MSymond1 Week 12

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  1. A list of biological terms from the paper I did not know the definitions for when I first read the article
    • transcription regulator activity: A molecular function that controls the rate, timing and/or magnitude of gene transcription. The function of transcriptional regulators is to modulate gene expression at the transcription step so that they are expressed in the right cell at the right time and in the right amount throughout the life of the cell and the organism. Genes are transcriptional units, and include bacterial operons (Gene Ontology, 2024).
    • transcription cis-regulatory region binding: Binding to a specific sequence of DNA that is part of a regulatory region that controls transcription of that section of the DNA. The transcribed region might be described as a gene, cistron, or operon (Gene Ontology, 2024).
    • respiratory electron transport chain: A process in which a series of electron carriers operate together to transfer electrons from donors such as NADH and FADH2 to any of several different terminal electron acceptors to generate a transmembrane electrochemical gradient (Gene Ontology, 2024).
    • Lysis: The disintegration or rupture of the cell membrane, resulting in the release of cell contents or the subsequent death of the cell (Biology Online, 2024).
    • immunoprecipitate: the precipitate formed in an antigen‐antibody reaction (Oxford Reference, 2006).
    • DNA ligation: The re-formation of a broken phosphodiester bond in the DNA backbone, carried out by DNA ligase (Gene Ontology, 2024).
    • Phylogeny: the scientific study of phylogeny. It studies evolutionary relationships among various groups of organisms based on evolutionary history, similarities, and differences. It makes use of molecular sequencing data (such as homologous sequences, protein sequences, nucleotide sequences, etc.) and morphological data matrices to understand and analyze the protein and gene evolutions of genetically-related groups of organisms (Biology Online, 2024).
    • PCR: A laboratory method used to make many copies of a specific piece of DNA from a sample that contains very tiny amounts of that DNA. PCR allows these pieces of DNA to be amplified so they can be detected. PCR may be used to look for certain changes in a gene or chromosome, which may help find and diagnose a genetic condition or a disease, such as cancer. It may also be used to look at pieces of the DNA of certain bacteria, viruses, or other microorganisms to help diagnose an infection. Also called polymerase chain reaction (National Cancer Institute, 2024).
    • Epitope: That part of an antigenic molecule to which the t-cell receptor responds, a site on a large molecule against which an antibody will be produced and to which it will bind (Biology Online, 2024).
    • Microarray: A laboratory tool used to analyze large numbers of genes or proteins at one time (National Cancer Institute, 2024).
  2. The main Findings of the paper are that the architecture of the promoter, meaning the arrangements of the DNA binding site, change depending on environmental conditions and can be predicted with confidence what the binding arrangement will be depending on the promoter and the environmental conditions.
  3. The significance of these findings is the fact that they combine genome-wide location data with phylogenetic conservation data. Using both these types of data allowed to cluster all significant results from the genome wide data location based upon their conservation data.
  4. The limitations of prior studies is the fact that it cannot be determined what the location is for the recognition sites of transcriptional regulators with phylogenetic sequence data alone, or with any other prior knowledge from any previous study. The fact that the sequences have been conserved through evolution indicates that they can be regulated, but does not reveal information about the binding process, or the conditions, or the architecture of such binding.
  5. They treated the Yeast cells by using PCR and they printed about 6000 DNA fragments to represent nearly all regions in the yeast genome.
  6. They used the W303 yeast strain from Saccharomyces cerevisiae, and it was haploid.
  7. They grew them in microarrays with PCR products. The article does not specify temperature or time, in the supplementary methods section, it does list that the times varies for each of the conditions. it is as low as 20 minutes for certain conditions (namely the moderately hypertonic condition). And the time is as high as 14 hours (namely the filamentation inducing condition). It does not specify the temperature for most of the conditions, except for the elevated temperature condition in which it specifies that it begins at 30 degrees celsius and is shifted to 37 degrees celsius.
  8. The controls group they used was an unenriched microarray to compare with the immunoprecipitated samples.
  9. They ran each program 50 times on a randomly selected set of sequences.
  10. The study conducted their genome wide location analysis by cross linking the proteins to the DNA, which then created precipitate which separated the DNA from the protein. These precipitates were then went through PCR procedures to hybridize them to a microarray of spotted PCR products, each representing a different location of the yeast genome. Such locations were used to compare the probabilities of binding interactions.
  11. They used an Axon 200B scanner to scan the microarrays, they compared the immunoprecipitated sample with the unenriched sample. They found the median of each channel to calculate a normalization factor. They then calculated the log ratio of the intensity of the test channel to the control channel. The log ratios were normalized by subracting the average log ratio of every spot across all arrays. Finally, they calculated an error model by calculating the significance of enrichment on each chip, and combining the data for all replicates to calculate an average ratio and significance of enrichment for every region in the genome.
  12. their supplementary tables are available to the public for download on nature.com and these tables have the results that they were able to calculate from their data, but their raw data and calculations are not to be found available to the public.
  13. The list of figures from the article
    • Figure 1 has 2 parts, part a essentially states that the conclusions from this study, regarding the identification of transcription factor binding site specificities, could only be concluded when using the three kinds of data they have. They had their genome-wide location data, their phylogenetic sequence conservation data, and other previous work. Part b shows the sequence specificities of some of the regulators. There are 2 columns, one of the columns displays sequences that had already been discovered and were rediscovered with this study, and the other column shows sequences that were newly discovered by this study. Each of the letters in the sequence have a size proportional to the product of their frequency and their information content
    • Figure 2 has three parts, part a displays the different chromosomes, as well as certain genes located on said chromosomes with , it also shows the locations of certain DNA sequences that are bound by transcriptional regulators. They obtained this information by mapping on the yeast genome sequences the motifs that they found to be bound by regulators at high confidence that were also conserved. The functions of the specific transcriptional regulators had already been previously established. Part b of the figure combines binding data with sequence conservation data. This part of the graph is in 3 parts, the first shows all sequence matches to DNA binding specificities. The second part shows all of the sequence matches to conserved sequences, and the third part shows all sequences that match with conserved sequences that are bound by regulators. Part c of the figure is a graph that shows the frequency of binding sites in relation to the distance from translational start site on the DNA sequence. The x axis is the distance from translational start site, and the y axis is the number of binding sites.
    • Figure 3 shows the different promoter architectures. The first one being single regulator, the second being repetitive motifs, the third being multiple regulators, and co-occuring regulators. They display this information by putting a different color box for each regulator on different lines representing different binding site sequences. They obtained this information through their microarray experiments in this study.
    • Figure 4 displays the environment-specific use of transcriptional regulatory code. It shows four different patterns of binding behavior in four different rows. The different patterns being Condition invariant, condition enabled, condition expanded, and condition altered. The regulators are represented by colored circles and shown above and below the genes/promoters, and there are lines connecting them to the genes that display their binding nature depending on their environments. To the right of these charts, there are 2 lines for each binding pattern representing different environments. The regulators are displayed near the genes, and they are shown in circles or colored boxes to show whether they are binding or not depending on the environment.
    • Supplementary figure 1 is a graph in which the x axis is the regulator under testing (1 through 203), and the y axis is the number of promoter regions bound to said regulator. There are 2 lines on the graph, one is in blue which is unadjusted per the number of conditions the regulator was profiled under, and another line is in pink in which it averages the distributions for the same set of p values among regulators and promoter regions. This information was also obtained by their microarray data.
    • Supplementary figure 2 represents the data calculations conducted in this study. First all of their motifs were identified by using a variety of methods as listed in the figure, which were then filtered to determine which were significant, and then clustered based upon representative motifs, they then used conservation data to identify which motifs had the highest confidence rate. The final step is the statistical test from specificity databases to assign a specific motif to each regulator.
    • Supplementary figure 3 is a photo that displays the binding of Cin5 to two different sequences. It shows 15 different lanes to demonstrate the different binding results of the protein with different sequences. The first lane shows it with no competitor, the lanes 2-8 show it binding with a competitor sequence found by one of the discovered motifs in this study, and lanes 9-15 show it binding with a previously established binding site for the regulator. And the concentration of the regulator was 27 times higher in the motif discovered in this study as opposed to the previously established sequence, meaning the results of the study were able to predict the binding capacity for this protein better than previously published literature.
    • Supplementary figure 4 is a bar graph in which the x axis is the regulators, and the y axis is the number of promoter regions bound for each of those regulators. This figure compares the number of promoter regions bound for each regulator depending on the environment they're growing in. The two different environments they compared in this figure are the rich medium environment and the amino acid starvation environment.
    • Supplementary figure 5 is a bar graph in which the x axis is the percent of maximum matching sites, which essentially translates to the quality of the matching sites found for each of them, which was determined based on to the best matching sequence to the Gcn4 binding specificity. The y axis is the frequency of matches found of that quality. The bars were clustered by which conditions they were grown in.
  14. This study does incorporate methods from other previous studies. Other previous studies have used conservation sequence data, but that has not allowed them to predict the binding sites and environments for each of the regulators with confidence that this study does.
  15. The authors could take the future direction of testing such transcription factors in higher eukaryotes, for if they are able to predict such binding mechanisms for yeast cells, they can likely do something similar for a higher level organism. Perhaps they will not be able to test as many regulators or have as high of a confidence rate, for I would assume it would be far more difficult to carry out such tests on another higher organism, for there are likely far more regulators and they may have a much larger genome to select from.
  16. I believe the authors in this study were able to support their conclusions well with the data acquired, but I do not believe the data was well presented or explained in the article. Much of the materials necessary to understand their methods or to know important details (temperature, time, conditions) of their experiment were not even on the article itself and had to be found in the supplementary section. And even in the supplementary section it was still a very dense topic that is very difficult to understand for anyone who is not a well established expert on the topic. Not to mention the fact there is no defined discussion or conclusion section of the paper. The final section of the article is the methods section which is rather unconventional.

Presentation

Slides

Meta Data

  1. 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
  2. link to the abstract from PubMed
  3. link to the full text of the article in PubMedCentral
  4. link to the full text of the article (HTML format) from the publisher web site.
  5. link to the full PDF version of the article from the publisher web site.
  6. The copyright is owned by the Author because it is an Author Manuscript
  7. 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
  8. The journal Nature is available for print, since it is available to subscribe to print.
  9. 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.
  10. Since 1989
  11. Yes, the articles in this journal are peer-reviewed.
  12. advisory board/editorial board of the journal.
  13. 64.8 was the 2 year impact factor and 60.9 was the 5 year impact factor (2022).
  14. The article was submitted on March 11 2004
  15. The article was accepted on July 1st 2004
  16. No/unknown, all it says is that it was published in final edited form 2004, Sept 2.
  17. The article was published on September 2 2004
  18. 7 months
  19. Whitehead Institute of Biomedical Research, Massachusetts Institute of Technology, MIT Computer Science and Artificial Intelligence Laboratory
  20. 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.
  21. Yes, “Some authors have filed a patent application covering aspects of this work and are pursuing commercialization.”
  22. There isn't data associated with the dataset.
  23. This article has cites 30 articles, and has been cited by 1671 articles.

Acknowledgements

I have been in contact with my group members for this week about the presentation and questions this week. We worked together in class and texted about the presentation. I also visited my professor, Dr. Dahlquist during her office hours to ask for help in interpreting the article for this week. Except for what is noted above, this individual journal entry was completed by me and not copied from another source.

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