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Title: Pr


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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

3
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)

4
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

5
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

6
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

8
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)

9
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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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
20
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)

23
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

24
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

25
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

26
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

30
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
32
Nature of IPG determines spot location on 2D-PAGE
33
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

35
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
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