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New Releases. Description The Biogenesis of Cellular Organelles represents a comprehensive summary of recent advances in the study of the biogenesis and functional dynamics of the major organelles operating in the eukaryotic cell. This book begins by placing the study of organelle biogenesis in a historical perspective by describing past scientific strategies, theories, and findings and relating these foundations to current investigations.
Reviews of protein and lipid mediators important for organelle biogenesis are then presented, and are followed by summaries focused on the endoplasmic reticulum, Golgi, lysosome, nucleus, mitochondria, and peroxisome. Product details Format Paperback pages Dimensions x x Other books in this series. Stage-Structured Populations Bryan Manly. Add to basket.
The Biogenesis of Cellular Organelles
Immunobiology of Carbohydrates Simon Wong. Calreticulin Paul Eggleton. Zinc Finger Proteins Shiro Luchi. Table of contents Preface 1. Mullock and J. Paul Luzio. Shiflett and Jerry Kaplan. Nucleogenesis; Sui Huang. Mitochondrial Biogenesis.
Curran and Carla M. Terlecky and Paul A. Learn about new offers and get more deals by joining our newsletter. Sign up now. To test this prediction, we performed spinning disc confocal microscopy on a budding yeast strain expressing the monomeric red fluorescent protein mRFP fused to the Golgi localized marker protein Anp1 Huh et al. Anp1-mRFP forms punctate spots Figure 2B marking the presence of individual Golgi, whose number we quantified in each cell to generate a Golgi abundance histogram from which we could calculate the Fano factor.
Furthermore, in order to reduce potentially confounding extrinsic sources of fluctuations due to variations in the phase of the cell cycle each cell in our population is in, we synchronized the cell cycle phases of the cells in our experiments by arresting them in S-phase of the cell cycle through treatment with mM hydroxyurea. We see that synchronizing the cell cycle phases of the cells we examine by microscopy does not affect the Fano factors of the measured abundance distributions for the Golgi or late Golgi Figure 2—figure supplement 2A—D. A Schematic depicting the biophysical processes that govern Golgi apparatus abundances.
C Histograms depicting the theoretically predicted Golgi apparatus abundance distribution blue trace and experimentally measured single haploid cell Golgi apparatus abundance distribution red trace. D Bar graph depicting theoretical prediction blue bar and experimental measurement red bar of the Golgi apparatus abundance distribution Fano factor. E Schematic depicting the biophysical processes that govern vacuole abundances. F Spinning disc confocal microscopy images of the vacuole as visualized by the fusion protein Vph1 fused to green fluorescent protein Vph1-GFP.
G Histograms depicting the theoretically predicted vacuole abundance distribution blue trace and experimentally measured single haploid cell vacuole abundance distribution red trace. H Bar graph depicting theoretical prediction blue bar and experimental measurement red bar of the vacuole abundance distribution Fano factor. Given an experimentally measured mean number of vacuoles of 2. This distinction between case 1 and case 2 is a reflection of the fact that vacuole abundance, the result of a balance between fission and fusion events, follows a shifted Poisson distribution.
Vph1-GFP forms discrete rings Figure 2F that we count in each cell to construct vacuole abundance distributions, as was done for the Golgi. It is important to note that the close match between theory and experiment here suggests that de novo vacuole biogenesis, which is observed only in mutant strains that specifically disable vacuole inheritance, appears not to play a quantitatively significant role in affecting vacuole abundance in wild-type yeast.
Taken together, the cases of the Golgi apparatus and vacuole fluctuations allow us to make two conclusions.
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First, budding yeast cells tolerate the maximum level of variability generated by the biogenesis pathways governing Golgi apparatus and vacuole abundance, evidenced by the fact that our model predicted the experimental data with high quantitative accuracy without invoking any feedback control mechanisms to control the number of organelles.
Second, as expected from theory, different biogenesis mechanisms generate differing levels of abundance fluctuations. At low mean organelle copy numbers, organelles governed by fission and fusion vacuole; Figure 2H inherently exhibit smaller abundance fluctuations than organelles governed by de novo synthesis and decay Golgi; Figure 2D.
In the case of the vacuole, our fluctuation analysis also sheds light on the quantitative role played by de novo vacuole biogenesis Catlett and Weisman, Thus we can use experimentally measured fluctuations in organelle abundance to make quantitative inferences about the relative contributions of different organelle biogenesis pathways for cases where the pathways are less understood. Given the success of the model in making predictions about the abundance distributions and magnitude of fluctuations in Golgi and vacuole abundances, in our last case we sought to use the model to infer mechanistic insight into organelle biogenesis.
We can thus use the Fano factor to infer whether de novo synthesis or fission dominates the production of peroxisomes; this is a topic of active debate Hoepfner et al. Specifically, in the absence of fusion, a Fano factor significantly larger than 1 indicates that fission dominates over de novo synthesis in generating an increased number of organelles, while a Fano factor close to 1 indicates that de novo synthesis dominates over fission. Furthermore, if de novo synthesis dominates over fission, we also expect to see that the organelle abundance distribution will closely match a Poisson distribution, as we observed for the Golgi apparatus, while if fission dominates then we expect a distribution broader than Poisson.
Notably, peroxisomes are greatly upregulated in number when yeast cells are cultured in fatty acid-rich medium; therefore it is of interest to measure Fano factors for these organelle abundance distributions in both glucose and fatty-acid rich media. A Schematic depicting the biophysical processes that govern peroxisome abundances. D Bar graph depicting measured peroxisome abundance distribution Fano factors in glucose-rich and 0. Figure 3—figure supplement 1 depicts a peroxisome biogenesis model, referred to as Model 2, alternative to the model depicted in panel A.
Figure 3—figure supplement 3 displays simulation results from Model 2 showing how increased pre-peroxisomal vesicle production affects the mean and Fano factor of the mature peroxisome abundance distribution. Figure 3—figure supplement 4 depicts the Fano factors of the Golgi apparatus and vacuole abundance distributions from cells grown in oleic acid-rich medium.
It is important to note, in applying our theory to peroxisome biogenesis, that these predictions are completely insensitive to whether or not the details of de novo peroxisome biogenesis, which occurs through fusion of pre-peroxisomal vesicles that bud off from the ER van der Zand, et al.
The model we use to calculate Fano factors with Figure 3A uses a simplified peroxisome biogenesis process in which de novo peroxisome biogenesis proceeds as a single step of a mature peroxisome budding from the ER. In an alternative, more biologically detailed model Figure 3—figure supplement 1A we explicitly keep track of the two vesicle types, one bearing the peroxisomal membrane proteins PMPs Pex2 and Pex10 on their surfaces and the other the PMPs Pex13 and Pex14 on their surfaces, that fuse in order to form the peroxisome import machinery that then imports the enzymes that allow the peroxisome to carry out its metabolic functions van der Zand, et al.
To check that our simplification of de novo peroxisome biogenesis does not introduce significant quantitative errors, we compare the mature peroxisome abundance statistics for the two alternative de novo biogenesis models one-step vs pre-peroxisomal vesicle fusion , isolated from any contributions from fission.
To visualize peroxisomes we imaged budding yeast strains containing fusions of the yeast enhanced monomeric Citrine yemCitrine protein to the peroxisome targeting signal PTS1, consisting of the amino acids serine, lysine, and leucine at the C terminus of the protein Figure 3B ; we also repeated our peroxisome experiments using a budding yeast strain containing a fusion of the peroxisome membrane protein Pex3 with the monomeric red fluorescent protein mRFP; Figure 3—figure supplement 2A. The PTS1 and Pex3 reporters gave virtually indistinguishable results.
Importantly, when the rates of pre-peroxisomal vesicle production and fusion are increased to match the increased mean mature peroxisome abundance observed in cells cultured in oleic acid medium in the alternative model of peroxisome biogenesis Figure 3—figure supplement 4A , the alternative model cannot explain this rise in the mature, import-competent peroxisome abundance distribution Fano factor upon transfer of cells to oleic acid containing medium Figure 3—figure supplement 4B. Therefore, we conclude that increased pre-peroxisomal vesicle production and fusion cannot explain the rise in the mature peroxisome abundance distribution Fano factor obtained from cells cultured in oleic acid medium.
Instead we infer that upon transfer to oleic acid containing medium budding yeast cells primarily generate new peroxisomes by fission of pre-existing peroxisomes. To test whether our results are specific to peroxisomes or a more general aspect of culturing in oleic acid rich conditions, we also measured Golgi and vacuole abundance distributions in oleic acid rich medium. Our model of organelle biogenesis makes predictions about how the magnitude of organelle abundance fluctuations will change if the mean organelle abundance is changed. We conjectured, motivated by the observation that organelle sizes scale with cellular volume and ploidy Weiss et al.
We constructed diploid versions of the strains used for our above measurements and repeated our imaging experiments on these diploid strains with fluorescently labeled organelles. We found that vacuole and peroxisome abundance distributions have increased means in diploid cells compared to haploid cells; the Golgi apparatus and late Golgi notably did not yield a statistically significant increase in mean abundance in diploid cells Figure 4—figure supplements 1, 2.
The diploid vacuole abundance distribution is still well described by a shifted Poisson distribution that is calculated from the experimentally measured mean, again with no fitting parameters Figure 4A , blue line. A Histograms depicting the theoretically predicted vacuole abundance distribution blue trace and experimentally measured single diploid cell vacuole abundance distribution red trace.
B and C Bar charts comparing experimentally measured vacuole abundance means B and Fano factors C in haploid and diploid cells cultured in glucose-rich medium. D Histograms depicting the theoretically predicted peroxisome distributions blue trace and experimentally measured single diploid cell peroxisome abundance distributions red trace. E and F Bar charts comparing experimentally measured peroxisome means E and Fano factors F in haploid and diploid cells cultured in glucose-rich or oleic acid OA medium.
Green line in panel F indicate Fano factor of 1. Figure 4—figure supplement 1 displays the Golgi apparatus abundance distribution and its Fano factor from diploid cells grown in glucose-rich medium. As mentioned above, the organelle abundance distribution should follow the Poisson distribution, even with the higher mean, because the underlying biogenesis mechanisms of de novo synthesis and decay should not have changed.
We calculated the Poisson distribution derived from the experimentally measured mean abundances and find that the predicted distributions, even with no fitting parameters, closely describe the experimentally measured abundance distributions Figure 4D. As before, we obtain indistinguishable results for peroxisomes labeled by fusing the peroxisomal membrane protein Pex3 to mRFP Figure 4—figure supplement 3. Interestingly, the Fano factors for the peroxisome abundance distribution in diploid cells grown in glucose are much smaller than the Fano factors measured in cells grown in oleic acid-rich medium Figure 4F despite the fact that their mean values are similar Figure 4E.
This provides additional evidence that peroxisome biogenesis involves fundamentally different mechanisms in glucose-cultured cells vs oleic acid-cultured cells, with our model pointing to the less noisy de novo synthesis pathway dominating the former and the more noisy fission pathway dominating the latter. Finally, we tested the mechanistic prediction that peroxisomes switch from de novo synthesis dominated production in glucose-containing medium to fission dominated production in oleic acid-containing medium.
Peroxisome fission is mediated by the proteins Vps1, Dnm1 and its accessory protein Fis1, with the dominant role played by Vps1 Kuravi et al. We engineered yeast strains containing fluorescently labeled peroxisomes and lacking either Dnm1, Vps1, or Fis1, cultured these cells in oleic acid medium, counted the number of peroxisomes in each cell, and calculated the mean and Fano factor of the peroxisome abundance distributions for each strain Figure 5.
We also repeated these experiments in yeast cells bearing peroxisomes labeled by fusing Pex3 with mRFP and obtained virtually indistinguishable results Figure 5—figure supplement 1. These results strongly suggest that fission dominates peroxisome proliferation in wild-type cells cultured in oleic acid medium. Single cell peroxisome abundance distributions were measured for each of these strains. The Fano factors are plotted as a function of mean peroxisome abundance extracted from the single cell peroxisome distributions.
Figure 5—figure supplement 1 shows data similar to Figure 5 but with Pex3-mRFP as the peroxisome marker. Here we formulate a stochastic model of organelle biogenesis and find that the diverse mechanisms that alter organelle abundance leave distinct signatures on the shape of the organelle abundance distribution. Specifically we find that fission or fusion dominated organelle abundance dynamics, respectively, increase or decrease the width of the organelle abundance distribution, as measured by the variance of the distribution divided by the mean termed the Fano factor , compared to de novo synthesis dominated organelle biogenesis.
We then applied this theory to predict the precision with which single budding yeast cells regulate their abundances of the Golgi apparatus and vacuoles. Our results show that budding yeast cells tolerate the theoretically predicted maximum Fano factors consistent with the known mechanisms of Golgi apparatus and vacuole biogenesis.
Having validated that the theory could make quantitatively accurate predictions, we then used it to quantitatively distinguish between de novo synthesis-based and fission-based models of peroxisome biogenesis. Our results showed that the peroxisome abundance distribution is consistent with a model in which the organelle is created primarily by de novo synthesis in glucose-cultured budding yeast cells, but then switch to a noisy, fission-dominated biogenesis when cells are cultured in a fatty acid rich environment in which organelle biogenesis is upregulated.
We presented additional lines of evidence for this picture in the form of measuring the fluctuations in peroxisome abundances in mutant yeast strains lacking organelle fission factors.
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Specifically, consistent with our theoretical prediction, we observed significantly decreased fluctuations in peroxisome abundance when these mutant strains were cultured in fatty acid rich medium compared to our measurements in wild-type cells. Perhaps the most surprising aspect of the comparison between our model of organelle abundances and measurements of endomembrane organelle abundance distributions is the close match between theory and experiment without the need to modify the model to take account feedback control mechanisms.
This is in stark contrast to the expectation that cells know how to count and tightly control the number of its various organelles Rafelski and Marshall, While some subcellular structures such as the nucleus and centrioles are clearly under the control of strong feedback control mechanisms that suppress fluctuations in their abundances Marshall, , the cell apparently tolerates the maximum amount of variability in endomembrane organelle abundance generated by a given set of biogenesis mechanisms.
It must be noted, however, that while the close match between our theory and experiment without invoking any feedback control implies that any feedback control mechanisms operating on endomembrane organelle copy numbers are no more precise than the biogenesis system could achieve without feedback, we do not explicitly rule out the presence of feedback regulation of the organelle copy numbers.
Our model is also very simple, and though we were able to obtain new insights into organelle biogenesis using the model we are likely suppressing details that could affect organelle abundance under certain conditions. For example, it has been recently shown that the rate of peroxisome decay via autophagy depends on the existence of a functional fission pathway Mao et al.
Nevertheless, it would be trivial to extend our model to incorporate such findings; in the case of fission-dependent autophagy rates, for example, one would simply need to rewrite the decay rate to be a function of the fission rate rather than just being a simple numerical parameter.
Such effects would of course require us to take greater care in interpreting the results of the model, but do not invalidate that model; indeed deviations from our simple model could even facilitate discovery of effects such as coupling between the different processes affecting organelle copy number and feedback control of organelle copy numbers.
Finally, the consequences of these fluctuations in organelle abundance on the fundamental cell biological processes controlled by these organelles, ranging from secretion to metabolism, remain to be explored. Assessing the dependence of cell biological processes on the abundance of the organelles governing these processes requires systematic, quantitative measurements such as, for example, how the rate of secretion depends on Golgi apparatus abundance or how protein degradation rates depend on vacuole abundances.
Our results suggest a resolution to the long-running debate over whether peroxisome biogenesis is the result of de novo synthesis or fission Hoepfner et al. With these quantitative tools in hand to characterize organelle abundance processes, it will be of great interest to uncover functional reasons why the cell employs de novo synthesis vs fission to proliferate organelles.
Given that peroxisomes appear to switch from de novo synthesis to a noisy, fission dominated creation, it will be particularly interesting to measure the degree to which the abundances of peroxisomes with other metabolic organelles such as the mitochondria or lipid droplets are correlated in single cells. These correlation measurements can allow us to infer the design principles underlying cellular responses to fatty acid rich conditions: anti-correlations in noisy peroxisome and lipid droplet production, for example, would suggest a model in which different cells specialize in lipid metabolism vs storage, while correlated production would favor a model in which only a subset of cells specialize in responding to the environmental change.fr.yhuvonemobeh.gq
De novo peroxisome biogenesis revisited
Along with previously developed frameworks examining variability in organelle number Hennis and Birky, ; Marshall, , our model can aid in examining the functional consequences of stochastic fluctuations in organelle abundance. Perhaps most importantly, the generality of our approach makes it amenable to analyzing the wealth of subcellular compartments and granules in prokaryotes Yeates et al. With previous examinations of organelle number control Hennis and Birky, ; Marshall and our analysis of the peroxisome fission pathway as guides, we hope that our model will be used as a framework in which to interpret future genetic studies that aim to uncover the biophysical pathways responsible for the biogenesis of subcellular structures.
All strains were taken from the collection of GFP fusion strains generated by Huh et al. This collection includes one set of strains used as organelle references against which the localization of all other fluorescently tagged proteins were scored.
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Strains from this reference strain set contain the monomeric red fluorescent protein mRFP fused to a protein that localizes to a specific organelle with high reliability. Where possible, as was the case for endosomes, Golgi apparatus, and peroxisomes, we selected these organelle reference strains for visualization. In order to visualize mature, import-competent peroxisomes, we engineered a yeast strain that expressed a fusion of the peroxisome targeting signal 1 PTS1 , consisting of the amino acids serine, lysine and leucine, to the extreme C terminus of the monomeric, yeast-enhanced Citrine fluorescent protein yemCitrine.
For the case of the vacuole, for which no mRFP reference strain was created, we used a strain from the library that was engineered to contain the green fluorescent protein GFP fused to the vacuolar membrane protein Vph1. Following washing of the cells twice with 50 ml complete synthetic medium without alpha factor to remove the alpha factor from the culture medium, the G1 synchronized cells were transferred into medium containing mM hydroxyurea to arrest the cells in S-phase. Following 2 hr of hydroxyurea treatment, the cells were fixed with 3.
Cell segmentation was performed manually using ImageJ. To obtain the number of organelles in each cell, we split the data into two cases. For those organelles that appear as discrete foci endosomes, Golgi apparatus, peroxisomes individual slices of each image stack were filtered with a Gaussian blurring filter to eliminate high frequency noise in the image, followed by a Laplacian second derivative filter to sharpen edges thereby enhancing the foci.
The filtered images were then thresholded to identify those pixels belonging to an organelle. Finally the organelles could be assigned to single cells using the manually segmented image.
All organelle identification and assignment to single cells was manually verified. In the second case, where the organelle vacuole appears as a discrete ring, all quantification of number of organelles per cell was done manually. An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent see review process. Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.
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In a fissioning cell population for high abundances, dilution can be approximated by a first-order decay term; but this is not true for low abundances. Furthermore, non-trivial behaviour can arise if partitioning between parent and daughter cells is not binomial. The authors would have to show by simulation or any other means why an explicit incorporation of partitioning during cell division is not required in their model.
It could be, for example, that in a budding organism the daughter does not significantly affect organelle properties in the mother, and therefore the authors' model is a reasonable approximation. I assume from the Methods section that the authors pool all cells together i. Using images alone, it is possible for the data to be partitioned by cell-cycle phase. Does this reveal identical organelle number distributions for all phases? If not, how much variation does one see? Another way to experimentally estimate the size of the extrinsic term is to see the change in Fano factor as a function of the mean.
The authors have shown how this can be achieved by a shift to oleic acid medium or by using diploid cells. Can they use the resulting measurements to bind the extrinsic term? Here, the authors infer, from obtaining a Fano factor that exceeds 1, that peroxisomes are generated primarily by fission in oleic acid medium. Since the organelle abundance data are from a mixed asynchronous population, it is important to establish that the increase in Fano factor is not due to a cell-cycle-dependent de-novo synthesis rate. The cell cycle specific issues could be addressed more clearly — experimentally — by arresting cells in a specific stage, S-phase, for example, and then re testing the model.
Either outcome is likely to be highly significant and should be tested experimentally. While, as the reviewers note below, the Golgi primarily decays through maturation Losev, et al. Nature , and thus partitioning is not expected to significantly affect our results, vacuole Catlett and Weisman, Curr Opin Cell Biol and peroxisome Fagaransu et al.
Below we will show that both simulation results and previous findings from the literature support the idea that our model is a reasonable approximation of the situation in growing and dividing cells. First we will detail the results of our simulations to probe how well our model, which we will call Model 1, approximates two alternative models, which we will call Models 2 and 3, that explicitly include partitioning between parent and daughter cells. In Model 2, we replace the first order decay term with a term that reduces the organelle copy number by half in regular intervals, thereby mimicking organelle partitioning upon cell division.
Reducing the organelle copy number by half upon each division of a parent cell into 2 daughter cells is necessary for all cells in the population to achieve steady state organelle copy numbers. Furthermore, studies of vacuole and peroxisome partitioning upon cell division that indicate that partitioning occurs much more equitably than expected from a random binomial process Weisman et al. We then ran the simulations of Model 2 and Model 3 10, times, built up the organelle copy number distributions resulting from each model and plot the Fano factors of these distributions below.
Comparing Fano factors obtained from simulations of models with alternative organelle decay processes. Model 1 implements first order decay, Model 2 implements reducing the organelle number by half at defined time intervals, and Model 3 implements reducing organelle number in a given cell by binomial partitioning of the organelles into daughter cells. We see that when we adjust the de novo synthesis rates in Model 1, Model 2, and Model 3 such that they produce the same mean organelle copy number, we observe no significant differences in the Fano factors obtained from simulations of Model 1, Model 2, and Model 3.
This analysis is now included as Figure 1—figure supplement 1. There are two intuitive ways to understand why our approximation of first order decay works well. First, for the Golgi apparatus and the peroxisome, the mean abundance is high enough empirically roughly 4 and higher that the dynamics of organelle loss in our first order decay approximation do not substantially differ quantitatively from the dynamics achieved through partitioning of organelles at a defined time.