Comparison of gene expression levels of ΔZMS1/ΔZMS2 and ΔZMS2 in Saccharromyces cerevisiae

 Background Information

The current microarray analysis that is being done as a group involves examining the effect of ΔZMS1/ΔZMS2 knockout strains on the up or down regulation of the yeast genome. We specifically looked for any alteration in genes involved in anti-oxidant pathways. From the nine microarrays that were analyzed our group extrapolated data from the double knockout slides only. Specifically, we used slides 5 and 9, and examined genes from grids 11 and 15 including their duplicates from the bottom of the slide.

 For my individual project I used the microarrays from slides 6 and 8 (both ΔZMS2 knockouts), and analyzed the genes from grids 11 and 15 as well as their duplicates (27 and 31 respectively) to determine if the same genes were up or down regulated compared to the ΔZMS1/ ΔZMS2 microarrays. The same criteria from the double knockouts was also used as a cutoff for determining which genes were up of down regulated. Using the MagicTool application, ΔZMS2 slides were corrected using mean normalization and standardization of log₂ data.

 Results

Using a maximum up regulation value of 3.7 on the log₂ scale, 7 genes were chosen as over expressed in the ΔZMS2 strains. In total 3 genes were found to be under expressed using the same criteria. The level of expression is noted in Table 1 for the selected genes. The gene that was most heavily up regulated in the ΔZMS2 strains was YPL096W. Table 2 shows that this gene encodes for the breakdown of misfolded or incomplete proteins. Since the up regulated genes were only seen in 1 out of 4 grids for all 7 genes, it was hard to confidently define them as over expressed. Contrarily, the genes that were determined to be down regulated all showed consistent down regulation across all four grids.

 Table 1: Selected up and down regulated genes using a value of 3.7 as the cutoff level. Top part of the table shows genes that are up regulated, which are noted in red. The most down regulated genes are found on the bottom. Down regulation is noted in green.

Table 2: Up or down regulated genes and their corresponding biological and molecular functions if known. All selected genes were over or under expressed using 3.7 fold criteria.

The results from the ΔZMS2 microarrays found in Table 1 and Table 2 can be compared to our groups previous results for ΔZMS1/ΔZMS2 data found here.

 Discussion

The level of up regulation of selected genes in Table 1 appears to be inconsistent across the four grids; because of this it is important to keep in mind that the up regulated genes from the ΔZMS2 strains might not be as over expressed.

The most highly up regulated gene in the ΔZMS2 strains was YPL096W, also known as PNG1. In Saccharromyces cerevisiae PNG1 encodes for a protein involved in proteasome-mediated degradation of misfolded glycoproteins. Although it does not have a direct redox related role in the cell, the up regulation of PNG1 could effect free radical levels. For instance, ceruloplasm is a Cu-binding glycoprotein that if it becomes fully loaded with metal it can enhance cellular oxidative stress (Halliwell, 1992). If the PNG1 protein is involved with the degradation of glycoproteins like ceruloplasm then it could in turn lower cellular oxidative stress.

 When the genes from ΔZMS2 strains were compared to the genes examined by the group ones in the ΔZMS1/ΔZMS2 strains no matching genes were found. Both analyses found completely different genes that were up regulated and down regulated in the two different yeast strains. However, there was one gene that was found to be down regulated in the double knockout, but was seen as highly up regulated in the ΔZMS2 knockout. Met6 was the name of the gene, and it is involved in methionine metabolism. Since the yeast strains used were methionine auxotrophs it is interesting that one strain would show over expression, while the other showed the opposite (Slekar, 2008).

If there was any mutation involved in methionine metabolism from the two different yeast strains, one would expect it to be a mutation to result in up regulation of the gene so that more Met could be metabolized. One hypothetical explanation for this difference could be that although methionine was up regulated in ΔZMS2 strains there could be some other gene involved in the double knockout strain that is acting to suppress the Met6 gene rather than induce it.

 The information gathered from the group research as well as this individual project only focused on genes from 2 grids out of the 16 grids that were spotted onto the microarray. Essentially, we were only analyzing 1/8^th of the yeast genome. In addition, each project only examined 2 microarray slides of the 9 that were hybridized. Due to the magnitude of information available it was hard to isolate a few genes that may be associated with the pentose phosphate pathway or oxidative stress pathways.  Future projects could include analyzing the rest of the grids that we were unable to explore, then looking at expression levels specifically for known genes involved in reducing free radicals.

Another problem that was encountered was the inconsistencies between expression in the four grids. For example, it was hard to find genes that were both highly over expressed and that were up regulated across all four grids. To counter this more statistical analysis could be done to more confidently assess which genes were over or under expressed.

References

1. Halliwell, B., Gutteridge, J.M.C., Cross, C.E. 1992. Free-Radicals, Antioxidants, and

Human Disease – Where are We Now. Journal of Laboratory and Clinical Medicine 119 598 - 620.

2. Slekar, Kimberly. "A Genetic Study of Anti-Oxidant Factors in Yeast."                                    

            James Madison University, Harrisonburg, VA. Oct. 2008.

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