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Identification of network motifs in lung disease

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

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Identification of network motifs in lung disease

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

  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), 2nd 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. <mgm.duke.edu> <www.imbb.forth.gr> 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/)

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

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

  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 • Log2 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. Up regulated in Cancer Down Regulated in Cancer Overlap Cancer Expression Values Up regulated in development ~ Down Regulated in Cancer Down regulated in development ~ Up 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 Down in development cell cycle Up in Development Angiogenesis and Cell Adhesion

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

  17. Conclusion • 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.

  18. 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

  19. 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 Genetics40, 408-417. Ernst J. & Bar-Joseph Z. (2006): STEM: a tool for the analysis of short time series gene expression data. BMC Bioinformatics7. Ernst J., Nau G.J., & Bar-Joseph Z. (2005): Clustering Short Time Series Gene Expression Data. Bioinformatics (Proceedings of ISMB 2005), 21, 159-168.

  20. 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 

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

  22. Questions?

  23. RNA extraction and amplification • Extract total RNA from sample • Reverse transcribe mRNA into cDNA which is more stable • Polymerize cDNA <http://www.ma.uni-heidelberg.de/inst/zmf/affymetrix/ bilder/ablauf_m.gif>

  24. Affymetrix Arrays • Transcribe cDNA back into RNA • Tag cRNA with biotin and fragment it • Apply cRNA to Affymetrix GeneChip <http://www-microarrays.u-strasbg.fr/images/affy/affyExpresPrinciple.jpg>

  25. Affymetrix Arrays • Affymetrix GeneChip • 500,000 of features, each containing millions of 25 bp DNA strands • Hybridization • Tagged cRNA hybridizes with complimentary DNA on GeneChip <www.affymetrix.com/> <www.affymetrix.com/>

  26. <www.affymetrix.com/> Affymetrix Arrays • Wash off unbound cRNA • Stain GeneChip with streptavidin phycoerythrin (SA_PE) • Scan with confocal laser • Higher intensity fluorescence at a location signifies higher expression of the respective gene in the tissue

  27. <www.bioteach.ubc.ca> 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

  28. <userwww.sfsu.edu> 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

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