``sage 10 - includes advances of sage
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SAGE TECHNOLOGY AND
ITS APPLICATIONS
PRESENTED BYDr. R.A.Siddique &
Dr.Anand Kumar
Animal Biochemistry Division
N.D.R.I., Karnal (Haryana)India, 132001
E-mail: [email protected]
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WHAT IS SAGE?
Serial analysis of gene expression (SAGE) isa powerful tool that allows digital analysis ofoverall gene expression patterns.
Produces a snapshot of the mRNA populationin the sample of interest.
SAGE provides quantitative andcomprehensive expression profiling in a givencell population.
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SAGE invented at Johns Hopkins University in
USA (Oncology Center) by Dr. Victor Velculescuin 1995.
An overview of a cellscomplete gene activity.
Addresses specific issues such as determination ofnormal gene structure and identification of
abnormal genome changes.
Enables precise annotation of existing genes anddiscovery of new genes.
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NEED FOR SAGE..
Gene expression refers to the study of howspecific genes are transcribed at a given point intime in a given cell.
Examining which transcripts are present in a cell.
SAGE enables large scale studies of DNAexpression; these can be used to create'expression profiles.
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Allows rapid, detailed analysis of thousands of
transcripts in a cell.
By comparing different types of cells, generate
profiles that will help to understand healthy cellsand what goes wrong during diseases.
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THREE PRINCIPLES UNDERLIE THE
SAGE METHODOLOGY:
A short sequence tag (10-14bp) contains sufficientinformation to uniquely identify a transcript provided that
the tag is obtained from a unique position within eachtranscript
Sequence tags can be linked together to from long serialmolecules that can be cloned and sequenced
Quantitation of the number of times a particular tag isobserved provides the expression level of thecorresponding transcript.
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PRE REQUISITES:
Extensive sequencing techniques
Deep bioinformatic knowledge
Powerful computer software (assemble and analyze resultsfrom SAGE experiments)
Limited use of this sensitive technique inacademic research laboratories
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STEPS IN BRIEF..
1. Isolate the mRNA of an input sample (e.g. a
tumour).
2. Extract a small chunk of sequence from a
defined position of each mRNA molecule.
3. Link these small pieces of sequence together to
form a long chain (or concatamer).
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4. Clone these chains into a vectorwhichcan be taken up by bacteria.
5. Sequence these chains using modern high-throughput DNA sequencers.
6. Process this data with a computer to countthe small sequence tags.
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SAGE FLOWCHART
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SAGE TECHNIQUE (in detail)Trap RNAs with beads
Messenger RNAs end with a long string of "As" (adenine)
Adenine forms very strong chemical bonds with another nucleotide,thymine(T)
Molecule that consists of 20 or so Ts acts like a chemical bait tocapture RNAs
Researchers coat microscopic, magnetic beads with chemical baits i.e."TTTTT" tails hanging out
When the contents of cells are washed past the beads, the RNAmolecules will be trapped
A magnet is used to withdraw the bead and the RNAs out of the"soup"
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cDNA SYNTHESIS
Double stranded cDNA is synthesized from the extracted
mRNA by means of biotinylated oligo (dT) primer.
cDNA synthesized is immobilised to streptavidin beads.
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ENZYMATIC CLEAVAGE OF cDNA.
The cDNA molecule is cleaved with a restrictionenzyme.
Type II restriction enzyme used.
Also known as Anchoring enzyme. E.g.NlaIII.
Any 4 base recognising enzyme used.
Average length of cDNA 256bp with sticky endscreated.
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The biotinylated 3 cDNA are affinity purified using strepatavidin
coated magnetic beads.
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LIGATION OF LINKERS TO BOUND
cDNA These captured cDNAs are divided into two
halves, then ligated to linkers A and B,respectively at their ends.
Linkers also known as docking modules.
They are oligonucleotide duplexes.
Linkers contain:
NlaIII 4- nucleotide cohesive overhangType IIS recognition sequence
PCR primer sequence (primer A or B).
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Type IIS restriction enzymetagging enzyme.
Linker/docking module:
PRIMER TE AE TAG
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CLEAVAGE WITH TAGGING
ENZYME Tagging enzyme, usuallyBmsFIcleave DNA 14-
15 nucleotides, releasing the linkeradapted
SAGE tag from each cDNA.
Repair of ends to make blunt ended tags using
DNA polymerase (Klenow) and dNTPs.
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FORMATION OF DITAGS What is left is a collection of short tags taken from each
molecule.
Two groups of cDNAs are ligated to each other, to create a
ditag with linkers on either end.
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Ligation using T4 DNA ligase.
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PCR AMPLIFICATION OF
DITAGS
The linker-ditag-linker constructs are
amplified by PCR using primers specific
to the linkers.
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ISOLATION OF DITAGS
The cDNA is again digested by the AE.
Breaking the linker off right where it was added in thebeginning.
This leaves a sticky end with the sequence GTAC (orCATG on the other strand) at each end of the ditag.
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CONCATAMERIZATION OF
DITAGS Tags are combined into much longer molecules, called
concatemers.
Between each ditag is the AE site, allowing the scientistand the computer to recognize where one ends and the next
begins.
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CLONING CONCATAMERS
AND SEQUENCING Lots of copies are required- So the concatemers are put
into bacteria, which act like living "copy machines" to
create millions of copies from the original
These copies are then sequenced, using machines that canread the nucleotides in DNA. The result is a long list of
nucleotides that has to be analyzed by computer
Analysis will do several things: count the tags, determinewhich ones come from the same RNA molecule, and figureout which ones come from known, well-studied genes and
which ones are new
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Quantitation of gene expression
And data presentation
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How does SAGE work?1. Isolate mRNA.
2.(b) Synthesize ds cDNA.
2.(a) Add biotin-labeled dT primer:
4.(a) Divide into two pools and add linker sequences:4.(b) Ligate.
3.(c) Discard loose fragments.
3.(a) Bind to streptavidin-coated beads.3.(b) Cleave with anchoring enzyme.
5. Cleave with tagging enzyme.
6. Combine pools and ligate.
7. Amplify ditags, then cleave with anchoring enzyme.
8. Ligate ditags.
9. Sequence and record the tags and frequencies.
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Vast amounts ofdata is produced, which
must be sifted and ordered for useful
information tobecome apparent.Sage reference databases:
SAGE map
SAGE Genie
http://www.ncbi.nlm.nih.gov/cgap
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What does the data look like?
TAG COUNT TAG COUNT TAG COUNT
CCCATCGTCC 1286 CACTACTCAC 245 TTCACTGTGA 150
CCTCCAGCTA 715 ACTAACACCC 229 ACGCAGGGAG 142
CTAAGACTTC 559 AGCCCTACAA 222 TGCTCCTACC 140
GCCCAGGTCA 519 ACTTTTTCAA 217 CAAACCATCC 140
CACCTAATTG 469 GCCGGGTGGG 207 CCCCCTGGAT 136
CCTGTAATCC 448 GACATCAAGT 198 ATTGGAGTGC 136
TTCATACACC 400 ATCGTGGCGG 193 GCAGGGCCTC 128
ACATTGGGTG 377 GACCCAAGAT 190 CCGCTGCACT 127
GTGAAACCCC 359 GTGAAACCCT 188 GGAAAACAGA 119
CCACTGCACT 359 CTGGCCCTCG 186 TCACCGGTCA 118
TGATTTCACT 358 GCTTTATTTG 185 GTGCACTGAG 118
ACCCTTGGCC 344 CTAGCCTCAC 172 CCTCAGGATA 114
ATTTGAGAAG 320 GCGAAACCCT 167 CTCATAAGGA 113
GTGACCACGG 294 AAAACATTCT 161 ATCATGGGGA 110
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FROM TAGS TO GENES
Collect sequence records from GenBank
Assign sequence orientation (by finding poly-A
tail or poly-A signal or from annotations)
Extract 10-bases -adjacent to 3-most CATG Assign UniGene identifier to each sequence with a
SAGE tag
Record (for each tag-gene pair)
#sequences with this tag
#sequences in gene cluster with this tag
Maps available at http://www.ncbi.nlm.nih.gov/SAGE
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DIFFERENTIAL GENE
EXPRESSION BY SAGE Identification of differentially expressed
genes in samples from different
physiological or pathological conditions.Application of many statistical methods
Poisson approximation
Bayesian methodChi square test.
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SAGE software searches GenBank for matchesto each tag
This allows assignment to 3 categories of tags:
mRNAs derived from known genes
anonymous mRNAs, also known as expressed sequence
tags (ESTs)
mRNAs derived from currently unidentified genes
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SAGE VSMICROARRAY
SAGEAn open system which detects both known and
unknown transcripts and genes.
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COMPARISON
SAGE
Detects 3 region of
transcript. Restriction site
is determining factor.
Collects sequence
information and copy no.
Sequencing error and
quantitation bias.
MICROARRAY
Targets various regions of
the transcript.Base
composition for specificity
of hybridization.
Fluorescent signals and
signal intensity.
Labeling bias and noise
signals.
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Contd
Features SAGE M icroarray
Detects unknown
transcripts
Yes No
Quantification Absolute measure Relative measure
Sensitivity High Moderate
Specificity Moderate High
Reproducibility Good for higher
abundance transcripts
Good for data from
intra-platformcomparison
Direct cost 5-10X higher than
arrays.
5-10 X lower than
SAGE
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RECENT SAGE APPLICATIONS
Analysis of yeast transcriptome
Gene Expression Profiles in Normal and Cancer Cell
Insights into p53-mediated apoptosis
Identification and classification of p53-regulated genesAnalysis of human transcriptomes
Serial microanalysis of renal transcriptomes
Genes Expressed in Human Tumor Endothelium
Analysis of colorectal metastases (PRL-3)Characterization of gene expression in colorectal adenomas
and cancer
Using the transcriptome to analyze the genome (Long SAGE)
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LIMITATIONS
Does not measure the actual expression level of a gene.
Average size of a tag produced during SAGE analysis is
ten bases and this makes it difficult to assign a tag to a
specific transcript with accuracy
Two different genes could have the same tag and the same
gene that is alternatively spliced could have different tags at
the 3' ends
Assigning each tag to an mRNA transcript could be madeeven more difficult and ambiguous if sequencing errors are
also introduced in the process
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Quantitation bias: Contamination of of large quantities of linker-dimer molecules.
low efficiency in blunt end ligation.
Amplification bias.
Depending upon anchoring enzyme and tagging enzyme
used, some fraction of mRNA species would be lost.
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Advances over SAGE
Generation of longer 3` cDNA from SAGE tags
for gene identification (GLGI)
Long SAGE
Cap Analysis of Gene Expression (CAGE)
Gene Identification Signature (GIS)
SuperSAGE
Digital karyotyping
Paired-end ditag
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Long SAGE
Increased specificity of SAGE tags for
transcript identification and SAGE tag
mapping. Collects tags of 21bp
Different TypeII restriction enzyme-Mmel
Adapts SAGE principle to genomic DNA.
Allows localisation of TIS and PAS.
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CAGE (Capped Analysis of Gene Expression)
Aims to identify TIS and promoters.
Collects 21 bp from 5 ends of cap purified cDNA.
Used in mouse and human transcriptome studies. The method essentially uses full-length
cDNAs , to the 5ends of which linkers are
attached.
This is followed by the cleavage of the first 20
base pairs by class II restriction enzymes,
PCR, concatamerization, and cloning of the
CAGE tags
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AAAAA
AAAAABiotin
Biotin +
Mmel
xBiotin
+Xma JI
Biotin
Biotin Mmel-PCR
BiotinUni-PCR
XmaJI tag1 tag2 XmaJI
Concatenation
Cloning
Sequencing
PCR amplification
Ligation to second linker
MmeI digestion of dsDN
ssDNA captureSecond strand synthesi
Full strand DNA synthesis
ssDNA release
Reverse transcription
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Micro SAGE
Requires 500-5000 fold less starting input RNA.
Simplifies by the incorporation of a one tube procedure
for all steps.
Characterization of expression profiles in tissue biopsies,
tumor metastases or in cases where tissue is scarce.
Generation of region-specific expression profiles of
complex heterogeneous tissues.
Limited number of additional PCR cycles are performed to
generate sufficient ditag.
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An expression profile can be obtained from as
little as 1-5 ng of mRNA.
Comparison between the twoSAGE MicroSAGE
Amount of input
material
2.5-5 ug RNA 1-5 ng of mRNA
Capture of
cDNA
Streptavidin coated
magnetic beads
Streptavidin coated PCR
tube
Multiple tube vs.
Single tube
reaction
Subsequent reactions in
multiple tubes
Multiple PCI extraction
and ethanol precipitation
steps
Single tube reaction
Easy change of buffers
No PCI extraction or
ethanol ppt step.
Fewer manipulations
PCR 25-28 cycles 28 cycles followed by re-PCR on excised ditag (8-
15)
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SuperSAGE
Increases the specificity of SAGE tags and
use of tags as microarray probes.
Type III REEcoP15Itag releasing
Collects 26 bp tags
Has been used in plant SAGE studies.
Study of gene expression in which sequence
information is not available.
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Flowchart of superSAGE
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Gene Identification Signature
(GIS) Identifies gene boundaries.
Collects 20bp LongSAGE tags from 3 and
5 end of the transcript.
Applied to human and mouse transcription
studies.
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DIGITAL KARYOTYPING
Analyses gene structure. Identification amplification and deletion in several
cancers.
PAIRED END DITAG
Identifies protein binding sites in genome.
Applied to identify p-53 binding sites in thehuman genome.
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1. SAGE: A LOOKING GLASS
FOR CANCER Deciphering pathways involved in tumor genesisand identifying novel
diagnostic tools, prognostic markers,and potential therapeutic targets.
SAGE is one of the techniques
used in the National Cancer Institutefunded Cancer GenomeAnatomy Project (CGAP).
A database with archived SAGE tag counts and on-line query toolswas
created - the largest source of public SAGE data.
More than 3 million tags from 88 different librarieshave been
deposited on the National Center for BiotechnologyEducation/CGAP
SAGEmap web site (http://www.ncbi.nlm.nih.gov/SAGE/).
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Several interesting patterns have emerged.
cancerous and normal cells derived from the same tissue typeare verysimilar.
tumors of the same tissue of origin but of different
histological type orgrade have distinct gene expression patterns
cancer cells usuallyincrease the expression of genes associated withproliferationand survival and decrease the expression of genes involved in
differentiation.
SAGE studies have been performed in patientswith colon, pancreatic,lung, bladder, ovarian, and breast cancers.
SAGE experiments validated in multiple tumor and normaltissue pairsusing a variety of approaches, including Northernblot analysis, real-
time PCR, mRNA in situ hybridization, and
immunohistochemistry.
Identification of an ideal tumor marker. E.g. Matrix metalloprotease1in ovarian cancer is overexpressed.
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p53- TUMOR SUPRESSOR GENE
p53is thought to play a rolein the regulation of cell cycle checkpoints,apoptosis, genomicstability, and angiogenesis.
Sequence-specific transactivationis essential forp53-mediated tumorsuppression.
The analysis of transcriptomes afterp53expressionhas determinedthatp53exerts its diverse cellular functionsby influencing theexpression of a large group of genes.
Identification of Previously Unidentified p53-Regulated Genes bySAGE analysis.
Variability exists with regardto the extent, timing, andp53dependenceof the expressionof these genes.
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2. IMMUNOLOGICAL STUDIES
Only a few SAGE analysis has been applied for the study ofimmunological phenomena.
SAGE analyses were conducted for human monocytes and their
differentiated descendants, macrophages and dendritic cells.
DC cDNA library represented more than 17,000 different genes. Genesdifferentially expressed were those encoding proteins related to cellmotility and structure.
SAGE has been applied to B cell lymphomas to analyze genesinvolved in BCRmediated apoptosis.- polyamine regulation isinvolved in apoptosis during B cell clonal deletion.
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Contd
LongSAGE has been used to identify genes of T cells with SLE that
determine commitment to the disease.
Findings indicate that the immatureCD4+ T lymphocytes may be
responsible for the pathogenesis of SLE.
SAGE has been used to analyze the expression profiles of Th-1 and Th-
2 cells, and newly identified numerous genes for which expression is
selective in either population.
Contributes to understanding of the molecular basis of Th1/Th2
dominated diseases and diagnosis of these diseases.
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3. YEAST TRANSCRIPTOME
Yeast is widely used to clarify the biochemical physiologicparameters underlying eukaryotic cellular functions.
Yeast chosen as a model organism to evaluate the powerof SAGE technology.
Most extensive SAGE profile was made for yeast.
Analysis of yeast transcriptome affords a unique view ofthe RNA components defining cellular life.
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4.ANALYSIS OF TISSUE
TRANSCRIPTOMES
Used to analyze the transcriptomes of renal, cervical
tissues etc.
Establishing a baseline of gene expression in normal tissue
is key for identifying changes in cancer.
Specific gene expression profiles were obtained, and
known markers (e.g., uromodulinin the thick ascending
limb of Henle's loop and aquaporin-2 inthe collecting duct)
were found.
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REFERENCES
Maillard, Jean-Charles, et al., Efficiency and limits of the Serial Analysis ofGene Expression., Veterinary Immunol. and Immunopathol. 2005., 108:59-69.
Man, M.Z. et al., POWER-SAGE: comparing statistical tests for SAGEexperiments., Bioinformatics 2000., 16: 953-959.
Polyak, K. and Riggins, G.J., Gene discovery using the serial analysis of geneexpression technique: Implications for cancer research., J. of Clin. Oncol.2001., 19(11):2948-2958.
Tuteja and Tuteja., Serial Analysis of Gene Expression: Applications inHuman Studies., J. of Biomed. And Biotechnol. 2004., 2: 113-120.
Tuteja and Tuteja., Serial analysis of gene expression: application in cancerresearch., Med. Sci. Monit. 2004., 10(6): 132-140.
Velculescu, V.E. et al. Serial analysis of gene expression., Science 1995.,
270:484-487. Wing, San Ming., Understanding SAGE data., Trends in Genetics 2006., 23:
1-12.
Yamamoto, M., et al., Use of serial analysis of gene expression (SAGE)technology., J. of Immunol. meth.2001., 250:45-66.
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