6.3.1
Tracking pheromone inactivation by Sxa2
To measure the decline in pheromone concentration due to Sxa2 produced by cells, a new assay was developed that could be performed using basic laboratory equipment. Although in principle a more expensive solution was possible in the form of reversed-phase HPLC, the purchase of the necessary technologies did not seem cost effective as they would have been used exclusively for this experiment, with seemingly no further benefit to other projects in the research group. It was also not possible to find any such equipment within the university that could be used for this purpose.
The new assay consisted in sampling the media of P-factor treated cells and in- activating Sxa2 through heat treatment, followed with the measurement of the remaining pheromone concentration by recording the pheromone-induced cell vol- ume increase of a sxa2∆ strain added to the test sample (Figure 4.1). Removing
the sxa2+ cells being tested was necessary to ensure that no Sxa2 activity was carried over to further stages of the assay, but also so that they did not skew the measurements of cell volume. Since the cell volume measurements were performed on a population of cells, a number of measures of central tendency could be used to characterise the changes in the population. The measure that provided the most consistent results was the median, as it is robust to outliers and the dis- tribution of the measured volumes normally displays heavy tails. To convert the cell volumes into P-factor concentrations, a standard curve was prepared through identical conditions as the samples, and the fitting of a dose-response equation to the standard curve allowed the interpolation of unknown concentrations from the cell volumes.
In previous years, brief attempts had been made by other people to measure the re- maining concentration of pheromone in their samples with mixed results (Graham Ladds, personal communication). In the assay introduced here, several pitfalls were discovered during its development that required optimising the conditions of the protocol. The assay required the transfer of media containing pheromone between tubes after removing thesxa2+ cells; however, the loss of pheromone due to adsorption to the tube walls was substantial enough to make the assay useless if not addressed properly (Figure 4.2). Interestingly, tubes manufactured for the express purpose of limiting protein loss due to adsorption were not effective for use with P-factor. The only solution that provided consistent results was to sat- urate the tube walls with a coating of purified BSA. Another confounding factor in the assay, which is usually not encountered in pheromone experiments, was the adverse effects caused by exposing cells to methanol if present by more than 1/100 of the culture volume.
The measurements obtained in this assay displayed very little variability between technical replicates; however, this reflects in part the limits of resolution capable by the cell counter used, as it was observed that the reported values fell into
discrete categories, instead of along a continuous scale, which implies that the equipment would round off measurements within a given interval to a single value. Nonetheless, the size of the intervals that could be resolved appeared not to distort the overall trends observed in the time course experiments.
From this assay a new data set was generated that reported the absolute quantifica- tion of changes in pheromone concentration in response to pheromone-stimulated Sxa2 production. The assay represents a cheap new addition to the tools available for studying pheromone signalling, especially with the use of sxa2+ strains which have typically been ignored due to a lack of tools for their study.
6.3.2
Fixing the scale of Sxa2 concentration
The preliminary analysis performed on the model illustrated the drawbacks of using relative quantification data for parameter estimation, because it requires the introduction of additional parameters that will add to the non-identifiabilities of the model structure. The scaling parameterSpep, which related the Sxa2 activities
in the Ladds et al. (1996) data to the corresponding Peptidase variable, had two significant correlations with other model parameters, and a completely flat PL as a result of its structural unidentifiability.
Providing the model with measurements of Sxa2 on an absolute scale would allow the value of Spep to be fixed, and remove its influence on the model parameters.
To generate these measurements, the information obtained from previous reports that attempted the quantification of Sxa2 proved invaluable (Ladds, 1998; Ladds and Davey, 2000), as they had established the need for concentrating the medium samples in order for the concentration of Sxa2 to be within detectable limits. This information made these the most straightforward experiments of the whole project. It was unexpected however, to find that adding a GFP tag would remove the ability of Sxa2 to degrade P-factor, although it was the first time to add
a fusion tag bigger than just a few residues in length. It is known that Sxa2 goes through extensive proteolytic processing during its traffic to the extracellular space, so it is likely that the tag prevents the full maturation of Sxa2.
6.3.3
Transcriptional dynamics of
sxa2
and
ste11
Quantifying the expression patterns of sxa2 and ste11 in response to pheromone was a natural step in the search for additional measurements to complement the protein data available for Sxa2, since gene expression analysis by qPCR is one of the most common measurements performed in current biological research, and as such the necessary equipment to perform these experiments is usually readily available.
The selection of reference genes for the normalisation of samples was facilitated by existing data sets of whole genome expression changes across a wide variety of perturbations, which allowed the list of ideal candidates to be narrowed down to a select few before having to perform any experimental validation. Since the efficiency of amplification plays a large role in determining the accuracy of qPCR measurements, great care was taken to design optimal primer pairs, with additional consideration of the amplicon that would be produced.
One of the most challenging aspects of generating this data set was the isolation of total RNA from yeast cells. Extracting RNA is usually technically demanding, but is notoriously more laborious to perform onS. pombe cells due to their tough external cell wall. Fortunately, the research groups that had to optimise this technique for the whole genome experiments mentioned above, also established the widely accepted gold standard protocol for RNA isolation from S. pombe (Lyne
The measurements from qPCR are usually made on a relative scale, where the changes in gene expression are expressed as fold differences with respect to a sam- ple that is chosen as the unit calibrator. To continue the trend of the absolute quantification data sets that were generated previously, it was desired to trans- form these data from relative to absolute values. This is possible in principle by generating a carefully constructed standard curve from in vitro transcribed RNA of the target of interest; however, in practice this option is rarely pursued since it is difficult to perform, and the accuracy of the standards decrease very rapidly due to instability (Collinset al., 1995). Thus, it was serendipitous that the oppor- tunity arose to quantify the existing time course cDNA samples by ddPCR. The measurements performed by ddPCR were not only superior by providing absolute quantification, but the robustness of the technique removed most of the technical variability that was observed with qPCR.
The time course gene expression profiling of sxa2 and ste11 by ddPCR provides the best characterisation of their transcriptional dynamics, both in accuracy and temporal resolution, that has been reported to date. Xue-Franz´enet al.(2006) had previously reported a pheromone-dependent upregulation of ste11 of ∼3-4 fold; however, their results were obtained from DNA microarrays which are subject to a greater amount of variability than both qPCR and ddPCR. The data presented here shows that at most a ∼1.5 fold upregulation occurs forste11 transcription in response to pheromone.