1. Identification of network motifs in lung disease Cecily Swinburne Mentor: Carol J. Bult Ph.D. Summer 2007

2. Goals  Use genome wide gene expression data to study lung cancer  Use normal lung development as a backdrop for comparing gene expression in diseased lung tissue  Identify signature pathways that are characteristic this disease

3.  Signature pathways could provide insights into the pathology of these diseases  Provide possible targets for diagnostics or treatments Significance

4. Background: Lung Cancer  Leading cause of cancer deaths in the US  Approximately 160,000 deaths annually in the US  Contributing factors: Smoking (90% of cases), 2 nd hand smoke, asbestos and other inhaled carcinogens  Two Types  Small Cell: rapidly spreading, almost only in smokers  Non-Small Cell: 75% of cases, more slowly progressing and easier to treat than small cell

5. Transcriptional Profiling with Microarrays  Provides genome wide gene expression data by measuring the presence of messenger RNA in a sample  Many different platforms, the two most common are spotted arrays and Affymetrix arrays

6. Lung Development  Analyze two mouse lung development time series  Bonner et al (2003) 14.5e, 17.5e, birth, 1w, 2w, and 4w (Jax A/J mice)  Jackson Laboratory 11.5e, 13.5e, 14.5e, 16.5e and 5days (C57B1/6J mice)  Shortened time series Bonner: 14.5e, 17.5e, 1w Jax: 14.5e, 16.5e, 5days

7. Analysis of time series using Short Time Series Expression Miner (STEM) (http://www.cs.cmu.edu/~jernst/stem/) Bonner ProfilesJackson Lab Profiles 1513121101381215273 Number of Genes 8775584943312349196141431320139

8. Find Biological Annotations with VLAD Cell Adhesion Anatomical Structure System Development Vasculature Development Blood Vessel Development Angiogenesis Profiles 12, 13 and 15, up over development

9. Find Biological Annotations with VLAD Cell Cycle Cell Division Mitotic Cell Cycle DNA Repair RNA Processing Mitosis Regulation of transcription Jax Profile 2, down over development

10. Analysis of Human Lung Cancer Data Set Downloaded microarray data from Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/)  Dehan and Kaminski (GSE1987) 16 Squamous Cell Carcinoma 7 Adenocarcinoma 9 Normal lung tissue samples

11. Generate Top Hits List  Log 2 normalize data  Calculate the analysis of variance  Identify a top-hits list of genes that are significantly up or down regulated  R Statistical Software (www.r-project.org)

12. Construct Pathways  Use Ingenuity Pathways Analysis (IPA) (www.ingenuity.com) to construct pathways based on the top hits gene list

13. Overlay Cancer genes onto lung development Profile up regulated in Development Profile down regulated in Development

14. Overlap Cancer Expression Values Up regulated in development ~ Down Regulated in Cancer Down regulated in development ~ Up Regulated in Cancer Up regulated in Cancer Down Regulated in Cancer

15. VLAD analysis of Cancer Genes Up Regulated in Cancer M Phase Cell cycle process Mitosis Cell division Cell cycle checkpoint DNA replication Down Regulated in Cancer Anatomical structure development Organ Development Biological Adhesion Cell Adhesion Cell Communication Angiogenesis Blood Vessel Morphogenesis Parallels lung development results Up in Development Angiogenesis and Cell Adhesion Down in development cell cycle

16. In IPA, overlapped genes which were: Down Regulated in both cancer samples Involved in Angiogenesis or Cell Adhesion Up regulated in development 23

17. Gene Biological Process (Ad=Adhesion; n=Angiogenesis) Lung Association? Cancer association? Previously used as biomarker for cancer? Acvrl1AnYes AgerAdYes AngAnYes Cd34AdYes Cdh5An, AdYes Cldn5AdYes Col4A3An, AdYes CtsgAdYes DptAd Edg1An, AdYes EngAn, AdYes Fbln1AdYes Fbln5AdYes Foxf1aAnYes Gja4AnYes Icam2AdYes KdrAnYes Mfap4Ad PtprbAdYes TekAn, AdYes Tgfbr2AnYes Tie1AnYes VwfAdYes

18.  The expression of these 23 genes in lung development and in cancer suggest that they are important to the pathology of lung cancer.  They could serve as potential biomarkers for diagnosis or prognosis of lung cancer. Conclusion

19. Further Work  Find genes which are involved in down regulated in development, up regulated in cancer and involved in cell cycle  Expand approach to other diseases such as pulmonary fibrosis

20. References Bonner A.E., Lemon W.J., & You M. (2003): Gene expression signatures identify novel regulatory pathways during murine lung development: implications for lung tumorigenesis. Journal of Medical Genetics 40, 408-417. Ernst J. & Bar-Joseph Z. (2006): STEM: a tool for the analysis of short time series gene expression data. BMC Bioinformatics 7. Ernst J., Nau G.J., & Bar-Joseph Z. (2005): Clustering Short Time Series Gene Expression Data. Bioinformatics (Proceedings of ISMB 2005), 21, 159-168.

21. Acknowledgements  Carol J. Bult Ph.D.  Benjamin L. King, M.S.  Jon Geiger  Randy O’Rouke  The Jackson Laboratory Summer Student Program  Jane D. Weinberger Endowed Scholarship Fund  The Horace W. Goldsmith Foundation

23. Network Comparison Network Motifs Normal Lung Microarray Data Gene List Networks Analysis Lung Cancer Microarray Data Gene List Networks Analysis

24. Questions?

25. RNA extraction and amplification  Extract total RNA from sample  Reverse transcribe mRNA into cDNA which is more stable  Polymerize cDNA

26. Affymetrix Arrays  Transcribe cDNA back into RNA  Tag cRNA with biotin and fragment it  Apply cRNA to Affymetrix GeneChip

27. Affymetrix Arrays  Hybridization  Tagged cRNA hybridizes with complimentary DNA on GeneChip  Affymetrix GeneChip  500,000 of features, each containing millions of 25 bp DNA strands

28. Affymetrix Arrays  Higher intensity fluorescence at a location signifies higher expression of the respective gene in the tissue  Wash off unbound cRNA  Stain GeneChip with streptavidin phycoerythrin (SA_PE)  Scan with confocal laser

29. Spotted Mircoarrays  Compares expression in two samples  Tag cDNA from each sample with a fluorescent marker  usually Cy3 (green) and Cy5 (red)  Mix in equal quantities and apply to plate to hybridize

30. Spotted Mircoarrays  Samples compete to bind at each location  The sample with the higher expression of a given gene will have more cDNA bind at that location  Scan array