Case story: Analysis of the Cell Cycle

Transcription

1 DNA microarray analysis, January 2 nd 2006 Case story: Analysis of the Cell Cycle Center for Biological Sequence Analysis

2 Outline Introduction Cell division and cell cycle regulation Experimental studies of cell cycle regulation DNA microarrays & cell culture synchronization Finding periodically expressed genes My Research Projects Project 1: Comparing computational methods Project 2: Finding weakly expressed genes Project 3: Regulation of protein complexes

3 Outline Introduction Cell division and cell cycle regulation Experimental studies of cell cycle regulation DNA microarrays & cell culture synchronization Finding periodically expressed genes My Research Projects Project 1: Comparing computational methods Project 2: Finding weakly expressed genes Project 3: Regulation of protein complexes

4 Cell division How does one cell become many?

5 The importance of research in cell division Cell division is one of the most fundametal processes of all living things and therefore of enormous importance for our understanding of life itself. Cell division research is important for: Cancer, cell factories, biomass increase, cell differentiation, stem cells, signalling, apoptosis, developmental disorders, fertility, etc. The 2001 Nobel Prize in Physiology or Medicine Leland Hartwell Tim Hunt Sir Paul Nurse for their discoveries of key regulators of the cell cycle

6 The cell cycle

7 A complex biological system

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 posttranslational 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 Periodically expressed genes Cell cycle regulated genes Simon et al., Cell, 2001 S phase S phase

11 Outline Introduction Cell division and cell cycle regulation Experimental studies of cell cycle regulation DNA microarrays & cell culture synchronization Finding periodically expressed genes My Research Projects Project 1: Comparing computational methods Project 2: Finding weakly expressed genes Project 3: Regulation of protein complexes

12 DNA microarray technology

13 But 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 Microarray analysis of cell cycle regulation Synchronized cell culture Microarrays Expression Profile 1. Take samples from a synchronized cell culture 2. Hybridize each sample to a microarray chip 3. Normalize to make the chips comparable 4. String the data-points together to a profile 5. Look for genes whose expression is periodic

16 Outline Introduction Cell division and cell cycle regulation Experimental studies of cell cycle regulation DNA microarrays & cell culture synchronization Finding periodically expressed genes My Research Projects Project 1: Comparing computational methods Project 2: Finding weakly expressed genes Project 3: Regulation of protein complexes

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 Quiz Synchronized cell culture Microarrays Expression Profile? You have 6000 profiles with about 20 time-points in each How do you find out which of them are regulated during the cell cycle and in which phase they are expressed?

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

20 Fourier score distribution

21 The thresholding problem Yeast Human

22 WARNING! The next slide may be the most important slide of your career so pay attention!

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

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 Cell division and cell cycle regulation Experimental studies of cell cycle regulation DNA microarrays & cell culture synchronization Finding periodically expressed genes My Research Projects Project 1: Comparing computational methods Project 2: Finding weakly expressed genes Project 3: Regulation of protein complexes

26 Microarray analysis of cell cycle regulation Synchronized cell culture Microarrays Expression Profile Periodicity Analysis Budding yeast Cho et al., 1998 Spellman et al., 1998 de Lichtenberg et al., 2005 Fission yeast Rustici et al., 2004 Peng et al., 2004 Oliva et al., 2005 Human cells Cho et al., 2001 Whitfield et al., 2002 Plant cells Menges et al., 2003 Computational methods Spellman et al., 1998 Zhao et al., 2001 Johansson et al, 2003 Wichert et al., 2004 Luan & Li, 2004 Lu et al., 2004 Liu et al., 2004 de Lichtenberg et al., 2005 Ahdesmaki et al., 2005

27 Benchmark of computational methods Expression Profile Periodicity Analysis Motivation? The overlap between different computational methods is remarkably poor when they are applied to the same data. Although, most analyses have concluded the periodically expressed subset of the yeast genome to comprise about 300 to 800 genes, nearly 1800 different genes have been proposed in total

28 Benchmarking of computational methods Computational Methods Benchmark sets 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 B1 B2 B3 113 known periodic genes from small scale experiments 352 genes bound by at least one of nine known cell cycle transcription factors, excluding those in set B1 518 genes annotated as cell cycle and DNA processing in MIPS, excluding meiosis and those in set B1 Luan & Li, 2004 cubic spline fitting Lu et al., 2004 Bayesian, mixture model de Lichtenberg et al., 2004/2005 permutation based statistics 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)

29 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.

30 Words of wisdom Before you criticize someone, you should walk a mile in their shoes. That way when you criticize them, you ll be a mile away and you ll have their shoes. -- Anonymous

31 Why are some methods better? 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.

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

33 Better methods give better overlap Why not perfect 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

34 Outline Introduction Cell division and cell cycle regulation Experimental studies of cell cycle regulation DNA microarrays & cell culture synchronization Finding periodically expressed genes My Research Projects Project 1: Comparing computational methods Project 2: Finding weakly expressed genes Project 3: Regulation of protein complexes

35 Predicting new cell cycle regulated genes Periodic genes {? Train area Model Grey zone Non-periodic genes periodic proteins non-periodic proteins

36 Prediction of cell cycle regulated proteins ANN score YIL169C C Protein of unknown function YNL322C KRE cell wall beta-glucan assembly YJL078C PRY S C Pathogen Related in Sc, contains homology to the plant PR-1 class of pathogen related proteins. YDL038C Protein of unknown function YOR009W TIR Protein of unknown function YJR151C DAN Delayed Anaerobic Gene YOL155C C Protein of unknown function YLR286C CTS K S C Endochitinase YOL030W GAS Z S Protein of unknown function YOR220W Protein of unknown function YNR044W AGA K S anchorage subunit of a-agglutinin YGR023W MTL Mid-Two Like 1 YMR317W Protein of unknown function YCR089W FIG Factor-Induced Gene 2: expression is induced by the mating pheromones, a and alpha factor YLR194C S Protein of unknown function YIL011W TIR S TIP1-related YGR161C Protein of unknown function YBR067C TIP K Z S C cold- and heat-shock induced protein of the Srp1p/Tip1p family of serine-alanine-rich proteins YNL327W EGT K S cell-cycle regulation protein, may be involved in the correct timing of cell separation after cytokinesis YLR332W MID Protein required for mating

37 Prediction of cell cycle regulated proteins ANN score YIL169C C Protein of unknown function YNL322C KRE cell wall beta-glucan assembly YJL078C PRY S C Pathogen Related in Sc, contains homology to the plant PR-1 class of pathogen related proteins. YDL038C Protein of unknown function YOR009W TIR Protein of unknown function YJR151C DAN Delayed Anaerobic Gene YOL155C C Protein of unknown function YLR286C CTS K S C Endochitinase YOL030W GAS Z S Protein of unknown function YOR220W Protein of unknown function YNR044W AGA K S anchorage subunit of a-agglutinin YGR023W MTL Mid-Two Like 1 YMR317W Protein of unknown function YCR089W FIG Factor-Induced Gene 2: expression is induced by the mating pheromones, a and alpha factor YLR194C S Protein of unknown function YIL011W TIR S TIP1-related YGR161C Protein of unknown function YBR067C TIP K Z S C cold- and heat-shock induced protein of the Srp1p/Tip1p family of serine-alanine-rich proteins YNL327W EGT K S cell-cycle regulation protein, may be involved in the correct timing of cell separation after cytokinesis YLR332W MID Protein required for mating

38 No bias towards strongly expressed genes Estimated gene expression level

39 Experimental validation of predictions

40 Experimental setup Synchronization with temperature sensitive mutant strain (Cdc15-2) Fermentation in chemostat Sampling every 20 min Custom made probes (OligoWiz) Custom made microarrays (febit ag) New computational method

41 The results

42 The results

43 Examples of new discoveries

44 New genes are weakly expressed Predicted genes de Lichtenberg et al., JMB, 2003 Confirmed genes de Lichtenberg et al., Yeast, 2005

45 Outline Introduction Cell division and cell cycle regulation Experimental studies of cell cycle regulation DNA microarrays & cell culture synchronization Finding periodically expressed genes My Research Projects Project 1: Comparing computational methods Project 2: Finding weakly expressed genes Project 3: Regulation of protein complexes

46 Modeling through data integration

47 Networks from topology to dynamics Dynamics Interactions Dynamic Models Jeong, Nature, 2001 Ideker & Lauffenburger, TRENDS in Biotechnology, 2003

48 Extracting a cell cycle interaction network Node selection Data sources Interactions list of periodically expressed proteins with peak timing cell cycle gene expression data include non-periodic proteins that are strongly connected to periodic proteins. protein-protein interaction data score and filter by network topology and subcellular localization extract cell cycle network

49 Visualizing the network Color represents transcriptional timing M G 1 Interacting proteins are usually expressed close in time G 2 S

50 Coverage of interaction space Most stable complexes were captured, at least partially, in the network. Still, no interactions could be found for 2/3 of the dynamic proteins. Why? Some may be missed subunits of complexes already in the network Some may not participate in protein-protein interactions But, the majority probably participate in transient interactions that are not so well captured by current interaction assays

51 Networks as discovery tools Obervation: The network places 30+ uncharacterized proteins in a temporal interaction context. The network thus generates detailed hypothesis about their function. Obervation: The network contains entire novel modules and complexes.

52 Static scaffold proteins play a major role Observation: Static (scaffold) proteins comprise about a third of the network and participate in interactions throughout the entire cycle

53 Just-in-time complex assembly Expression based time mapping corresponds closely to the timing of complex assembly and activity. Hypothesis: We propose a just-in-time assembly mechanism where the expression of the dynamic subunits of each complex control the timing of final assembly, and thereby the complex function.

54 Phosphorylation of dynamic proteins The yeast cyclin-dependent kinase, Cdc28/Cdk1, regulates of a large number of cell cycle proteins by phosphorylation. By detailed analysis of the network we discover that this regulation specifically affects the dynamic proteins, but not the static proteins. An experimentally determined* set of 332 Cdc28 targets constitute: 6% of all yeast proteins 8% of the static proteins 27% of the dynamic proteins This over-representation is significant at P < 10-4 * Ubersax et al., Nature,2003

55 Degradation signals in dynamic proteins Transcriptional PEST degradation signals are found in the sequence Targeted regulation of many proteins known degradation to be selectively targeted Dynamic for degradation. We proteins discover that PEST signals are significantly more frequent among the dynamic Post-translational proteins and among the regulation Cdc28-targets.

56 Are the special properties of dynamic proteins conserved? Transcriptional regulation Dynamic proteins Targeted degradation Post-translational regulation What s the chance of finding the overrepresentation of PEST regions observed among the dynamic proteins by random? Budding yeast (S. cerevisiae): P < 10-2 Fission yeast (S. pombe): P < 10-2 Human (HeLa cells): P < 10-7

57 Acknowledgments Center for Biological Sequence Analysis, Denmark Søren Brunak Thomas Skøt Jensen Anders Fausbøll Rasmus Wernersson Henrik Bjørn Nielsen The rest of CBS! EMBL Biocomputing, Heidelberg, Germany Lars Juhl Jensen Peer Bork Sean Hooper

58 Further reading Comparison of Computational Methods for the Identification of Cell Cycle Regulated Genes, Lars Juhl Jensen, Anders Fausbøll, Thomas Skøt Jensen, Peer Bork and Søren Brunak Bioinformatics, 21(7), , 2005 New weakly expressed cell cycle regulated genes in yeast, Rasmus Wernersson, Thomas Skøt Jensen, Henrik Bjørn Nielsen, Peer Schmidt, Flemming Bryde Hansen, Steen Knudsen and Søren Brunak Yeast, 22(15), , 2005 Dynamic Complex Formation During the Yeast Cell Cycle, Lars Juhl Jensen, Søren Brunak and Peer Bork Science, 307 (5710), , 2005 All data and supplementary information available at: