BIONF/BENG 203: Functional Genomics

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BIONF/BENG 203Functional Genomics
Sources of Functional DataLectures 1 and 2

• Lecture TI 1
• Trey Ideker
• UCSD Department of Bioengineering

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Grading

• 40 Problem Sets (best 4 of 5)
• 30 Midterm
• 30 Final Project

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Outline of the course
Biological data sources (2)
Data pre-processing (6)
Total of 17 lectures
Project Presentations (2)
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Functional Genomics Data

• Expression
• mRNA, protein
• Molecular interactions
• Protein, mRNA, small molecules
• Knockout phenotypes
• 1st, 2nd, higher orders
• SNP sequence (polymorphism) data
• Imaging data
• Sub-cellular localization
• Cell morphology
• Gene ontology

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Dividing the data into two classes of
informationBiological Networks and Network
States

• Directly observe the network wires themselves
• Protein-protein interactions
• Two-hybrid system, coIP, protein antibody arrays
• BIND, DIP
• Protein-DNA interactions
• Chromatin IP
• BIND, Transfac, SCPD
• Other types not yet possible
• e.g., protein-small molecule

• Observe molecular states that result from the
  interaction wiring
• DNA/RNA Gene expression
• DNA microarrays, SAGE
• Protein levels, locations, and modifications
• Mass spectrometry, fluorescence microscopy,
  protein arrays
• Gross phenotypes
• e.g., growth rates of single and double deletion
  strains

1)
2)
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High-throughput methods for measuring cellular
states

• Gene expression levels RT-PCR, arrays
• Protein levels, modifications mass specProtein
  locations fluorescent tagging
• Metabolite levels NMR and mass spec
• Systematic phenotyping

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The transcriptome and proteome

• The transcriptome is the full complement of RNA
  molecules produced by a genome
• The proteome is the full complement of proteins
  enabled by the transcriptome
• DNA ? RNA ? protein
• Genome ? transcriptome ? proteome
• 30,000 genes ? ??? RNAs ? ??? proteins?
• For example, the drosophila gene Dscam can
  generate 40,000 distinct transcripts through
  alternative splicing.
• What is the minimum number of exons that would be
  required?

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Expression High-throughput approaches

• RNA
• DNA Microarrays
• cDNA / EST sequencing
• RT-PCR
• Differential display
• SAGE
• Massively parallel signature sequencing (MPSS)
• Proteins
• 2D PAGE
• Mass spectrometry

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Gene expression arrays

• They are really, really, really, really, really,
  really, really, really, really, really, really,
  really, really important

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Microarrays

• Monitors the level of each gene
• Is it turned on or off in a particular
  biological condition?
• Is this on/off state different between two
  biological conditions?
• Microarray is a rectangular grid of spots printed
  on a glass microscope slide, where each spot
  contains DNA for a different gene

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Two-color DNA microarray design
Reverse Transcription
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cDNA-chip of brain glioblastoma
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Types of microarrays

• Spotted (cDNA)
• Robotic transfer of cDNA clones or PCR products
• Spotting on nylon membranes or glass slides
  coated with poly-lysine
• Synthetic (oligo)
• Direct oligo synthesis on solid microarray
  substrate
• Uses photolithography (Affymetrix) or ink-jet
  printing (Agilent)
• All configurations assume the DNA on the array is
  in excess of the hybridized samplethus the
  kinetics are linear and the spot intensity
  reflects that amount of hybridized sample.
• Labeling can be radioactive, fluorescent
  (one-color), or two-color

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Microarray Spotter
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Affymetrix High Density Arrays
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Microarrays (continued)

• Imaging
• Radioactive 32P labeling Autoradiography or
  phosphorimager
• Fluorescent labeling Confocal microscope
  (invented by Marvin Minsky!!)
• Feature density
• Nylon membrane macroarrays ? 100-1000 features
• Glass slide spotted array ? 5,000 features / cm2
• Synthesized arrays ? 50,000 features / cm2

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Microarrayconfocal scanner

• Collects sharply defined optical sections from
  which 3D renderings can be created
• The key is spatial filtering to eliminate
  out-of-focus light or glare in specimens whose
  thickness exceeds the immediate plane of focus.
• Two lasers for excitation
• Two color scan in less than 10 minutes
• High resolution, 10 micron pixel size

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cDNA / EST sequencing projects

• cDNA complementary or copy DNA
• EST Expressed Sequence Tag
• The microarray could be described as a closed
  system because information about RNAs is limited
  by the targets available for hybridization. RNAs
  not represented on the array are not
  interrogated.
• Direct sequencing of cDNAs (yielding ESTs)
  overcomes this problem by large-scale random
  sampling of sequences from a whole-cell RNA
  extract
• Statistical counting of distinct sequences
  provides an estimate of expression level
• Conversely, cDNA library can be normalized to
  capture rare messages
• Requires large scale sequencing to get
  statistical significance

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cDNA / EST SequencingPreparation of a cDNA
library in phage l vector
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SerialAnalysis ofGeneExpression
SAGE Technology
Takes idea of sequence sampling to the
extreme Generates short ESTs (9-14nt) which are
joined into long concatamers and then
sequenced 49 is 262,144, 5-fold the number of
human genes The count of each type of tag
estimates RNA copy number gt50X more efficient
than cDNA sequencing because many RNAs are
represented in a single sequencing run
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Steps to SAGE

• Copy mRNA ? ds cDNA using biotinylated (dT)
• Cleave with anchoring enzyme (AE) which cleaves
  within 250bp of poly-A tail at 3 end.
• Capture this segment on streptavidin beads
• Ligate to linkers containing a type IIs
  restriction site, which cleave DNA 14 bp away
  from this site.
• Ligate sequences to each other and PCR amplify
• Cleave with AE to remove linkers
• Concatenate, clone, and sequence

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Velculescu et al. Science (1995)
WHY DI-TAGS? Ditags are used to detect bias in
the PCR amplification step. The probability of
any two tags being coupled in the same ditag is
small. Biased amplification can be detected as
many ditags always having the same 2 tags present.
B
A
B
A
B
A
PrimerA
PrimerB
PrimerA
PrimerB
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SAGE (continued)
Example of a concatemer
CATGACCCACGAGCAGGGTACGATGATACATGGAAACCTATGCACCTTGG
GTAGCACATG
TAG1
TAG2
TAG3
TAG4
Counting the tags
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Proteomics

• SDS PAGE
• 2D PAGE
• MS/MS

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An example SDS-PAGE
How many proteins are in a band?
Protein stains Silver Copper Coomassie Blue
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2D-PAGE
Dimension 2 size
Dimension 1 Isoelectric focusing gel
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2D gel from macrophage phagosomes
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Mass spectrometry

• Mass spectrometers consist of three essential
  parts
• Ionization source Converts peptides into
  gas-phase ions (MALDI ESI)
• Mass analyzer Separates ions by mass to charge
  (m/z) ratio (Ion trap, time of flight,
  quadrupole)
• Ion detector Current over time indicates amount
  of signal at each m/z value

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MS/MS Overview
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MS/MS Overview
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A raw fragmentation spectrum
By calculating the molecular weight difference
between ions of the same type the sequence can be
determined. SEQUEST uses the fragmentation
pattern to search through a complete protein
database to identify the sequence which best fits
the pattern.
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Tandem Mass Spec (MS/MS)
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Typical nanoelectrospray source
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Isotope Coded Affinity Tags (ICAT)
Mass spec based method for measuring relative
protein abundances between two samples
Heavy reagent d8-ICAT (Xdeuterium) Normal
reagent d0-ICAT (Xhydrogen)
ICAT Reagents
O
N
N
O
O
O
I
N
O
O
N
S
Biotin tag
Linker (d0 or d8)
Thiol specific reactive group
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Protein Quantification Identification via ICAT
Strategy
100
Mixture 1
Light
Heavy
0
550
560
570
580
m/z
ICAT-labeled cysteines
Quantitation
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NH2-EACDPLR-COOH
Combine and proteolyze (trypsin)
Affinity separation (avidin)
Mixture 2
0
200
400
600
800
m/z
ICAT Flash animation http//occawlonline.pearsone
d.com/bookbind/pubbooks/bc_mcampbell_genomics_1/me
dialib/method/ICAT/ICAT.html
Protein identification
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ICAT continued

• The heavy (blue) and light (gray) peptides are
  separated and quantified to produce a ratio for
  each peptide here, a single peptide ratio is
  shown
• Each peptide is subjected to CID fragmentation in
  the second MS stage in order to identify it

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Metabolomic measurements

• 2D NMR or mass spectrometry
• Currently not global and in less widespread use
  than microarrays, but have tremendous potential

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Gene knockout and RNAi libraries for model
speciesExample from yeast

• Replacement of yeast ORFS with kanMX gene flanked
  by unique oligo barcodes Yeast Deletion Project
  Consortium

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YFP tagging for protein localization
YPF is green, transmitted light is red
NIC96 Nuclear Pore
TUB1 Tubulin cytoskeleton
HHF2 Histone Nucleus
BNI4 Bud neck
Images courtesy T. Davis lab See also recent work
byWeissman and OShea labs at UCSF
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Systematic phenotyping
Barcode (UPTAG)
CTAACTC
TCGCGCA
TCATAAT

Deletion Strain
Growth 6hrs in minimal media (how many doublings?)
Rich media
Harvest and label genomic DNA
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Systematic phenotyping with a barcode arrayRon
Davis and friends

• These oligo barcodes are also spotted on a DNA
  microarray
• Growth time in minimal media
• Red 0 hours
• Green 6 hours

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Molecular Interactions

• Among proteins, mRNA, small molecules, and so on

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Also like sequence, protein interaction data are
exponentially growing
DIP Database Growthtotal interactions
EMBL Database Growthtotal nucleotides (gigabases)
10
5
0
1980
2000
1990
(As are the false positives!!!)
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High-throughput methods for measuring interaction
networks

• 2-hybrid
• co-immunoprecipitation w/ mass spec
• chIP-on-chip
• systematic genetic analysis

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Yeast two-hybrid method
Fields and Song
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Detection of protein interactions with antibody
arrays
McBeath and Schreiber
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Kinase-target interactions
Mike Snyder and colleagues
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High-throughput methods for measuring networks

• 2-hybrid
• co-immunoprecipitation w/ mass spec
• chIP-on-chip
• systematic genetic analysis

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Protein interactions by protein
immunoprecipitation followed by mass spectrometry
TEV Tobacco Etch Virus proteolytic site CBP
Calmodulin binding peptide Protein A IgG
binding from Staphylococcus
Gavin / Cellzome
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TAP purification
Image courtesy of Bertrand Seraphin
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High-throughput methods for measuring networks

• 2-hybrid
• co-immunoprecipitation w/ mass spec
• chIP-on-chip
• systematic genetic analysis

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ChIP-chip measurement of protein?DNA interactions
From Figure 1 of Simon et al. Cell 2001
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High-throughput methods for measuring networks

• 2-hybrid
• co-immunoprecipitation w/ mass spec
• chIP-on-chip
• systematic genetic analysis

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Genetic interactions synthetic lethals and
suppressors

• Genetic Interactions
• Widespread method used by geneticists to discover
  pathways in yeast, fly, and worm
• Implications for drug targeting and drug
  development for human disease
• Thousands are now reported in literature and
  systematic studies
• As with other types, the number of known genetic
  interactions is exponentially increasing

Adapted from Tong et al., Science 2001
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Most recorded genetic interactions are synthetic
lethal relationships
A
B
A
DB
DA
B
DA
DB
Adapted from Hartman, Garvik, and Hartwell,
Science 2001
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Synthetic-lethal protein interaction
A
B
X
A
B
Suppressor protein interaction
A
B
B
DB
X
A
B
DB
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Interpretation of genetic interactions (Guarente
T.I.G. 1990)
Parallel Effects (Redundant or Additive)
Sequential Effects (Additive)
GOAL Identify downstream physical pathways
Single A or B mutations typically abolish their
biochemical activities
Single A or B mutations typically reduce their
biochemical activities