SERIAL ANALYSIS OF GENE EXPRESSION AND CANCER RESEARCH

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welcome!!
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SAGE TECHNOLOGY AND ITS APPLICATIONS

• PRESENTED BY
• Dr. R.A.Siddique
• Dr.Anand Kumar
• Animal Biochemistry Division
• N.D.R.I.,
  Karnal (Haryana)India, 132001
• E-mail riazndri_at_gmail.com

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WHAT IS SAGE?

• Serial analysis of gene expression (SAGE) is a
  powerful tool that allows digital analysis of
  overall gene expression patterns.
• Produces a snapshot of the mRNA population in the
  sample of interest.
• SAGE provides quantitative and comprehensive
  expression profiling in a given cell population.

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• SAGE invented at Johns Hopkins University in USA
  (Oncology Center) by Dr. Victor Velculescu in
  1995.
• An overview of a cells complete gene activity.
• Addresses specific issues such as determination
  of normal gene structure and identification of
  abnormal genome changes.
• Enables precise annotation of existing genes and
  discovery of new genes.

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NEED FOR SAGE..

• Gene expression refers to the study of how
  specific genes are transcribed at a given point
  in time in a given cell.
• Examining which transcripts are present in a
  cell.
• SAGE enables large scale studies of DNA
  expression 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
  cells and what goes wrong during diseases.

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THREE PRINCIPLES UNDERLIE THE SAGE METHODOLOGY

• A short sequence tag (10-14bp) contains
  sufficient information to uniquely identify a
  transcript provided that the tag is obtained from
  a unique position within each transcript
• Sequence tags can be linked together to from long
  serial molecules that can be cloned and sequenced
• Quantitation of the number of times a particular
  tag is observed provides the expression level of
  the corresponding transcript.

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PRE REQUISITES

• Extensive sequencing techniques
• Deep bioinformatic knowledge
• Powerful computer software (assemble and analyze
  results from SAGE experiments)
• Limited use of this sensitive technique in
  academic 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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• Clone these chains into a vector which can be
  taken up by bacteria.
• Sequence these chains using modern
  high-throughput DNA sequencers.
• Process this data with a computer to count the
  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 to capture 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 RNA molecules 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 restriction
  enzyme.
• 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 ends
  created.

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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 overhang
• Type IIS recognition sequence
• PCR primer sequence (primer A or B).

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• Type IIS restriction enzyme
  tagging enzyme.
• Linker/docking module

PRIMER TE AE TAG
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CLEAVAGE WITH TAGGING ENZYME

• Tagging enzyme, usually BmsFI cleave DNA 14-15
  nucleotides, releasing the linker adapted 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 the beginning.
• This leaves a sticky end with the sequence GTAC
  (or CATG 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
  scientist and 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 can read 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,
  determine which ones come from the same RNA
  molecule, and figure out 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?
3.(c) Discard loose fragments.
9. Sequence and record the tags and frequencies.
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• Vast amounts of data is produced, which must be
  sifted and ordered for useful information to
  become 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?
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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 method
• Chi square test.

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• SAGE software searches GenBank for matches to
  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 VS MICROARRAY

• SAGE An 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 Microarray
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-platform comparison
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 genes
• Analysis 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 made even 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 5 ends 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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Reverse transcription
AAAAA

• Full strand DNA synthesis
• ssDNA release

AAAAA
Biotin
Biotin

• ssDNA capture
• Second strand synthesis

x
Biotin
MmeI digestion of dsDNA
Mmel

Biotin
Ligation to second linker
Xma JI
Biotin
Mmel-PCR
PCR amplification
Biotin
Uni-PCR

• Concatenation
• Cloning
• Sequencing

XmaJI tag1 tag2 XmaJI
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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 two

SAGE 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 RE EcoP15I tag 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 the
  human genome.

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APPLICATIONS
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1. SAGE A LOOKING GLASS FOR CANCER

• Deciphering pathways involved in tumor genesis
  and identifying novel diagnostic tools,
  prognostic markers, and potential therapeutic
  targets.
• SAGE is one of the techniques used in the
  National Cancer Institutefunded Cancer Genome
  Anatomy Project (CGAP).
• A database with archived SAGE tag counts and
  on-line query tools was created - the largest
  source of public SAGE data.
• More than 3 million tags from 88 different
  libraries have been deposited on the National
  Center for Biotechnology Education/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 type are very similar.
• tumors of the same tissue of origin but of
  different histological type or grade have
  distinct gene expression patterns
• cancer cells usually increase the expression of
  genes associated with proliferation and survival
  and decrease the expression of genes involved in
  differentiation.
• SAGE studies have been performed in patients with
  colon, pancreatic, lung, bladder, ovarian, and
  breast cancers.
• SAGE experiments validated in multiple tumor and
  normal tissue pairs using a variety of
  approaches, including Northern blot analysis,
  real-time PCR, mRNA in situ hybridization, and
  immunohistochemistry.
• Identification of an ideal tumor marker. E.g.
  Matrix metalloprotease1 in ovarian cancer is
  overexpressed.

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p53- TUMOR SUPRESSOR GENE

• p53 is thought to play a role in the regulation
  of cell cycle checkpoints, apoptosis, genomic
  stability, and angiogenesis.
• Sequence-specific transactivation is essential
  for p53-mediated tumor suppression.
• The analysis of transcriptomes after p53
  expression has determined that p53 exerts its
  diverse cellular functions by influencing the
  expression of a large group of genes.
• Identification of Previously Unidentified
  p53-Regulated Genes by SAGE analysis.
• Variability exists with regard to the extent,
  timing, and p53 dependence of the expression of
  these genes.

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2. IMMUNOLOGICAL STUDIES

• Only a few SAGE analysis has been applied for the
  study of immunological 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. Genes differentially expressed
  were those encoding proteins related to cell
  motility and structure.
• SAGE has been applied to B cell lymphomas to
  analyze genes involved in BCR mediated
  apoptosis.- polyamine regulation is involved 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
  physiologic parameters underlying eukaryotic
  cellular functions.
• Yeast chosen as a model organism to evaluate the
  power of SAGE technology.
• Most extensive SAGE profile was made for yeast.
• Analysis of yeast transcriptome affords a unique
  view of the 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

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  limits of the Serial Analysis of Gene
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• Man, M.Z. et al., POWER-SAGE comparing
  statistical tests for SAGE experiments.,
  Bioinformatics 2000., 16 953-959.
• Polyak, K. and Riggins, G.J., Gene discovery
  using the serial analysis of gene expression
  technique Implications for cancer research., J.
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• Tuteja and Tuteja., Serial Analysis of Gene
  Expression Applications in Human Studies., J. of
  Biomed. And Biotechnol. 2004., 2 113-120.
• Tuteja and Tuteja., Serial analysis of gene
  expression application in cancer research., Med.
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• Velculescu, V.E. et al. Serial analysis of gene
  expression., Science 1995., 270484-487.
• Wing, San Ming., Understanding SAGE data., Trends
  in Genetics 2006., 23 1-12.
• Yamamoto, M., et al., Use of serial analysis of
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  Immunol. meth.2001., 25045-66.

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