Analyzing transcription modules in the pathogenic yeast Candida albicans

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

4. Background (2) – Tools • “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)

5. Better understanding of CA via SC data • 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? • Large scale expression analysis of CA (Dr. Barkai’s group and Prof. Judith Berman) • Use the homology between SC and CA • focus on selected annotated SC transcription modules • use the information from SC TMs to study CA

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

7. Main goal of the project (2) Measures (cont.): • 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

8. Tools and methods • 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

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

10. Identifying co-regulation Candida Refined Module Yeast Module Candida Homologue 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 Average Correlation+Z-score Average Correlation+Z-score > <

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

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

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

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

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

16. Results – co-regulation of modules

17. Modules are anti-regulated Modules are co-regulated Results – co-regulation between SC modules

18. Modules are anti-regulated Modules are co-regulated Results – co-regulation between CA modules

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

20. Results – cis-regulatory elements chart # of Genes CRE % Mean CRE

21. Conclusions • 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

22. Future research tasks • 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

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