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Investigating Transcription Factor Dynamics in Gene Expression: Insights from mRNA Analysis

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aleda
17 diapositives · 325 Views · Aug 20, 2014

This study examines the dynamics of transcription factors (TFs) and their role in gene expression at the genomic level through mRNA abundance analysis. We investigate pivotal questions regarding TF binding, activation patterns, and collaborations among factors. Methods include ChIP for assessing transcription factor binding to DNA, with a focus on reducing false positives and utilizing advanced error modeling. Our results reveal autoregulated genes and multicomponent regulatory loops, providing insights into gene regulatory networks and potential pathways for further research.

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Investigating Transcription Factor Dynamics in Gene Expression: Insights from mRNA Analysis
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  1. Goal • Once we have the ability to examine gene expression (please keep in mind that this is mRNA abundance) at the genomic level –what are some of the questions we would ask? • What transcription factors (TFs) are doing this • What is the dynamic of TF binding and activation • How do TFs work together

  2. Background • TFs bind to promoters • Recruit other TFs, including chromatin-modifying proteins (histone acetylases, etc.) • A small sample of this analysis had been done either looking at expression of genes in TF mutants or Chromatin Immuno-precipitation

  3. ChIP • Isolate chromatin with tagged TF • Reversible cross link of protein to DNA • Shear DNA • Immunoprecipitate protein-DNA complex • Reverse cross linker • Label DNA • Hybridize to intergenic DNA microarrays

  4. Methods • Added myc tag to 141 transcription factors • Checked expression with PCR, protein with western blot (have myc antibody) • Could detect 106 of 124 by western blot! • 17 constructs couldn’t be recovered (mcy tag interfered significantly with their activity – these are essential proteins)

  5. Assay • ChIP has many false positives • Did experiments in triplicate in YPD • Looked at ratio of IP-DNA to control DNA • Used Rosetta error model • Were concerned about errors: cell populations, DNA binding factors capable of binding specifically and non-specifically, and the expectation of noise.

  6. Procedure

  7. Distribution of transcription factors 37% bound by at least one Average = 38

  8. Network motifs

  9. Results • Found 10 autoregulated genes – suggests 10% of the genes are autoregulated • Found 3 multicomponent loops – could be bistable, i.e. have two alternative states • Found 39 regulators in 49 feed forward loops potentially controlling 240 genes

  10. Fkh1p forms a single-input regulatory motif (look up recent Struhl paper about ribosomal proteins) Also, multi-input involving Fkh1p Ribosomal protein genes

  11. Assembling motifs into network structures • Define G genes bound by S regulators with a P value threshold of .001 • Look for other genes with similar expression patterns • Examine these genes to see if S regulators are bound to them

  12. Go to the website for more discussion • Could check more TFs • Could check other growth conditions • May be too rigorous in excluding false negatives