July 10, 2012 § Leave a comment
For decades now, the biological community has been focused on the question of how cells transmit information from place to place. It’s a central problem if you want to understand pretty much anything about cell behavior. A signal to grow, for example, might start when a growth factor arrives on the outside of a cell, say in your tissue culture dish when you add fresh medium with growth-factor-containing serum in it. The information that it’s time to grow might be transmitted across the membrane by a membrane-spanning receptor, triggering a series of events such as a cascade of phosphorylations that cause enzymes within the cell to change activity. The final result might be a change of activity of a transcription factor; the presence of a signal outside the cell has thus been converted into a change in the gene expression profile inside the nucleus of the cell. We chiefly think of these processes as linear — a pathway — with a well-defined flow of information from A to B to C. We draw diagrams that show A near the cell membrane, passing information to B (closer to the nucleus) and then to C (closer still). But of course this is just an analogy we use to make it easier for us to think about what’s going on, and like all convenient analogies it has the potential to be seriously misleading. Our so-called “pathways” loop and branch and pass information forward and backward and sideways, losing precision all the way; A, B and C are most often distinguished by the timing of their activation, rather than by their location in the cell; and while it’s easy to tell a general story about how an external stimulus leads to a response inside the cell, it’s still hard to know why the response is the size it is, or happens at the time it does.
One of the most puzzling aspects of signal transduction is what happens when multiple signals impinge on the same mediator — when “paths” cross, or diverge, or merge. In the case of the important anti-oncogene p53, we draw several paths coming in to p53 and several paths going out again. The downstream consequences of p53 activation vary dramatically, from transient cell cycle arrest to senescence and apoptosis. How does this single protein receive and transmit several different types of information?
One idea is that the p53 network is in fact many different, distinct pathways, each using a different p53 isoform (say, p53′, p53” and so on). All these pathways look as if they overlap because they all involve an increase in the total level of p53 protein, but p53 can be modified in many ways (phosphorylation, acetylation, ubiquitination, methylation… ) at many different sites, producing modified versions of p53 that have varying functions. It’s well established that this happens, and that the modifications do indeed modulate p53’s behavior. But there’s another dimension, literally, to explore here: time. Although activating the p53 pathway always causes p53 protein levels to increase — by definition — that doesn’t mean that the timing and duration of the response is always the same. The role of protein dynamics in the transmission and processing of information in biology is seriously under-explored.
Here’s a dramatic example: exposure to gamma radiation, which causes double-strand breaks in DNA, leads to repeated individual pulses of p53 that have a stereotyped size and shape and appear at defined intervals. Increasing the dose of radiation doesn’t increase the average size of the pulses; instead, it increases the number of pulses. Irradiation with ultraviolet light also causes damage to DNA, but this time the breaks are primarily single-stranded. The response of p53 to UV is quite different from its response to gamma. Instead of repeated pulses of unchanging average size, you get a single wave whose size varies depending on the amount of irradiation: the bigger the radiation dose, the bigger the wave. But what do these differences mean? The Lahav lab has been pursuing this question pretty much ever since the lab began, and now they think they have an answer (Purvis et al. (2012) p53 dynamics control cell fate. Science doi:10.1126/science.1218351).
June 29, 2012 § 1 Comment
There was a time when we viewed bacterial cells as mere bags of randomly mixed molecules. Lacking the obvious compartmentalization of eukaryotic cells, bacteria were viewed as being completely unstructured. But increasing numbers of studies seem to show clearly defined localization patterns for proteins in bacteria. One example is that the main proteases responsible for regulated proteolysis in bacteria — the Clp proteases (pronounced “clip”) — have been observed in several studies to form a single bright proteolytic focus, detected by fluorescent protein labeling.
The Paulsson lab spotted these observations and became intrigued. One of the major interests in the lab is variation between individual cells at the RNA and protein level, and this looks like a potentially significant place where variation may happen. If all proteolysis in a cell is localized into a single spot, then when a cell divides something interesting must happen: either the spot also divides, or one of the two daughter cells gets all of the Clp proteases in the cell while the other daughter gets nothing. The second option would lead to a potentially enormous difference in the ability of the two daughters to perform proteolysis. So a graduate student, Dirk Landgraf, set out to look at whether this difference exists, and if so how long it lasts (Landgraf et al. 2012, Segregation of molecules at cell division reveals native protein localization. Nature methods doi: 10.1038/nmeth.1955).
The first step was to ask what happens to the proteolytic focus at cell division. Landgraf et al. made movies of cells carrying fusions of a Clp family member, ClpP, with two different fluorescent proteins, Venus and superfolder GFP. In each case they saw a single focus of fluorescence, and when the cell divided the whole fluorescent focus went to one daughter. After a few generations, fluorescent foci (one per cell) reappeared in the line of cells descending from the other daughter. This strongly suggested that there should be significant variation in the level of proteolysis going on in different cells. If regulated proteolysis is an important function for the cell — which we believe it is — this seems odd, and therefore interesting. So the authors tested this possibility directly using another fluorescent tag (mCherry) fused to a Clp substrate, allowing them to measure the variation in the degradation of the substrate in pairs of daughter cells from a single division event.
This is where things get surprising, not to say shocking. Yes, the lines in which ClpP was labeled with Venus or superfolder GFP showed very significant daughter-to-daughter variation. But in the wild type strain, in which the ClpP was unmodified, very little daughter-to-daughter variation was seen. The inescapable conclusion is that the fluorescent protein tags are changing the behavior of the protein being studied. And this is not a small change: the whole notion that ClpP self-organizes into a single localized focus, which has led for example to the idea that protein degradation needs to be compartmentalized, appears to be an artifact.
Fluorescent proteins have swept the world of cell biology. What better way could there be to study the behavior of your favorite protein than to put a brightly glowing tag on it and watch it going about its normal business? The images you get are beautiful and compelling, and make great figures in your paper. We’ve become so comfortable with the essential benignity of fluorescent protein fusions that we barely bother to worry about whether adding an extra 238 amino acids to a protein changes its behavior. Partly this is because we can see so much with fluorescent protein fusions that we could never see before, so there is no easy way to be sure that the behavior of the protein under study hasn’t changed. But partly, too, it’s because the standard in the field has shifted. Fluorescent proteins are the gold standard now. If your results from an older and apparently cruder technique, such as immunofluorescence, don’t match the results from live-cell imaging using fluorescent proteins, then the immediate suspicion is that the older technique is wrong. And probably this is often true. What Landgraf et al. show, however, is that in the case of the Clp family the older methods are the better methods. Immunofluorescent staining shows many small Clp foci, probably corresponding to individual protease complexes, located throughout the cell in the wild type, but also detects the large single clump induced when fluorescent tags are added. « Read the rest of this entry »
June 22, 2011 § Leave a comment
Once again, an interesting Theory Lunch talk has inspired me to write a blog post. Last Friday’s talk was from Mike White, who described (among other things) his lab’s efforts to understand the transcriptional behavior of the prolactin gene. This gene is primarily expressed in the pituitary, and controls the production of milk in breastfeeding mothers. On a cell-by-cell level, its expression is very variable in pituitary tissue; neighboring cells express the gene to very different extents. And yet the random expression patterns in individual cells together add up to a coordinated response at the tissue level. If we hope to build from an understanding of how cells behave to an understanding of how organisms behave, we need to know what underlies this kind of “wisdom of crowds” effect. And so White and colleagues set out to determine why this gene shows variable expression (Harper et al. 2011. Dynamic analysis of stochastic transcription cycles. PLoS Biology doi:10.1371/journal.pbio.1000607) and how this expression might be coordinated on a population level.
Cell-to-cell variability in the levels of proteins and mRNAs has been much studied in bacteria, where at least two factors are likely to be important: first, key regulatory molecules may be present in the cell at very low numbers, leading to randomness in gene activation; and second, unequal partitioning of components at cell division may create additional variation that is pretty much indistinguishable from the fluctuations caused by sporadic gene activation. There have been fewer studies in eukaryotes, so far, but people are already speculating about additional sources of differences: perhaps genes are moved in and out of “transcription factories” at different times in different cells, or perhaps the differences are caused by chromatin remodeling.
January 12, 2011 § 2 Comments
Ever since we’ve been able to look at the internal components of cells, we’ve become more and more fascinated by the fact that individual cells are — well — individual. Two genetically identical cells sitting next to each other in a dish may harbor very different sets of messenger RNAs and proteins (or other components), and may therefore respond differently to a stimulus. We’ve seen this in the context of apoptosis in mammalian cells, and also bacterial responses to antibiotics. Where do these differences come from?
The dominant story, so far, is that the variation in mRNA and protein levels we see is due to gene expression noise. In a normal cell, you have at most 2 copies of any given gene, and each gene may be either in an “on” state for transcription (producing mRNA) or an “off” state. This can produce bursts of mRNA production (think of the sudden stream of cars that comes through a traffic light when it turns green, then stops when the light turns red), which in turn can produce even larger bursts of protein production. This will make protein levels fluctuate randomly over time, and different cells are not very likely to fluctuate in synchrony. So there are bound to be differences between individual cells: noise is a fact of life.
But is gene expression noise the only source of fluctuations? A recent paper (Huh and Paulsson, 2010, Non-genetic heterogeneity from stochastic partitioning at cell division, Nature Genetics doi:10.1038/ng.729) argues that cell-to-cell differences created at cell division when components are unequally split between the two daughters (called partitioning error) may be just as important as — or more important than — gene expression noise.
December 29, 2010 § 3 Comments
Over the last 10 years or so — thanks to tools that allow us to study the behaviors of individual cells — we’ve become increasingly aware of and interested in cell-to-cell variation in genetically identical populations. For example, in response to a challenge, some cells may live and others die (I’ve written before about examples of this in both mammalian cells and bacterial cells). There seem to be many possible reasons for this variability, including an inherent randomness in the expression of individual genes that leads to individual cells having distinct complements of proteins. Here’s a new one, which personally I find quite surprising: according to a recent paper (Pires des Neves et al., 2010, Connecting variability in global transcription rate to mitochondrial variability, PLoS Biology 12 e1000560, doi:10.1371/journal.pbio.1000560) the overall level of transcription may vary from cell to cell due to variations in the level of ATP, which are in turn caused by individual differences in mitochondrial mass as a result of uneven partitioning at cell division.
This paper is written more like a detective story than are most scientific papers: instead of starting with a hypothesis and setting out to test it, they start with the corpus delicti and set out to discover the perpetrator. The evidence that there is indeed variation in the overall transcription rate in individual cells — not just the rate of transcription of individual genes — comes from experiments in which they add bromo-uridine (BrU) to cells, and track the incorporation of BrU into RNA transcripts using anti-BrU antibodies and imaging. There’s considerable cell-to-cell variability in the amount of BrU-labeled RNA, and thus we can infer that the rate of RNA transcription varies from cell to cell. This experiment was done on a variety of mammalian cell lines, including — importantly — primary cell lines. The authors ruled out two obvious explanations right away: the variation is not due to cells being in different stages of the cell cycle, nor is it due to a major variation in the total number of RNA polymerase molecules. « Read the rest of this entry »
October 14, 2010 § Leave a comment
Many of you know that as a post-doc in Uri Alon’s lab, Galit Lahav caused a small revolution in our understanding of how the p53 network responds to DNA damage. By looking at single cells instead of populations, she showed that individual cells responding to the damage caused by gamma-irradiation show a series of stereotyped pulses (shown in this movie); different cells show different numbers of pulses, and as you increase the amount of damage, the number of pulses per cell increases. Now the Lahav lab has identified another previously unsuspected feature of the p53 response (Loewer A, Batchelor E, Gaglia G, Lahav G. 2010. Basal Dynamics of p53 Reveal Transcriptionally Attenuated Pulses in Cycling Cells Cell 142 89-100. PMID: 20598361). It turns out that p53 is being activated in normal growing cells all the time. Because the cell cycle of cells in culture is unsynchronized, this activation can only be seen by looking at single cells. Since p53 may be the most studied protein on the planet, discovering something completely new and unexpected about its activities isn’t an everyday event.
The story started with an experiment that was originally intended as a control, looking at unstressed cells. Unexpectedly, in these unstressed, undamaged cells they found p53 pulses that are indistinguishable in shape from the pulses seen in gamma-irradiated cells. The first clue to where these pulses come from was the observation that they’re correlated with specific stages of the cell cycle, primarily happening right after mitosis. Loewer et al. used a Cdk inhibitor to show that when the cell cycle is stopped, the pulses go away. And the pulses were also selectively stopped when the ATM/DNA-PK pathway, which monitors double-stranded DNA breaks, was inhibited. It appears that these pulses are triggered by transient DNA damage that is a routine part of the cell cycle.
September 8, 2010 § 1 Comment
Darwin never knew what a mutation was. He inferred that the hereditary material of a species could change, and that changes could be positively or negatively selected, but he knew nothing of the “central dogma” of molecular biology: genes make RNA make protein. Until Watson and Crick came along with their coy but memorable statement, “it has not escaped our notice that the specific pairing we have postulated immediately suggests a possible copying mechanism for the genetic material”, we did not know how information was stored in biological molecules, and therefore did not know how the content of the information could change.
DNA sequencing has given us a window on how genomes have changed over the course of evolution: we can see how the sequence of a gene varies in different species of yeast, or between chimpanzees and man (or even between Neandertals and man). More recently, we’ve been able to perform evolution experiments in a test tube and see how mutations accumulate as a species evolves. But all of this has been after the fact; though we can sometimes deduce when mutations happened, and in what order, we’re not exactly watching them in real time. But now, a new approach recently published in Current Biology (Elez et al. Seeing mutations in living cells Curr Biol. 20 1432-7 PMID: 20674359) allows us to watch DNA mismatches in the act of turning into mutations.
September 3, 2010 § Leave a comment
One of the fundamental problems in systems biology is that many important decisions get made at the level of individual cells, but most of the measurement techniques we have (mass spectroscopy, Western blots…) report data on the behavior of populations. And much of the time it’s hard to relate what you see on the single-cell level to what you see at the population level. A recent paper (Pfeifer et al. 2010 Model-based extension of high-throughput to high-content data. BMC Syst Biol. 4 106.PMID: 20687942) aims to address this issue to some extent, by providing a method for comparing and merging data from microscopy and FACS analysis.
Even if this paper hadn’t said anything else interesting, I would still want to cover it because of the first sentence of the abstract, which states:
“High-quality quantitative data is a major limitation in systems biology.”
It’s so refreshing, given the dominance of the meme that biologists are drowning in floods of data, to see an acknowledgement that often the reverse is the case: we’re starving [I guess that should be thirsting, but it doesn’t have quite the same resonance] for the right data. This is why much of this blog is about new ways to measure things, quantitatively, so that we can get a quantitative description of how the biological circuits we aim to understand are behaving. It’s true that there’s a lot of low-quality (by which I don’t mean carelessly done, but low information content) data about; many brave souls are addressing the problem of extracting meaningful information from these large datasets. But in case you were in any doubt, no, this is not the time for experimentalists to down tools and retrain as computational biologists. The ability to make quantitative measurements is still a limiting factor in understanding biological systems.
August 23, 2010 § Leave a comment
Apoptosis is everywhere: it formed the spaces between your fingers and toes as you developed in utero, it prevents the development of T cells that would attack the cells of your body, and it shapes the structure of your brain. Too little apoptosis can cause cancer; over-zealous apoptosis causes much of the damage in a stroke or heart attack, and contributes to the failure of organ transplants. Not surprisingly, apoptosis is extremely tightly controlled at many levels. The key event in the decision to go ahead and die is the permeabilization of the mitochondrial outer membrane (a.k.a. MOMP). Cells given an apoptotic signal may wait for quite a long time before MOMP happens, and can be rescued if the conditions around them change; but once MOMP begins, under normal circumstances, there’s no going back. Proteins released from inside the mitochondrion unleash the activity of the so-called “executioner” caspases, setting off a vicious cycle that ends with most of the contents of the cell being chewed up. In fact, MOMP itself is usually enough to kill a cell, even when the downstream caspases are inhibited.
Usually, but not quite always. Recently, it’s been noticed that MOMP alone is not always a death sentence. If the caspases are inhibited, a few cells survive. Galit Lahav pointed me to an interesting paper that shows that the cells that survive do so because they have a few intact mitochondria left, which can divide and gradually repopulate the cell (Tait et al. 2010 Resistance to caspase-independent cell death requires persistence of intact mitochondria. Dev Cell. 18 802-13. PMID: 20493813)
August 4, 2010 § 2 Comments
Something odd happens when you treat a population of bacteria with antibiotics. Even if the culture is supposed to be homogeneous, and sensitive to the antibiotics you’re using, some fraction of the cells will typically survive. This is not because the survivors have mutated and developed antibiotic resistance; we know this because if you grow up the survivors and then hit them again with the same antibiotic, the same fraction die, and the same fraction survive. (This is partly why you’re told to take every last pill in the bottle when your doctor prescribes antibiotics for you: you’re hitting the bacteria repeatedly in the hope of catching the ones that didn’t die in response to the first treatment). The survivors are called “persisters”, and over the last several decades a great deal of effort has been focused on answering the clinically important question of what on earth is going on.