1. Analyzing transcription modules in the pathogenic yeast Candida albicans Elik Chapnik Yoav Amiram Supervisor: Dr. Naama Barkai

2. Background (1) – C. albicans Opportunistic fungal pathogen Genome was recently sequenced Lack of sufficient annotation of genes Distant cousins: S. cerevisiae –SC is the yeast model organism –SC is used as a model to study CA –comparative genomics: what are the tools?

3. Genes Conditions BLAST DNA Microarrays –monitors 1000’s of genes simultaneously –co-expression patterns can provide functional links Cluster Analysis, SVD –limited size of data sets –mutually exclusive clusters –expression analyzed under all conditions Background (2) – Tools

4. “Transcription Modules” (TMs): –a self-consistent regulatory unit –co-regulated genes and their regulating conditions Signature Algorithm –global decomposition into TMs –robust, fast –integration of external data –if no a-priory information exists, can be applied iteratively (ISA) Background (2) – Tools

5. Expression levels of SC have been measured for over 1000 conditions Emerging quantities of CA microarray experiments Genomes are both fully sequenced What can be done with all this? 1.Large scale expression analysis of CA (Dr. Barkai’s group and Prof. Judith Berman) 2.Use the homology between SC and CA −focus on selected annotated SC transcription modules −use the information from SC TMs to study CA Better understanding of CA via SC data

6. Measures: 1.computing pair-wise correlations between genes in TMs (Pearson correlation coefficient) Annotating C. albicans ORFs with unknown functions Main goal of the project (1)

7. Measures (cont.): 2.Search for cis-regulatory elements (CREs) in the upstream region of genes –find over represented sequence in the upstream region of genes in the SC modules, using computational DNA pattern recognition methods –search for previously identified cis-regulatory elements in the CA homologue modules Main goal of the project (2)

8. Programming software: MATLAB 6.5 Cluster analysis tools: GeneHopping Sequence data: Stanford Genome Technology center Expression data: C. albicans expression data was provided by Prof. Berman’s lab Software for CRE prediction: MEME, TESS, EPD, CONSENSUS Tools and methods

9. Generating modules BLAST signature algorithm Yeast Module Candida Refined Module Candida Homologue Module And the modules are: 0 1

10. Identifying co-regulation Yeast Module Candida Homologue Module Candida Refined Module Find all pair-wise correlation in the module genes using the Pearson correlation coefficient Apply statistical significance tests: generate random modules to compute Z-scores Average Correlation+ Z-score ><

11. 1.Generate random modules by reshuffling genes in whole genome database 2.Compute average correlations for the random and “real” modules 3.Calculate mean and standard deviation from random modules set 4.Calculate Z-scores of “real” modules 5.High Z-score (>2) represents a statistically significant correlated module Statistical analysis

12. BLAST signature algorithm Yeast Module Candida Refined Module Candida Homologue Module Two slides ago…

13. Yeast Module Candida Homologue Module Rejected Overlapped Candida Refined Module Included Find common CRE in Yeast Module Rejected Identification of cis-regulatory elements

14. Candida Homologue Module Rejected Overlapped Yeast Module Overlapped Candida Refined Module Included Rejected our prediction for CRE % and Mean CRE in each module CRE CRE ? Identification of cis-regulatory elements

15. Average Correlation 0.34816 Z-Score = 106.9 Results – co-regulation of SC aa Module

16. Module type Module name S. cerevisiae C. albicans homologue module C. albicans refined module Amino acid Biosynthesis 0.34816 ± 0.0029 [106.9] 0.043325± 0.0038 [7.5693] 0.26942± 0.0082 [31.038] Cell Cycle G1 0.2921± 0.0028 [90.0693] 0.0475± 0.0047 [7.0945] 0.18± 0.0079 [20.926] rRNA Processing 0.674± 0.0045 [142.113] 0.3216± 0.0051 [60.2796] 0.3097± 0.0023 [127.507] Proteosome Subunits 0.4211± 0.0054 [71.2679] 0.1611± 0.0078 [18.8772] 0.2342± 0.0045 [48.9743] 0.9-1.0 0.8-0.9 0.7-0.8 0.6-0.7 0.5-0.6 0.4-0.5 0.3-0.4 0.2-0.3 0.1-0.2 0.0-0.1 Mean Correlation± Standard Deviation [Z-Score] Results – co-regulation of modules

17. Amino acid Biosynthesis (13.7) Cell Cycle G1 (12.9) rRNA Processing (12.6) Proteosome subunits (11.31) Amino acid Biosynthesis (13.7) --- -0.0216± 0.0017 [-35.0476] 0.0042± 0.0025 [-13.9315] 0.0337± 0.0031 [-1.6166] Cell Cycle G1 (12.9) -0.0216± 0.0017 [-35.0476] --- 0.0779± 0.0024 [16.1595] 0.0203± 0.0025 [-7.2475] rRNA Processing (12.6) 0.0042± 0.0025 [-13.9315] 0.0779± 0.0024 [16.1595] --- -0.1241± 0.0033 [-48.9049] Proteosome subunits (11.31) 0.0337± 0.0031 [-1.6166] 0.0203± 0.0025 [-7.2475] -0.1241± 0.0033 [-48.9049] --- Modules are anti-regulated Modules are co-regulated Results – co-regulation between SC modules

18. Amino acid Biosynthesis (13.7) Cell Cycle G1 (12.9) rRNA Processing (12.6) Proteosome subunits (11.31) Amino acid Biosynthesis (13.7) --- -0.0078± 0.0051 [-4.5555] 0.0622± 0.0032 [14.8978] -2.02E-04± 0.0041 [-3.5271] Cell Cycle G1 (12.9) -0.0078± 0.0051 [-4.5555] --- 0.0117± 0.0034 [-0.9320 0.0341± 0.0041 [4.7324] rRNA Processing (12.6) 0.0622± 0.0032 [14.8978] 0.0117± 0.0034 [-0.9320] --- -0.0028± 0.0026 [-6.6787] Proteosome subunits (11.31) -2.02E-04± 0.0041 [-3.5271] 0.0341± 0.0041 [4.7324] -0.0028± 0.0026 [-6.6787] --- Modules are anti-regulated Modules are co-regulated Results – co-regulation between CA modules

19. Candida Homologue Module Rejected Overlapped Yeast Module Overlapped Candida Refined Module Included Rejected TGACTC CRE CRE ? Results - cis-regulatory elements in the aa modules 46%, 1.25 34%, 1.06 54%, 1.29 53%, 1.22 29%, 1.00 52%, 1.18 CRE %, Mean CRE

20. Results – cis-regulatory elements chart Module type Module name S. cerevisiae C. albicans homologue module Rejected genes Included genes Overlapped genes C. albicans refined module Amino acid Biosynthesis 156 46% 1.25 98 34% 1.06 77 29% 1 13 54% 1.285 21 52% 1.181 34 53% 1.222 rRNA Processing 12.6 61 67% 1.585 55 42% 1.304 9 44% 1.25 219 32% 1.225 46 41% 1.315 265 34% 1.24 Protesosome subunits 10.14 41 37% 1 37 19% 1.428 11 18% 1 38 16% 1.166 26 19% 1.6 64 17% 1.363 Protesosome subunits 11.31 45 62% 1.071 39 23% 1 13 23% 1 38 13% 1 26 23% 1 64 17% 1 Cell Cycle G1 12.9 124 59% 1.41 71 46% 1 52 42% 1 14 29% 1 19 58% 1 33 45% 1 Cell Cycle G1 16.4 158 52% 1.378 88 45% 1.025 67 40% 1.037 13 23% 1 21 62% 1 34 47% 1 # of Genes CRE % Mean CRE

21. Co-regulation: –Different co-regulation schemes can point out alternative gene function between SC and CA –Investigate the relations between “real” CA modules and refined CA modules with a similar annotation cis-regulatory elements: –CRE as a function of homology –CRE as a function of co-regulation –Low expression of SC CRE as an indicator for biological importance –Not all CREs are conserved between the organisms: GCN4 vs. GAL4 Conclusions

22. Experimental validation of functional assignment : –verify if the cis-regulatory elements found in C. albicans are biologically active –test the conservation of function across homologue modules of S. cerevisiae and C. albicans Future research tasks

23. Naama Barkai – Weizmann Institute Judith Berman – University of Minnesota Sven Bergmann – Barkai’s group Jan Ihmels – Barkai’s group Acknowledgements