and the explanation that you find in the literature for this decreas in growth trate is the fact that it has less
Ribosomes so it has less ribosomes so it’s it’s growing less okay why not the second thing that we have measured is the fact that diffusion is larger in that mutant and again we can explain that maybe with less ribosome less biomass inside the cytoplasm so more diluted cytoplasm but when you increase
Pressure cing still increases not exactly at the same rate but it still increases so everything that I’ve been telling you about the fact that as pressure increases cing increases this is still true but what may not be true is how growth rate changes in that case and the big surprise was actually there
So we did the experiment a dozen time because it was very puzzling to us at the beginning so here what you see is the growth rate of the W type as a function of pressure and that’s decreasing that’s what I’ve been telling you but what you see in blue is the
Growth rate of this mutant and it’s actually increasing as a function of time and not decreasing as a function of time and actually there can be a nice and easy reason for that I me easy quot and quote easy if you plot Now growth rate as a
Function of diffusion so you just get rid of pressure what you end up with is a plot like that so here pressure goes in that direction ction so it’s the difference it’s different from cring concentration because it’s diffusion so when concentration increases diffusion decreases so if you plot growth rate as
A function of diffusion you see that for these two guys here you have the mutant so it’s starting at a as a lower growth rate and lower and a higher diffusion and it goes in that direction when pressure increases where as the wild type it seems to be sitting at the S of
Maximum of this bell-shaped curve and then it’s decreasing ing when the growth rate increases so for now we just have two mutants we actually found a third one that that should be so the thing is that we cannot go to higher pressure because then we start to have a nutrient
Effect where the cell starts to be starved and that’s not good but we have found a mutant that starts there so our hope is that we can uh maybe see first an increase and then a decrease to see how that could work for different mutants but um but to us that’s very
Interesting because it means that indeed there can be some sort of optimum for the cells so I’m not sure it’s I’m not sure that the wild type sits at the maximum I think it’s actually the other way around we have optimized the growth conditions so that the W type is growing
At the maximum rate because I mean the growth media that we have they’ve been optimized for decades and decades and decades and they have been optimized to maximize growth mainly so maybe that’s why the the wild type sits there but then if you use the same medium for
Everyone this is the kind of curve that we find um so to finish I have a a game for you ah okay so you don’t see nicely the colors I think that will work still so here we start 5050 mutant cells in red while type in green they will be
Confined there will be build up of pressure question who wins so the green the wild type okay and the mutant okay the answer as I’m from Normandy Normandy we always say okay that depends uh that depends that depends because here you start there’s no pressure when there’s no pressure the W Ty
Proliferates much faster and then there’s pressure so at the beginning the W type overtakes the population and then pressure builds up and then poof the mutant takes up because then the mutant has a much larger proliferation rate than the wild types which keeps on decreasing so to us that’s a very so
It’s yeast I know it’s not Mamon cells but to us it’s a very interesting experiment because if you think of a tumor case there’s always different clones appearing at the at a given time but they don’t appear at zero mechanical stress or zero given chemical environment uh they appear as the tumor
Evolves and maybe some of the mutants that occur in a tumor setting can also emerge when the tumor is confined and experiencing a bit of mechanical stress and maybe they do emerge at that time because it gives them a proliferating Advantage when the cell is confined but
When we study it without confinement we don’t really see that we just see a decrease in proliferation rate not necessarily the increase that we would see in the case of that tumor setting all right so that’s uh that’s it for me uh uh to to summarize um I’m not
Saying that there’s no biology so I’m I’m a physicist but I I acknowledge a lot of biology I’m just saying that any type of mechanical stress or stress that you do chemical or mechanical it will have two consequences it will have some physical modifications of the cell of
The membrane tension but also of the cring inside the cell in the cytoplasm in the nucleus maybe in other different compartments and the physical modification that can trigger some physical changes that can affect a given response but I can also trigger maybe some uh interaction with the biological
Integration that will also occur we have found that in that case there’s also a lot of different Pathways that are turned on I’m not NE neglecting that and they many know also to regulate cell division which I haven’t talked about but the fact about cell division will
Also be impacted by the change of these physical modifications basically that’s the point all right thank you [Applause] You