1. Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic Scale Joseph L. DeRisi, Vishwanath R. Iyer, Patrick O. Brown Science Vol. 278 October 24, 1997 p.680-6

2. What they asked… Question - What changes occur at the genetic level when yeast shifts from anaerobic to aerobic metabolism? Question - What changes occur at the genetic level when yeast shifts from anaerobic to aerobic metabolism? Glucose Ethanol + CO 2 Ethanol + O 2 “ Diauxic Shift ” CO 2 and ATP

3. How can you approach this question? Changes to proteins Changes to proteins Expression and post-translational modification Expression and post-translational modification Changes to cell structure / morphology Changes to cell structure / morphology Changes in gene expression: Changes in gene expression: Occurs relatively quickly Occurs relatively quickly Gives you a good idea which processes are being affected Gives you a good idea which processes are being affected MICROARRAY technology now available MICROARRAY technology now available Changes to proteins Changes to proteins Expression and post-translational modification Expression and post-translational modification Changes to cell structure / morphology Changes to cell structure / morphology Changes in gene expression: Changes in gene expression: Occurs relatively quickly Occurs relatively quickly Gives you a good idea which processes are being affected Gives you a good idea which processes are being affected MICROARRAY technology now available MICROARRAY technology now available

4. Measuring Gene Expression How to measure changes in RNA How to measure changes in RNA Signal Regulation of RNA transcription (  or  ) Use a viral enzyme (Reverse Transcriptase) to make single-stranded DNA from mRNA (cDNA). Amplify the cDNA with PCR for detection on gel Northern Blot

5. Microarray Technology YeastcDNALibrary AnaerobicAerobic

6. Interpreting Microarrays Increase Decrease No Change Control (Cy3) Treated (Cy5)

7. Why use a Microarray? Quantitative comparison of transcript levels Quantitative comparison of transcript levels Look at thousands of genes in one experiment Look at thousands of genes in one experiment Can theoretically assess changes in all genes of an organism Can theoretically assess changes in all genes of an organism Quantitative comparison of transcript levels Quantitative comparison of transcript levels Look at thousands of genes in one experiment Look at thousands of genes in one experiment Can theoretically assess changes in all genes of an organism Can theoretically assess changes in all genes of an organism

8. Experimental Design Took samples from a fermenting yeast culture seven times over 2 hours. Took samples from a fermenting yeast culture seven times over 2 hours. Isolated RNA from cells and prepared cDNA labelled with Cy3 (green) for initial measurement (time zero) and Cy5 (red) for successive samples. Isolated RNA from cells and prepared cDNA labelled with Cy3 (green) for initial measurement (time zero) and Cy5 (red) for successive samples. Equal amounts of cDNA were mixed from time zero and each time point. Equal amounts of cDNA were mixed from time zero and each time point. Took samples from a fermenting yeast culture seven times over 2 hours. Took samples from a fermenting yeast culture seven times over 2 hours. Isolated RNA from cells and prepared cDNA labelled with Cy3 (green) for initial measurement (time zero) and Cy5 (red) for successive samples. Isolated RNA from cells and prepared cDNA labelled with Cy3 (green) for initial measurement (time zero) and Cy5 (red) for successive samples. Equal amounts of cDNA were mixed from time zero and each time point. Equal amounts of cDNA were mixed from time zero and each time point.

9. What they found…

10. Organization and Analysis They set a threshold of 2-fold change They set a threshold of 2-fold change Not many genes changes in first few hours Not many genes changes in first few hours As yeast switched from fermentation to respiration, started to see large changes in some genes. As yeast switched from fermentation to respiration, started to see large changes in some genes. They set a threshold of 2-fold change They set a threshold of 2-fold change Not many genes changes in first few hours Not many genes changes in first few hours As yeast switched from fermentation to respiration, started to see large changes in some genes. As yeast switched from fermentation to respiration, started to see large changes in some genes.

11. Changes in gene expression during diauxic shift:  ALD2 and ACS1 (fuel the TCA cycle)  ALD2 and ACS1 (fuel the TCA cycle)  Pyruvate carboxylase and  Pyruvate decarboxylase (fuel TCA cycles and gluconeogenesis)  Pyruvate carboxylase and  Pyruvate decarboxylase (fuel TCA cycles and gluconeogenesis)  PCK1 and  FBP1 (pushing reversal of glycolysis pathway toward production of glucose-6- phosphatase  PCK1 and  FBP1 (pushing reversal of glycolysis pathway toward production of glucose-6- phosphatase Trehalose synthase and glycogen synthase (promotes storage of glucose) Trehalose synthase and glycogen synthase (promotes storage of glucose)  genes associated protein synthesis  genes associated protein synthesis On the contrary,  in mitochondrial ribosomal proteins observed, emphasizing need for mitochondrial biogenesis (requirement for more energy?) On the contrary,  in mitochondrial ribosomal proteins observed, emphasizing need for mitochondrial biogenesis (requirement for more energy?) Changes in gene expression during diauxic shift:  ALD2 and ACS1 (fuel the TCA cycle)  ALD2 and ACS1 (fuel the TCA cycle)  Pyruvate carboxylase and  Pyruvate decarboxylase (fuel TCA cycles and gluconeogenesis)  Pyruvate carboxylase and  Pyruvate decarboxylase (fuel TCA cycles and gluconeogenesis)  PCK1 and  FBP1 (pushing reversal of glycolysis pathway toward production of glucose-6- phosphatase  PCK1 and  FBP1 (pushing reversal of glycolysis pathway toward production of glucose-6- phosphatase Trehalose synthase and glycogen synthase (promotes storage of glucose) Trehalose synthase and glycogen synthase (promotes storage of glucose)  genes associated protein synthesis  genes associated protein synthesis On the contrary,  in mitochondrial ribosomal proteins observed, emphasizing need for mitochondrial biogenesis (requirement for more energy?) On the contrary,  in mitochondrial ribosomal proteins observed, emphasizing need for mitochondrial biogenesis (requirement for more energy?)

12. Gene families and temporal relationships

13. Other observations… Genes containing a carbon source response element (CSRE) in the promoter. Genes containing a carbon source response element (CSRE) in the promoter. As concentration of glucose went down, repression was relieved on gene transcription. As concentration of glucose went down, repression was relieved on gene transcription. Genes containing a carbon source response element (CSRE) in the promoter. Genes containing a carbon source response element (CSRE) in the promoter. As concentration of glucose went down, repression was relieved on gene transcription. As concentration of glucose went down, repression was relieved on gene transcription.

14. Promoter analysis of effected genes revealed: Promoter analysis of effected genes revealed: Stress response elements (STRE) Stress response elements (STRE) HAP responsive genes (respiration) HAP responsive genes (respiration) Rap1-regulated genes (ribosomal protein translation) Rap1-regulated genes (ribosomal protein translation) Also, increased expression of HAP4 and SIP4, transcription factors which control respiration Also, increased expression of HAP4 and SIP4, transcription factors which control respiration Promoter analysis of effected genes revealed: Promoter analysis of effected genes revealed: Stress response elements (STRE) Stress response elements (STRE) HAP responsive genes (respiration) HAP responsive genes (respiration) Rap1-regulated genes (ribosomal protein translation) Rap1-regulated genes (ribosomal protein translation) Also, increased expression of HAP4 and SIP4, transcription factors which control respiration Also, increased expression of HAP4 and SIP4, transcription factors which control respiration Identification of Gene Families and Pathways

15. Limitations of Experimental Design Used primers for KNOWN / PREDICTED open reading frames in yeast Used primers for KNOWN / PREDICTED open reading frames in yeast Not useful if you want to identify new genes Not useful if you want to identify new genes Can use primers which non-specifically amplify all mRNAs. Can use primers which non-specifically amplify all mRNAs. Threshold of 2-fold - may miss important genes that are changing slightly, but have large effects Threshold of 2-fold - may miss important genes that are changing slightly, but have large effects Multiple time points good (allows plotting of changes over time and acts as an internal control) - but always hard to know when the optimal times to choose are. Multiple time points good (allows plotting of changes over time and acts as an internal control) - but always hard to know when the optimal times to choose are. Adaptive changes in mutant strains. Adaptive changes in mutant strains. Used primers for KNOWN / PREDICTED open reading frames in yeast Used primers for KNOWN / PREDICTED open reading frames in yeast Not useful if you want to identify new genes Not useful if you want to identify new genes Can use primers which non-specifically amplify all mRNAs. Can use primers which non-specifically amplify all mRNAs. Threshold of 2-fold - may miss important genes that are changing slightly, but have large effects Threshold of 2-fold - may miss important genes that are changing slightly, but have large effects Multiple time points good (allows plotting of changes over time and acts as an internal control) - but always hard to know when the optimal times to choose are. Multiple time points good (allows plotting of changes over time and acts as an internal control) - but always hard to know when the optimal times to choose are. Adaptive changes in mutant strains. Adaptive changes in mutant strains.

16. Other applications of microarrays: Effects of individual mutations Create a strain of yeast with a mutation in one protein and compare to wild-type strain. (Gain or loss of function) Create a strain of yeast with a mutation in one protein and compare to wild-type strain. (Gain or loss of function) Gives information about the genes regulated by that protein. Gives information about the genes regulated by that protein. Mutated Tup1 (tup1  ) and compared to WT. Mutated Tup1 (tup1  ) and compared to WT. Tup1 known to repress gene transcription in response to glucose. Tup1 known to repress gene transcription in response to glucose. Create a strain of yeast with a mutation in one protein and compare to wild-type strain. (Gain or loss of function) Create a strain of yeast with a mutation in one protein and compare to wild-type strain. (Gain or loss of function) Gives information about the genes regulated by that protein. Gives information about the genes regulated by that protein. Mutated Tup1 (tup1  ) and compared to WT. Mutated Tup1 (tup1  ) and compared to WT. Tup1 known to repress gene transcription in response to glucose. Tup1 known to repress gene transcription in response to glucose.

17. Identification of possible overlapping functions DiauxicShift tup1  10% Shared Therefore, tup1 might be integral in the repressing the diauxic shift in the presence of glucose.

18. Gain of Function: Over- expression of YAP1 Yap1 - transcription factor Yap1 - transcription factor Identified genes belonging to a class of dehydrogenases/ oxidoreductases Identified genes belonging to a class of dehydrogenases/ oxidoreductases Might play a protective role during oxidative stress. Might play a protective role during oxidative stress. 2/3 of genes induced had Yap1 binding sites in the promoter. 2/3 of genes induced had Yap1 binding sites in the promoter. Yap1 - transcription factor Yap1 - transcription factor Identified genes belonging to a class of dehydrogenases/ oxidoreductases Identified genes belonging to a class of dehydrogenases/ oxidoreductases Might play a protective role during oxidative stress. Might play a protective role during oxidative stress. 2/3 of genes induced had Yap1 binding sites in the promoter. 2/3 of genes induced had Yap1 binding sites in the promoter.

19. Conclusions and Directions: Data agrees with what is already known about the pathways, therefore demonstrates the robustness of this technique. Data agrees with what is already known about the pathways, therefore demonstrates the robustness of this technique. Effected genes often grouped into distinct functional classes - specificity and accuracy. Effected genes often grouped into distinct functional classes - specificity and accuracy. Target genes often had predicted binding sites for transcription factors. Target genes often had predicted binding sites for transcription factors. Reproducible (0.87 correlation between duplicate experiments, 95% differed by a factor of less than 2-fold) Reproducible (0.87 correlation between duplicate experiments, 95% differed by a factor of less than 2-fold) Data agrees with what is already known about the pathways, therefore demonstrates the robustness of this technique. Data agrees with what is already known about the pathways, therefore demonstrates the robustness of this technique. Effected genes often grouped into distinct functional classes - specificity and accuracy. Effected genes often grouped into distinct functional classes - specificity and accuracy. Target genes often had predicted binding sites for transcription factors. Target genes often had predicted binding sites for transcription factors. Reproducible (0.87 correlation between duplicate experiments, 95% differed by a factor of less than 2-fold) Reproducible (0.87 correlation between duplicate experiments, 95% differed by a factor of less than 2-fold)

20. Microarray Technology Strengths of this approach Strengths of this approach Lots of data! Lots of data! Great starting point when screening for new targets or pathways Great starting point when screening for new targets or pathways Screen many genes at one time with small amounts of RNA Screen many genes at one time with small amounts of RNA Quantitative Quantitative Weaknesses of the approach Weaknesses of the approach Only investigate changes in RNA levels (transcription) Only investigate changes in RNA levels (transcription) Poorly expressed genes (sensitivity) Poorly expressed genes (sensitivity) Small changes can translate to large biological effects (statistical error and elimination) Small changes can translate to large biological effects (statistical error and elimination) Limited by the genes spotted on the chip Limited by the genes spotted on the chip Cross-hybridization of cDNA with highly homologous sequences (isoforms, families) Cross-hybridization of cDNA with highly homologous sequences (isoforms, families) Statisitical analysis - false positives/negatives, validation by other methods Statisitical analysis - false positives/negatives, validation by other methods Strengths of this approach Strengths of this approach Lots of data! Lots of data! Great starting point when screening for new targets or pathways Great starting point when screening for new targets or pathways Screen many genes at one time with small amounts of RNA Screen many genes at one time with small amounts of RNA Quantitative Quantitative Weaknesses of the approach Weaknesses of the approach Only investigate changes in RNA levels (transcription) Only investigate changes in RNA levels (transcription) Poorly expressed genes (sensitivity) Poorly expressed genes (sensitivity) Small changes can translate to large biological effects (statistical error and elimination) Small changes can translate to large biological effects (statistical error and elimination) Limited by the genes spotted on the chip Limited by the genes spotted on the chip Cross-hybridization of cDNA with highly homologous sequences (isoforms, families) Cross-hybridization of cDNA with highly homologous sequences (isoforms, families) Statisitical analysis - false positives/negatives, validation by other methods Statisitical analysis - false positives/negatives, validation by other methods

21. Applications for a Microarray Drug screens: Drug screens: Search for desired patterns Search for desired patterns of gene activation Identify potential toxic or Identify potential toxic or beneficial effects of chemicals Tumor classification / prognosis Tumor classification / prognosis Assess effects of mutations Assess effects of mutations Identify the functions of unclassified genes Identify the functions of unclassified genes Drug screens: Drug screens: Search for desired patterns Search for desired patterns of gene activation Identify potential toxic or Identify potential toxic or beneficial effects of chemicals Tumor classification / prognosis Tumor classification / prognosis Assess effects of mutations Assess effects of mutations Identify the functions of unclassified genes Identify the functions of unclassified genes