Home

Materials & Methods
RNA Isolation from Yeast
Quantification of RNA
Examining RNA Degradation
Preparation of cDNA Probe
Hybridization of cDNA Probe
Microarray Analysis

»Results
RNA Isolation from Yeast
Gridding and Segmenting
Transformation of Microarray Data
Microarray Analysis

Discussion
Yeast Growth Phase Analysis
RNA Quality and Quantity
Microarray Analysis
Genes of Interest

Literature Cited

Picture taken from http://www.wormbook.org/chapters/www_germlinegenomics/germlinegenomicsfig1.jpg.

           The purpose of the experiment was to determine whether transcription factors are involved in the cellular response to oxidative stress, and if so, which transcription factors are responsible.  Based on Dr. Sleckar’s research, it was predicted that genes involved in oxidative stress response would be down-regulated in the knock-out mutants, ΔZMS 1 and ΔZMS 1/ΔZMS2.  Following Biology 480 Laboratory Protocol, we collected and analyzed data in order to determine if our hypothesis could be supported.

RNA Isolation from Yeast Cultures

            In the first part of the experiment, we prepared spheroplasts from cultured yeast cells, and then isolated RNA from the cells using Qbiogene RNAase safe kits reagents.  The optical densities of the wild-type, ΔZMS 1, and ΔZMS 2 cultures were 0.624 ODU, 0.592 ODU, and 0.604 ODU, respectively.  The optical densities were below the desired ODU range of 1.5 – 2.5, and were associated with yeast cells in the mid log-phase of growth.

            Following Qbiogene RNA Isolation protocol, RNA was successfully isolated from each yeast culture, and RNA concentrations and purity were determined using a Nano-Drop spectrophotometer (Table 1).  The concentrations of yeast cultures CM (ΔZMS1), LM (ΔZMS1), MJ (ΔZMS2), DR (ΔZMS2), KTS (wild-type), and MF (wild-type), were 143.7 ng/mL, 359.3 ng/mL, 555.8 ng/mL, 716.4 ng/mL, 1035 ng/mL, and 427.9 ng/mL, respectively, with purities of 1.98, 2.14, 2.02, 2.07, 2.11, and 2.06, respectively.

TABLE 1.  Concentrations (ng/mL) and purities of RNA isolated from yeast cultures.  Yeast cultures used were wild-type, ΔZMS1, and ΔZMS2.  RNA was isolated using Qbiogene RNAase safe kit reagents and protocol.  Data was obtained using Nano-Drop spectrophotometer.

            To determine which isolated RNA samples to use for construction of labeled-cDNA via reverse transcription, the samples were run on a gel to determine whether RNA degradation was present.  The gel was analyzed using a chemiluminescent scanner (Figure 1).  Lane 6 contains the isolated RNA from yeast culture DR (ΔZMS2).  There are two distinct bands (marked by arrows) indicating the 28S and 18S rRNA subunits.  Lane 1 contains the isolated RNA from yeast culture LM (ΔZMS1).  There are faint distinct bands associated with the 28S and 18S rRNA subunits, but much more RNA between the two faint bands.  According to the concentration and purity of the isolated RNA samples and the degradation study, yeast cultures CM (DZMS1), KTS (wild-type), and DR (DZMS2) were used to in construction of labeled-cDNA probes.

FIGURE 1.  1.2% Agarose Gel containing isolated RNA from yeast cultures to determine whether RNA is being degraded in each sample.  Lane 6 contains isolated RNA from yeast culture DR (DZMS2) and shows two distinct bands from the 18S (lower arrow) and 28S (higher arrow) ribosomal RNA subunit.  Lane 1 contains isolated RNA from yeast culture LM (DZMS1) and shows the same, but much fainter, distinct bands and many bands in  between the distinct bands.  All other lanes are samples from other groups.

Gridding and Segmenting Microarray Slides using ScanAlyze Software

            Following hybridization of labeled-cDNA probes and scanning of microarray slides at Davidson College, it was determined that our data could not be used to show statistical significant over- or under-expression.  Using microarray slide scans from 2004 and the ScanAlyze software, the gridding and segmenting were done to yield data to analyze (Figure 2, 3).

FIGURE 2.  Photo of grid and segmented data obtained from TR104_w594.tiff (Channel 1) and TR104_w685.tiff (Channel 2).  Channel 1, green dye, was the wild-type yeast, and Channel 2, red dye, was the ∆ZMS1 knock-out mutant yeast.  Blue dots indicated flagged genes, which could not be analyzed due to interference on the microarray slide, such as dust and streaks.  Photo taken using ScanAlyze software.

FIGURE 3.  Photo of grid and segmented data obtained from TR106_w594.tiff (Channel 1) and TR106_w685.tiff (Channel 2).  Channel 1, green dye, was the wild-type yeast, and Channel 2, red dye, was the ∆ZMS1/∆ZMS2 knock-out mutant yeast.  Blue dots indicated flagged genes, which could not be analyzed due to interference on the microarray slide, such as dust and streaks.  Photo taken using ScanAlyze software.

Transformation and Standardization of Microarray Data using Magic Tool Software

            The data obtained from the ScanAlyze software was further analyzed using the Magic Tool software.  The data was transformed and standardized to help lessen dye bias and aid in analysis of the data (Figure 4, 5).  Figure 3 illustrates how dye bias was corrected.  Prior to standardization (Figure 4), the mean was approximately -1.5.  After standardization (Figure 5), the mean was approximately 0.25.

FIGURE 4.  Box plot of the microarray channel ratios.  Data was transformed with log base 2.  Data sets 1.0 and 2.0 contained ratios from DZMS1.  Data sets 3.0 and 4.0 contained ratios from DZMS1/DZMS2.  All data sets had a mean of approximately -1.5.

FIGURE 5.  Box plot of the microarray channel ratios after standardization.  Data was transformed with log base 2 and then standardized with a mean of zero and a standard deviation of one.  Data sets 1.0 and 2.0 contained ratios from DZMS1.  Data sets 3.0 and 4.0 contained ratios from DZMS1/DZMS2.  All data sets had a mean of approximately 0.25.

Microarray Analysis

            Following analysis protocol (see Methods and Materials), twelve genes were determined to have significant under- or over-expression (Table 2).  In the DZMS1 mutant, there was under-expression associated with genes Q0143, YFL014W, YGL239C, YLR327C, and YKR035C, and over-expression with genes YLR243W, YOR202W, and YNL145W.  In the DZMS1/DZMS2 mutant, there was under-expression associated with gene YBR072W, and over-expression with genes YJL212C, YDR512C, and YHR111W. 

TABLE 2.  Yeast genes with significant over- or under-expression.  Ratios were determined by dividing Channel 2 (mutant) by Channel 1 (wild-type).  Negative (-) values indicate under-expression and positive (+) values indicate over-expression.  Expression was considered significant if it was greater than 2.5 or less than -2.5 with a standard deviation less than or equal to 10% of the mean.

            Functions of the genes with significant expression were identified using yeast genome lists obtained from the Genome Consortium for Active Teaching (GCAT) website (Table 3).

TABLE 3.  Functions of yeast gene with significant over-expression or under-expression.  Gene function list obtained from the Genome Consortium for Active Teaching (GCAT) website.