Genomica - Microarreglos de DNA

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  • Genomica - Microarreglos de DNA

    1. 1. Prof. Ulises Urzúa ICBM, Facultad de Medicina, Universidad de Chile [email_address] DNA microarrays in genomics and cancer Clase ToxGen-Nov08
    2. 3. (1982 - 2002)
    3. 4. Actualizado, 31 Dic 2008
    4. 5. One gene…or many genes? Environment and life-style are major contributors to the pathogenesis of complex diseases
    5. 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?"
    6. 7. Genetic Information Nondiscrimination Act of 2008
    7. 8. Medicina personalizada http://www.jyi.org/features/ft.php?id=1047
    8. 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
    9. 10. <ul><li>Microarray : ordered arrangement of known DNA sequences on a solid- planar substrate which allows the hybridization binding of labeled sample RNAs or DNAs. </li></ul><ul><li>A single microarray contains from few hundreds to 400.000 microscopic elements of uniform size and spacing. </li></ul><ul><li>Immobilized DNAs are oligonucleotides (20-80 mer), cDNAs (0.5-5 Kb) or BAC clones (10-50 Kb). Substrates are rigid, thermostable, optically flat surfaces like nylon, glass or silica. </li></ul><ul><li>DNAs are spotted onto chemically modified substrates and then immobilized using UV. Oligonucleotides can be either spotted or synthesized in situ . </li></ul>
    10. 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 * * * * *
    11. 12. Affimetrix GeneChip ® 5´ 3´ Oligo arrays Gene PerfectMatch Mismatch Multiple oligo probes on off 24 µm
    12. 13.  The photolitographic technique used in Affimetrix GeneChips TM allows obtaining ultra high-density microarrays (up to 10 6 probes/cm 2 ) GeneChip workstation
    13. 14. <ul><li>Glass slide microarrays </li></ul><ul><li>Up to 48,000 spotted “off-line” DNA probes </li></ul><ul><li>Spotted cDNA clones, ESTs or oligos </li></ul><ul><li>Gene expression, CGH, and SNPs </li></ul>
    14. 15. A comparative hybridization experiment
    15. 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
    16. 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
    17. 18. A 32 pin holder with pins loaded
    18. 19. Telechem pins <ul><li>- Deposits ~0.4nl a spot </li></ul><ul><li>Each spot ~100µm diameter </li></ul>Total uptake volumes 0.25 0.6 2.5 µl Contact deposition
    19. 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
    20. 21. Crosslinking of DNA to polylysine coated glass - GAPS (gamma aminopropyl silane) coating.
    21. 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
    22. 23. Fluorescence scanners ScanArray Lite (Perkin-Elmer) GenePix 4000B (Axon)
    23. 24. Exercise # 1 <ul><li>GenePix Pro 3.0 (Axon) Local </li></ul><ul><li>mAdb (NCI`s Microarray database) </li></ul><ul><li>http://nciarray.nci.nih.gov/ </li></ul>
    24. 25. Experimental design and variability <ul><li>Sources of variability: </li></ul><ul><li>Due to attributes or conditions </li></ul><ul><li>Biological variation is intrinsic; influenced by genetic & environmental factors, as well as whether samples are from populations or individuals </li></ul><ul><li>Due to technical issues, results during sample extraction ( quality ), labeling and hybridization </li></ul><ul><li>Due to fluorophore stability during laser scanning and fluorescence detection </li></ul>
    25. 26. Microarray data workflow <ul><li>Marcaje </li></ul><ul><li>Hibridacion </li></ul><ul><li>Escaneo </li></ul><ul><li>Análisis de </li></ul><ul><li>imagen </li></ul>Experimental Analisis numérico Interpretacion <ul><li>Normalización </li></ul><ul><li>Valores perdidos </li></ul><ul><li>Imputación </li></ul><ul><li>Distribución </li></ul><ul><li>Clustering </li></ul><ul><li>t-test </li></ul><ul><li>Anova </li></ul><ul><li>Correlación </li></ul><ul><li>PCA </li></ul><ul><li>Clasificación </li></ul><ul><li>Pathways </li></ul><ul><li>Gene ontology </li></ul><ul><li>PubMed </li></ul><ul><li>Gene networks </li></ul><ul><li>Validación (?) </li></ul>Corrección técnica (experimental)
    26. 28. Print-tip Loess normalization 3 6
    27. 29. Array #3 Print-tip display
    28. 30. Array #6 Print-tip display
    29. 31. Array #3 “MA” plot M = log 2 R - log 2 G A = (log 2 R + log 2 G ) / 2
    30. 32. Array #6 “MA” plot
    31. 33. Scale adjustment
    32. 34. Microarray data workflow <ul><li>Marcaje </li></ul><ul><li>Hibridacion </li></ul><ul><li>Escaneo </li></ul><ul><li>Análisis de </li></ul><ul><li>imagen </li></ul>Experimental Analisis numérico Interpretacion <ul><li>Normalización </li></ul><ul><li>Valores perdidos </li></ul><ul><li>Imputación </li></ul><ul><li>Distribución </li></ul><ul><li>Clustering </li></ul><ul><li>t-test </li></ul><ul><li>Anova </li></ul><ul><li>Correlación </li></ul><ul><li>PCA </li></ul><ul><li>Clasificación </li></ul><ul><li>Pathways </li></ul><ul><li>Gene ontology </li></ul><ul><li>PubMed </li></ul><ul><li>Gene networks </li></ul><ul><li>Validación (?) </li></ul>Corrección técnica (experimental) Corrección estadística
    33. 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
    34. 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
    35. 37. Case # 1 <ul><li>An in vitro mouse model of ovarian cancer </li></ul>
    36. 38. Ovarian cancer: risk factors and possible etiology <ul><li>There is no reliable screening test for early detection. Over 75% is detected late (5-year survival is below 30%). </li></ul><ul><li>S ymptoms are often vague and easily confused with other diseases. </li></ul><ul><li> risk: No children, continuous ovulation (never used birth control pills) </li></ul><ul><li> risk: Pregnancies, lactation </li></ul>
    37. 39. Generation of a mouse model Roby et al., Carcinogenesis 21, 585-594, 2000. pass 5 MOSE (mouse ovarian surface epithelial) cells
    38. 41. MOSE clonal cells produce tumors in immunocompetent mice Roby et al., Carcinogenesis 21, 585-594, 2000 .
    39. 43. Self organizing tree algorithm (SOTA) clustering Urzúa et al. (2006) J. Cell. Physiol. 206, 594-602
    40. 44. Human-Mouse Comparison <ul><li>SOM clustering of IG10 and IF5 cell lines compared to human ovarian tumors based on 872 genes with equivalent biological function. Samples description is as follows: </li></ul><ul><li>1.- LMP 2.- Stage III 3.- Serous BOT 4.- Mucinous BOT 5.- Mouse IG-10 6.- Mouse IF-5 </li></ul>Urzúa et al. (2006) J. Cell. Physiol. 206, 594-602
    41. 45. Microarray-CGH concept
    42. 50. Gene expression array Microarray-CGH Microarray-CGH… How to deal with the genome complexity? RNA (cDNA) hybridized Genomic DNA hybridized
    43. 51. <ul><li>RNA vs DNA raw data distribution </li></ul><ul><li>Test RNA and DNA obtained from the same source were hybridized against their respective reference RNA and DNA. Statistical values are shown for 13,417 clones from the NIA-15K cDNA mouse clone set. Upper and lower box boundaries indicate the 75 th and 25 th percentile, respectively. Whiskers above and below the box indicate the 90 th and the 10 th percentiles. A line within the box mark the median . </li></ul>
    44. 52. Microarray-CGH, experimental optimization Urzúa et al. (2005) Tumor Biol. 26, 236-44
    45. 53. Conventional-CGH vs microarray-CGH Z-score Frankenberger et al. (2006) Appl. Bioinformatics 5, 125-30
    46. 54. Exercise # 2 Web-aCGH, microarray CGH data analysis and display. http://129.43.22.27/WebaCGH/welcome.htm
    47. 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
    48. 56. Case # 2 LSGs expression pattern in Sjogren´s syndrome patients - Correlation with clinical parameters
    49. 57. Isolation of epithelial cells
    50. 59. Positively correlated gene expression Negatively correlated gene expression Epithelial gene expression VS focus score
    51. 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
    52. 61. Are they chromosomal neighbors? Gene expression phenotype correlation (1) Positively correlated gene expression Negatively correlated gene expression
    53. 62. Gene expression phenotype correlation (2) Functional (GO, KEGG, BioCarta) and literature (PubMed) linked genes
    54. 63. Strength of correlation respective to transcriptional activity Urzúa et al. (2008) in preparation
    55. 64. Correlation between expression profiles and multiple phenotypes Ovarian tumor frequency Number of litters Litter size 89 280 73 0 0 0 145
    56. 65. Case # 3 Gene expression profiling in ovarian carcinomas
    57. 66. Gene expression differences - IOSE vs EOC III (t-test) actin cytoskeleton   regulation of protein metabolism   blood coagulation   response to wounding   apoptosis  
    58. 67. Gene expression differences - IOSE vs EOC III (Anova)
    59. 68. NGF signaling pathway and related genes
    60. 69. Case # 4 Wine yeast genomics
    61. 70. L846 (cepa nativa) L846 ura- (mutante espontánea para uracilo, producto de esporulación) Spt2 overexpression is concomitant with transposable elements repression <ul><li> Spt2 , negative regulator of ty transcription </li></ul><ul><li>transposon ty2 protein b </li></ul><ul><li> transposon ty2 protein a </li></ul><ul><li> transposon ty1 protein a </li></ul><ul><li>ty1a protein </li></ul><ul><li>transposon ty1 protein a </li></ul><ul><li>mevalonate pirophosphate decarboxylase </li></ul><ul><li>phosphomevalonate kinase </li></ul><ul><li>farnesyltransferase (essential for yeast?) </li></ul><ul><li>Lanosterol 14-alpha demethylase (citP450 family) </li></ul>?
    62. 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
    63. 72. Case # 5 Norovirus genotyping
    64. 73. SNPs in viral genome allow viruses classification
    65. 74. Real-time Q-PCR validation Spp1 Mt1 ——  —— 18S rRNA ——  —— Rps16 Urzúa et al. (2006) J. Cell. Physiol. 206, 594-602
    66. 75. Real-time Q-PCR validation (2) Urzúa et al. (2008) in preparation
    67. 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.
    68. 77. Agradecimientos <ul><li>Dra Carmen Romero (Hosp Clinico U de Chile) </li></ul><ul><li>Dr Luigi Devoto (IDIMI, U de Chile) </li></ul><ul><li>Dr. David Munroe (NCI Frederick) </li></ul><ul><li>Dra Julieta Gonzalez, (Prog Biol Cel Mol, ICBM) </li></ul><ul><li>Dr Claudio Martínez (USACH) </li></ul><ul><li>Dra Sandra Ampuero (Virología, ICBM) </li></ul><ul><li>VID, U de Chile, Proyecto de Iniciación </li></ul>Colaboradores
    69. 78. Gracias! Dr. Ulises Urzúa [email_address] Fono 978-6877
    70. 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/
    71. 80. Seminarios 9 de Diciembre, 2008 http://www.ncbi.nlm.nih.gov/pubmed/18596974 http://www.ncbi.nlm.nih.gov/pubmed/17766027

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