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What next?

• High quality genome sequencing and annotation
  (2003)
• Complete sequencing the genomes of other model
  organisms (e.g. Mouse)
• The next step Functional Genomics
• Determine what our genes do through systematic
  studies of function on a large scale
• Transcriptomics - Comparative analysis of mRNA
  expression /splicing
• Proteomics - Comparative analysis of protein
  expression and post-translational modifications
• Structural genomics - Determine 3-D structures of
  key family members
• Intervention studies - Effects of inhibiting gene
  expression
• Comparative genomics - Analysis of DNA sequence
  patterns of humans and well studies model
  organisms

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Beyond Genomics Systems Biology

• Human Genome 30,000 to 60,000 genes
• Human Proteome 300,000 to 1,200,000 protein
  variants
• Human Metabalome metabolic products of the
  organism (lipids,carbohydrates, amino acids,
  peptides, prostaglandins, etc)

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Functional Genomics

• Whole genome
• Once the whole genome is truly known and the
  whole genome sequences become available for an
  organism, the challenge turns from identifying
  parts to understanding function
• Functional genomics
• The post-genomic era is defined as functional
  genomics
• Assignation of function to identified genes
• Organisation and control of genetic pathways that
  come together to make up the physiology of an
  organism

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Functional Genomics

• 42 of human genes of unknown function have been
  found in the human genome
• assigning function to these genes using
  systematic high throughput methods is required

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The Periodic Table
Functional grouping of Chemical Elements
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Biologists Periodic Table
Genomics

• Will not be two-dimensional
• Will reflect similarities at diverse levels
• Primary DNA sequence in coding and regulatory
  regions
• Polymorphic variation within a species or
  subgroup
• Time and place of expression of RNAs during
  development, physiological response and disease
• Subcellular localisation and intermolecular
  interaction of protein products

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Gene Expression analysis

• Array of hope?
• Arrays offer hope for global views of
  biological processes
• Systematic way to study DNA and RNA variation
• Standard tool for molecular biology research
  clinical diagnostics
• Labelled nucleic acid molecules can be used to
  interrogate nucleic acid molecules attached to
  solid support (remember Southern Blotting?)
• (Refer to January 1999, Nature Genetics
  Supplement, Volume 21)

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Gene Expression analysis

• DNA chips Also known as gene chips, biochips,
  microarraysbasically DNA-covered pieces of glass
  (or plastic) capable of simultaneously analysing
  thousands of genes at a time they can be high
  density arrays of oligonucleotides or cDNA
• Chips allow the monitoring of mRNA expression on
  a big scale (i.e many many genes at the same
  time)

Pre-1995, Northern Blots used to look at gene
expression
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Gene Expression analysis
Incyte
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Gene Expression analysis
Affymetrix
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Nanogen_Movie_2
Nanogen_Movie_3
Affymetrix_Movie_3
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Determining gene function
sequence homology
sequence motif
tissue distribution
chromsme localisation
function .
expression in disease
biochemical assays
proteomics .
expression in models
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Protein synthesis
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RNA synthesis and processing
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Alternatively spliced mRNA
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The transcriptome

• DEFINITION
• The mRNA collection content, present at any
  given moment in a cell or a tissue, and its
  behaviour over time and cell states
• (Adam Sartel, COMPUGEN).
• The complete collection of mRNAs and their
  alternative splice forms is sometimes referred to
  as the trancriptome. The transcriptome is teh
  set of instructions for creating all of the
  different proteins found in an organism.
• (From Genome to Transcriptome, Incyte)

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Genome, proteome and transcriptome
The Transcriptome
Golden path
Proteome information in
DNA technology
The Proteome
- Index to a range of possible proteins
- Useful as a map and for inter-organisms
analysis
- Describes what actually happens in the cell
- Complex tools, partial results
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Use of transcriptome analysis

• Discovery of new proteins
• that are present in specific tissues
• that have specific cell locations
• that respond to specific cell states
• Discovery of new variants
• of important genes
• that work to increase/decrease the activity of
  the native protein
• The transcriptome reflects tissue source (cell
  type, organ) and also tissue activity and state
  such as the stage of development, growth and
  death, cell cycle, diseased or healthy, response
  to therapy or stress..

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Beyond genomicsproteomics

• Proteomicswhere the genome hits the road
• Proteomics refers to the simultaneous, large
  scale analysis of all (or many) of the proteins
  made in a cell at one time to get a global
  picture of what proteins are made in cells and
  when
• Hopefully then we can determine the whys and
  what we can thus do about it very important for
  drug development
• The proteome is the protein complement encoded by
  a genome and the term was first proposed by an
  Australian post-doc, Marc Wilkins in 1994

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Beyond the genome Proteomics

• Genomics involves study of mRNA expression-the
  full set of genetic information in an organism
  contains the recipes for making proteins
• Proteins constitute the bricks and mortar of
  cells and do most of the work
• Proteins distinguish various types of cells,
  since all cells have essentially the same
  Genome their differences are dictated by which
  genes are active and the corresponding proteins
  that are made
• Similarly, diseased cells may produce dissimilar
  proteins to healthy cells
• However task of studying proteins is often more
  difficult than genes (e.g. post-translational
  modifications can dramatically alter protein
  function)

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Beyond the genome Proteomics

• Identification of all the proteins made in a
  given cell, tissue or organism
• Identification of the intracellular networks
  associated with these proteins
• Identification of the precise 3D-structure of
  relevant proteins to enable researchers to
  identify potential drug targets to turn protein
  on or off
• Proteomics very much requires a coordinated focus
  involving physicists, chemists, biologists and
  computer scientists

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Beyond the genome Proteomics

• Major challenge-how do we go from the treasure
  chest of information yielded by genomics in
  understanding cellular function
• Genomics based approaches initially use
  computer-based similarity searches against
  proteins of known function
• Results may allow some broad inferences to be
  made about possible function
• However, a significant percentage (gt30) of the
  sequences thus far ascertained seem to code for
  proteins that are unrelated at this level to
  proteins of known function

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Beyond the genome Proteomics

• Beyond the genetic make-up of an individual or
  organism, many other factors determine gene and
  ultimately protein expression and therefore
  affect proteins directly
• These include environmental factors such as pH,
  hypoxia, drug treatment to name a few
• Examination of the genome alone can not take into
  account complex multigenic processes such as
  ageing, stress, disease or the fact that the
  cellular phenotype is influenced by the networks
  created by interaction between pathways that are
  regulated in a coordinated way or that overlap

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Beyond the genome Proteomics

• Genomic analysis has certainly provided us with
  much insight into the possible role of particular
  genes in disease
• However proteins are the functional output of the
  cell and their dynamic nature in specific
  biological contexts is critical
• The expression or function of proteins is
  modulated at many diverse points from
  transcription to post-translation and very little
  of this can be predicted from a simple analysis
  of nucleic acids alone
• There is generally poor correlation between the
  abundance of mRNA transcribed from the DNA and
  the respective proteins translated from that mRNA
• Furthermore, transcript splicing can yield
  different protein forms
• Proteins can undergo extensive modifications such
  as glycosylation, acetylation, and
  phosphorylation which can lead to multiple
  protein products from the same gene

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Proteomics Tools

• The core methodologies for displaying the
  proteome are a combination of advanced separation
  techniques principally involving two-dimensional
  electrophoresis (2D-GE) and mass spectrometry

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2D-GE basic methodology

• Sample (tissue, serum, cell extract) is
  solubilized and the proteins are denatured into
  polypeptide components
• This mixture is separated by isoelectric focusing
  (IEF) on the application of a current, the
  charged polypeptide subunits migrate in a
  polyacrylamide gel strip that contains an
  immobilized pH gradient until they reach the pH
  at which their overall charge is neutral
  (isoelctric point or pI), hence producing a gel
  strip with distinct protein bands along its
  length
• This strip is applied to the edge of a
  rectangular slab of polyacrylamide gel containing
  SDS. The focused polypeptides migrate in an
  electric current into the second gel and undergo
  separation on the basis of their molecular size

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2D-GE basic methodology

• The resultant gel is stained (Coomassie, silver,
  fluorescent stains) and spots are visualized by
  eye or an imager. Typically 1000-3000 spots can
  be visualized with silver. Complementary
  techniques, e.g. immunoblotting allow greater
  sensitivity for specific molecules.
• Multiple forms of individual proteins can be
  visualized and the particular subset of proteins
  examined from the proteome is determined by
  factors such as initial solubilization
  conditions, pH range of the IPG and gel gradient

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General schematic of 2D-PAGE for protein
identification in Toxicology
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General strategy for proteomic analysis
Sample solubilization
Sample growth
Isoelectric focusing (IPG)
2D-PAGE
Immunoblot (Western)
Image analysis
Isolation of spots of interest
Trypsin digestion of proteins
MS analysis of tryptic fragments
Identification of proteins
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Nature of IPG determines spot location on 2D-PAGE
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Limitations of 2D-GE

• In the large scale analysis of proteomics, 2D-GE
  has been the major workhorse over the last 20
  years-its unique application in being able to
  distinguish post-translational modifications and
  is analytically quantitative
• However despite the significant improvements
  (e.g. immobilized pH gradients) to the technique
  and its coupling with MS analysis it is still
  difficult to automate
• Although at first glance the resolution of 2D
  seems very impressive, it still lags behind the
  enormous diversity of proteins and thus
  comigrating protein spots are not uncommon
• This is especially of concern when trying to
  distinguish between highly abundant proteins e.g.
  actin (108 molecules/cell) and low abundant like
  transcription factors (100-1000)-this is beyond
  the dynamic range of 2D
• Enrichment or prefractionation can often overcome
  such discrepancies

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Limitations of 2D-GE

• Chemical heterogeneity of proteins also presents
  a major limitation
• Thus the full range of pIs and MWs of proteins
  exceeds what can routinely be analyzed on 2D-GE.
  However improvements to IPGs is expected to
  overcome some of these constraints and greatly
  imrpove the coverage of the entire proteome of
  the cell
• Problems liked with extraction and solubilization
  of proteins prior to 2D-GE present an even
  greater challenge-especially for extremely
  hydrophobic proteins, such as membrane and
  nuclear proteins. Again recent advances in buffer
  composition has diminished the scale of this
  problem

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Differential Gel Electrophoresis (DiGE)
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Protein identification and characterization

• Specialized imaging software allows for a more
  detailed analysis of spot identification and
  comparison between gels, and treatments
• By a process of subtraction, differences (e.g.
  presence, absence, or intensity of proteins or
  different forms) between healthy and diseased
  samples can be revealed
• Cross-references to protein databases allow
  assignment by known pIs and apparent molecular
  size. Ultimate protein identification requires
  spot digestion (enzymatic) and analysis of charge
  and mass by mass spectrometry (MS)
• Spot cutter tools can be coupled to image
  analysis tools and in gel tryptic digestion
  techniques in 96 or 384 well format can greatly
  reduce the bottle-neck in sample identification
  by MS

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Protein analysis by MS

• Compared to sequencing, MS is more sensitive
  (femtomole to attomole concentrations) and is
  higher throughput
• Digestion of excised spot with trypsin results in
  a mixture of peptides. These are ionized by
  electrospray ionization from liquid state or
  matrix-assisted laser desorption ionization from
  solid state (MALDI-TOF) and the mass of the ions
  is measured by various coupled analyzers (e.g.
  time of flight measures the time for ions to
  travel from the source to the detector, resulting
  in a peptide fingerprint
• The resultant signature is compared with the
  peptide masses predicted from theoretical
  digestion of protein sequences found in
  databases-identification of protein!
• Tandem MS allows one to obtain actual protein
  sequence information-discrete peptide ions can be
  selected and further fragmented, and complex
  algorithms employed to correlate exp data with
  database derived peptide sequences

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MS analysis
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MS analysis
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Antibody arrays
Good for low-abundance proteins Problem is
antibody specificity
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Protein microarrays
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Caveats

• The technology of proteomics is not as mature as
  genomics, owing to the lack of amplification
  schemes akin to PCR. Only proteins from a natural
  source can be analyzed
• The complexities of the proteome arise because
  most proteins seem to be processed and modified
  in complex ways and can be the products of
  differential splicing
• in addition protein abundance spans a range
  estimated to be 5 to 6 orders of magnitude in
  yeast and 10 orders of magnitude in humans.

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challenges

• Complexity some proteins have gt1000 variants
• Need for a general technology for targeted
  manipulation of gene expression
• Limited throughput of todays proteomic platforms
• Lack of general technique for absolute
  quantitation of proteins