Identification of compounds to affect radiosensitivity of cells

1. Identification of compounds to affect radiosensitivity of cells Pellegrini Lab—UCLA SoCalBSI 2007 Joshua Smith Bazyl Nettles

2. Outline • Biological Significance • Overall Objectives • Basic Methodology • Tools • Background • Experimental Approach

3. Biological Significance • Results from our project could be used in development of drugs to affect cells’ radiosensitivity • Decreased radiosensitivity possibly beneficial to people that have been exposed to radiation • Increased radiosensitivity beneficial to potentially increase effectiveness of radiotherapy (cancer treatment)

4. Project Objectives • From gene expression information from cells exposed to 167 bioactive compounds: • Identify transcription factors that are activated in response to drugs • Identify which compounds activate the same factors as those activated by exposure to radiation

5. Basic Methodology • Changes in gene expression are regulated by the binding of transcription factors to promoters • The activity of a transcription factor often depends on co-factors and post translational modification and cannot therefore be reliably estimated from mRNA levels of the factor • Transcriptional regulation is inherently combinatorial

6. Basic Methodology • Thus, we use multivariate regression to estimate transcription factor activities

7. Major Tools • Matlab 2007 • Bioinformatics Toolkit • MS Excel • Perl www.mathworks.com www.microsoft.com www.perl.org

8. Background • Data taken from “The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease” by Lamb, et al • “…we have created the first installment of a reference collection of gene-expression profiles from cultured human cells treated with bioactive small molecules…” • “Connectivity Map” can be used to find connections among small molecules, expression, genes, etc.

9. Experimental Approach • Five basic steps: • Ordering and Gathering Data • Probe, Gene and Promoter Identification • Transcription Factor Data • Generate Models • Compare Model

10. ^SAMPLE = GSM119282 !Sample_title = 5202764005789148112904.A10 !Sample_geo_accession = GSM119282 !Sample_status = Public on Sep 27 2006 … ID_REF VALUE ABS_CALL 1007_s_at 495.3 P 1053_at 278.2 P 117_at 3713.4 P 121_at 44.7 P 1255_g_at 2.6 A 1294_at 16 A 1316_at 5.2 A 1320_at 4.4 A 1405_i_at 16 A 1431_at 21.2 A 1438_at 7.6 A … GSM118720 453 Drug/Contro Ratios GSM118721 564 Samples (microarrays) … 22280 probes GSM119282 22280 probes GSM119282 http://www.ncbi.nlm.nih.gov/geo/ Ordering and Gathering Data • “Connectivity Map” data retrieved from NCBI’s Gene Expression Omnibus (167 compounds) • Using Matlab’s Bioinformatics Toolkit, imported 564 expression profiles • Using Matlab, MS Excel, and Perl divided data into 453 “experiments” • Using SQL, detected and averaged duplicate experiments, leaving us with 314 experiments The Connectivity Map The Connectivity Map (GSE5258)

11. Probe, Gene and Promoter Identification • Retrieved human promoters from UCSC Genome Browser • Retrieved microarray and probe information from GEO for our data • Found variance for each probe across 314 unique experiments • Using top 2000 by variance, revealed 1704 probe/gene/promoter sets

12. 314 Unique Experiments Variance 22280 Probes Keep Top 2000 Withpromoterdata Probe Variance ------------------------ 3 5423.535 12799 3647.582 17745 550.7743 5991 253.0915 3192 250.4694 1704 genes& promoters Probe, Gene and Promoter Identification Normalized expression ratio

13. Promoter ATGCCCTTGCTATCTGCATGCTATCTGCACTGGACGT… Transcription Factor Data • TRANSFAC® and JASPAR® are the databases of transcription factors, their genomic binding sites and DNA-binding profiles. • For each TF PWM, we move along the promoter sequence calculating the probability of binding • The maximum binding probability calculated along a sliding window is kept for each promoter

14. ~940 TFs from TRANSFAC & JASPAR 1704 gene promoters Transcription Factor Data • Then the maximum score for each promoter is compiled into a matrix probability a TF will bind to a particular promoters

15. Model Generation • Generate models using Multivariate Adaptive Regression Splines (MARS) to correlate • occurrences of TF binding motifs in the promoter DNA • their interactions to the gene expression levels • “Model” refers to set of Transcription factors that can explain a high percentage of the current variance in expression activity

16. Model Generation • An overabundance of data led to a predicted modeling time of 20 hrs for each of our 314 experiments • This led to a decision to reduce the number of TFs used for computation from all 940 to ~40 “relevant” TFs • This could be used as to identify likely experiments that could be run with all TFs

17. Relevant Factors • Ataxia telangiectasia mutated • Protein kinase that plays a critical role in response to certain types of DNA damage • Produced in all cells, it is activated once DNA damage has occurred. (Hawley and Friend, 1996; Banin et al., 1998; Canman et al., 1998) • Used list of ATM dependent factors that are activated in response to radiation damage (prior work) • Compare with models • Attempt to find experiments (compounds) activate the same factors as those activated by exposure to radiation

18. Results • We generated models for these relevant factors and found several experiments with a high reduction in variance (RIV) • RIV • The percentage of variance in expression accounted for by the factors in a model

19. Results RIV for our 314 experiments

20. Results • The top 10 drugs by RIV were: HC toxin Pirinixic acid Ionomycin Phenanthridinone Tioguanine Fasudil Prochlorperazine Amitriptyline 1 7. Valproic acid 10.Colchicine 2 1. Valproic acid enhances brain tumor cell radiosensitivity. Immunotherapy Weekly (2005-06-01) 2. Modification of Radiation Response of Tissue by Colchine. International Congress of Radiology (1965-09-27)

21. References • Debopriya Das, Nilanjana Banerjee, and Michael Q. Zhang. Interacting models of cooperative gene regulation. PNAS, 2004. • Justin Lamb, et al. The Connectivity Maps: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Diseases. Science, 2006. • Debopriya Das, Zaher Nahle, and Michael Q. Zhang. Adaptively inferring human transcriptional subnetworks. Molecular Systems Biology, 2006. • Shawn Cokus, et al. Modelling the network of cell cycle transcription factors in the yeast Saccharomyces cervisiae. BMC Bioinformatics, 2006.

22. Acknowledgements • UCLA and the Pellegrini Lab • Dr. Matteo Pellegrini • Dr. David Casero Díaz-Cano • SoCalBSI Instructors and Fellow Students • National Institutes of Health • National Science Foundation • LA / Orange County Biotechnology Center www.ucla.edu