top of page

Student Group

Public·11 members

Mating Pictures WORK

Communication between cells is a ubiquitous feature of cell populations and is frequently realized by secretion and detection of signaling molecules. Direct visualization of the resulting complex gradients between secreting and receiving cells is often impossible due to the small size of diffusing molecules and because such visualization requires experimental perturbations such as attachment of fluorescent markers, which can change diffusion properties. We designed a method to estimate such extracellular concentration profiles in vivo by using spatiotemporal mathematical models derived from microscopic analysis. This method is applied to populations of thousands of haploid yeast cells during mating in order to quantify the extracellular distributions of the pheromone α-factor and the activity of the aspartyl protease Bar1. We demonstrate that Bar1 limits the range of the extracellular pheromone signal and is critical in establishing α-factor concentration gradients, which is crucial for effective mating. Moreover, haploid populations of wild type yeast cells, but not BAR1 deletion strains, create a pheromone pattern in which cells differentially grow and mate, with low pheromone regions where cells continue to bud and regions with higher pheromone levels and gradients where cells conjugate to form diploids. However, this effect seems to be exclusive to high-density cultures. Our results show a new role of Bar1 protease regulating the pheromone distribution within larger populations and not only locally inside an ascus or among few cells. As a consequence, wild type populations have not only higher mating efficiency, but also higher growth rates than mixed MATa bar1Δ/MATα cultures. We provide an explanation of how a rapidly diffusing molecule can be exploited by cells to provide spatial information that divides the population into different transcriptional programs and phenotypes.

Mating Pictures


The pheromone response of budding yeast (Saccharomyces cerevisiae) is an example for a eukaryotic cell communication system. Yeast cells occur either in the haploid forms MATa and MATα, or as MATa/α diploid. Haploid and diploid cells are both able to replicate vegetatively. Mating of two haploid cells with opposite mating types yields diploid cells, while haploid cells are formed through spore formation in meiosis [6], [7].

Moreover, recent theoretical findings support the theory, that secretion of Bar1 in the extracellular medium does not help to align gradients in the direction of the opposing mating type [16]. In summary, the role of Bar1 is still controversially discussed. Also, none of the models proposed so far has investigated interactions of more than a few cells or the four haploid spores in an ascus, even though mating occurs not only inside the ascus, but also in a cell population which was shown by new findings where a remarkably high outcrossing rate from asci was reported [18]. This indicates that mating yeast cells interact with quite a number of potential mating partners in a natural environment. Furthermore, a recent study has shown the potential of simple secrete and sense motifs to exhibit surprising effects on the population level [19].

Therefore, we designed a method to identify the most likely α-factor distribution within mixed haploid yeast populations of thousands of cells directly from confocal microscopic images with fluorescently tagged marker proteins. Here, an RD model is used to simulate interactions of a few hundred cells at the same time. In MATa cells, the protein Fus1, which is strongly expressed upon pheromone stimulation, is tagged with GFP to record pheromone pathway activation and serves as a proxy for Bar1 induction. Therefore, the experimentally observed pheromone activation level of each MATa cell is integrated into the model and compared to the experiments. MATα cells are modified to express mCherry from the TDH3 promoter to indicate their mating type and location. In a unique way we coupled physical RD models with experimental imaging in order to quantify the spatial distribution of extracellular α-factor. The use of simple marker constructs, that altered neither α-factor nor Bar1, served for minimal interference with the biological system. We used this approach to directly estimate the influence of Bar1 on the distribution of α-factor in a mixed yeast population and suggest a novel function of Bar1 to enable the coordination of mating and growth in a yeast population in vivo.

Location, shape, and mating type of the cells were extracted from out-of-focus images in the brightfield and mCherry channel (Figure S1 in Text S1). The cells' spatial arrangement on the images was transferred into locally refined triangular meshes for the model (Figure S2 in Text S1) and used to calculate the extracellular spatial distributions of Bar1 and α-factor.

We observed large differences in the estimated local α-factor concentrations between wild type cell populations and cell populations with a bar1Δ background. Dense wild type cell populations showed a strongly localized α-factor distribution at sites of high MATα cell density, with α-factor concentration quickly declining with distance. Consequently, MATa cells far away from a cluster of MATα cells experienced significantly lower local α-factor concentrations than close-by cells, and hence were often non-permissive for induction of the pheromone response (Figure 3). Populations with bar1Δ background showed an almost uniform distribution of very high pheromone concentrations, resulting in global pathway activation as evidenced by high Fus1-GFP expression. Nevertheless, the global (over-) activation led to reduced mating events.

Virtual wild type populations exhibited a strong gain for the information content of the α-factor distribution as the population size increases (Figure 4A). This was accompanied by increasing α-factor gradients across MATa cells (Figure 4B). In contrast, populations not secreting Bar1 showed information contents close to zero (Figure 4A) as well as insignificant pheromone gradients (Figure 4B), both independently of population density. We noted that the overall pheromone concentration remained within a range of up to 20 nM in wild type, but in the mutant linearly increased with population density (see Figure S6 in Text S1). This observation indicates that the gradients and, thus, the reachability of nearby mating partners can only be detected faithfully in cell populations secreting Bar1, particularly in high cell densities.

In order to test the validity of this prediction and its dependency on Bar1 we observed mating between MATa cells (here marked with Rpl9A-GFP) and MATα cells (marked with mCherry) and quantified their growth rates with FACS analysis (Figure 6) and OD measurements in wild type and in bar1Δ populations during incubation (Figure 7).

Looking at population growth during mating, we found strong differences between wild type and bar1Δ cultures (Figure 7B). For bar1Δ cultures, the global activation of the pheromone response in effectively all MATa cells of the population led to an almost complete loss of population growth (Figure 7B,C). This also caused a characteristic population phenotype with many pheromone-stimulated MATa cells being significantly larger than normal MATa cells and showing multiple mating projections (Figure 7E,F). This phenotype was never encountered in unperturbed wild type mixtures of MATa and MATα cells, but could be induced by swirling them rapidly to inhibit cell fusion (Figure S8 in Text S1). Thus, this phenotype appears associated with induction of pheromone response in vivo under conditions where a cell cycle arrest has been induced but successful mating is inhibited.

The effect of Bar1 secretion on haploid growth rates was even more prominent when looking at the MATa/MATα ratio in the population (Figure 7C). There is no known secretion of an extracellular protease described for MATα cells equivalent to Bar1. Co-cultured wild type MATa cells strongly outperform MATα cells in growth during mating to an extent that within 5 hours MATa is the predominant haploid cell type in the population. This cannot be observed in bar1Δ background where the MATa/MATα ratio remains constant, presumably because both haploid cell types are equally inhibited in growth. In summary, secretion of Bar1 enables a high mating rate on a population level, but also strongly optimizes the population growth rate by avoiding unnecessary cell cycle arrest when mating is improbable (Figure 7F).

In order to further validate our findings about the role of Bar1 in the mating process, we mixed MATα cells with different ratios of wild type and bar1Δ MATa cells. We measured the amount of haploid and diploid cells after 4 h of incubation by FACS analysis (Figure 8). For labeling of MATα cells we again used mCherry, whereas MATa BAR1 wild type cells were labeled with Rpl9a-GFP and bar1Δ MATa cells with Rpl9a-TagBFP2. For equal ratios of wild type cells of both mating types at time 0 h we obtained a diploid fraction of 14% at time 4 h (leftmost columns). However, MATα cells assumed about 23% while MATa cells reached more than 62% of the total population, again supporting the observation that a part of the MATa population engages in mating and other cells continue to grow. 041b061a72

  • About

    Welcome to the group! You can connect with other members, ge...

    Group Page: Groups_SingleGroup
    bottom of page