The document discusses various applications and techniques of DNA microarrays, including summarizing key points about Affymetrix GeneChips, spotted microarrays, experimental design, data analysis, and several case studies on various topics like ovarian cancer, Sjogren's syndrome, wine yeast genomics, and norovirus genotyping. Microarrays allow analysis of gene expression patterns and copy number variations across genomes through comparative hybridization experiments. The document provides an overview of microarray technology and applications in genomic and biomedical research.

1. Prof. Ulises Urzúa ICBM, Facultad de Medicina, Universidad de Chile [email_address] DNA microarrays in genomics and cancer Clase ToxGen-Nov08

3. (1982 - 2002)

4. Actualizado, 31 Dic 2008

5. One gene…or many genes? Environment and life-style are major contributors to the pathogenesis of complex diseases

6. Legal Issues in Genomic Medicine "We won't be able to offer you a position with our company. The results of our genetic tests suggest that you have a predisposition to attention deficit disorder. Mr. Jones? Mr. Jones?"

7. Genetic Information Nondiscrimination Act of 2008

8. Medicina personalizada http://www.jyi.org/features/ft.php?id=1047

9. Tumor classification, risk assessment, prognosis prediction Microarray CGH Drug development, therapy development, disease progression Mutation &Polymorphism analysis Drug development, drug response, therapy development Transcriptional analysis Application Approach Major microarray applications

11. Affimetrix GeneChip ® MicroArrays 20µm Millions of copies of a specific oligonucleotide probe Image of Hybridized Probe Array >400,000 different complementary probes Single stranded, labeled RNA target Oligonucleotide probe 1.28cm GeneChip Probe Array Hybridized Probe Cell Suited for both expression profiling and genotyping * * * * *

12. Affimetrix GeneChip ® 5´ 3´ Oligo arrays Gene PerfectMatch Mismatch Multiple oligo probes on off 24 µm

13.  The photolitographic technique used in Affimetrix GeneChips TM allows obtaining ultra high-density microarrays (up to 10 6 probes/cm 2 ) GeneChip workstation

15. A comparative hybridization experiment

16. Mouse NIA 15K cDNA microarray, block 15 (from 32 total) - Cy5 mouse ovarian cell line (total RNA) - Cy3 reference whole newborn mouse (total RNA) Microarrays allows only comparative (relative) measurements Genes up-regulated in mouse ovarian cells Genes up-regulated in the reference RNA Genes equally expressed in both samples

17. BioRobotics Arrayer Plate loader and lid remover Refrigerated Biobank (holds up to 24 microtiter plates) Wash baths for cleaning the pins The four platforms are capable of holding 120 slides

18. A 32 pin holder with pins loaded

20. 50% DMSO Advantages : denatures the DNA; low evaporation rate; interacts well with GAPS coating thus generating uniform spots. Disadvantages : Strong irritant; tends to form spots of large diameter, sometimes causing them to merge; DNA aggregates when DMSO concentration is above 70%. 3X SSC Advantages : Aqueous solvent; produces spots of small diameter, allowing high printing density. Disadvantages : Does not denature the DNA; evaporates quickly so that carefully controlled printing environment is required. 150 mM NaPO4, pH 8.5 Similar to 3X SSC in terms of advantages and disadvantages Spotting solutions

21. Crosslinking of DNA to polylysine coated glass - GAPS (gamma aminopropyl silane) coating.

22. Hybridization Manual hybridization chambers (TELECHEM-Arrayit ) - 20 to 50 µ l of hyb cocktail - prone towards significant experimental variability. Automatic hybridization station: - Over 120 µ l of hyb cocktail - less variability in replicates - washing also automated

23. Fluorescence scanners ScanArray Lite (Perkin-Elmer) GenePix 4000B (Axon)

28. Print-tip Loess normalization 3 6

29. Array #3 Print-tip display

30. Array #6 Print-tip display

31. Array #3 “MA” plot M = log 2 R - log 2 G A = (log 2 R + log 2 G ) / 2

32. Array #6 “MA” plot

33. Scale adjustment

35. Differentially expressed genes: the problem of multiple testing - ANOVA test, 40 arrays, 7 samples - FWER (family wise error rate), type I error or false positive. Urzúa et al. (2006) J. Cell. Physiol. 206, 594-602

36. Dataset structure -filtering hierarchy Statistical tests Co-expression Correlation, etc (multiple test control) Raw dataset Processed and normalized subset Candidate genes Functional groups Pathway analysis Text-mining (interpretation) Interaction gene/groups networks

39. Generation of a mouse model Roby et al., Carcinogenesis 21, 585-594, 2000. pass 5 MOSE (mouse ovarian surface epithelial) cells

41. MOSE clonal cells produce tumors in immunocompetent mice Roby et al., Carcinogenesis 21, 585-594, 2000 .

43. Self organizing tree algorithm (SOTA) clustering Urzúa et al. (2006) J. Cell. Physiol. 206, 594-602

45. Microarray-CGH concept

50. Gene expression array Microarray-CGH Microarray-CGH… How to deal with the genome complexity? RNA (cDNA) hybridized Genomic DNA hybridized

52. Microarray-CGH, experimental optimization Urzúa et al. (2005) Tumor Biol. 26, 236-44

53. Conventional-CGH vs microarray-CGH Z-score Frankenberger et al. (2006) Appl. Bioinformatics 5, 125-30

54. Exercise # 2 Web-aCGH, microarray CGH data analysis and display. http://129.43.22.27/WebaCGH/welcome.htm

55. Focus: clusters of > 50 lymphocytes Focus score : number of focus/40mm 2 glandular tissue (0  4) Ducto Acino Focal lip sialadenitis in Sjogren`s syndrome

56. Case # 2 LSGs expression pattern in Sjogren´s syndrome patients - Correlation with clinical parameters

57. Isolation of epithelial cells

59. Positively correlated gene expression Negatively correlated gene expression Epithelial gene expression VS focus score

60. Top ranked LSG acini expressed genes correlated to focus score a n.r.d. 2,13 1.09 (+), 0.74 RING1 , Ring finger protein 1 n.r.d. 2,33 1.22 (+), 0.85 FYB , FYN binding protein (FYB-120/130) n.r.d. 1,97 0.98 (+), 0.79 IL10RA , interleukin 10 receptor, alpha Ohyama et al. (1995), 7621031 3,73 1.90 (+), 0.82 CD69 , CD69 antigen (p60, early T-cell activation antigen) n.r.d. 2,41 1.27 (+), 0.89 SERPINB1 , Serpin peptidase inhibitor, clade B (ovalbumin), member 1 Dimitriou et al. (2002), 11876766 6,11 2.61 (+), 0.82 HLA-DRA , Major histocompatibility complex, class II, DR alpha Kay et al. (1995), 7558918 2,81 1.49 (+), 0.84 TRBV2 , T cell receptor beta variable 2 n.r.d. 3,39 1.76 (+), 0.84 NQO2 , NAD(P)H dehydrogenase, quinone 2 Ogawa et al. (2002), 12384933 5,17 2.37 (+), 0.83 CXCL9 , chemokine (C-X-C motif) ligand 9 Fei et al. (1991), 1685512 3,29 1.72 (+), 0.77 HLA-DQA1 , Major histocompatibility complex, class II, DQ alpha 1 n.r.d. 4,86 2.28 (+), 0.89 HLA-DMA , major histocompatibility complex class II, DM alpha n.r.d. 3,10 1.63 (+), 0.85 LCP1 , L plastin, actin binding protein Loiseau et al. (2001), 11423179 5,54 2.47 (+), 0.82 HLA-A , major histocompatibility complex, class I, A n.r.d. d 2,62 1.39 (+), 0.83 RAC2 , ras-related C3 botulinum toxin substrate 2 Azuma et al. (2002), 11947921 3,23 1.69 (+), 0.86 LAPTM5 , Lysosomal associated multispanning membrane protein 5 Previously reported in SS, PMID Gene expression shift (ratio) Gene expression shift (log 2 ) c Direction of correlation, R 2 value b GENE SYMBOL , description

61. Are they chromosomal neighbors? Gene expression phenotype correlation (1) Positively correlated gene expression Negatively correlated gene expression

62. Gene expression phenotype correlation (2) Functional (GO, KEGG, BioCarta) and literature (PubMed) linked genes

63. Strength of correlation respective to transcriptional activity Urzúa et al. (2008) in preparation

64. Correlation between expression profiles and multiple phenotypes Ovarian tumor frequency Number of litters Litter size 89 280 73 0 0 0 145

65. Case # 3 Gene expression profiling in ovarian carcinomas

66. Gene expression differences - IOSE vs EOC III (t-test) actin cytoskeleton regulation of protein metabolism blood coagulation response to wounding apoptosis

67. Gene expression differences - IOSE vs EOC III (Anova)

68. NGF signaling pathway and related genes

69. Case # 4 Wine yeast genomics

71. Yeast genome may undergo genomic changes when exposed to the environment S288C v/s S288C S288C v/s S288C EC1118 v/s S288C L-1333 v/s S288C L-957 v/s S288C S288C= cepa estándar de laboratorio EC1118= cepa comercial francesa L-1333= cepa aislada en Casablanca L-957= cepa aislada en Mendoza

72. Case # 5 Norovirus genotyping

73. SNPs in viral genome allow viruses classification

74. Real-time Q-PCR validation Spp1 Mt1 ——  —— 18S rRNA ——  —— Rps16 Urzúa et al. (2006) J. Cell. Physiol. 206, 594-602

75. Real-time Q-PCR validation (2) Urzúa et al. (2008) in preparation

76. Tal como una casa se construye con ladrillos, la ciencia se construye en base a hechos... Pero un conjunto de hechos no constituye por sí sólo ciencia, tal como un montón de ladrillos no constituyen una casa. Henri Poincaré La Science et l'Hypothese, Paris, 1908.

78. Gracias! Dr. Ulises Urzúa [email_address] Fono 978-6877

79. Systems biology integrates different levels of information to understand how biological organisms function. In contrast to molecular biology, systems biology does not break down a system into all of its parts and study one part of the process at a time. Systems biologists argue that this reductionist approach is not robust, either because of nature's redundancy and complexity, or because we have not understood all the parts of the processes. The ultimate goal of systems biology is to mathematically model biological processes. Such models are used to predict how different changes affect the phenotype of a cell, and can be iteratively tested to prove or disprove the model. Adapted from http://en.wikipedia.org/

80. Seminarios 9 de Diciembre, 2008 http://www.ncbi.nlm.nih.gov/pubmed/18596974 http://www.ncbi.nlm.nih.gov/pubmed/17766027