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..

• Isolate the mRNA of an input sample (e.g. a
  tumour).
• Extract a small chunk of sequence from a defined
  position of each mRNA molecule.
• 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
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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

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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

• Maillard, Jean-Charles, et al., Efficiency and
  limits of the Serial Analysis of Gene
  Expression., Veterinary Immunol. and
  Immunopathol. 2005., 10859-69.
• 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.
  of Clin. Oncol. 2001., 19(11)2948-2958.
• 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.
  Sci. Monit. 2004., 10(6) 132-140.
• 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
  gene expression (SAGE) technology., J. of
  Immunol. meth.2001., 25045-66.

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