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Prof. Ulises Urzúa ICBM, Facultad de Medicina,
Universidad de Chile
[email protected]@med.uchile.cl
DNA microarrays in DNA microarrays in genomics and cancergenomics and cancer
Clase ToxGen-Nov08
(1982 - 2002)
Actualizado, 31 Dic 2008
One gene…or many genes?
Environment Environment and life-style are and life-style are
major major contributors to contributors to
the the pathogenesis of pathogenesis of
complex complex diseasesdiseases
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?"
Genetic Information Nondis crimination Act of
2008
Tumor classification, risk assessment, prognosis predictionMicroarray CGH
Drug development, therapy development, disease progression
Mutation&Polymorphism
analysis
Drug development, drug response, therapy developmentTranscriptional
analysis
ApplicationApproach
Major microarray applicationsMajor microarray applications
• MicroarrayMicroarray: : ordered arrangement of ordered arrangement of known DNA sequences on a solid-known DNA sequences on a solid-planar planar substrate which allows the hybridization substrate which allows the hybridization binding of labeled sample RNAs or binding of labeled sample RNAs or DNAs.DNAs.
• A single microarray contains from few hundreds to 400.000 microscopic elements of uniform size and spacing.
• Immobilized DNAs are oligonucleotidesoligonucleotides (20-80 mer), cDNAscDNAs (0.5-5 Kb) or BACBAC clones (10-50 Kb). Substrates are rigid, thermostable, optically flat surfaces like nylon, glass or silica.
• DNAs are spotted onto chemically modified substrates and then immobilized using UV. Oligonucleotides can be either spotted or synthesized in situ.
Affimetrix GeneChip® MicroArrays
20µm20µm
Millions of copies of a specificMillions of copies of a specificoligonucleotide probeoligonucleotide probe
Image of Hybridized Probe Array
>400,000 different>400,000 differentcomplementary probes complementary probes
Single stranded, Single stranded, labeled RNA targetlabeled RNA target
Oligonucleotide probeOligonucleotide probe
* **
**
1.28cm1.28cm
GeneChip Probe ArrayHybridized Probe Cell
Suited for both expression profiling
and genotyping
Affimetrix GeneChip®
5´5´ 3´3´Oligo arraysOligo arraysGeneGene
PerfectMatchPerfectMatchMismatchMismatch
MultipleMultiple oligo probesoligo probes
onon
offoff
2424 µm µm
• The photolitographic technique used in Affimetrix GeneChipsTM allows obtaining ultra high-density microarrays (up to 106 probes/cm2)
GeneChip workstation
• Glass slide microarraysGlass slide microarrays
• Up to 48,000 spotted “ off-line” DNA probes Up to 48,000 spotted “ off-line” DNA probes • Spotted cDNA clones, ESTs or oligosSpotted cDNA clones, ESTs or oligos• Gene expression, CGH, and SNPsGene expression, CGH, and SNPs
A comparative A comparative hybridization hybridization experimentexperiment
Mouse NIA 15K cDNA microarray, Mouse NIA 15K cDNA microarray, block 15 (from 32 total)block 15 (from 32 total)
- - Cy5Cy5 mouse ovarian cell line (total RNA) mouse ovarian cell line (total RNA)
- - Cy3Cy3 reference whole newborn mouse reference whole newborn mouse (total RNA)(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
BioRobotics Arrayer
Plate loader and lid remover
Refrigerated Biobank (holds upto 24 microtiter plates)
Wash baths for cleaning the pins
The four platforms are capable of holding 120 slides
A 32 pin holder with pins loaded
Telechem pins
- Deposits ~0.4nl a spot- Each spot ~100µm diameter
Total uptake volumesTotal uptake volumes 0.25 0.6 2.5 µl0.25 0.6 2.5 µl
Contact depositionContact deposition
50% DMSOAdvantages: 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 SSCAdvantages: 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 8.5Similar to 3X SSC in terms of advantages and disadvantages
Spotting solutions
Crosslinking of DNA to polylysine coated glass
- GAPS (gamma aminopropyl silane) coating.
Hybridization
Manual hybridization chambers (TELECHEM-Arrayit )
- 20 to 50 µl of hyb cocktail - prone towards significant experimental variability.
Automatic hybridization station:
- Over 120 µl of hyb cocktail - less variability in replicates - washing also automated
Fluorescence scanners
ScanArray Lite (Perkin-Elmer) GenePix 4000B (Axon)
Exercise # 1Exercise # 1
- GenePix Pro 3.0 (Axon) GenePix Pro 3.0 (Axon) LocalLocal
- mAdb (NCI`s Microarray database)mAdb (NCI`s Microarray database)
http://nciarray.nci.nih.gov/http://nciarray.nci.nih.gov/
Experimental design and variabilityExperimental design and variability
Sources of variability:• Due to attributes or conditions
• Biological variation is intrinsic; influenced by genetic & environmental factors, as well as whether samples are from populations or individuals
• Due to technical issues, results during sample extraction (quality), labeling and hybridization
• Due to fluorophore stability during laser scanning and fluorescence detection
Microarray data workflowMicroarray data workflow
• Marcaje
• Hibridacion
• Escaneo
• Análisis de
imagen
Experimental Analisis numérico Interpretacion
• Normalización
• Valores perdidos
• Imputación
• Distribución
• Clustering
• t-test
• Anova
• Correlación
• PCA
• Clasificación
• Pathways
• Gene ontology
• PubMed
• Gene networks
• Validación (?)
Corrección técnica
(experimental)
Print-tip Loess normalization
3
6
Array #3 Print-tip display
Array #6 Print-tip display
Array #3 “MA” plot
M = log2R - log2G A = (log2R + log2G) / 2
Array #6 “MA” plot
Scale adjustment
Microarray data workflowMicroarray data workflow
• Marcaje
• Hibridacion
• Escaneo
• Análisis de
imagen
Experimental Analisis numérico Interpretacion
• Normalización
• Valores perdidos
• Imputación
• Distribución
• Clustering
• t-test
• Anova
• Correlación
• PCA
• Clasificación
• Pathways
• Gene ontology
• PubMed
• Gene networks
• Validación (?)
Corrección técnica
(experimental)Corrección estadística
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
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
Case # 1Case # 1
- An An in vitroin vitro mouse mouse model of ovarian model of ovarian
cancercancer
Ovarian cancer: risk factors and possible etiologyOvarian cancer: risk factors and possible etiology
• There is no reliable screening test for early detection. Over 75% is detected late (5-year survival is below 30%).
• Symptoms are often vague and easily confused with other diseases.
➩ risk: No children, continuous ovulation (never used birth control pills)
➪ risk: Pregnancies, lactation
Generation of a mouse modelGeneration of a mouse model
Roby et al., Carcinogenesis 21, 585-594, 2000.
pass 5
MOSE (mouse ovarian surface epithelial) cells
MOSE cells: highest ranking differentially expressed genes
Gene symbol, description Mean difference, adjusted p-value a
Reported in ovarian cancer b [References]
Up-regulated
Spp1, osteopontin +3.77, 0.0e0 Yes [18, 19]
Cyp1b1, cytochrome P450, family 1, subfamily 1, polypeptide 1 +2.85, 3.1e-3 Yes [22]
Nupr1, nuclear protein 1 +2.83, 9.0e-4 Pancreas c
Gsto1, glutathione S-transferase ω1 +2.52, 0.0e0 Breast d
Btg1, B-cell translocation gene +2.41, 0.0e0 Yes [24]
Down-regulated
Mt1, metallothionein 1 −4.77, 0.0e0 Yes [27]
Mt2, metallothionein 2 −4.66, 0.0e0 Yes [27]
Igfbp5, insulin-like growth factor binding protein 5 −3.93, 2.0e-4 Yes [28, 30]
Gstm1, glutathione S-transferase, µ1 −3.32, 0.0e0 Yes [32, 33]
Alcam, activated leukocyte cell adhesion molecule −3.08, 1.7e-2 Breast i
MOSE clonal cells produce tumors in MOSE clonal cells produce tumors in immunocompetent miceimmunocompetent mice
Roby et al., Carcinogenesis 21, 585-594, 2000.
Self organizing tree algorithm (SOTA) clustering
Urzúa et al. (2006)
J. Cell. Physiol. 206, 594-602
Human-Mouse Comparison
• 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:
1.- LMP 2.- Stage III 3.- Serous BOT 4.- Mucinous BOT 5.- Mouse IG-10 6.- Mouse IF-5
Urzúa et al. (2006)
J. Cell. Physiol. 206, 594-602
Microarray-CGH conceptMicroarray-CGH concept
Gene expression array Microarray-CGH
Microarray-CGH… Microarray-CGH… How to deal with the genome complexity?How to deal with the genome complexity?
RNA (cDNA) hybridized Genomic DNA hybridized
RNA vs DNA RNA vs DNA raw data raw data distributiondistribution
• 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 75th and 25th percentile, respectively. Whiskers above and below the box indicate the 90th and the 10th percentiles. A line within the box mark the median.
Microarray-CGH, experimental optimizationMicroarray-CGH, experimental optimization
Urzúa et al. (2005) Tumor Biol. 26, 236-44
Conventional-CGH vs microarray-CGHConventional-CGH vs microarray-CGH
Z-scoreZ-score
Frankenberger et al. (2006) Appl. Bioinformatics 5, 125-30
Exercise # 2Exercise # 2
Web-aCGH, microarray CGH data Web-aCGH, microarray CGH data analysis and display.analysis and display.
http://129.43.22.27/WebaCGH/welcome.htmhttp://129.43.22.27/WebaCGH/welcome.htm
Focus: clusters of > 50 lymphocytes
Focus score: number of focus/40mm2 glandular tissue (0 4)
Ducto
Acino
Focal lip sialadenitis in Sjogren`s syndrome
Case # 2Case # 2
LSGs expression pattern in Sjogren´s LSGs expression pattern in Sjogren´s syndrome patients -syndrome patients -
Correlation with clinical parameters Correlation with clinical parameters
Isolation of epithelial cells
Positively correlated
gene expression
Negatively correlated
gene expression
Epithelial gene expression
VS
focus score
Top ranked LSG acini expressed genes correlated to focus score a
n.r.d.2,131.09(+), 0.74RING1, Ring finger protein 1
n.r.d.2,331.22(+), 0.85FYB, FYN binding protein (FYB-120/130)
n.r.d.1,970.98(+), 0.79IL10RA, interleukin 10 receptor, alpha
Ohyama et al. (1995), 7621031
3,731.90(+), 0.82CD69, CD69 antigen (p60, early T-cell activation antigen)
n.r.d.2,411.27(+), 0.89SERPINB1, Serpin peptidase inhibitor, clade B (ovalbumin), member 1
Dimitriou et al. (2002), 11876766
6,112.61(+), 0.82HLA-DRA, Major histocompatibility complex, class II, DR alpha
Kay et al. (1995), 7558918
2,811.49(+), 0.84TRBV2, T cell receptor beta variable 2
n.r.d.3,391.76(+), 0.84NQO2, NAD(P)H dehydrogenase, quinone 2
Ogawa et al. (2002), 12384933
5,172.37(+), 0.83CXCL9, chemokine (C-X-C motif) ligand 9
Fei et al. (1991), 1685512
3,291.72(+), 0.77HLA-DQA1, Major histocompatibility complex, class II, DQ alpha 1
n.r.d.4,862.28(+), 0.89HLA-DMA, major histocompatibility complex class II, DM alpha
n.r.d.3,101.63(+), 0.85LCP1, L plastin, actin binding protein
Loiseau et al. (2001), 11423179
5,542.47(+), 0.82HLA-A, major histocompatibility complex, class I, A
n.r.d. d2,621.39(+), 0.83RAC2, ras-related C3 botulinum toxin substrate 2
Azuma et al. (2002), 11947921
3,231.69(+), 0.86LAPTM5, Lysosomal associated multispanning membrane protein 5
Previously reported in SS, PMID
Gene expression shift (ratio)
Gene expression shift (log2)
c
Direction of correlation,R2 value b
GENE SYMBOL, description
Gene expression phenotype correlation (1)
Positively correlated
gene expression
Negatively correlated
gene expression
Gene expression phenotype correlation (2)
Functional (GO, KEGG, BioCarta) and literature (PubMed) linked genes
Strength of correlation respective to transcriptional activity
Urzúa et al. (2008) in preparation
Ovarian tumor frequency
Number oflittersLitter size
89
280
73 00
0 145
Correlation between expression profiles and multiple phenotypes
Case # 3Case # 3
Gene expression profiling in ovarian Gene expression profiling in ovarian carcinomas carcinomas
actin cytoskeleton
regulation of protein metabolism
blood coagulation
response to wounding
apoptosis
Gene expression differences - IOSE vs EOC III (t-test)
Gene expression differences - IOSE vs EOC III (Anova)
*
*
*
**
-2.5 -1.5 -0.5 0.5 1.5 2.5
VEGF
TGFb1
TrkA
FSHR
ER alfa
ER beta
AKT1
AKT2
AKT3
PTEN
MAPK1
GSK3b
E-cad
FOXL2
HIF1A
log2 ratio
IOSE
CaOv
NGF signaling pathway and related genes
Case # 4Case # 4
Wine yeast genomics Wine yeast genomics
L846 (cepa nativa)
L846 ura- (mutante espontánea para uracilo, producto de esporulación)
Spt2 overexpression is concomitant with transposable elements repression
⇑ Spt2, negative regulator of ty transcription
⇓ transposon ty2 protein b
⇓ transposon ty2 protein a
⇓ transposon ty1 protein a
⇓ ty1a protein
⇓ transposon ty1 protein a
⇓ mevalonate pirophosphate decarboxylase
⇓ phosphomevalonate kinase
⇓ farnesyltransferase (essential for yeast?)
⇓ Lanosterol 14-alpha demethylase (citP450 family)
?
Yeast genome may undergo genomic changes when exposed to the environment
288 / 288S C v s S C
1118 / 288EC v s S C
-1333 / 288L v s S C
-957 / 288L v s S C
S288C= cepa estándar de laboratorio
EC1118= cepa comercial francesa
L-1333= cepa aislada en Casablanca
L-957= cepa aislada en Mendoza
Case # 5Case # 5
Norovirus genotyping Norovirus genotyping
SNPs in viral genome allow viruses classification
Real-time Q-PCR validationReal-time Q-PCR validation
Spp1 Mt1
———— 18S rRNA ——●—— Rps16
Urzúa et al. (2006) J. Cell. Physiol. 206, 594-602
Real-time Q-PCR validation (2)Real-time Q-PCR validation (2)
Urzúa et al. (2008) in preparation
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.
AgradecimientosAgradecimientos
• Dra Carmen Romero (Hosp Clinico U de Chile)
• Dr Luigi Devoto (IDIMI, U de Chile)
• Dr. David Munroe (NCI Frederick)
• Dra Julieta Gonzalez, (Prog Biol Cel Mol, ICBM)
• Dr Claudio Martínez (USACH)
• Dra Sandra Ampuero (Virología, ICBM)
• VID, U de Chile, Proyecto de Iniciación
ColaboradoresColaboradores
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/
Seminarios 9 de Diciembre, 2008Seminarios 9 de Diciembre, 2008
http://www.ncbi.nlm.nih.gov/pubmed/18596974
http://www.ncbi.nlm.nih.gov/pubmed/17766027