Marmas Week 11

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Effects of the Pesticide Thiuram: Genome-wide Screening of Indicator Genes by Yeast DNA Microarray

Team Information
Project Manager: Michael Armas
Quality Assurance:  Iliana Crespin
Data Analysis: Emma Young, Kaitlyn Nguyen
Coder: Michael Armas

Purpose

The purpose of this assignment is to begin in-depth analysis of the paper assigned for the final project and presentation of BIOL 367-01. The article assigned to this group, FunGals, is titled "Effects of the Pesticide Thiuram: Genome-wide Screening of Indicator Genes by Yeast DNA Microarray." This week, ten unknown terms from the paper will be defined for clarification, and an outline of the article will be posted to summarize the reading.

Ten Terms

  1. Thiuram: "The counting and/or measuring of cells in a fluid suspension" (Oxford Dictionary of Biochemistry and Molecular Biology, 2008).
  2. Mutagen: "Any physical or chemical agent that is capable of increasing the frequency of mutation above the spontaneous, background level"(Oxford Dictionary of Biochemistry and Molecular Biology, 2008).
  3. Disulfiram: "Trivial name for tetraethylthiuram disulfide; bis(diethylthiocarbamoyl) disulfide; a drug used to deter alcohol abuse in the treatment of alcoholism. Alcohol ingestion after disulfiram causes vasomotor disturbances, nausea, vomiting, and even unconsciousness and death. It acts by inhibiting the enzyme acetaldehyde dehydrogenase and hence slows the removal of acetaldehyde. It occurs naturally in the otherwise edible fruit body of the agaric mushroom Coprinus atramentarius. One proprietary name is Antabuse" (Oxford Dictionary of Biochemistry and Molecular Biology, 2008).
  4. Erythrocyte: "A mature red blood cell; in mammals it is non‐nucleated and lacks mitochondria. Erythrocytes contain, but are no longer capable of synthesizing, hemoglobin and they function in the transport of oxygen. Mammalian erythrocytes obtain energy from anaerobic glycolysis and also metabolize glucose via the phosphogluconate pathway"(Oxford Dictionary of Biochemistry and Molecular Biology, 2008).
  5. Cytometry: "The counting and/or measuring of cells in a fluid suspension"(Oxford Dictionary of Biochemistry and Molecular Biology, 2008).
  6. Glutathione: "A tripeptide that is widely distributed in most if not all cells. It acts as a coenzyme for some enzymes and as an antioxidant in the protection of sulfhydryl groups in enzymes and other proteins; it has a specific role in the reduction of hydrogen peroxide and oxidized ascorbate, and it participates in the γ ‐glutamyl cycle. Oxidation links two molecules by a disulfide bond (represented as GSSG). For clarity, glutathione is sometimes termed reduced glutathione (or the reduced form of glutathione). It is involved in the synthesis of certain leukotrienes"(Oxford Dictionary of Biochemistry and Molecular Biology, 2008).
  7. Glutathione Dehydrogenase: "An enzyme that catalyses the reduction by two molecules of glutathione of dehydroascorbate to form oxidized glutathione and ascorbate"(Oxford Dictionary of Biochemistry and Molecular Biology, 2008).
  8. Base Excision Repair: "One of the intracellular mechanisms for the repair of DNA lesions (single‐strand breaks, damaged bases, etc.). It occurs in the following stages: (1) recognition of the damaged region; (2) removal of the damaged oligonucleotide by two enzymic nucleolytic reactions (excision); (3) synthesis by DNA polymerase of the excised oligonucleotide using the second (intact) DNA strand as template; and (4) covalent joining by DNA ligase of the newly synthesized segment to the existing ends of the originally damaged DNA strand. The process is light‐independent"(Oxford Dictionary of Biochemistry and Molecular Biology, 2008).
  9. Transformant: "A bacterial cell that has undergone transformation, i.e. one that contains integrated donor genes that can be detected by plating on media selective for some or all of the donor genes"(Oxford Dictionary of Biochemistry and Molecular Biology, 2008).
  10. Oxidative Stress: "A state of metabolic imbalance within cells that favours pro‐oxidant substances (e.g. superoxide, hydrogen peroxide, hypochlorous acid, nitric oxide, peroxynitrite) rather than antioxidants (e.g. glutathione, ascorbic acid) and antioxidant enzyme systems (e.g. superoxide dismutases, catalase). This leads to oxidative damage to all classes of the major biomolecules. Depending on the degree of imbalance, a cell may die or it might survive in a changed state. Such stress can be important in causing a wide variety of degenerative states, including atherosclerosis, ischemia/reperfusion injury in heart and brain, mutagenesis, and chronic inflammatory disease"(Oxford Dictionary of Biochemistry and Molecular Biology, 2008).

Article Outline

  1. What is the main result presented in this paper?
    • This paper showed that the DNA microarray data is used to understand toxicity of chemicals in the environment. Specifically, YKL071W, YCR102C, YLR303W, YLL057C were selected for in the resulting microarray data and used for the promoted activity assay. The thiuram treatment directly affected the promoted of these genes. These results mean that this technique can be used for the selection of biomarkers.
  2. What is the importance or significance of this work?
    • With the discoveries from the microarray data, these techniques can be used to find biomarkers for thiuram. By characterizing the toxicity in yeast cells treated with thiuram, yeast will serve as a biomarker as its levels of toxicity can be observed to see the presence of thiuram in an environment.
  3. What were the limitations in previous studies that led them to perform this work?
    • Previous studies have shown that the thiuram pesticide is a potentially toxic chemical. Other studies have shown the effects of thiuram on yeast, but its toxic effects have not been observed using a DNA microarray experiment.
  4. How did they treat the yeast cells (what experiment were they doing?)
    • Saccharomyces cerevisiae S288C was the strain used for analysis of this experiment. Yeast cultures (OD_660 = 1.0) were treated with 300µL of 50mM thiuram. Samples were prepared that differed by the amount of time allowed to grow in the mixture. These times varied from 15 minutes to 2 hours, after which, the cells were harvested using centrifugation. RNA was extracted by hot-phenol method and purified using a Oligotex-dT30 mRNA purification kit.
  5. What strain(s) of yeast did they use? Were the strain(s) haploid or diploid?
    • The researchers used Saccaromyces cerevisiae S288C as the experimental strain. According to Engel et al., S288C is a haploid yeast strain.
  6. What media did they grow them in? What temperature? What type of incubator? For how long?
    • The yeast cells were grown in a YPD media at 25˚C overnight. For incubation, a Biomek 2000 Laboratory Automation Workstation from Beckman Counter, Inc. was used at 25˚C.
  7. What controls did they use?
    • The control used for this experiment was simply not treated with the thiuram pesticide. Additionally, the control was labeled with a Cy3 fluorescent label, while the thiuram-treated samples were labeled with a Cy5 fluorescent label.
  8. How many replicates did they perform per treatment or timepoint?
    • The replicates differed in the time treated with thiuram before cell harvesting. The samples were treated for timepoints between 15 minutes and 2 hours in increments of 15 minutes. However, the most discussed results come from the control group (0 minutes), the 30 minute group, and the 120 minute group.
  9. What method did they use to prepare the RNA, label it and hybridize it to the microarray?
    • RNA was extracted by using a hot-phenol method, and poly(A)+ RNA was purified from about 400µg of RNA using a Oligotex dT30 mRNA purification kit. At least 4µg of mRNa and a 0.4µM oligo dT primer were denatured at 70˚C for 5 minutes. This produced the labeled cDNA. Hybridiation onto the DNA microarray was carried out at 65˚C for 24 to 48 hours, after which, the microarray slides were washed with wash buffer twice and rincsed with SCC, centrifuged, and dried.
  10. What mathematical/statistical method did they use to analyze the data?
    • Mathematical and statistical methods were not used to analyze the data. However, much of the analysis was performed using Munich Information Center for Protein Sequences (MIPS), a yeast database.
  11. Are the data publicly available for download? From which web site?
    • The data for this article was not found
  12. Briefly state the result shown in each of the figures and tables, not just the ones you are presenting.
    • What do the X and Y axes represent?
      • Figure 1A: OD_650 versus Time(hours)
      • Figure 1B: Relative growth for 2 hours (%) versus Concentration of thiuram (µM)
      • Figure 2: Intensity of Cy5 versus Intensity of Cy3
      • Figure 3B: MIPS Classifications versus number of induced genes
      • Figure 4B: MIPS Classifications versus number of repressed genes
    • How were the measurements made?
      • Figure 1A: Yeast growth was measured by comparing OD_650 at different thiuram treatment levels over time.
      • Figure 1B: Yeast growth was measured comparatively by percentage by concentration of thiuram at OD = 1.
      • Figure 2: All three graphs measure the fluorescence of each label compared to the control Cy3 tag. this was measured by the intensities represented by the DNA microarray experiments.
      • Figure 3A: The overlapping of genes induced by each treatment. Acquired using databases analysis.
      • Figure 3B: Using MIPS, classifications are determined for the function of each gene. This is then compared to the levels of induction.
      • Figure 4A: The overlapping of genes repressed by each treatment. Acquired using databases analysis.
      • Figure 4B:Using MIPS, classifications are determined for the function of each gene. This is then compared to the levels of repression.
    • What trends are shown by the plots and what conclusions can you draw from the data?
      • Figure 1A: Samples treated with little thiuram increased in ocular density, while those treated with more thiuram struggled to increase in ocular density.
      • Figure 1B: Relative growth decreases as concentration of thiuram increases.
      • Figure 2: In all three graphs, the intensity of Cy5 increases as the intensity of Cy3 increases.
      • Figure 3A: As more time elapses, the more genes are induced. While some induced genes overlap with different time increments, some genes are not induced as time progresses.
      • Figure 3B: The MIPS Classifications individually increase as time progresses. This graph only shows time increments at 30 minutes and 120 minutes.
      • Figure 4A: As more time elapses, the more genes are repressed. While some repressed genes overlap with different time increments, some genes are not repressed as time progresses.
      • Figure 4B: The MIPS Classifications individually increase as time progresses. This graph only shows time increments at 30 minutes and 120 minutes.
  13. How does this work compare with previous studies?
    • Previous studies have looked at the toxicity of thiuram, but none have observed the toxicity on the classification of gene function via DNA microarray analysis. From this, the effects of thiuram on yeast can be used as a biomarker to detect an environment polluted with thiuram.
  14. What are the important implications of this work?
    • When testing to see if an environment is polluted with thiuram, the gene regulation of the biomass in the surrounding area can be observed using DNA microarray analysis.
  15. What future directions should the authors take?
    • This experiment has identified a biomarker for thiuram pollution in an environment. The researchers should continue the research in other pesticides. It is important to realize that pesticide pollution does not only stem from one type of pesticide. The applications of DNA microanalysis are vast and can be used to further pollution identification.
  16. Give a critical evaluation of how well you think the authors supported their conclusions with the data they showed. Are there any major flaws to the paper?
    • I believe that this paper was, for the most part, very straight forward and understandable. There were very few words that I was unable to understand, which is great when readers are not completely on board with the field of study. Some of the figures and graphs are poorly labeled, which made their analysis somewhat difficult. While the methods section was extensive, I felt lost at times when trying to reference this section with the figures and tables in the results section.

Annotated Bibliography

  1. Hinkle, K. L., & Olsen, D. (2018). Exposure to the lampricide TFM elicits an environmental stress response in yeast. FEMS yeast research, 19(1), foy121. doi: 10.1093/femsyr/foy121
  1. Pierron, A., Mimoun, S., Murate, L. S., Loiseau, N., Lippi, Y., Bracarense, A. P. F., ... & Oswald, I. P. (2016). Intestinal toxicity of the masked mycotoxin deoxynivalenol-3-β-D-glucoside. Archives of toxicology, 90(8), 2037-2046. doi: 10.1007/s00204-015-1592-8
  • Database Search with Database Tools
    • PubMed
      • I used keywords "microarray data" and "toxicity."
      • When using these keywords, I found that there were many articles that came up. I had to do some searching to find articles I wanted to use. 1130 results came up.
    • Google Scholar
      • I used keywords "microarray data" and "toxicity," but this time I added "yeast."
      • After trying to use the same keywords that I used on PubMed, I added the keyword "yeast" to narrow down the search. Google is much more vast in data that PubMed, and it gave me about 59,000 results.
    • Web of Science
      • I once again used keywords "microarray data," "toxicity," and "yeast."
      • These keywords were the same as the ones I chose to use on Google Scholar as I wanted to use these keywords on a smaller database. Web of Science pulled up 23 results.
  • Advanced search functions
    • PubMed
      • I found the "title" category to the most useful. It allowed me to put in words I wanted to find in a title and brought them accurately.
    • Google Scholar
      • While I really liked Google Scholar's normal search function and its interface as a whole, I was not too fond of it's advanced search function. I found it too specific on search parameters I didn't want to use, and lacking in those I did want to use, such as the ability to specifically search the title or abstract.
    • Web of Science
      • The different options for the advanced search on Web of Science were vast and specific. It even gave the option to search in different languages. Additionally, they have different tabs for author and cited reference searches.
  • Reflect
    • Keywords have always been my preffered way to search for an article. I can keep refining my search of keywords until I find something that I want. Using long phrases often makes the search complicated and inefficient. Choosing the right keywords that are broad enough to find articles is key to finding what you are looking for.
    • Google Scholar's interface and data is unrivaled. It perfectly meshes with university credentials and is very easy to use. However, it's advanced search function was average at best. Web of Science lacks the amount of data that Google Scholar has, but its advanced search functions are unparalleled. I have yet to see a data base or search engine with the same specificity as Web of Science. In my opinion, PubMed was average all around. I really didn't use it too much and stuck mostly with Google Scholar. Since I am not a big advocate for using advanced search fields, I did not use Web of Science's advanced search since Google Scholar's regular search sufficed.

Conclusion

The assigned article was analyze to understand the purpose and significance of the article. The exercise of examining the article for unknown words made understanding the article much easier. Additionally, other supporting papers were found to further understand the current research being done in microarray analysis involving toxicity and stress. This article is now more understood and the purpose for our bioinformatics study can progress with confidence.

Acknowledgments

  • I would like to acknowledge Dr. Dahlquist for the assistance with organization and the lecture about this group project.
  • I would like to acknowledge my group, the FunGals, for their continued support. Team members other than myself are Kaitlyn Nguyen, Emma Young, and Iliana Crespin.

Except for what is noted above, this individual journal entry was completed by me and not copied from another source.
Marmas (talk) 23:39, 13 November 2019 (PST)

References

  • Engel, S. R., Dietrich, F. S., Fisk, D. G., Binkley, G., Balakrishnan, R., Costanzo, M. C., ... & Weng, S. (2014). The reference genome sequence of Saccharomyces cerevisiae: then and now. G3: Genes, Genomes, Genetics, 4(3), 389-398. doi: 10.1534/g3.113.008995.
  • Kitagawa, E., Takahashi, J., Momose, Y., & Iwahashi, H. (2002). Effects of the pesticide thiuram: genome-wide screening of indicator genes by yeast DNA microarray. Environmental science & technology, 36(18), 3908-3915. doi: 10.1021/es015705v.
  • Smith, A. (2008). Oxford Dictionary of Biochemistry and Molecular Biology: 2nd Edition. Oxford University Press.
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