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Introduction Microarray Technology Oxidative Stress in Yeast Hypothesis & Predictions Experimental Precedent
Materials & Methods
Results
»Discussion
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In order to test out hypothesis that the ZMS knock out mutants will have reduced expression of genes involved with oxidative stress response, we used RNA isolated from yeast knock-out cultures to construct cDNA probes for use in microarray detection. To successfully isolate RNA from yeast culture, cells must be obtained from mid- to late-log phase of growth before the accumulation of waste forces the cells to make physiological adaptations to their new environment. We measured the optical density at 600 nm to determine when cultures where appropriate for harvest. With an optical density of 0.600 ODU for all three yeast cultures, it was determined that the cultures were in the mid-log phase, which we deemed acceptable for proceeding with the isolation procedure. After the RNA isolation procedure, concentration and purity were measured to determine which samples to use for probe construction. From the data collected, samples LM, DR, and KTS were chosen because of their higher concentrations for use in probe construction (Table 1) . The purity of an RNA sample is measured by a ratio of absorbencies at 600 nm (A260/A280), because nucleic acids absorb light with the wavelength of 260nm and nucleic acids and aromatic proteins absorb light at 280nm. A pure RNA sample will typically have a ratio between 1.9 and 2.2. With purity ratios within the desired range, all of the isolated RNA samples were predicted to have little protein contamination (Table 1). To check for degradation in the RNA, samples were run on an agarose gel (Figure 1). RNA by its nature is an unstable molecule: it is constantly digested and synthesized within a normal cell. Because of this, any presence of RNAase could cause significant damage to the isolate and prevent the construction of a cDNA library. Storage at cold temperature, RNAase inhibitors, and strict laboratory procedure help protect against RNA degradation. Despite these measures, the gel electrophoresis showed partial degradation of the RNA sample in lane 1 (LM), indicating it was not fit for use. The sample RNA run in lane 5 (DR) shows the distinct 28S and 18S bands characteristic of an intact RNA, which is ideal for the creation of the cDNA library. The original microarray produced for this experiment did not return useable results. To continue our project, we used slides created by the 2004 class for statistical analysis (see Methods & Materials for slide numbers). The lack of useable information on our original slide is most likely due to human error during the hybridization procedure. In order to analyze the data obtained from the microarray chips, the data had to be adjusted using the Magic Tool software. First background signals were removed from the data set, and then a log base 2 transformation of the data was preformed. The log base 2 transformation reduces the range of the data while maintaining the differences between genes. This step makes the data more suitable for later statistical analysis by reducing the range of values between -16 and 16. Additionally, a standardization was preformed using a mean of 0 and standard deviation of 1, in order to reduce the amount of dye bias. Ideally, a single dye experiment or a complementary array with dye reversal is used to correct for the difference in dye intensity, but due to the lack of data available, a standardization was the best approach. The results of this standardization can be seen in Figures 4 and 5. The means moved closer together and close to zero while the ranges of the data sets were reduced. Because the box plots do not have means of zero, we were only able to fully reduce dye-bias, not eliminate it completely. After the statistical modifications, twelve genes were identified as having significant expression differences compared to the wild-type (Table 2). We found six genes to be down regulated, including one gene (YFL014W) known to be involved in the response to oxidative stress and another (YBR072W) known to be involved in general stress response (Table 3). This indicates that the ZMS1 transcription factor likely controls the expression of these two genes, and when it is removed the cell, it cannot transcribe normal levels of mRNA from these genes. The four other genes determined to be down regulated, are of unknown function. We hypothesize that these are also involved in the cellular response to stress, and suggest that further exploration into their function will lead to a more complete picture of the oxidative stress response mechanisms. Conversely, six genes were determined to be up regulated (Table 3), however their function varied widely from histidine synthesis to protein modification and signal transduction during conjugation. This suggests that the ZMS1 transcription factor may play a role in the regulation of other processes in the cell. Another possible explanation is that the up regulated genes are involved in a compensatory pathway to aid in cell survival when normal stress response pathways are removed. In conclusion we support our hypothesis, that genes involved in oxidative stress will be down regulated in ZMS knock out mutants. We also suggest that Dr. Slekar investigate the function of the four unknown genes that were down regulated, because the data indicates a possible relationship with ZMS1. Another improvement for future experiments would be to increase the sample size to eight slides. Due to the fact we had a sample size of two (n = 2) in the experiment, we could not utilize a Student's statistical T-test to analyze the data, which would have been necessary to label the results as significant. We also found that it in many cases where the expression ratio was greater than 2.5 or less than -2.5, the standard deviation was greater than 10% of the mean, so it was inappropriate to classify the gene as a possible candidate of further study. Thus, increasing sample number would not only increase the validity of the results with the use of statistical tests, but possibly identify more genes that could be involved in the cell oxidative stress response. To continue this experiment and support the data collected from the microarray, a RT-PCR reaction should be preformed on the genes of interest. This is the standard method of confirming microarray results in current literature research (Liu, 2009).
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