Lucia I. Ramirez Week 10: Difference between revisions

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*yeast genes are also frequently regulated through combinations of regulators
*yeast genes are also frequently regulated through combinations of regulators


Figure 2
Genome-wide distribution of transcriptional regulators
*More than 1/3 of the promoter regions bounded by regulators were bound by 2 or more regulators
*Similar to expected distribution from randomized data, there was a high number of promoter regions that were bounded by four or more regulators
*Resulted in an underestimate of regulator density because of stringency of the p-value threshold
====Motifs====
Figure 3
*network motifs: simplest building blocks found in transcriptional regulatory network architecture that provides specific regulatory capacities such as positive and negative feedback loops. Motifs suggest models for regulatory mechanisms that can be tested
#''Autoregulation'': regulator that binds to the promoter region of its own gene (10% of yeast gene)
#*Advantages:
#**Reduced response time to environmental stimuli
#**Decreased biosynthetic cost of regulation (takes less energy)
#**Increased stability of gene expression
#''Multicomponent loops'': a regulatory circuit whose closure involves two or more factors (only observed in 3 of the 106 regulators)
#*Advantages:
#**closed-loop structure provides the capacity for feedback control (stable bc not affected by external eniv)
#**offers the potential to produce bistable systems that can switch between two alternative states
#''Feedforward loops'': contain a regulator that controls a second regulator so both regulators bind a common target gene (39 regulators are involved in 49 feedforward loops potentially controlling 240 genes in the yeast network)
#*Advantages:
#**may act as a switch (expression of the target gene may depend on the rest of levels of the master and secondary regulators)
#**provides a form of multistep ultrasensitivity
#''Single-input'': contain a single regulator that binds a set of genes under a specific condition (i.e. several genes of the leucine biosynthetic pathway are controlled by the Leu3 transcriptional regulator)
#''Multi-input'': consist of a set of regulators that bind together to a set of genes (295 combinations of two or more regulators found), which offers the potential for coordinating gene expression across a wide variety of growth conditions.
#*each of the regulators bound to a set of genes can be responsible for regulating those genes in response to a unique input. In this manner, two different regulators responding to two different inputs would allow coordinate expression of the set of genes under these two different conditions
#''Regulator chain'': consist of chains of 3 or more regulators in which one regulator binds the promoter for a second regulator, the second binds the promoter for a third regulator, and so on (188 regulator chain motifs found, which varied in size from 3 to 10 regulators)
#*the chain represents the simplest circuit form for ordering transcriptional events in a temporal sequence. The most straightforward form of this appears in the regulatory circuit of the cell cycle, where regulators functioning at one stage of the cell cycle regulate the expression of factors required for entry into the next stage of the cell cycle
===Results===
Figure 4
*Assembling Motifs into Network Structures
**Algorithm created examines over 500 expression experiments
**Genome is scanned for genes common to phase G. Matches are examined for regulators common to S. P value is then relaxed to “recapture” data that was not used
**Motifs used to create replica of cell cycle based only on the location/data of the regulators with no prior cell cycle knowledge
**Yeast Cell Cycle Model: transcriptional regulatory network created from binding and expression data
Computational Model
*Created model based on peak expression of common expression multi-input motifs
*Three notable results:
**Model correctly assigned all the regulators to previously proven stages of the cell cycle
**2 relatively unknown regulators could be assigned based strictly on binding data
**No prior knowledge required (automatic)
*Regulators
**Abf1 bound the largest number (181) of promoter regions.
**Regulators that should be active under growth conditions other than yeast extract, peptone, and dextrose were typically found to bind the smallest number of promoter regions.
**This was among the regulators that bound the smallest number (3) of promoters.
**Identification of a set of promoter regions that are bound by specific regulators allowed investigators to predict sequence motifs that are bound by these regulators
===Significance of regulatory network information===
*Identified network motifs that provide specific regulatory capacities for yeast
*Motifs can be used as building blocks to construct large network structures through an automated approach that combines genome-wide location and expression data (without prior knowledge) 
*Future research will  require knowledge of regulator binding sites under various growth conditions and experimental testing of models that emerge from computational analysis of regulator binding, gene expression, and other information.
*'''Purpose''': The interactions between genes and transcription factors can be mapped using the model described, which can then be used to improve our understanding of human health and design new strategies to combat disease.
==Presentation==
Partners:[[User: Kristen M. Horstmann| Kristen M. Horstmann]] and [[User:Tessa A. Morris| Tessa A. Morris]]
[[Media:TMKHLRJournal Club 2.pptx| Presentation]]




Latest revision as of 23:07, 23 March 2015

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Lee, T. I., Rinaldi, N. J., Robert, F., Odom, D. T., Bar-Joseph, Z., Gerber, G. K., Hannett, N. M., Harbison, C. T., Thompson, C. M., Simon, I., Zeitlinger, J., Jennings, E. G., Murray, H.L ., Gordon, D. B., Ren, B., Wyrick, J. J., Tagne, J. B., Volkert, T. L., Fraenkel, E., Gifford, D. K. & Young, R. A. (2002). Transcriptional regulatory networks in Saccharomyces cerevisiae. Science, 298(5594), 799-804. DOI: 10.1126/science.1075090

Link to PDF version of article

Biological Terms

  1. myc epitope tag:provide a method to localize gene products in a variety of cell types, study the topology of proteins and protein complexes, identify associated proteins, and characterize newly identified, low abundance or poorly immunogenic proteins when protein specific antibodies are not available. Antibodies against c-myc epitopes recognize overexpressed proteins containing Myc epitope tag fused to either amino- or carboxy-termini of targeted proteins (http://www.pierce-antibodies.com/Myc-Epitope-Tag-antibody-clone-MycA7-Monoclonal--MA121316.html)
  2. physiologic: of, or pertaining to physiology or normal functioning or state of an organism (http://www.biology-online.org/dictionary/Physiologic)
  3. immunoblot analysis: is a rapid and sensitive assay for the detection and characterization of proteins that works by exploiting the specificity inherent in antigen-antibody recognition. It involves the solubilization and electrophoretic separation of proteins, glycoproteins, or lipopolysaccharides by gel electrophoresis, followed by quantitative transfer and irreversible binding to nitrocellulose, PVDF, or nylon (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2583035/)
  4. immunoprecipitated: is the small-scale affinity purification of antigens using a specific antibody and is one of the most widely used methods for antigen purification and detection (https://www.lifetechnologies.com/us/en/home/life-science/protein-biology/protein-biology-learning-center/protein-biology-resource-library/pierce-protein-methods/immunoprecipitation-ip.html)
  5. stringency: reaction conditions, notably temperature, salt, and pH that dictate the annealing of single-stranded DNA/DNA, DNA/ rNA, and RNA/RNA hybrids. at high stringency, duplexes form only between strands with perfect one-to-one complementarity, lower stringency allows annealing between strands with some degree of mismatch between bases (http://www.biology-online.org/dictionary/Stringency)
  6. biosynthetic: The production of a complex chemical compound from simpler precursors in a living organism, usually involving enzymes (to catalyze the reaction) and energy source (such as ATP) (http://www.biology-online.org/dictionary/Biosynthesis)
  7. pheromone: chemical substances which, when secreted by an individual into the environment, cause specific reactions in other individuals, usually of the same species. The substances relate only to multicellular organisms (http://www.biology-online.org/dictionary/Pheromones)
  8. transient: short-lived; passing; not permanent; said of a disease or an attack (http://www.biology-online.org/dictionary/Transient)
  9. FHL1: Four and a half LIM domains protein 1 (http://amigo.geneontology.org/amigo/gene_product/UniProtKB:Q5JXI8)
  10. assaying: analysis (as of an ore or drug) to determine the presence, absence, or quantity of one or more components (http://www.merriam-webster.com/dictionary/assay)

Outline

Introduction

  • Cells, the product of specific gene expression programs involving regulated transcription of thousands of genes, move through the cell cycle, where transcriptional programs are modified
  • Gene expression programs are dependent on the recognition of specific promoter sequences by transcriptional regulatory proteins
  • Regulatory proteins recruit and regulate chromatin-modifying complexes and components of the transcriptional apparatus

Main result presented in this paper

  • Knowing the sites bound by the transcriptional regulators encoded in a genome gives the information necessary to create models for transcriptional regulatory networks
  • Capable of identifying set of target genes bound in vivo by all of the transcriptional regulators using the complete genome sequence and the development of a method for genome-wide binding analysis

Methods

Figure 1

  • Genome-wide location analysis was used to see how yeast transcriptional regulators bind to promoter sequences throughout the genome
  • Observed all 141 transcription factors listed in the Yeast Proteome Database that were reported to have DNA binding and transcriptional activity
  • Myc epitope tagging (at COOH terminus of each regulator) was used to identify transcription factors in each yeast strain, might have affected the function of some transcriptional regulators

Analysis

  • Immunoblot analysis showed 106 of the 124 tagged regulator proteins could be detected when yeast cells were grown in rich medium (yeast extract, peptone, and dextrose)
  • Performed genome-wide location analysis experiment for the 106 yeast strains that expressed epitope-tagged regulators
  • Genome-wide location data were subjected to quality control filters and normalized.
    • Confidence value (p-value = 0.001) calculated for each spot from each array using error model
    • Weighted average method used for combination of three samples of each data
    • Average take after each ratio was weighted by p-value
    • Calculate Final p-values for combined ratios
    • Total number of protein-DNA interactions in the location analysis data was found
  • Approximately 4000 interactions were observed between regulators and promoter regions
  • The promoter regions of 2342 of 6270 yeast genes (37%) were bound by one or more of the 106 transcriptional regulators
  • yeast genes are also frequently regulated through combinations of regulators

Figure 2 Genome-wide distribution of transcriptional regulators

  • More than 1/3 of the promoter regions bounded by regulators were bound by 2 or more regulators
  • Similar to expected distribution from randomized data, there was a high number of promoter regions that were bounded by four or more regulators
  • Resulted in an underestimate of regulator density because of stringency of the p-value threshold

Motifs

Figure 3

  • network motifs: simplest building blocks found in transcriptional regulatory network architecture that provides specific regulatory capacities such as positive and negative feedback loops. Motifs suggest models for regulatory mechanisms that can be tested
  1. Autoregulation: regulator that binds to the promoter region of its own gene (10% of yeast gene)
    • Advantages:
      • Reduced response time to environmental stimuli
      • Decreased biosynthetic cost of regulation (takes less energy)
      • Increased stability of gene expression
  2. Multicomponent loops: a regulatory circuit whose closure involves two or more factors (only observed in 3 of the 106 regulators)
    • Advantages:
      • closed-loop structure provides the capacity for feedback control (stable bc not affected by external eniv)
      • offers the potential to produce bistable systems that can switch between two alternative states
  3. Feedforward loops: contain a regulator that controls a second regulator so both regulators bind a common target gene (39 regulators are involved in 49 feedforward loops potentially controlling 240 genes in the yeast network)
    • Advantages:
      • may act as a switch (expression of the target gene may depend on the rest of levels of the master and secondary regulators)
      • provides a form of multistep ultrasensitivity
  4. Single-input: contain a single regulator that binds a set of genes under a specific condition (i.e. several genes of the leucine biosynthetic pathway are controlled by the Leu3 transcriptional regulator)
  5. Multi-input: consist of a set of regulators that bind together to a set of genes (295 combinations of two or more regulators found), which offers the potential for coordinating gene expression across a wide variety of growth conditions.
    • each of the regulators bound to a set of genes can be responsible for regulating those genes in response to a unique input. In this manner, two different regulators responding to two different inputs would allow coordinate expression of the set of genes under these two different conditions
  6. Regulator chain: consist of chains of 3 or more regulators in which one regulator binds the promoter for a second regulator, the second binds the promoter for a third regulator, and so on (188 regulator chain motifs found, which varied in size from 3 to 10 regulators)
    • the chain represents the simplest circuit form for ordering transcriptional events in a temporal sequence. The most straightforward form of this appears in the regulatory circuit of the cell cycle, where regulators functioning at one stage of the cell cycle regulate the expression of factors required for entry into the next stage of the cell cycle

Results

Figure 4

  • Assembling Motifs into Network Structures
    • Algorithm created examines over 500 expression experiments
    • Genome is scanned for genes common to phase G. Matches are examined for regulators common to S. P value is then relaxed to “recapture” data that was not used
    • Motifs used to create replica of cell cycle based only on the location/data of the regulators with no prior cell cycle knowledge
    • Yeast Cell Cycle Model: transcriptional regulatory network created from binding and expression data

Computational Model

  • Created model based on peak expression of common expression multi-input motifs
  • Three notable results:
    • Model correctly assigned all the regulators to previously proven stages of the cell cycle
    • 2 relatively unknown regulators could be assigned based strictly on binding data
    • No prior knowledge required (automatic)
  • Regulators
    • Abf1 bound the largest number (181) of promoter regions.
    • Regulators that should be active under growth conditions other than yeast extract, peptone, and dextrose were typically found to bind the smallest number of promoter regions.
    • This was among the regulators that bound the smallest number (3) of promoters.
    • Identification of a set of promoter regions that are bound by specific regulators allowed investigators to predict sequence motifs that are bound by these regulators

Significance of regulatory network information

  • Identified network motifs that provide specific regulatory capacities for yeast
  • Motifs can be used as building blocks to construct large network structures through an automated approach that combines genome-wide location and expression data (without prior knowledge)
  • Future research will require knowledge of regulator binding sites under various growth conditions and experimental testing of models that emerge from computational analysis of regulator binding, gene expression, and other information.
  • Purpose: The interactions between genes and transcription factors can be mapped using the model described, which can then be used to improve our understanding of human health and design new strategies to combat disease.

Presentation

Partners: Kristen M. Horstmann and Tessa A. Morris

Presentation


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