Asandle1 Week 12
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Contents
Assignment Pages
Journals
Individual
Class Journals
Class Journal Week 12 Presentation
Biological Term's
In Vivo: In the body (NCI Dictionary of Cancer, 2024)
Amino Acid Transporter: Amino acid transporters are membrane-bound transport proteins that mediate transfer of amino acids into and out of cells or cellular organelles. (Amino acid transporters revisited: New views in health and disease - PubMed, 2024)
Phylogenetic’s: the study of evolutionary relationships among biological entities (Biology Online, 2024)
Phylogenetic Conservation: the keeping of the same genetic processes across organisms. Specifically cis-regulatory sequences in this case. I built this definition myself after finding out the definition of phylogenetic's. Therefor there is no source and it could be incorrect. My source for phylogenetic's is above in the previous definition.
Cis-regulatory Sequences: sequences that control when, where, and with what intensity genes are expressed. Key to this control is the recruitment of transcription factors (TFs) that bind to regulatory sequences, such as promoters, enhancers, repressors, and insulators. (Worsley-Hunt, 2024)
Sensu Stricto: (Science: zoology) In the strict sense, in the narrow sense. most often used to indicate the nominate subordinate taxon etc.). Or it may just indicate exclusion of similar taxa sometimes united with it. (Biology Online, 2024)
Respiratory Electron Transport Chain: The electron transport chain is a series of four protein complexes that couple redox reactions, creating an electrochemical gradient that leads to the creation of ATP in a complete system named oxidative phosphorylation. It occurs in mitochondria in both cellular respiration and photosynthesis. In the former, the electrons come from breaking down organic molecules, and energy is released. In the latter, the electrons enter the chain after being excited by light, and the energy released is used to build carbohydrates. (Ahmad, 2024)
Transcription Initiation Apparatus: the transcription apparatus is a huge molecular machine. It detects the time-varying concentrations of transcriptional activators and initiates mRNA transcripts at appropriate rates. (Wang, 2024)
G1 Phase of the Cell Cycle: G1 is the stage where the cell is preparing to divide. (NIH Glossary of Genomic and Genetic Terms, 2024)
Cell Cycle: The name we give the process through which cells replicate and make two new cells. (NIH Glossary of Genomic and Genetic Terms, 2024)
Chromatin Immunoprecipitation: Combining Genome-Wide Location Analysis + DNA Microarray Analysis to identify protein-DNA interactions within cells. (Lee, 2024)
Answer's to Questions
1) What is the main result presented in this paper?
They created a basic model of yeasts transcriptional regulatory code that shows how the cis-regulatory sequences have transcriptional regulators attach to them and how the interactions are affected by different environmental conditions.
2) What is the importance or significance of this work?
The paper is important because of how comprehensive they were in tackling their studying of gene regulation in yeast. They analyzed over 6000 pieces of. DNA and analyzed TR's under a ton of different conditions. It offered a more expansive map of the TR network in yeast than we had before including the figure that showed how they created a detailed map of the TR network in yeast.
3) What were the limitations in previous studies that led them to perform this work?
They say that people have used comparison across different strains of yeast to try and find regulatory sequences that might be the same.
4) How did they treat the yeast cells (what experiment were they doing?)
They did a genome-wide location analysis to figure out where in the genome the DNA-binding transcription regulators would attach in different environmental conditions.
5) What strain(s) of yeast did they use? Were the strain(s) haploid or diploid?
They used a strain called W303 and then they modified it with a Myc-epitope tag to turn it into a stain of yeast called Z1256. I think this is because any molecules that interact with them get tagged although Im not sure. I don’t think it directly mentioned haploid vs diploid, but it makes more sense to use haploid if they are doing gene editing because of the ability to see the results of the edits. I am very iffy about my answer to this question and I feel like this is not a complete answer but I can't figure out for sure.
6) What media did they grow them in? What temperature? What type of incubator? For how long?
They grew the cells within:
- YPD (1% Yeast Extract, 2% Peptone, 2% Glucose)
- YEP + Galactose or Raffinose
- Synthetic Complete Medium
This image shows the supplemental methods the team attached to their paper.
They only specify the elevated temperature strain which they grew at 30ºC and shifted to 37º C for 45 minutes.
The growth time was measured in Optical Density (OD600) not in time for most of them and they grew them anywhere from 0.5 - 0.8.
7) What controls did they use?
They used Unenriched Samples and then tagged other samples. They did their analysis in triplicate, which I looked up to make sure I understood the meaning and it is indeed referring to 3 times. They also used an error model to check if something was an actual binding event where a transcription factor attached to the cis-regulatory sequence versus background noise. They also used negative control probes, which I am guessing means they picked probes for the microarray that they did not expect to have anything happen due to them being known to not have any relation to the TF’s. They also did a statistical analysis to make sure what they were seeing wasn’t due to chance.
8) How many replicates did they perform per treatment or timepoint?
It does not seem to mention this.
9) What method did they use to prepare the RNA, label it and hybridize it to the microarray?
In this paper they use DNA, not RNA. They used chromatin immunoprecipitation combined with microarray analysis. This is ChIP-chip.
10) What mathematical/statistical method did they use to analyze the data?
They mention an error model, but not the specifics. They also mention motif discovery but not the method. “From these motifs we derived the most likely specificity for each regulator through clustering and stringent statistical tests.”
11) Are the data publicly available for download? From which web site?
Yes, I initially thought no, but it turned out I was having some technical difficulties. I eventually found and downloaded the Excel File for the "OSStrainList" but now I can't figure out how I did it or find it again. The rest of the figures are on the main paper page.
12) Figure 1 is showing the process of finding binding-site specificities for the TR’s in Yeast. It’s showing how cis-regulatory sequences were identified by combining the genome-wide location data, the “phylogenetically conserved sequences”, and the information from prior publications. Also in the chart with the letters, the size of the letters is related to how often they show up in specific sequences. Dr. Dahlquist explained this to me because I had never read a chart like this before. The measurements were made the ChIP-chip experiments. I had to read about ChIP-chip, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3004291/. From my understanding and I could be wrong that there is some sort “motif discovery algorithm” used to then find motifs (patterns) from the raw data.
Figure 2 is showing the transcriptional regulatory map of yeast. Part A and B are not so useful to me because I know most of those sequences/genes/promoters and would have to look them up. Part C is more useful because I can see that the data is not random. It looks like the TR’s are binding at a very specific distance. I could not tell you why.
Figure 3 is showing some of the architectures. Not really sure why they felt it was important to include. It does show that there are lots of different types of promoter architecture, single, repetitive, multiple regulators, co-occuring regulators. It is sort of showing the complexity of the systems here and that its not all a single on off promoter switch.
Figure 4 is showing environment-specific use of transcriptional regulatory code. It is showing how specific regulators bind with specific genes in specific circumstances .
13) How does this work compare with previous studies?
Earlier studies were less expansive if we take this papers word for it. They map almost all the predicted transcriptional regulators in yeast which is a big step up. Earlier studies also used individual factors but didn’t utilize the whole tool set.
14) What are the important implications of this work?
It gives a model for understanding how gene regulation works. It also identified motifs that were common across yeast species, which means they found some common regulatory themes. Since that is what motifs are. It also becomes a resource for future research and can help with predicting the effects of edits to genes.
15) What future directions should the authors take?
Adding more environmental conditions could be promising. I don’t know enough about yeast but I wonder how other stresses would affect them such as increased atmospheric pressure, humidity, predation, etc. And if we would witness any other regulatory changes.
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?
They may have missed functional changes that could happen in very different environments or at different cell stages since they only used the 0.5-0.8. Also just because a regulator was bound to DNA doesn’t mean it had an effect. Im not sure how to best word this, but something can attach and do nothing.
Meta Data Evaluation
- Harbison, C. T., Gordon, D. B., Lee, T. I., Rinaldi, N. J., Macisaac, K. D., Danford, T. W., Hannett, N. M., Tagne, J. B., Reynolds, D. B., Yoo, J., Jennings, E. G., Zeitlinger, J., Pokholok, D. K., Kellis, M., Rolfe, P. A., Takusagawa, K. T., Lander, E. S., Gifford, D. K., Fraenkel, E., & Young, R. A. (2004). Transcriptional regulatory code of a eukaryotic genome. Nature, 431(7004), 99–104. https://doi.org/10.1038/nature02800
- [1]
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3006441/
- [2]
- [3]
- The copyright is owned by the Author because it is an Author Manuscript
- Once I open the full text, I do see "Public Access".
- The article is open access.
- Accessing it from NIH was free, however to access it from "nature" it says that it is a subscription content and must be accessed via the institution
- The journal Nature is available for print, since it is available to subscribe to print.
- The publisher of the Journal is Nature Portfolio, which is part of Springer Nature, they are for profit. They are not a member of OAPA.
- Since 1989
- Yes, the articles in this journal are peer-reviewed.
- https://www.nature.com/nature/editors
- 64.8 was the 2 year impact factor and 60.9 was the 5 year impact factor (2022).
- The article was submitted on March 11 2004
- No/unknown, all it says is that it was published in final edited form 2004, Sept 2.
- The article was published on September 2 2004
- 7 months
- Whitehead Institute of Biomedical Research, Massachusetts Institute of Technology, MIT Computer Science and Artificial Intelligence Laboratory
- One of the authors, Christopher T Harbison, had published a paper in 2002 on Transcriptional Regulatory Networks in Saccharomyces cerevisiae. He also published a paper on Genome-wide map of nucleosome acetylation and methylation in yeast in 2005. Another author, D Benjamin Gordon, also published a paper relating to transcription factors in 2004, as well as An improved map of conserved regulatory sites for Saccharomyces cerevisiae in 2006.
- Yes, “Some authors have filed a patent application covering aspects of this work and are pursuing commercialization.”
- There isn't data associated with the dataset.
- This article has cites 30 articles, and has been cited by 1671 articles.
Bibliography
Acknowledgements
- I would like to acknowledge Dean, Natalija, and Dr. Dahlquist.
- The group questions answers are mine for 3,6,9,11. Then I lost track of the order. But we did an even amount of work. I was the last one to work on the blanks that were mine so I just filled in the remaining questions.
- I texted with Natalija and Dean to coordinate pretty much through the whole week.
All work here is my own except where otherwise stated. Asandle1 (talk)
References
- LMU BioDB 2024. (2024). Week 12. Retrieved April 11, 2024, from https://xmlpipedb.cs.lmu.edu/biodb/Spring2024/index.php/Week_12
- Ahmad, (2024). The Electron Transport Chain. Retrieved April 10, 2024 from https://www.ncbi.nlm.nih.gov/books/NBK526105/
- Lee, (2024). Chromatin Immunoprecipitation: Combining Genome-Wide Location Analysis + DNA Microarray Analysis. Retrieved April 10, 2024 from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3004291/
- NCI Dictionary of Cancer. (2024). In Vivo. Retrieved April 10, 2024 from https://www.cancer.gov/publications/dictionaries/cancer-terms/def/in-vivo
- NIH Glossary of Genomic and Genetic Terms. (2024). Cell Cycle. Retrieved April 9, 2024 from https://www.genome.gov/genetics-glossary/Cell-Cycle
- NIH Glossary of Genomic and Genetic Terms. (2024). G1 Phase of the Cell Cycle. Retrieved April 10, 2024 from https://www.genome.gov/genetics-glossary/Cell-Cycle
- PubMed. (2024). Amino Acid Transporters Revisited: New Views in Health and Disease. Retrieved April 10, 2024 from https://pubmed.ncbi.nlm.nih.gov/30177408/
- Wang, (2024). The Transcription Initiation Apparatus. Retrieved April 10, 2024 from https://www.nature.com/articles/srep00422
- Worsley-Hunt, (2024). Cis-Regulatory Sequences. Retrieved from https://genomemedicine.biomedcentral.com/articles/10.1186/gm281
- Biology Online. (2024). Phylogenetics. Retrieved from https://www.biologyonline.com/dictionary/phylogenetics
- Biology Online. (2024). Sensu Stricto. Retrieved from https://www.biologyonline.com/dictionary/sensu-stricto
