Austindias Week 3

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The purpose of this assignment is to understand how to interpret and analyze research relating to microarray data. In addition the goal is to infer broad conclusions from the figures that are presented within this research study.


  1. Molecular chaperone: Any of a group of proteins in living cells that assist newly synthesized or denatured proteins to fold into their functional three-dimensional structures (Rennie, 2016).
  2. Hybridization: the process of allowing single-stranded nucleic acids to form a duplex (Lackie, 2010).
  3. Nucleolin: A highly expressed acidic phosphoprotein (707 aa) found mainly in dense fibrillar regions of the nucleolus (Lackie, 2010).
  4. Normalization: The action of removing unimportant differences from data, especially text, to simplify subsequent processing (Butterfield & Ngond, 2016).
  5. Photomultiplier: A sensitive type of *photoelectric cell in which electrons emitted from a photocathode are accelerated to a second electrode (Rennie, 2015).
  6. Budding: A method of asexual reproduction in which a new individual is derived from an outgrowth (bud) that becomes detached from the body of the parent (Hine, 2015).
  7. Stationary Phase: The stage in the growth of a culture of microorganisms in a batch-operated bioreactor where the rate of growth ends (Schaschke, 2014).
  8. Dendrogram: A diagram, similar to a family tree, that indicates some type of similarity between different organisms (Hine, 2015).
  9. Menadione: a precursor to various types of vitamin K (Lackie, 2010).
  10. Posttranslational: describing any phenomenon or process occurring after completion of translation of messenger RNA into polypeptide (Smith, 2006).



  • Unicellular organisms face constant environmental changes (nutrients, acidity, osmolarity, and temperature, exposure to toxic agents & radiation).
  • Cells display certain response to different types of stress including protein phosphorylation and degradation, as well as, transcription alterations.
  • Previous research has focused on heat shock, but not much attention has been paid to the mechanistic biochemical effect of cold temperatures on yeast.
  • General Stress Response: transcription of a common set of genes is altered when many stresses are applied.
  • Mapping the the transcription of the entire genome in response to heat shock reveals that ~10% of the genome is either induced or repressed under this stress.
    • Induced ESR genes contribute to protein folding and degradation, transport, and carbohydrate metabolism.
    • Repressed ESR genes are involved in growth (RNA metabolism, nucleotide biosynthesis, secretion, ribosomal performance).
  • Cold leads to changes in membrane fluidity (slower diffustion of membrane proteins, lower enzymatic activity, and less instance of membrane transport.



  • Subjected yeast cells to cold shock, heat shock, and other environmental stresses (menadione, oxidative stress, osmotic stress, disulfide reducing agent)

Yeast Strains

  • They used diploid strains BY4743 (MATa/α, wild-type) and BSY25 (same as BY4743, except homozygous for Δmsn2::kanMXΔmsn4::kanMX met15).

Media & Incubation

  • Yeast cultures were grown in YPD medium and were inoculated at 30 degrees for one night.


  • Control microarrays done using independently grown cultures at 30°C.
    • Reliable data gathered for 5559 genes and only 14 genes (0.25%) showed an average variation >1.5-fold.

Replicates and Timepoints

  • Two repeats conducted for wild-type strain at time points 0,2, and 12h.
  • Three repeats at timepoints 10 min, 30 min, and 60 h.
  • Two repeats for Δmsn2Δmsn4 strain and three repeats at 12h timepoint.

RNA Preparation & Hybridization

  • RNA was isolated using hot-phenol method.
    • utilizes acid phenol, vortexing, centrifuging, and incubating cultures at different temperatures.
  • mRNA purified using the Oligotex Spin-Column Protocol.
  • mRNA was labeled by using Cy3- and Cy5-dCTP through reverse transcription.
  • cDNA then hybridized onto genomic DNA microarrays.

Mathematical/Statistical Methods

  • Microarray slides scanned using a ScanArray lite scanner.
  • Quality controls established:
  1. signal intensity had to be significantly greater than background
  2. signal intensity had to be within the dynamic range of the photomultiplier tube
  3. raw intensities of duplicates spots for each gene had to be within 50% of one another
  • Ratio of intensities was normalized using the mean ratio of all 400 spots that passed the three established criteria.
    • normalized each subarray individually for data replication purposes.
  • averaged Log2 of the ratios for each duplicate spot.
  • GeneSpring software used for statistical analysis and visualization.

The entire data set is available here:


Figure 1
  • At the top of Figure 1A the horizontal dendrogram shows similarities in gene expression patterns, while dendrogram on the left side of the figure shows similarities between different times of exposure to cold. The x-axis of Figure 1A shows many different genes while the y-axis displays up-regulation or down-regulation of those genes at different points in time. Figures 1B and 1C show specific categories of gene association on the x-axis and the number of genes that are up or down-regulated on the y-axis. Ratios of transcriptional change were calculated by dividing experimental values by the reference samples illustrated by the green-red color scale. The plot shows the functional categories that are most effected by cold shock and whether these genes were up-regulated or down-regulated. This figure allowed the research team to identify ECR and LCR genes as well as conclude that expression levels were higher in LCR most notable at 12 hours.
Figure 2
  • Figure 2 represents gene expression in ECR genes when the temperature is reduced from 37 C to 25 C. The x-axis shows the amount of time that has passed, while the y-axis uses a color map to show the fold change in ECR genes. The majority of genes in cluster A showed repressed ECR genes were also repressed with decrease in temperature. 47% of genes in cluster B showed increase in expression with reduction in temperature.
Figure 3
  • Figure 3A depicts the ECR gene expression at 2 hours to different stresses at different times. Time is shown on the x-axis and the amount of expression is indicated on the y-axis by the color scale. Interestingly Figure 3A shows that many ECR genes displayed reciprocal where induced genes were repressed and vice versa. The groups labeled I and II show that half of the repressed ECR genes were induced during heat shock and 40 % of induce heat shock genes were repressed. Figure 3B has the same x and y-values presented however comparing LCR genes to various other genes under different stresses. Despite the reciprocal behavior observed in ECR genes, LCR genes show reversion to the general stress response. Figure 3C shows the genes that were induced or repressed in ECR,LCR, ESR or a co-expression of both cold response and environmental stress response. This observation indicates LCR involves ESR, but ECR only consists of "cold specific" gene responses.
Figure 4
  • Figure 4 attempts to uncover the question of whether Msn2p/Msn4p are necessary for induction of LCR genes and if they are involved in regulation of ECR genes. The x-axis shows 2 hour and 12 hour time points and the y-axis illustrates the average gene expression ratio for each gene. Unaltered transcriptional abundance indicates genes that are independent of Msn2p/Msn4p and that are co-expressed in both strains. A large amount (78%) of LCR genes were not affected by the loss of Msn2p/Msn4p, meaning other transcriptional factors are involved in LCR genes. Also the lack of difference between transcriptional wild-type and Δmsn2Δmsn4 strains support that there is a cold specific response during the ECR phase.
Figure 5
  • Figure 5 relates amounts of trehalose and glycogen on the y-axis and different time points after temperature reduction from 30C to 10 C in wild type and Δmsn2Δmsn4 strains on the x-axis. The graph indicates the average of three individual trials. From the graphs it is clear that after 2 hours there is no aggregation of glycogen or trehalose after being exposed to cold for 2 hours, but after 12 hours there is an increase in the amounts of both. This data supports the microarray data because genes related to reserve carbohydrate metabolism was induced at this time point.
Figure 6
  • Figure 6 gives a comparison of gene responses to cold shock at 10C observed in this research to that reported by Sahara et al. (2002). Similarly to many of the other heat map figures presented in this research, the different colors indicate the ratios of transcription (y-axis) and the x-axis consists of different time points. Gray color means that there was no data available for those genes in Sahara et al. (2002). Figures 6A and 6B zoom in on a cluster of genes that encode ribosomal proteins and a cluster of genes that showed increased transcript abundance in response to cold in both studies. One interesting difference can be observed between the results of the two studies. Sahara et al. (2002) found that the induction of ribosomal genes during short cold treatments, while this study discovered a decrease in transcript abundance for ribosomal genes.


Main Findings

  • Gene expression cold response of S. cervisiae has two distinguishable phases, an early and late phase
    • The early phase could potentially be related to changes to membrane fluidity and destabilization of RNA secondary structures for protein production via translation. Reciprocal behavior in the early phase suggests involvement of other transcription factors.
    • The late phase is most likely linked to the environmental stress response and general stress response as a result of the altered physiology of the cell.

Importance of Study

  • This study was important because it identified relationships between cold shock and other environmental stresses. In particular, the discovery of two distinct phases throughout the cold shock time lapse is very intriguing. It also raises many questions such as to why early cold response transcription was not similar to other environmental stress, whereas, late cold response was. Mapping gene expression and understanding which transcription factors are responsible for controlling specific genes under certain environmental in a model organism could help us make inferences about the human genome. This information could be incredibly valuable in medicine and biotechnology to be able to target specific translation factor to prevent expression or repression of gene segments related to disease or illness.

Critical Evaluation

  • I believe the authors supported the conclusions that they made at an adequate level and they were supported by their data. One aspect of the study that I struggled to accept was the number of trials for each time point. I felt this could have been increased. Considering they took the average for each time point, an outlier could have drastically affected their results. This work was original and innovative in that they performed cold shock and compared it to other environment stress stimuli. I was also interested that they created a figure that compared their findings to that of another paper and found completely different results. An important implication of this research is that cold shock results in the involvement of gene expression that differs from general stress response. This finding could lead them to conduct further research regarding the early cold response and try to discover what transcriptional factors are regulating genes during the early cold shock response.


  • I would like to acknowledge my homework partner Edward TalaTala who I met with a few times outside of class to discuss our figure and to discover the main conclusions of this research paper.
  • Previous lecture slides from Dr. Dahlquist and Dr.Fitzpatrick provided the resources necessary to complete this task, such as the links to useful dictionaries.

Except for what is noted above, this individual journal entry was completed by me and not copied from another source.

Austindias (talk) 21:49, 6 February 2019 (PST)


Butterfield, A., & Ngondi, G. E. (2016). A Dictionary of Computer Science. Oxford: Oxford University Press.

Dahlquist, K. and Fitzpatrick, B. (2019). BIOL388/S19:Week 3. [online] Available at:Week 3 Assignment Page [Accessed 6 Feb. 2019].

Hine, R. (2015). A Dictionary of Biology. Oxford, United Kingdom: Oxford University Press.

Lackie, J. (2010). A Dictionary of Biomedicine. Oxford: OUP.

Rennie, R. (2016). A Dictionary of Chemistry. Oxford: Oxford Univerity Press.

Schade, B., Jansen, G., Whiteway, M., Entian, K. D., & Thomas, D. Y. (2004). Cold adaptation in budding yeast. Molecular biology of the cell, 15(12), 5492-5502. DOI: 10.1091/mbc.e04-03-0167

Schaschke, C. (2014). A Dictionary of Chemical Engineering. Oxford: Oxford University Press.

Smith, A. D. (2006). Oxford Dictionary of Biochemistry and Molecular Biology. Oxford: Oxford university press.

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