Toxicology in the omics era'

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Toxicology in the omics era.

• Chris Evelo
• BiGCaT Bioinformatics Group BMT-TU/e UM

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Where the cat hunts
BiGCaT Bioinformatics
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BiGCaT, bridge between two universities
TU/eIdeas Experience in Data Handling
Universiteit Maastricht Patients,
Experiments,Arrays and Loads of Data
BiGCaT
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BiGCaT Bioinformatics
Nutritional EnvironmentalResearch
CardiovascularResearch
BiGCaT
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The Cardiovascular Triangle
Platformproviders
microarrays, clones, proteomicsannotation/databas
es
BiGCaT
biomedical researchpatientsBiGCaTs lair
informaticsmathematicscomputer science
UM
BMT TU/e
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Gene expression

• Genes are part of the chromosomes in the cell
  nucleus.
• Genes are transcribed to messenger RNA (mRNA).
• mRNA is processed further in the nucleus.
• Complete mRNAs leave the nucleus and are
  translated to protein in the cytosol.

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Alberts et al. Molecular Biology of the Cell, 3rd
edn.
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mRNA processing

• Genes contain
• Expressed regions (exons)
• Non expressed regions (introns)
• During gene splicing introns are removed and
  exons connected
• A poly-adenosine (poly-A) tail is added
• Complete mRNAs leave the nucleus

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Figure 9-87. Control of the poly-A tail length
affects both mRNA stability and mRNA translation.
(A) Most translated mRNAs have poly-A tails that
exceed a minimum length of about 30 As. The tails
on selected mRNAs can be either elongated or
rapidly cleaved in the cytosol, and this will
have an effect on the translation of these mRNAs.
(B) A model proposed to explain the observed
stimulation of translation by an increase in
poly-A tail length. The large ribosomal subunits,
on finishing a protein chain, may be directly
recycled from near the 3' end of an mRNA molecule
back to the 5' end to start a new protein by
special poly-A-binding proteins (red).
Alberts et al. Molecular Biology of the Cell, 3rd
edn.
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Genes and vulnerability

• Genes can be
• Absent (e.g. GST mu deletion)
• Broken (e.g. single nucleotide point mutations
  SNPs)
• Differently expressed (e.g. P450 class of
  enzymes)
• Intra individual differences (vulnerability)
• As a result of exposure (BEM)

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What about the human genome?

• Smart people copied chromosomal sequences to
  computer hard discs.
• So now you can read it (although I still prefer
  a good novel).
• If you are good at it (and care to read it 6
  times over) you can even predict genes.
• But even if you are among the best you cant
  predict proteins or their function

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Tell me about your proteins

• Hard working biochemists and physiologists
• did spend a century
• to describe proteins, their function, structure
  and sequence.
• Molecular biologists
• used decades
• found huge amounts of expressed mRNA sequences
  (ESTs) and tried to relate them to function.
• And
• they failed.
• Cluttering up the databases with things like EST
  found in very seldom tumor so and so (this could
  still be myoglobin mRNA)

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So what can we do?

• Take the EST sequences and cluster them to full
  mRNA sequences (Unigene!)
• Build the full coding sequences from this
  (useful part of EMBL)
• Translate that into hypothetical proteins
  (trEMBL)
• Check whether that happens to be a known
  protein (Swissprot)
• Use all that to find microarray reporter
  sequences for known proteins

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DNA useful after all?

• Yes, if you know from population genetics or
  animal experiments about loci important for
  trades. Your gene might be in such a locus.
• And to find regulatory sequences

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Past, present and (near) future

• Toxicologists detect
• Enzyme activity classic clinical chemistry.
• Single gene DNA identity, PCR.
• Single gene expression at the mRNA level (RT-PCR)
• Transcriptomics. Full genome mRNA expression
  (microarray, expression libraries)
• Proteomics. Full genome protein expression
  (proteomics, 2D-gels with MS, antibody arrays)

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Gene expression measurement
DNA ? mRNA ? protein

• Functional genomics/transcriptomics
• Changes in mRNA
• Gene expression microarrays
• Proteomics
• Changes in protein levels

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Gene expression arrays

• Microarrays relative fluorescense signals.
  Identification.

Macroarrays absolute radioactive signal.
Validation.
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Gene expression microarrays

• Contain many immobilized unique cDNA sequences
  (20,000)
• Sample mRNA is transcribed to complementary DNA
  (cDNA)
• Sample cDNA is made fluorescent using 2 different
  dyes
• cDNAs will bind (hybridize) specifically to
  their own complementary spotted cDNA
• Fluorescence is read using laser technology

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Layout of a microarray experiment

• Get the cells
• Isolate RNA
• Make fluorescent cDNA
• Hybridize
• Laser read out
• Analyze image

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Next slide shows data of one single actual
microarray

• Normalized expression shown for both channels.
• Each reporter is shown with a single dot.
• Red dots are controls
• Note the GEM barcode (QC)
• Note the slight error in linear normalization
  (low expressed genes are higher in Cy5 channel)

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Next slide shows same data after processing

• Controls removed
• Bad spots (lt40 average area) removed
• Low signals (lt2.5 Signal/Background) removed
• All reporters with lt1.7 fold change removed (only
  changing spots shown)

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Final slide shows information for one single
reporter

• This signifies one single spot
• It is a known genean UDP glucuronyltransferase
• Raw data and fold change are shown

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Microarrays can detect

• Differences in mRNA expression (thats what they
  were made for)
• Can compare the individual to the population
• Or the exposed group with the control group
• Gene deletions
• SNPs (single nucleotide point mutations)

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We could now

• Isolate mRNA from an individuals white blood
  cells.
• Run a 10,000 gene mRNA expression array
• Put the results on a personal microchip or CD-ROM
• And know his vulnerabilities

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And we could

• Sell the results to his insurance company

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Are you ready?

• Are you ready to hop on the genomics wagon?
• It may be a bit awkward
• But you will have to

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Hop, Step and
Jump
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Slides will be made available at

• http//www.BiGCaT.nl