ANALYSIS OF GENE REGULATION IN T-LYMPHOCYTES USING MICROARRAYS AND GENE EXPRESSION DATABASES   Dr A. Mouzaki,  Dr C. Argyr...
Aims & Objectives <ul><li>Hypothesis-driven application of high-throughput gene quantification technologies </li></ul><ul>...
Starting point … <ul><li>Experimental work from early 90s suggesting that IL-2 gene is actively repressed  in resting naïv...
The distal   NF-AT site in the IL-2 gene  &  HIV-1 LTR are co-regulated <ul><li>Experimental system  :  Χ. laevis   oocyte...
Background data <ul><li>Repressor(s) is selectively expressed in  resting naive T-lymphocytes </li></ul><ul><li>Disappears...
From gene (expression) to protein <ul><li>Problem specification  :  </li></ul><ul><ul><li>looking for DNA-Binding Proteins...
 
Crash Course on BAYESIAN   Statistics <ul><li>Bayesian :  named after   Rev Bayes who described Bayes theorem in the 18th ...
How Does it Work ? <ul><li>A three step procedure :  </li></ul><ul><li>Clearly state what the hypotheses or models are, al...
Experimental Design <ul><li>Bayesian methods are ideally suited to contribute to experimental design   because :  </li></u...
Decision structure of EMSA experiments <ul><li>a set of available actions ( α i ) </li></ul><ul><li>a set of uncertain eve...
The role of Databases <ul><li>Microarrays help to expand the action horizon by providing quantitative data about uncertain...
The role of mathematics <ul><li>max   i   {P DN (TF i ) * u(c i ) + P NDN (TF i ) * u(c * )} = max   i  {P DN (TF i ) * u(...
<ul><li>Two possible outcomes for every gene: </li></ul><ul><li>either   δ < 0  (gene is down regulated upon T cell activa...
The role of  public  data sources <ul><li>The Lymphochip dataset :  Alizadeh AA, Eisen MB, Davis RE  et al . Distinct type...
Bayesian analysis pinpoints differentially regulated genes RESULTS
DOWN-REGULATED TRANSCRIPTION FACTORS
COMBINING UTILITIES AND PROBABILITIES
The role of (depletion) EMSAs <ul><li>Nuclear extracts of  resting naive  Τ  cells   </li></ul><ul><li>Enrichment   x 10 <...
HENCE  ... <ul><li>An ets-2 like factor most likely represses IL-2 gene </li></ul><ul><li>Most likely another  DNA-BP also...
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ANALYSIS OF GENE REGULATION IN T-LYMPHOCYTES USING MICROARRAYS AND GENE EXPRESSION DATABASES

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Powerpoint presentation of a talk I gave in the15th European Immunology Congress (EFIS 2003), June 2003 Rhodes Greece

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ANALYSIS OF GENE REGULATION IN T-LYMPHOCYTES USING MICROARRAYS AND GENE EXPRESSION DATABASES

  1. 1. ANALYSIS OF GENE REGULATION IN T-LYMPHOCYTES USING MICROARRAYS AND GENE EXPRESSION DATABASES Dr A. Mouzaki, Dr C. Argyropoulos
  2. 2. Aims & Objectives <ul><li>Hypothesis-driven application of high-throughput gene quantification technologies </li></ul><ul><li>Bioinformatics as an experimental tool </li></ul><ul><li>Application of formal Bayesian Statistical Inference techniques in molecular biology experimental design </li></ul><ul><li>Elucidation of gene regulation interactions in lymphocyte biology </li></ul>
  3. 3. Starting point … <ul><li>Experimental work from early 90s suggesting that IL-2 gene is actively repressed in resting naïve T-lymphocytes </li></ul><ul><li>The same repressor(s) seems to be involved in HIV-1 regulation and in autoimmune diseases (childhood ITP) </li></ul><ul><li>Protein purification has been a daunting task </li></ul>
  4. 4. The distal NF-AT site in the IL-2 gene & HIV-1 LTR are co-regulated <ul><li>Experimental system : Χ. laevis oocytes </li></ul><ul><li>Transfections using various promoters to drive expression CAT –plasmids </li></ul><ul><li>Microinjection of nuclear and cytoplasmic extracts from T-lymphocytes </li></ul><ul><li>Experimental findings : </li></ul>Interpretation : ? Repressor in resting naive κύτταρα Similar size complexes in EMSA experiments Hot – cold competition EMSA experiments IL-2 promoter Δ PRRE IL-2 promoter HIV-LTR Δ PRRE HIV-LTR ++ ++ ++ ++ activated T-cells + 0 + 0 resting naive T cells
  5. 5. Background data <ul><li>Repressor(s) is selectively expressed in resting naive T-lymphocytes </li></ul><ul><li>Disappears and (never comes back) after T – cell activation </li></ul><ul><li>Binds to : </li></ul>HIV–1 PRRE -279 AGGCCAATGAAGGAGAGAACAACAGCTTGT -250 IL-2 PRRE -292 AAGAAAGGAGGAAAAACT-GT -273 HIV-1 NF - AT –252 TGTTACACCCTATFAGCCTGCATGGGATGGAGGACGC -216 HIV-1 NF-κB -108 ACAAGGGACTTTCCGCTGGGGACTTTCCA - 80
  6. 6. From gene (expression) to protein <ul><li>Problem specification : </li></ul><ul><ul><li>looking for DNA-Binding Proteins that are down-regulated upon T cell activation </li></ul></ul><ul><ul><li>Can bind to the common motif: AAGGAG </li></ul></ul><ul><li>In theory: test candidate TFs in EMSA experiment </li></ul><ul><li>Q: how do you find candidate factors ? </li></ul><ul><li>A: Bayesian statistics+TF Databases+Gene Expression Databases </li></ul>
  7. 8. Crash Course on BAYESIAN Statistics <ul><li>Bayesian : named after Rev Bayes who described Bayes theorem in the 18th century . </li></ul><ul><li>Probability : a real-number-valued measure of the plausibility of a proposition when incomplete knowledge does not allow us to establish its truth or falsehood with certainty </li></ul><ul><li>Probability theory is just common sense reduced to numbers, and probability represents the observer’s belief that a certain event is true </li></ul>
  8. 9. How Does it Work ? <ul><li>A three step procedure : </li></ul><ul><li>Clearly state what the hypotheses or models are, along with all the background information and data </li></ul><ul><li>Use the language of probability to assign prior probabilities to the hypotheses investigated </li></ul><ul><li>Use probability calculus in order to arrive to numerical values for the hypotheses in light of the available data </li></ul>
  9. 10. Experimental Design <ul><li>Bayesian methods are ideally suited to contribute to experimental design because : </li></ul><ul><li>information is usually available prior to experimentation </li></ul><ul><li>uncertainties can be combined with numerical measures of utility of consequences </li></ul><ul><li>The optimal experimental design is the one maximizing the expected utility of an experiment </li></ul>
  10. 11. Decision structure of EMSA experiments <ul><li>a set of available actions ( α i ) </li></ul><ul><li>a set of uncertain events (E i,j ) : E DN and E NDN </li></ul><ul><li>a set of consequences </li></ul><ul><li>Preferences about the uncertain scenarios depends on the attitudes towards the consequences involved and is codified in a utility function </li></ul>E NDN E DN α i-1 α i+1 α i c * c i U (c * ) = 0 0 ≤ U (c i ) ≤ 1
  11. 12. The role of Databases <ul><li>Microarrays help to expand the action horizon by providing quantitative data about uncertain events </li></ul><ul><li>Specialized databases (i.e. TRANSFAC) complement microarrays by providing a wealth of data to aid numerical codification of utilities in a specific situation. </li></ul><ul><li>Common sense implies a simple utility function : </li></ul><ul><li>the utility c * of the event E NDN is set equal to zero </li></ul><ul><li>The utility of any factor in the case of down-regulation is a semi-quantitative binding site “similarity” score, with 1 being a perfect match and lesser degrees of similarity coded accordingly </li></ul>
  12. 13. The role of mathematics <ul><li>max i {P DN (TF i ) * u(c i ) + P NDN (TF i ) * u(c * )} = max i {P DN (TF i ) * u(c i ) } </li></ul><ul><li>P DN given by the Behrens-Fisher distribution in Bayesian statistics (bypasses sample size limitations and heteroscedasticity of measurements) </li></ul><ul><li>Probabilities easier conceptualized as odds </li></ul><ul><li>Calculated using the Bayes Theorem </li></ul><ul><li>Computer algebra software takes care of integrals </li></ul>
  13. 14. <ul><li>Two possible outcomes for every gene: </li></ul><ul><li>either δ < 0 (gene is down regulated upon T cell activation) , </li></ul><ul><li>or δ  0 (gene is not down-regulated) . </li></ul><ul><li>“ Bayesian” significance testing : </li></ul><ul><li>H 0 : δ < 0 null hypothesis and H 1 : δ  0 alternative one </li></ul><ul><li>Before we collect any gene expression data, we could assume that each outcome is equiprobable (I stands for available background knowledge ) </li></ul><ul><li>P(H 0 |I) = P(H 1 |I) = ½ </li></ul><ul><li>After observing data, we calculate the POR: </li></ul>Significance Testing …
  14. 15. The role of public data sources <ul><li>The Lymphochip dataset : Alizadeh AA, Eisen MB, Davis RE et al . Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000; 403: 503-511 provided the relevant array experiment </li></ul><ul><li>The TRANSFAC database : Wingender E, Chen X, Hehl R et al . TRANSFAC: an integrated system for gene expression regulation. Nucleic Acids Res. 2000; 28: 316-319 helped to calculate utilities for factors </li></ul>
  15. 16. Bayesian analysis pinpoints differentially regulated genes RESULTS
  16. 17. DOWN-REGULATED TRANSCRIPTION FACTORS
  17. 18. COMBINING UTILITIES AND PROBABILITIES
  18. 19. The role of (depletion) EMSAs <ul><li>Nuclear extracts of resting naive Τ cells </li></ul><ul><li>Enrichment x 10 </li></ul><ul><li>Remove TF using specific antibody and Protein A Sepharose </li></ul><ul><li>Band Signal Reduction  Super Shift in typical EMSA experiments </li></ul>
  19. 20. HENCE ... <ul><li>An ets-2 like factor most likely represses IL-2 gene </li></ul><ul><li>Most likely another DNA-BP also plays a role (work in progress using affinity columns to isolate the complex) </li></ul><ul><li>Statistics can be extremely useful, provided computer algebra systems take care of “double” integrals </li></ul>

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