1. CBS Ph.D. course, 14. april 2005 Using Microarrays to Study Cell Cycle Regulation Ulrik de Lichtenberg Center for Biological Sequence Analysis The Technical University of Denmark

2. Outline Introduction to cell division and the cell cycle Cell culture synchronization and arrest methods Microarrays and periodically expressed genes Our own research

3. The Origin of Life How does one cell become many?

4. The cell cycle

5. The cell cycle – a hot scientific topic Papers about microarray analysis of the cell cycle have been extensively cited Spellman et al., 1998 (yeast) 697 citations Cho et al., 1998 (yeast) 385 citations Cho et al., 2002 (human) 61 citations Leland Hartwell Tim Hunt Sir Paul Nurse The 2001 Nobel Prize in Physiology or Medicine "for their discoveries of key regulators of the cell cycle“ Annual number of new articles in MEDLINE about “cell cycle” Cell cycle related research topics: Cancer, apoptosis, development, metabolic engineering, biomass increase, fertility, protein degradation, phosphorylation, signalling, etc.

6. A complex biological system

7. Evolutionary Conservation & Check-points The core machinery of the cell cycle is largely conserved throughout eukaryotes (from yeast to humans) Especially the multi-cellular organisms have extensive and sophisticated control mechanisms (check-points) that check if one step (e.g. DNA replication) is completed and error-free, before proceeding to the next steps (e.g. segregation of the chromosomes)

8. Different levels of control in the cell The activity of a protein depends on –regulation of its transcription (by activator/inhibitors) –its mRNA stability –the efficiency of translation (from mRNA to protein) –stability of the mature protein –the subcellular localization of the protein –regulation of the protein function by binding of activator and inhibitors –regulation of the protein function by post-translational modification (e.g. phosphorylation) –targeted degradation of the protein (e.g. by ubiquitination)

9. The “just-in-time” principle Some products you use all the time, while others are only needed once in a while People tend to buy or order things right before they need them…

10. Just-in-time synthesis in the cell cycle

11. Periodically expressed genes G1 SG2M G1 SG2M G1 SG2M G1 SG2M

12. Outline Introduction to cell division and the cell cycle Cell culture synchronization and arrest methods Microarrays and periodically expressed genes Our own research

13. In a normal culture, cells grow out of synchrony

14. Arrest-and-release synchronization Temperature sensitive mutants – unable to pass a certain point in the cell cycle at high temperatures Alpha factor – all cells arrest in the same stage until a factor is supplied to the culture

15. Outline Introduction to cell division and the cell cycle Cell culture synchronization and arrest methods Microarrays and periodically expressed genes Our own research

16. Using microarrays with synchronized cells Synchrone yeast culture Microarrays Gene expressionExpression Profile Take samples from a synchronized cell culture Hybridize each sample to a microarray chip Normalize to make the chips comparable String the data-points together to a profile Look for genes whose expression is periodic

17. Time-series versus comparative studies Most microarray experiments compare measurements from condition A with condition B to find those genes that are differentially expressed –the typical analysis is a statistical test, e.g. t-test Time-series experiments are usually aimed at studying how gene expression changes over time (i.e. identifying genes that show a specific pattern of expression) –you can’t make a t-test, instead you have to look for some pattern!

18. Look for sine/cosine-like waves G1 SG2M G1 SG2M G1 SG2M G1 SG2M

19. Measuring Periodicity Fourier-scoring: extracting the ”signal” at the interdivision frequency.

20. Fourier score distribution

21. The thresholding problem YeastHuman

22. Sensitivity versus Specificity edible poisonous Sensitivity: how many of the edible mushrooms do you accept Specificity: how many of the mushrooms you accept are edible

23. Specificity Sensitivity Sensitivity versus Specificity find little, of which most is right find a lot, of which most is right find little, of which most is wrong find a lot, of which most is wrong Sensitivity: how many of the edible mushrooms do you accept Specificity: how many of the mushrooms you accept are edible edible poisonous x

24. The thresholding problem Every threshold corresponds to a sensitivity and a specificity There is (almost always) a trade-off between the two

25. Outline Introduction to cell division and the cell cycle Cell culture synchronization and arrest methods Microarrays and periodically expressed genes Our own research

26. Benchmark of computational methods Many different computational methods have been developed to find periodically expressed genes, but we were the first to compare them to see which ones work the best!

27. Benchmarking of periodicity analysis methods Computational Methods Cho et al., 1998 –visual inspection Spellman et al., 1998 –fourier scoring and correlation Zhao et al., 2001 –single pulse model Johansson et al., 2003 –partial least squares regression Luan & Li, 2004 –cubic spline fitting Lu et al., 2004 –Bayesian, mixture model de Lichtenberg et al., 2004/2005 –permutation based statistics Benchmark sets B1113 known periodic genes from small scale experiments B2352 genes bound by at least one of nine known cell cycle transcription factors, excluding those in set B1 B3518 genes annotated as “cell cycle and DNA processing” in MIPS, excluding “meiosis” and those in set B1 How do we benchmark? We assume that the benchmark sets B1-B3 are enriched in cell cycle regulated genes. We then benchmarks the ability of each computational method to retrieve these genes from the data (rank them high)

28. Comparison of different methods --- random Cho et al. Spellman et al. Zhao et al. Johansson et al. Luan & Li Lu et al. de Lichtenberg et al.

29. Our periodicity analysis of microarray data “is the gene significantly regulated?” –p(regulation): compare actual standard deviation to randomly sampled background distribution “is the expression pattern periodic?” –p(periodicity): compare the actual fourier signal to distribution made by randomly shuffling the data point within each profile Combined score with penalty function –Combi score: Combination of p-values for regulation and periodicity, with penalty terms that require both components to be significant.

30. Regulation versus periodicity --- random regulation alone periodicity alone combined combined + re-norm combined + consistency

31. Better methods give better overlap Different experimental conditions, such as cell culture synchronization methods Handling of samples, amplification, hybridization, etc. DNA microarray data are noisy and replicates rarely overlap completely. Signal-to-noise ratio low for weakly expressed –and regulated genes Why not perfect overlap?

32. Finding the weakly expressed genes List of putative cell cycle regulated genes

33. Validation of the predictions Used a temperature sensitive mutant yeast strain to synchronize a cell culture in a batch fermentor Took out samples every 20 min Treated and froze the samples Extracted RNA from the samples hybridized to custom made DNA microarrays, where the probes were designed with our own software tool, OligoWiz Performed the raw data treatment and normalization Analyzed the data with our own permutation- based method, which is good at finding weakly expressed –or regulated genes.

34. Experimental setup - sampling –Samples were shaken with ice (2°C I a few sec) –Centrifuged 1-1/2 min. –Supernatant discarded –Frozen in liquid N2 (-196°C) –Stored at -80°C until RNA extraction

35. Monitoring synchrony The culture exhibited synchrony with an interdivision time of ~148 minutes, meaning that the experiment covers two entire cell cycles

36. The results

38. New genes are weakly expressed

39. Examples of new discoveries

40. Mapping gene expression on networks cell cycle microarra y data Physical PPI interactions with confidence scores Expand the set of proteins to include non-periodic proteins that are strongly connected to periodic proteins. Raw DataNode selection List of periodically expressed proteins with peak time Interactions Require compatible compartments and high confidence Extract cell cycle network

41. Dynamics of protein complex formation

42. CBS, BioCentrum-DTU, Denmark Søren Brunak (Professor, Center director) Steen Knudsen (Professor) Rasmus Wernersson (synchronization, sampling, probe/array design) Thomas Skøt Jensen (design & data analysis) Henrik Bjørn Nielsen (array normalization & data analysis) Anders Fausbøll (data analysis) Flemming Bryde Hansen (sampling) EMBL, Heidelberg, Germany Lars Juhl Jensen (Prediction and benchmarking) Peer Bork (group leader) Thanks to all the other people involved! http://www.cbs.dtu.dk/cellcycle

43. New Weakly Expressed Cell Cycle Regulated Genes in Yeast Ulrik de Lichtenberg, Rasmus Wernersson, Thomas Skøt Jensen, Henrik Bjørn Nielsen, Anders Fausbøll, Peer Schmidt, Flemming Bryde Hansen, Steen Knudsen and Søren Brunak Submitted, 2005 Dynamic Complex Formation During the Yeast Cell Cycle Ulrik de Lichtenberg, Lars Juhl Jensen, Søren Brunak and Peer Bork Science, 307 (5710), 724-727, 2005 [PubMed]PubMed Comparison of Computational Methods for the Identification of Cell Cycle Regulated Genes Ulrik de Lichtenberg, Lars Juhl Jensen, Anders Fausbøll, Thomas Skøt Jensen, Peer Bork and Søren Brunak Bioinformatics, 2004 (advance online access) [PubMed]PubMed Protein Feature Based Identification of Cell Cycle Regulated Proteins in Yeast Ulrik de Lichtenberg, Thomas Skøt Jensen, Lars Juhl Jensen and Søren Brunak Journal of Molecular Biology, 239(4), 663-674, 2003 [PubMed]PubMed Further reading… http://www.cbs.dtu.dk/cellcycle