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The Effect of Knockout ZMS1 and ZMS1/ZMS2 on the Expression of TCA Cycle Genes in Saccharomyces cervisiae

Introduction

Saccharomyces cervisiae is a single cell eukaryotic model organism which contains fundamental antioxidant genes found in other eukaryotic organisms, including humans. Because yeast have a fully sequenced genome, this organism is ideal for studying how antioxidant genes function to protect the cell from damage caused by free radical oxygen species such as hydrogen peroxide, super oxide anions and hydroxyl radicals (Slekar 2008). These reactive oxygen species can cause major damage to DNA, proteins and cell membranes and in addition have been implicated in a number of disease states including cancer, cardiovascular disease and neurodegenerative disease (Slekar 2008).

The yeast gene ZWF1 codes for the protein Glucose 6-phosphate dehydrogenase (G6PD) which is the first protein to act in the pentose phosphate pathway. G6PD converts Glucose 6-P to 6-phosphogluconate and in the process produces a molecule of NADPH (Slekar 2008). The pentose phosphate pathway converts Glucose-6-phosphate into simple sugars which are critical to many cellular processes.  Additionally, the production of NADPH, a reducing agent in the cell, plays a critical role in the prevention of oxidative stress (Juhnke 1996).

            A yeast strain with a ZWF1 knockout (Δzwf1) is sensitive to oxygen and other oxidizing agents (Slekar 2008). Two multicopy suppressors of this mutation have been identified by the Slekar lab as ZMS1 and ZMS2.  Δzwf1 yeast strains containing a ZMS1 knockout (Δzms1) and a ZMS2 knockout (Δzms2) as well as a double knockout Δzms1Δzms2 have been generated by the Slekar lab. These two genes, ZMS1 and ZMS2 code for potential zinc-finger transcription factors which are able to suppress the mutation caused by a knockout of ZWF1 (G6PD) (Slekar 2008).

            In addition to studying the effect of these knockout mutations on oxidative stress and other antioxidant genes directly, this study analyzed the effect of these mutations on the 15 nuclear genes in Saccharomyces cervisiae which code for the 8 major enzymes of classic TCA cycle (CIT1, ACO1, FUM1, MDH1, SDH1-4, IDH1, IDH2, KGD1, KGD2, LPD1, LSC1, and LSC2) (McCammon 2003). It has been determined that mutations in TCA cycle genes can in addition to altering expression within the TCA cycle, alter the gene expression of oxidative genes (McCammon 2003). Gene expression in the Δzwf1 yeast strains which are also Δzms1 (3 trials) and Δzms1Δzms2 (2 trials) were analyzed using microarray analysis and compared to wild type yeast strains. It is hypothesized that gene expression for the TCA cycle genes will be differential expression between the double knockout Δzms1Δzms2 and the single knockout Δzms1, but that the three trials of the Δzms1 and two trials of the Δzms1Δzms2 will have consistent expression levels within their trials.

Methods and Materials

See Methods and Materials used by Group 4 (Methods)
For this Individual Project specific TCA cycle genes from all 16 grids of five different microarray slides were analyzed using Magic Tool version 2.1 (see Table 1).

           Table 1. Microarray slides used to analyze the TCA Cycle gene expression in Δzms1 and Δzms1Δzms1 mutant yeast. Cy5 is a red dye and Cy3
           is a green dye.

Results

    The fifteen genes which encode the eight proteins of the classic TCA Cycle in Saccharomyces cervisiae originally selected for study by microarray analysis are listed below in Table 2. The ORF name, gene name, chromosome location and biological function are given.

Table 2. The fifteen genes which code for the eight major proteins of the TCA cycle selected for analysis from five different microarray slides.

    Data for these fifteen genes were isolated from the raw data (16 grids) of the microarray slides and the intensity ratio of mutant (either Δzms1 or Δzms1Δzms2) to wild type were calculated. Table 3 contains the ORF name for each gene and the calculated intensity ratio. Negative ratios occurred when the background intensity was higher than the measured dye intensity. These ratios were removed and are represented by blank cells in the table.

Table 3. Raw expression ratios for the fifteen genes of the TCA Cycle for each of the five microarray slides. Expression ratios are defined
as intensity ratios of the mutant compared to the wild type. Negative ratios are eliminated and represented by a blank cell in the table.

    The data was transformed using a log base of 2 and then standardized to a mean of zero and standard deviation of one in Magic Tool v.2.1. Figure 1 is a box plot which shows the mean and ranges of data for each slide analyzed before the standardization but after the transformation to log base of 2 (slides in the same order as listed in Table 3). Figure 2 below shows the box plot for the slides analyzed after standardization, making the slides more directly comparable.

Figure 1. Box Plot of the ratios for each of the five microarray slides before standardization.
Image from Magic Tool v. 2.1.

Figure 2. Box Plot of the ratios for each of the five microarray slides after standardizing
to a mean of zero and standard deviation of one. Image from Magic Tool v. 2.1.

    Following standardization, the ratios between each of the different slides were analyzed for up-regulation or down-regulation of expression compared to the wild type.  Not all fifteen genes could be analyzed by Magic Tool 2.1 software because one or more of the spots on the microarray slides resulted in a negative intensity ratio. This happens when the background has a higher intensity than the dye (see Table 3).  These eight genes and their expression ratios are represented below in Table 4. In the colored table, red cells indicate the gene was up-regulated in the mutant (either Δzms1 or Δzms1Δzms2) compared to the wild type, green cells indicate that the gene was down-regulated in the mutant compared to the wild type and finally the black cells represent no differential expression between the mutant and wild type. Additionally, the gene name and function for these eight genes which were able to be analyzed are re-listed from Table 3 in Table 5 below.

Table 4. List of the eight TCA cycle genes analyzed in Magic Tool v. 2.1. Negative ratios in the table are represented in green and
denote down-regulation in the mutant (either compared to the wild type. Positive ratios in the table are represented in red and denote
up-regulation in the mutant compared to wild type. Ratios which are approximately zero are represented in black and denote no
differential expression between the mutant and wild type yeast. The different microarray slides are listed in the same order as Table 3.

 

Table 5. The eight genes of the TCA cycle analyzed for differential expression in the two mutants (either Δzms1 or Δzms1Δzms2) compared to wild type.

Discussion

    In a previous study by McCammon et al it was determined by microarray analysis that mutations of TCA cycle genes in Saccharomyces cervisiae affect not only the expression of genes in the TCA cycle but also the expression of some oxidative stress genes. In this experiment, two knockout yeast strains (Δzms1 and Δzms1Δzms2) were analyzed by microarray analysis to determine if altering oxidative stress genes had an effect on fifteen genes of the TCA cycle.  It was hypothesized that gene expression for the TCA cycle genes will have differential expression between the double knockout Δzms1Δzms2 and the single knockout Δzms1, but that the three trials of the Δzms1 and two trials of the Δzms1Δzms2 will have consistent expression levels within their trials. After analysis of the five microarray slides, it was determined that the hypothesis was not correct for all the genes analyzed. 
    Even though the hypothesis did not turn out to be totally correct, because multiple trials were conducted for each mutant, there is still usable data and information to gain. First of all, not all fifteen genes originally selected for analysis could be analyzed because their intensity ratios were negative, due to a high background intensity (see Table 3). Eight genes were analyzed in Magic Tool v 2.1. Before comparing the ratios between the five microarray slides the ratios were transformed using the log base of 2 and standardized in Magic Tool to a mean of zero and a standard deviation of one. It is evident in comparing the box plots of Figure 1 and 2 that standardization brought the ratios into a much more comparable range between the five slides. In Figure 1 the mean ratio ranged from 2 to greater than -6, while in Figure 2 standardization allowed the means to hover just above and below zero.
    With comparable slides, it was determined that not all of the genes selected had consistent expression within the repeated trials (Table 4). This is likely due to human error in one or multiple steps of creating and hybridizing the microarray slides, error in the reading of the slide intensities and/or it is also likely that dye intensities could have been faded due to ozone exposure. However, keeping these errors in mind, in order to still obtain usable information from this data, genes which are consistent in two of the three Δzms1 trials and in both the Δzms1Δzms2 trials will be considered.
    Based on these standards, there are four genes of interest to consider, FUM1, SDH1, SDH2 and SDH4. FUM1 on chromosome 16 is involved in fumarate hydratase activity encoding a single enzyme fumarse and was down-regulated in comparison to the wild type in both the ZMS1 and double knockout mutant .SDH1-4 encode for subunits of the enzyme succinate dehydrogenase. SDH1 is located on chromosome 11, involved in succinate dehydrogenase activity and was up-regulated in two out of three ZMS1 mutants and down-regulated in both of the ZMS1ZMS2 double knockout trials. SDH2 is also involved with succinate dehydrogenase activity, located on chromosome 12 and is down-regulated in all three ZMS1 mutants and up-regulated in both double mutant trials. Finally SDH4 which is on chromosome 4 and involved with succinate dehydrogenase activity was up-regulated in two of three ZMS1 mutants and down-regulated in both double knockout trials.
    The interaction of these genes with the potential zinc-transcription factors of ZMS1 and ZMS2 should be explored further using microarray analysis. Due to uncertainty because of human error and dye intensity, the experiment should be repeated to obtain more usable and consistent data, allowing all fifteen genes to be considered instead of only eight. Additionally, three trials of the Δzms1Δzms2 should be done so that data consistent between two of three trials could be considered valid.

Literature Cited

Slekar, KH. (2008) Lecture:  “A Genetic Study of Anti-Oxidant Factors in Yeast.” James Madison University.

Juhnke, H., Krems, B., Kotter, P.,  and Entian, K.D. Mutants that show increased sensitivity to hydrogen peroxide reveal an important role for the pentose-phosphate pathway in protection of yeast against oxidative stress. Mol. Gen. Genet. 252 (1996), pp. 456–464.

McCammon, M.T., Epstein, C.B., Przybyla-Zawislak, B., McAlister-Henn, L., Butown, R.A., Global Transcription Analysis of Krebs Tricarboxylic Acid Cycle Mutants Reveals an Alternating Pattern of Gene Expression and Effects of Hypoxic and Oxidative Genes. Molecular Biology of the Cell. 14 (2003), 958-972.