BIOL21351 Past Paper 1-Oct-2024 (PDF)

BIOL21351 - 21-Oct, automated transcript 1-Oct-2024 --- Okay, everyone, it's just after nine, so we'll make a start. So, good morning, everyone. I've put the QR code up for the attendance system. I'll put it up at the end if anyone's not got it. So, this is my second lecture for the course Biol 21,...

BIOL21351 - 21-Oct, automated transcript 1-Oct-2024 --- Okay, everyone, it's just after nine, so we'll make a start. So, good morning, everyone. I've put the QR code up for the attendance system. I'll put it up at the end if anyone's not got it. So, this is my second lecture for the course Biol 21, 351, Molecules and Cells in Human Disease. So, I lectured to you on Friday. There's still a lot of you here, so that's very well done. This is like the two worst lecture slots you can get Friday afternoon and Monday morning, so thanks for coming in. There'll be a question and answer session this Friday. Same place as last Friday. I think Andrew has made a padlet, so I think if you look on Blackboard, you may be able to post questions. So, there's some things for me to talk about in that session. So, this second lecture we'll pick up from Friday. I'll talk about the mechanical properties of the extracellular matrix. I'll talk again about fibrosis, and more specifically about cancer, and we'll look at breast cancer as a case study of the mechanical properties and role of that in cancer. So, like the lecture on Friday, there'll be three parts to this lecture. Each of these three parts will end with a summary, and we'll have a case study in the middle. I'll again highlight key concepts in little red boxes. Again, there is reading, so this is a paper I recommend to accompany this lecture. Extracellular matrix modulates the hallmarks of cancer by pick-up and co-authors. I'll also highlight on the slides the references that I've used for the particular bits of data that I show if you wanna follow up on any of those papers. Okay, so picking up from where we were on Friday, I showed you this graphic, well, cartoon of a human body, and emphasized that we have diverse mechanical properties in our bodies. I showed you how we can measure these, well, plot these different stiffnesses on a scale. That scale is of elasticity. Brain being soft on the left-hand side of this scale with a stiffness of around one kilopascal, like bone marrow is also very soft. Internal organs are in the middle of this scale, and then on the right-hand side, we have sort of stiff connective tissue like cartilage, like tendons, and then the far right of this scale, we have bone, which is very stiff. So the reason why we have these diverse range of properties is that those properties allow function, so we need stiff connective tissue, we need rigid bones, but they also allow communication with cells, so that's one of the bits of information that cells use in different parts of our bodies to tell them to do the right jobs for those tissues that they're in. So what I wanna talk about in the first part of this lecture is the features of the extracellular matrix that allow these mechanical properties to be tuned. So again, this will form a list. The first of those features of the matrix is the identities of the proteins in that matrix, and I wanna introduce here the concept of the matrosome. So the matrosome is the set of all proteins that are composing or are associated with the extracellular matrix. So you may be aware of the end of words, finishing in ohm, and this tends to mean sort of large collections of data, so proteome is the set of proteins in a system, genome is the set of genes, transcriptome is the set of transcripts, and typically we collect ohmic data sets in an untargeted, unbiased way, so we don't aim to pick out one gene or one protein, we analyze the entire system, and we quantify everything we see. And this sort of methodology has come around in the last 20, 30 years, and it's changed how we do biology to some extent, where it's less targeted and more general, and we use those general analyses to sort of form hypotheses by trying to characterize the whole system. So the matrosome project was an attempt to characterize all the proteins present in the extracellular matrix. It started out as an in silico prediction, so they analyzed the genome, and they predicted which proteins from the genome encoded by the genome they thought would be associated with the matrix, and then they followed up using mass spectrometry proteomics to try and find those proteins secreted by cells or in various tissues. The matrosome is broken down into classifications, two of those classifications are shown here, the core matrosome, which consists of structural components, like collagens, like glycoproteins, and proteoglycans, which we'll discuss in a moment. And then there are matrosome associated proteins. These are proteins that are secreted and that associate with the extracellular matrix or modify its properties, and we'll talk about those as well later in this lecture. So I wanna briefly mention what mass spectrometry does, because I've just mentioned it now. So this is a method of analyzing all the proteins present in a system. This is an ohmic method, it doesn't target one protein, it just tries to find everything in a system. So the methodology follows this workflow. You take your tissue or your population of cells, and you lyse it, so you solubilize as much protein as possible, you break down any structures within that tissue or any cell membranes, and you make sure that those proteins are all solubilized. You then use an enzyme to chop up those proteins into smaller parts. And trypsin is a good enzyme for doing this because it chops up proteins in a predictable way. So you then have a very complicated mixture of peptides. So just to give you an idea of how complicated these mixtures are, our genome encodes for about 20,000 proteins, each of those proteins might be chopped up into between one, two, and dozens and dozens of peptides. So you might have tens of thousands, hundreds of thousands of peptides present in your solution. You then analyze that very complex mixture using a mass spectrometry system. So the way this works is it is a liquid chromatography system coupled to a mass spectrometer. And what this does is it separates out all the peptides by hydrophobicity. So you simplify that very, very complex solution. And then you detect each of the peptides and you measure how much of the peptide is present, and you fragment it so you can identify it. So what you end up with is a catalog of these thousands and thousands of peptides quantified and identified by how they fragment. So this is a very complicated data set. You therefore need sophisticated computational tools, bioinformaticians to help analyze that data, piece back all that quantitative peptide data to give you a picture of the proteins that were present in your initial system, in your tissue or in your lysate. So what the MatriZone project did was to analyze lots of tissues and then look for the matrix-associated proteins or the matrix-derived proteins in their final data sets. So breaking down the annotations within the MatriZone. So the first of those annotations was the core MatriZone. One family of proteins within the core MatriZone are the collagens. There are dozens of different types, well, more than a dozen different types of collagen. One example is type I collagen. These type I collagen in particular forms a structural component within connective tissues. You remember I said that collagen makes up a large amount of the proteins in our bodies. So if you take out all the water, and then so we're about 80% water, something like this, the remaining 20%, sort of a third of that remaining protein is gonna be collagen. So that's a significant amount of our protein is this extracellular material. Collagen forms this characteristic triple helix. If you look at it under electron microscope, you see this characteristic banding pattern that forms where these helices join into fibrils. Second family of core MatriZone proteins are proteoglycans. Examples of proteoglycans are perlican, biglycan, lumican. These are proteins functionalized by glycoso amino glycans, which is abbreviated to GAGS. These are long linear repeats of disaccharides. These are big sugar molecules. So a big chunk of the molecular mass of these modified proteins is made up by these big carbohydrate chains. The purpose of proteoglycans is to bind water and salt within the tissue, and this hydrates the tissue, and then that provides space filling within a tissue, and it also provides lubrication. So you find a lot of proteoglycans in your joints, for example, where you have cushioning and lubrication within your knees or your hips, et cetera, provided by these big space filling molecules. Third family within the core MatriZone is glycoproteins. Examples here are fibronectin and laminins. There are many kinds of laminin. These are proteins also functionalized by sugar modifications, but these are more complicated oligosaccharide chains, and so these are complex modifications, and these provide a range of functions. They allow assembly. So some of these electron micrographs at the bottom of the screen here show the structures of laminin, for example, which assembles into this sort of cruciform structure. Tenacin forms into this sort of, it's not really an octopus, but a six-armed structure as well, and this allows adhesion within these matrices, and these complex sugar groups allow cell recognition, cell signaling, and cell adhesion to the matrix. Okay, so the MatriZone is very complicated, and one of the things that complicates the MatriZone, unlike the genome, which is very similar in most of our cells, is that different tissues have different matrix proteins present, and those matrix proteins will also vary in different parts of the tissue, and also at different times, and also in states of health and disease and development. So this builds up a very complicated system. So looking first at this spatial resolution of the MatriZone, what this graphic here shows is across the top there are lots of different tissues listed, and then down the left-hand side there are lots of different collagens listed, so there's a couple of dozen different kinds of collagen, and the heat map shows the quantity of each of those proteins expressed in those different tissues in a sort of grid method. And what this shows you is that different tissues have different combinations of these different matrix molecules. Some are unique to particular tissues, some are common throughout, but there are also big variations in concentration. So these variations in composition and concentration define the different properties, both biochemical and biomechanical, of those different tissues. This is variation in space. The MatriZone also varies in time. So this is a recent study done in Manchester that looked at circadian rhythmicity in the matrix. So circadian rhythmicity is a day and night change, in this case, in the proteins of the tendon of a mouse, its tail tendon. So what this graphic shows is down the left-hand side we have lots of different proteins listed, and the heat map shows things going up in red, sorry, the other way around, up in blue and down in red, and then across the y-axis, across the bottom, we have variation in time over two days. And what you see is this sort of banding pattern means that many, many of these proteins are going up and down in a wave over those two days. So these are proteins that in our matrix are varying over a daily cycle. You can see collagen, there's one example of this picked out here, showing this wave-like form. And what we think is happening here is that you have these cycles that align to your behavior. So these are in mice, so mice are asleep during the day, and they are awake and active during the night. And so this alignment of these waves allows the repair of that matrix while the animals are asleep, and then it presumably is turned over while they're active in the daytime. Sorry, active at night, the other way around. Another feature that I mentioned was besides being resolved in time and space, the matrizone can also be affected by disease. So what this graphic shows is a Venn diagram of the proteins present in different tissues at different states of cancer. So it compares the matrix present in the normal liver, a colonic cancer in its primary site, and then where that cancer has metastasized to a secondary site in the liver. And you can see that a lot of those proteins, 154 proteins in the matrix, are common within all three of those environments. So there are features that are common. However, there are also unique features. So the fact that there's a cancer has changed, or a cancer is different between, or the matrix of a cancer is different between the colon and the liver, but also when that cancer moves to the liver, again, that matrix is now different to the original cancer and also the liver. So this is a complex environment, and it's changing as a function of that disease state. Okay, so to summarize on the matrizone, this key concept, we can resolve the matrizone in different tissues, and they vary in space and time. They also vary as a state of disease, and also, I've not really emphasized here, but they also change in development, and they also change as we age. So this is a very dynamic system. So taking a step back, you remember that, started out this lecture by saying, I was gonna talk about the mechanical, how the features of the matrix define the mechanical properties of a tissue, and we got as far as the first one, which was to say that the composition of the matrix affects its mechanical properties, and we sort of got sidetracked into talking about the matrizone. So I wanna pick up these features. So feature one was the mechanical properties of the matrix are defined by the kinds of molecules that are present. The second feature is that the mechanical properties are affected by the concentrations of those proteins present. The reason concentration is quite important in this is that most matrix molecules, collagens, for example, are polymers. So a property of polymers is that the higher their concentration, the more you drive their assembly from monomers, which would have one property, into polymers, which are these sort of much more structured, or fibrillar structures, and they have different mechanical properties. So many matrix proteins behave as biological polymers. You drive their assembly by having higher concentrations. You are familiar with this concept. In your kitchens, you may have jelly. Jelly is made out of collagen. If you make the jelly with lots of water, low concentration, it will be soft. If you make it at much higher concentration with a small amount of water, it will be much stiffer. So the collagen in your body works the same way. That's shown in this graph here. So across the x-axis, we have increasing stiffnesses of tissues. So the left-hand side is our soft tissues, like brain and marrow. The right-hand side is very stiff tissues, like cartilage and muscle and tendon. And then up the y-axis, there are concentrations of collagen one, as detected and quantified by mass spectrometry. And what you see is there is a scaling law, such that tissues with low quantities of collagen are correspondingly soft, and tissues with high concentrations of collagen are correspondingly very stiff. Okay, the third feature is how those proteins are organized. So this is again looking at the example of collagen. Collagen is expressed as a monomer. It is assembled into a trimer, this triple helix. These triple helices are processed by cleaving off the ends. And then they are assembled into fibrils. And again, I've shown an electron micrograph, which shows this characteristic banding structure, which is formed because these fibers are positioned end to end. And then these fibers assemble into fibers. So we can see that mechanical properties are affected here, because there are different mechanical properties across different dimensions of this tissue. So the tendon, which these fibers are formed part of, is strong along one axis, but relatively weak along the second axis. And that's because we have this lengthwise organization of our fibers. Okay, and then the last feature to mention is that we can chemically modify our matrix proteins to tether them together. This is called cross-linking. So again, looking at collagen one as an example, you could have cross-links between molecules in a trimer, or you could have cross-links between different trimers, or you could have cross-links within a single molecule. And the effect of these covalent cross-links is to strengthen the tissue and make it stiffer. We'll talk about how these cross-links are formed later on. Okay, so to summarize this first little chunk of the lecture, again, we talked about mechanical properties of tissues, how they're defined by the matrix primarily. Collagen is an extremely high concentration, or the most common protein in our bodies, making up a significant chunk of our mass. And it's collagen in these tissues that in part defines mechanical properties. Also talked about and listed a set of matrix features that allow the mechanical properties to be controlled and defined. And those were that we looked at the identity of the matrix proteins present, and I referred to the MatriZone project, the set of all proteins within a system. I talked a little bit about how we can quantify proteins using mass spectrometry. The second important feature is how much of each of those proteins is present, because they are polymers. The more you have a concentration of a polymer, the more you drive its assembly. Organization is important, for example, collagen fibrils. It's important how those proteins are modified, such as having cross-links. Another feature which I've not discussed specifically is that some of our tissues also contain mineral deposits. So one of the reasons why our bones are very, very stiff is that those collagen fibers are joined by mineral deposits, which drives bones up to being as stiff as plastic or almost glass, so very, very stiff on that scale. Okay, so the second part, we'll look at the misregulation of the extracellular matrix and how that can be considered as one of the hallmarks of cancer. So hallmarks of cancer is something you'll come back to several times in this course. Most cancers acquire the same functional capabilities, and that's what we're referring to as the hallmarks of cancer. How each of the cancers gets those different hallmarks may vary from disease to disease. It may be different specific mutations. It may be slightly different pathways, but these are common features that are considered to be conserved across many types of cancer. So these were first sort of put together as a concept in a paper in Cell 20-something years ago, the year 2000. This is Hanahan and Weinberg, and they initially listed these six features as being the characteristics common to many cancers. So a sustained proliferative signaling, evasion of growth suppressors, so this is meaning your cell populations are increasing. There is an activation of invasion, so this is leading to metastasis, the formation of secondary tumors. There is replicative immortality, so these cells don't know when to stop dividing. There is angiogenesis, so as you get a growing tumor, these cells are going to need nutrition, and they're going to need oxygen, and so you need blood vessels, and so the tumors form their own blood vessel system. And also there's resistance to cell death. The paper also described ways that we can look at cancer, and sort of a model of cancer, or how do we think about the cancer tumor. So a very reductionist view is just to think of it as a population of out-of-control cells. So clonal cells, one cell goes wrong, and then it starts dividing uncontrollably until you have many, many copies of that cell present. So reductionist views can be useful. They can be useful models. They let you understand fundamental properties, but they're overly simplistic for many means of understanding. So this is perhaps a model that you could have by growing cancer cells in a dish, in a lab, but it doesn't reflect all the features of the disease. In order to reflect more features of the disease, you either need a more complex model or to look in actual patients or in animals. So a second slight advance over this initial reductionist view was to consider the fact that cancers actually have many different cell types. So they have blood vessels, they have immune cells infiltrating into them, and they also have fibroblasts. So these are cells producing extracellular matrix and making that tumor stiffer. The initial hallmarks of cancer were revised 11 years later. The authors added in a couple more hallmarks, a dysregulation of metabolism, an ability to avoid the immune system and avoid being cleared and removed by the immune system. The fact that tumors can promote inflammation and also that tumors accelerate or have instability in their genomes, which means they accumulate more mutations, which can drive further development of the cancer. As well as adding these new hallmarks, they also made a slightly more complicated model of how we think about the cell environment or the cells within the tumor. So they added, as well as these out-of-control cancer cells, the tumor cells, there are cancer stem cells, there are various kinds of immune cells. The blood vessels also have parasites associated with them. There are cancer-associated fibroblasts. And also from the point of view of these lectures, they also appreciated that the matrix is very important. So many cancers are stiffer than the surrounding healthy tissue. That's how we find a lot of cancers is because we have a lump form and that lump is cells, but it's also that extracellular matrix, that stiffer matrix. So the cancer environment, because of that matrix, the cells are in a different environment than the cells in healthy tissue. If the cancer metastasizes, the cells have to break out of that initial tumor and they again find themselves in a second new matrix. And if that cancer metastasizes into a different organ, those cells again move perhaps into an entirely different matrix. So this environment, these matrices, biochemical properties, mechanical properties, and the cells have to, or the cancer cells have to be able to grow in these different conditions. And just referring back to the data I showed a few slides ago. So this was the comparison between the matrizone of healthy liver tissue, a colon cancer, and a colon cancer metastasizing into the liver. There are common features of the matrix, but there are also substantial differences. So this now is the updated hallmarks of cancer. Which is 10 features. What I wanna look at now is how these features might map onto some of the things we talked about in the lecture on Friday, which is mechanical properties. So if you think back to Friday, we talked about some of the cell behaviors that are influenced by the mechanical properties of their environments. And I've mentioned now that cancers are stiff. They have a very different matrix. We find cancers, cancers are diagnosed because they are stiff lumps quite often. So remember for Friday that changes in tissue stiffness can drive changes in cell morphology. They can affect how the cells interact with their environments. The cells pull harder on their environments. Stiffness can affect propagation rate. It can suppress apoptosis. It can drive cell movement, the property of geotaxis. And it can also affect cells commitment to lineage, the process of differentiation. So looking again at the hallmarks of cancer, a key concept here is some of these cancer hallmarks are gonna be affected by the properties of the matrix and matrix mechanics. So for example, top left of this picture, sustained proliferative signaling. We know that that stiff environment of the tumor is gonna contribute to greater rates of cell proliferation. On the left-hand side of the picture, resistance to cell death. One of those characteristics of cells on stiff substrate was that apoptosis was suppressed. So again, this is an overlap. Activation of invasion and metastasis. This is a little bit like geotaxis. So we have cells moving through a gradient of stiffness from the tumor into the surrounding healthy tissue or from one site into a metastatic site. Okay, so if you look at the reading that I recommended at the start of this lecture, pick up at all, there's a little bit more on the pathways involved. This is summarized in this graphic here. So we have extracellular matrix proteins outside of a cell so that the cell in green is embedded within this matrix structure in blue. There are various different kinds of matrix present, such as collagen, such as laminin, fibronectin. And these matrix proteins are detected or interact with proteins on the cell surface. So there are receptors for these matrix proteins like DDR, which I have to look up, looks for discolored in domain receptor. Integrins, which bind to laminins and collagen. There are syndicons and there are specific laminin receptors. So these are on the cell surface, they identify features of the matrix. And they also head up signaling cascades when they interact with their ligands. And those signaling cascades affect the pathways which is shown in the nucleus of this cell. And those map onto specific cancer hallmarks. So to pick out a few of those, and we have focal adhesion kinase, FAC, which limits sensitivity to growth inhibitors and apoptosis. There is map kinase ERK signaling, which promotes proliferation. There is VEGF, which induces angiogenesis to the formation of blood vessels. There's ROROC, which we talked about on Friday, which drives cell contractility, cell movement, and helps invasion and metastasis. And there's also various immune cell signaling, which again, you have immune cells infiltrating into your tumor, and they are also affecting the extracellular matrix. So you'll hear a lot more about the hallmarks of cancer and these specific pathways when Adam Hurlston lectures to you in two or three weeks. Okay, so I wanna move now, end of this section, by looking at a specific case study, and talk about breast cancer. So again, trying to motivate why this is such an important thing to study. Breast cancer is one of the most prevalent types of tumor. There's a substantial lifetime risk for women. There are 55,000 diagnoses per year, many in the world, and it's a fatal disease in many cases. So this is something we want to try and better understand. And what I wanna talk about here is how matrix changes are associated with both the disease but also with disease risk, and this remains sort of an open question as to why this is the case. So you'll be aware of mammographic screening. So this is where you use low-intensity X-rays to shine or pass through the tissue, and you look at where those X-rays are being attenuated, and those show up in these images as mammographic density, which is abbreviated to MD, and these X-rays are blocked by matrix, effectively, by dense matrix. And so where you have lumps, tumors, they will show up as white occlusions within your mammographic density. And so these mammograms are used for screenings. They're a means of finding cancer, and so ideally, you want to catch cancer as early as possible, and then that maximizes the chances of being able to treat it successfully. So many women are screened, and what we find is that not only does this mammographic density quantification allow us to find cancer, it also correlates with the risk of cancer. So these are big studies that have taken place, and we know that the greater the density of the tissue is shown by a mammogram, the greater the risk of developing the cancer in the future, even if you don't currently have the disease. So this is potentially very instructive. It perhaps gives you a way of identifying people most at risk and therefore having some kind of intervention, and perhaps maybe even this density is something we can intervene on as a pathway of reducing risk. So mammographic density is one of the highest risk factors we can identify. The risk factors for getting breast cancer are age and people who have specific mutations, which give them propensity to disease. After those two factors, mammographic density is sort of the biggest predictor. So what does this actually mean in terms of the biology of the system? What does high mammographic density mean? So this was worked on in Manchester. This is a quantification of matrix properties as a function of mammographic density. So we have in the graph on the left-hand side, mammographic density on the x-axis and collagen on the y-axis. So high MD tissue has more organized collagen and correspondingly also has altered mechanical properties. So again, we have low and high mammographic density and where you have high mammographic density, that tissue is stiffer. So what we have therefore is a picture where you have mammographic density reporting on disease risk, where you have high mammographic density, that matrix, the collagen in those tissues is more organized and denser. The tissue is stiffer and that is leading to high risk. And this is even without having the disease itself. If you have the disease, again, you have very high density tissue because you have a tumor and that is correspondingly very stiff. What we don't know currently is what the pathway, what is the mechanism that links stiffness and the matrix to that disease risk. So again, work being done currently here in Manchester is suggesting this is perhaps a regulation of metabolism. It may also be accumulation of DNA damage. So the stiff tissue somehow is driving faster accumulation of DNA damage, driving the accumulation of mutations. Okay, so summarizing the second part, cancers are caused by different sets of mutations. Cancer is a diverse range of diseases, but there are common features that we refer to as the hallmarks of cancer. Matrix is important in understanding cancer, particularly in tissues, sorry, in solid tumor cancers, where the properties of the tumor and the metastasis in terms of biochemistry and biomechanics are gonna be different to the healthy tissues. And matrix and matrix mechanical properties can influence and map onto those cancer hallmarks, like regulation of proliferation, regulation of apoptosis and gradients of stiffness. And we looked at the case study of breast cancer. Okay, so final part is to look at cancer as a fibrotic disease. So we wanna look through some of the progression of cancer of the disease. So here we have a cell highlighted in green. That cell has oncogenic mutations, and that is gonna be the starting point of our cancer. It is an epithelial cell, so it's on this epithelial layer, which is sat on a matrix feature called the basement membrane. And the basement membrane separates the epithelium from the stroma, which is more collagen-based matrix, and that is where we find fibroblasts, so the cells that produce that matrix. So the epithelial cell starts to divide uncontrollably. This forms the basis of our tumor. And then we have the other cells in the environment start to get involved with the formation of this tumor. We have fibroblasts moving towards that as a site of injury. Those fibroblasts start to modify the matrix. They produce enzymes. There are enzymes that cross-link the matrix. We'll talk about this in a moment. Enzymes such as lox, lysloxidase, and also enzymes that reorganize the matrix, perhaps by breaking it down, and they can be proteases, enzymes that chop up matrix. We'll talk about those in a moment as well. So all these features change the mechanical properties of that growing tumor. Cancer cells can start to invade the surrounding tissues. And so we have other processes taking place. We have highlighted in four on the diagram, remodeling of the surrounding matrix. Five on the diagram, we have formation of blood vessels, angiogenesis. And six, we have metastasis, so cancer cells entering the circulation and perhaps moving to secondary. So again, fibroblasts are an important part of this process. We talked about these on Friday. There are cancer-associated fibroblasts. So fibroblasts moving to cancer as a site of injury, becoming activated, sort of misusing that ability to heal wounds, but now contributing towards this growing pathology of the system. So producing excessive matrix, effectively forming a scar, which is the tumor. And again, we can think back to this idea of a solid tumor being an uncontrolled fibrotic process, where instead of having homeostasis, which ends up in scarring, healthy scarring, and then repair, you have this persistent tumor because of these uncontrolled cell proliferation, which is driving an immune response, changing tissue mechanics, dysregulation of matrix. This adds to dysfunction. And then this sort of secondary fibrotic cycle that's mapped onto the cancer properties there. So what I wanna end up discussing in this lecture is some of the factors that limit metastasis. So if you imagine you've got your uncontrolled proliferating tumor cells and around them is a stiffer matrix, what is perhaps preventing or slowing down metastasis is those cells have got to move through that new matrix, that stiffer matrix, to get out of that system in order to enter the circulation. So there's two possible factors that I wanna consider. One is that those cells are slowed down because you have to deform the cell. And the second is that those cells are slowed down because you have to deform or remodel the matrix. So there's two limiting factors. Yeah, so there we go. Invasion is limited either by defamation of the cell or remodeling and defamation of the matrix. So the first of those factors is looking at the remodeling of the cell. So if you imagine the cell is something you have to deform in order to get into the surrounding environment, the nucleus is the biggest and the stiffest of the organelles in the cell. And so that can act like an anchor. In order to squeeze through a small hole, in order to get to the circulation system, you have to deform the nucleus. What prevents the nucleus from deforming is the mechanical structures within the nucleus. And those are made up by a family of proteins called the lamins. This is something we mentioned again on Friday in the context of the mechano-transmission. So lamins are proteins that polymerize into filaments. They form this network structure on the inside of the nucleus. We can measure the mechanical properties of the nucleus as a function of lamin concentration using this assay shown on the right-hand side here. So what happens in this is you try and deform the nucleus using suction. The more lamin you have in that nucleus, the harder it is to deform. And so more lamin means stiffer nucleus. So in this experiment, cells were cultured on a transwild plate, which is a plate that has lots of little perforations in it. And you measure the rate at which the cells pass through those small holes. We changed the amount of lamin. And what we find is if we increase the amount of lamin in those cells in our model system, it slows down how fast those cells can pass through the little holes because you're making the nuclei stiffer. Conversely, if you reduce the amount of lamin, you initially increase the rate at which the cells pass through the holes because the nuclei are more deformable. However, it's not a linear relationship because eventually you start to make the cells and the nuclei too fragile. And so there's a protective property of this lamin. It protects the nucleus. If you have too little protection, then you have apoptosis. So there's a sort of sweet spot for the amount of lamin. Too much lamin or too little lamin is problematic. Another experiment this was shown in was putting tumors into mice. And again, modulating the amount of lamin. So in this experiment, lamin was overexpressed. And if you overexpress lamin in a tumor, you slow down its movement into the surrounding tissue. So again, you're making that tumor or that you're making the cells nuclei within that tumor harder to deform by increasing the stiffness of the nucleus. And therefore it slows down movement into the surrounding tissue. And so you might ask, can we use lamin as a marker of disease risk? Or could we maybe intervene on lamin as a means of modulating the disease? And what unfortunately is the case is that this is a very complicated relationship. So some cancers, this is a table of the changes in the lamin in lots of different kinds of cancer. And it doesn't seem that there is a sort of consistent trend where you can say more lamin is good or more lamin is bad for prognosis. And so this perhaps comes back to this idea that there is not a linear relationship between the fate of the cells and the quantity of lamin because lamin both has a restrictive effect on the cell's motion, but it also has a protective effect. And so you need to balance up these factors. So there's no clear unfortunate message from that. Okay, and then the last factor I wanna consider is having thought about the difficulty of deforming the cell. The second thing that you can modify is the properties of the matrix. So this could constrain cells within a tumor. So enzymes can modify the properties of matrix by adding covalent bonds. So one of those enzymes, which is a cross-linking enzyme is transglutaminase. So this is covalently joining two peptides here. If you covalently tie peptides together, you increase the stiffness of the matrix. This is shown as well in another enzyme called lysoloxidase. This also modifies the matrix, forms cross-links, increases stiffness. The experiment at the bottom of this page, so that this slide shows where fibroblasts, the expressed lox have been put into a mouse. You then look at the organization of the matrix, which is shown on the left-hand side in red. You look at the sort of formation of fibrils, which is shown in the middle in green. And you look at the mechanical properties, which is shown on the right-hand side. Expression of lysoloxidase is cross-linking together the collagen, it's organizing the collagen, it's making the tissue stiffer. So lysoloxidase is part of disease processes. Most of our cells, healthy cells, don't express a lot of lox. However, it is upregulated in cancer. It increases cross-linking, it increases tissue stiffness, and it is amplified by the lack of oxygen present, that the hypoxia within tumors. And it can lead to many of these effects. So that increase modulation stiffness, increases proliferation, changes migration, increases invasion, decreases apoptosis, and allows angiogenesis. These are all signaling factor, or matrix modification factors derived of lysoloxidase. However, this is a complex picture. And again, like lamin, there's no easy modification to lox that we've discovered yet, although work is ongoing, and that allows us to modulate tumor properties. Okay, and then the last concept is looking at enzymes that can degrade matrix. And so what's shown in this picture is the activity of enzymes chewing up matrix, which is allowing cells to pass through the matrix. So this is a synthetic matrix made of collagen shown in green. And then there is an antibody allowing a sustain where collagen has been chewed up using an enzyme, and that's shown in red. We can also look at the cells, which is shown in blue. And what you can see in this diagram is the cells are moved from the bottom left of the picture, and they've chewed up the collagen, allowing them to push through the matrix, and they're leaving behind a trail of chewed up collagen fragments. So enzymes that can do this chewing up of the matrix include MMPs, matrix metalloproteinases. Some of those are shown on the right-hand side of this figure. There's complex family proteins. They have in common a zinc active site in the enzymatic active site. Some are associated with cell membranes. Some are soluble and are present in the extracellular matrix. Another family of proteins are Adamts, which I won't read that out, but you can read from the slide what that stands for. Again, these are to do or have a function of collagen processing, chopping up collagen, proteoglycan cleavage. So again, we might ask, can we use MMP, matrix metalloproteinases, or enzymes that modify the matrix as either markers of cancer or reporters of whether cancer is gonna be resolvable, or whether we can target it for therapy? And again, this is under investigation, but like LOX and like LAMIN, the picture is unfortunately very complicated. So this diagram here maps different functions of MMP family proteins onto those hallmarks of cancer. And some of these functions of MMP, either through signaling or modifying the matrix, promote the hallmarks of cancer, and some suppress them. But there's no sort of overall picture where we can say MMP is universally good or bad, which again makes it difficult to use as a reporter, and it makes it difficult to use as a target for intervention. Okay, final summary. Cancer cells, cancer-associated fibroblasts produce a matrix that is unique or different in cancer than it is to healthy tissue. We can affect the ability of cancer cells to move into their surroundings, either by changing the properties of the cell or by changing the properties of the matrix. Okay, so I'm gonna put up this code again. I'll see you on Friday. And remember, if you have any questions, to look up Blackboard for the Padlet page. I don't know why they sound so similar, but if LAMIN is in the nucleus, LAMININ is in the extra cell of the matrix. Big separation too, very different. Yeah.