Introduction to Physiological Genomics: Defining the Discipline and its Methods

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Introduction to Physiological Genomics Defining
the Discipline and its Methods

• 2005 IUPS Congress
• Timothy P. OConnor, Ph.D.
• Department of Genetic Medicine
• Weill Cornell Medical College
• tio2002_at_med.cornell.edu

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Topics to be Covered

• Terminology jargon
• Potential applications of genomics
• Tools and methods
• Microarrays
• Online resources
• (SNP chips)
• Examples of studies
• Thoughts on incorporating genomics into a
  curriculum

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What is Physiological Genomics?

• Physiological genomics is the study of the
  functioning of gene products in the context of
  the whole organism and its environment.

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Multi-dimensional Integrative Physiology
See Figure 2 from Liang et al. Journal of
Physiology 554 22-30, 2003 http//jp.physoc.org/c
gi/content/full/554/1/22 Free access
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Large Scale Approaches

• Genomics
• Functional genomics
• Proteomics The identification, characterization
  and quantification of all proteins involved in a
  particular pathway that can be studied in concert
  to provide accurate and comprehensive data about
  that system.
• Metabolomics characterization of the
  physiological state of a sample by determining
  the concentration of all the small molecules that
  contribute to metabolism
• Genotyping (SNP) chips

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Large Scale Approaches Genomics and Functional
Genomics

• Genomics
• Determining the sequences of the genome of an
  organism and ordering these sequences into
  individual genes, gene families, and chromosomes
• Identification of coding sequences as well as
  regulatory elements
• Determining the patterns of gene expression (gene
  expression profiles or signatures)
• Functional genomics
• Understanding the biological role of each gene
• Mechanism underlying the regulation of gene
  expression
• Regulatory interactions among genes
• Identifying the functional transcriptome

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Challenge of Functional Genomics

• Capacity for collecting data has surpassed the
  data analysis techniques, and it is only getting
  worse
• Converting data (information) into knowledge is a
  bottleneck
• Currently requires expertise and a
  labor-intensive hands-on approach
• Ultimate goal is to provide more automation to
  the process of knowledge discovery

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Lag Between Functional and Genomics
Source UC Davis Genomics Initiative, Technical
Report, 2001
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Bioinformatics

• Bioinformatics helps bridge the gap between
  functional and genomics
• Field at the interface of computer science,
  statistics, and biology
• Goal of the field is to refine and organize
  biological information into biological knowledge
  using computers

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Gene Expression Patterns

• Genes are expressed when they are copied into
  mRNA or RNA (transcription)
• Differential gene expression which genes are
  expressed in which cells or tissues at a given
  point in time or in the life of the organism.
• Total RNA can be isolated from cells or tissues
  under different experimental conditions and the
  relative amounts of transcribed RNA can be
  measured
• The change in expression pattern in response to
  an experimental condition, environmental change,
  drug treatment, etc. sheds light into the dynamic
  functioning of a cell

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What is a microarray?

• A tool for analyzing gene expression that
  consists of a small membrane or glass slide
  containing samples of thousands of genes arranged
  in a regular pattern.

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The Boom of Microarray Technology Number of
Publications with Affymetrix Chips
1200
1000
800
Number of publications
600
400
200
0
1991
1992
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1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
Year
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Whats the Point?

• Large scale (genome-wide) screening
• Eliminate bias of pre-selecting candidate genes
• Test multiple hypotheses simultaneously
• Generate new hypotheses by identifying novel
  genes associated with experiment
• Identify novel relationships/patterns among genes

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Applications of Microarray Technology

• Gene expression profiling
• In different cells/tissues
• During the course of development
• Under different environmental or chemical stimuli
• In disease state versus healthy
• Molecular diagnosis
• Molecular classification of diseases
• Drug development
• Identification of new targets
• Pharmacogenomics
• Individualized medicine

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Types of Microarrays

• Spotted DNA arrays (cDNA arrays)
• Developed by Pat Brown (Stanford)
• PCR products (or long oligos) from known genes
  (100 nt) spotted on glass, plastic, or nylon
  support
• Customizable and off the shelf
• Oligonucleotide arrays Affymetrix Gene Chips
• Large number of 20-25mers/gene
• Enabled by photolithography from the computer
  industry
• Off the shelf
• Ink-jet microarrays (Agilent)
• 25-60mers printed directly on glass
• Four cartridges A, C, G, and T

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Challenges in Microarray Studies

• What are the difficulties?
• Many potential sources of random and systematic
  measurement error in the microarray process

• Examples
• Experimental design
• Hypothesis testing vs. exploratory fishing
  expedition
• Statistical Analysis
• Small number of samples compared to large number
  of variables (genes) leads to problems with false
  positives
• Data mining
• Annotated lists
• What is the function of the differentially
  expressed genes?
• Extensive use of online resources

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How to Add Functional to Genomics?

• Some automated annotations
• NetAffx www.affymetrix.com
• Batch query with list of gene IDs
• Lots of hands-on annotating, one gene at a time,
  using online databases
• Entrez www.ncbi.nlm.nih.gov
• GeneCards http//bip.weizmann.ac.il/g.html
• Can try this out with public databases
• GEO gene expression omnibus via NCBI

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GEO Public Database Example
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Clinical Relevance
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Gene Expression Signature as a Predictor of
Survival
See figures from van de Vijver et al. New England
Journal of Medicine 347 1999-2009,
2002 http//content.nejm.org/content/vol347/issue2
5/index.shtml Subscription access
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Summary Present and Future Use of Physiological
Genomics

• Molecular diagnosis
• Redefining disease
• Discovery of new targets for therapeutic
  intervention
• Pharmacogenomics
• Variable drug effects depending on individual
  profiles
• Multi-dimensional integrative physiology for
  applications in any subdiscipline
• Comparative physiology
• Ecological physiology
• Evolutionary physiology

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Multi-dimensional Integrative Physiology
See Figure 2 from Liang et al. Journal of
Physiology 554 22-30, 2003 http//jp.physoc.org/c
gi/content/full/554/1/22 Free access
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Thoughts on incorporation of genomics into
curriculum

• Advantages
• availability of public databases containing real
  data
• basic analyses can be done with Excel
• outstanding online databases for annotating gene
  lists
• Challenges
• not effective if boiled down to a lab exercise or
  2
• how to effectively convey the integrative
  potential when you might be working at only 1
  level

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Useful References

• King, H.C. and A.A. Sinha, 2001. Gene expression
  profile analysis by DNA microarrays. JAMA,
  2862280-2288
• Duggan, D.J. et al., 1999. Expression profiling
  using cDNA microarrays. Nature Genetics Supp.
  2110-14.
• Lipshutz, R.J. et al. 1999. High density
  synthetic oligonucleotide arrays. Nature Genetics
  Supp. 2120-24.
• Hackett, N.R. et al., 2003. Variability of
  antioxidant-related gene expression in the airway
  epithelium of cigarette smokers. Am. J. Respir.
  Cell Molec. Biol. 29331-343.
• Cowley Jr., A.W. 2003 Physiological genomics
  tools and concepts. J. Physiol. 5543.
• Liang et al. 2003. High throughput gene
  expression profiling a molecular approach to
  integrative physiology. J Physiol. 554 22-30.

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Useful Websites

• www.ncbi.nih.gov/About/primer/
• NCBIs primer on arrays, SNPs, molecular
  genetics, pharmacogenetics, etc.
• www.affymetrix.com
• Useful information about new microarrays and
  publications using Affy chips
• NetAffx tools for automated annotations