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BioSci D145 Lecture #10
• Bruce Blumberg ([email protected])
– 4103 Nat Sci 2 - office hours Tu, Th 3:30-5:00 (or by appointment)
– phone 824-8573
• TA – Bassem Shoucri ([email protected])
– 4351 Nat Sci 2, 824-6873, 3116 – office hours M 2-4
• lectures will be posted on web pages after lecture
– http://blumberg.bio.uci.edu/biod145-w2015
– http://blumberg-lab.bio.uci.edu/biod145-w2015
Please work through the posted final exam
BioSci D145 lecture 1
page 1
©copyright
Bruce Blumberg 2010. All rights reserved
Genomics - linking biological variation to disease pathophysiology
Biological system
Tissues
Populations
Cells
Animal strains
Patients
Clinical trial volunteers
Tissues
Stimulated / non-stimulated
Resistant / susceptible
Cases / controls
Responders / non-responders
Normal / treated-diseased
Multivariate!
Experimental system
protein
DNA
Variant between
individuals / populations
Genome sequence
Genotyping variation
Epigenomic analysis
RNA
Variant between tissues
Variant between tissues
RNA seq
cDNA sequence (EST)
DNA microarrays
2D-electrophoresis / LC
Mass spectroscopy
( Yeast 2 hybrid )
What are genomic approaches to aid in these studies?
BioSci D145 lecture 10
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©copyright
Bruce Blumberg 2009. All rights reserved
The rise of -omics
• The -omics revolution of science
– http://www.genomicglossaries.com/content/omes.asp
• What does it all mean?
– Transcriptomics – large scale gene profiling (usually microarray)
– Proteomics – study of complement of expressed proteins
– Functional genomics – vague term, typically encompasses many others
– Structural genomics – prediction of structure and interactions from
sequence (Rick Lathrop, Pierre Baldi)
– Pharmacogenomics – transcriptional profiling of response to drug
treatment – often looking for genetic basis of differences
– Toxicogenomics – transcriptional profiling of response to toxicants (often
includes pharmacogenomics
• Seeks mechanistic understanding of toxic response
– Metabolomics – analysis of total metabolite pool ("metabolome") to
reveal novel aspects of cellular metabolism and global regulation
– Interactomics – genome wide study of macromolecular interactions,
physical and genetic are included
– Bibliomics – identifying words that occur together in abstracts of papers!
BioSci D145 lecture 10
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©copyright
Bruce Blumberg 2009. All rights reserved
The rise of –omics (contd)
• What do we want to know for drug development?
– How do individuals respond to drugs differently – pharmacogenomics
– How do individuals respond differently to toxicants (or toxic effects of
drugs) - toxicogenomics
Target identification
Protein
Assay
Target validation
All of them!!
Compound library
Hit identification (HTS)
Hit
Genes
Hit to lead (Lead identification)
Lead optimization
Candidate drug
Effort
Clinical trials
BioSci D145 lecture 10
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©copyright
Bruce Blumberg 2009. All rights reserved
Toxicogenomics
• Lump pharmacogenomics and toxicogenomics together in the context of drug
Non-monotonic dose
development
responses,
Barney?
• Toxicology is the study of effects of toxicant
exposure
– Traditional toxicology focuses on exposure, dose, effect
I don't want
to hearincorrect
any
– “dose makes the poison” – overly simplistic
and probably
more about it!
BioSci D145 lecture 10
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©copyright
Bruce Blumberg 2009. All rights reserved
Toxicogenomics
• Mechanistic Toxicology (academic and regulatory)
– Investigative toxicology
• Hypothesis generation for grants and studies
– Risk assessment
• Understanding the mechanism of toxicity at the molecular level
• EPA and NIEHS very concerned with this
• Predictive toxicology
– Compound avoidance
• Elimination of liabilities (pharma, chemical industry)
– Compound selection
• Select compound with least toxic liability from a series (pharma)
– Compound management
• Tailor conventional studies and perform timely investigational
toxicology studies
BioSci D145 lecture 10
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©copyright
Bruce Blumberg 2009. All rights reserved
Toxicogenomics (contd)
• Where predictive and mechanistic toxicology fit into drug development
– The road from hit to marketed drug is long
– 8/9 drug candidates fail due to toxic effects or unfavorable metabolism
–
~10 years
Drug
Discovery
PreClinical
Testing
Clinical
Development
FDA
Mechanistic studies
Pattern-based
Mechanism-based
Predictive screens
BioSci D145 lecture 10
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©copyright
Bruce Blumberg 2009. All rights reserved
Phase
IV
Toxicogenomics (contd)
• Bioinformatics ties together toxicogenomic studies
• Overall goal is predictive, personalized medicine
– Provide personalized prescriptions to best help each patient
• Especially cancer therapy
Infrastructure
Clinical and experimental material
SNP Genotyping
Genome data
DNA
Novel targets
Novel pathways
Novel diagnostic indicators
Mining
Novel biomarkers
Predictive toxicology
Modelling Predictive pharmacology
Analysis
Microarray data
EST / cDNA data
RNA
protein
Proteomics
Predictive medicine
Functional readouts
Metabolic space
Chemistry space
function
BioSci D145 lecture 10
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©copyright
Bruce Blumberg 2009. All rights reserved
Novelty, mechanism & prediction - toxicogenomics
Can we replace
animal studies with
genomics analyses?
Rat tissues
Normal and treated
Timecourses
BioSci D145 lecture 10
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©copyright
Bruce Blumberg 2009. All rights reserved
Toxicogenomics (contd)
• What is toxicogenomics good for?
– Obtaining a high level view of a biological system
– Rapid generation of response profiles to
• Unravel mechanisms
• Discriminate among compounds
– Signature of exposures?
– Probably not a single method to identify toxicity
• Problems that must be solved
– Interlab variation – different labs use slightly different methods and get
results that may not be strictly applicable
• Japanese solution is to designate a single lab for entire country
– Most genes change expression at high doses of exposure
• Relevant?
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©copyright
Bruce Blumberg 2009. All rights reserved
• Aim - associate genetics with susceptibility to
environmental agents (loosely defined)
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Bruce Blumberg 2009. All rights reserved
Metabolomics
• Metabolomics detects and quantifies the low molecular weight molecules,
known as metabolites (constituents of the metabolome), produced by active,
living cells under different conditions and times in their life cycles
– Metabonomics is near synonym, suggests metabolomics under some stress
(disease, toxic exposure,dietary change) and often uses NMR
– Metabolomics typically studies normal metabolism and uses mass
spectrometry.
• Types of metabolomic analysis
– Targeted – assay a fixed group of known molecules – not much material
required for full analysis
• Amino acids
• Sugars
• Carnitine
• Acylcarnitines
• Hydroxy and dihydroxycarnitines
• Sphingomyelins
• phosphatidylcholines
BioSci D145 lecture 10
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©copyright
Bruce Blumberg 2009. All rights reserved
Metabolomics
– Targeted metabolomics can give you a
picture of what is changing within a
cell
– Can’t detect unknown metabolites,
though.
– Halama paper this week.
– Can expand the number of molecules
tested to include entire set of KEGG
(Kyoto Encyclopedia of Genes and
Genomes)
• Basically all of known biochemical
pathways in a cell
• Seeing what is altered tells what
has been functionally changed
– allows one to focus on
particular pathways
– Transcriptomics tells only of
potential changes
BioSci D145 lecture 10
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©copyright
Bruce Blumberg 2009. All rights reserved
Metabolomics
• Combine metabolomics with other approaches for more power
– Metabolomics + GWAS (Illig paper this week)
» Look at GWAS studies and identify associations between
genetic changes and metabolomic profiles
– Metabolomics + transcriptomics
» Match changes in metabolites with corresponding changes in
gene expresssion.
– Non-targeted metabolomics
• Group metabolites that change under some condition
• Identify what these are (probably most unexpected)
• Tedious, low throughput, difficulties in identification
• Good to link with KEGG maps
• Requires much more material (several hundred uL at least)
BioSci D145 lecture 10
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©copyright
Bruce Blumberg 2009. All rights reserved
Figure 6.3 Integration of genomic, transcriptomic, and metabolomic data
Figure 6.3 Integration of genomic, transcriptomic, and metabolomic data
(Part 1)
Figure 6.3 Integration of genomic, transcriptomic, and metabolomic data
(Part 2)
Figure 6.3 Integration of genomic, transcriptomic, and metabolomic data
(Part 3)
Figure 6.4 Visualization of metabolic pathways
Genomic technology - implications
• Genetics and reverse genetics
– gene transfer and selection technology speeds up genetic analysis by
orders of magnitude
– virtually all conceivable experiments are now possible
• all questions are askable
• BUT should all questions be asked?
– much more straightforward to understand gene function using knockouts
and transgenics
• gene sequences are coming at an unprecedented rate from the
genome projects
• Knockouts and transgenics remain very expensive to practice
– other yet undiscovered technologies will be required to
understand gene function.
BioSci D145 lecture 10
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©copyright
Bruce Blumberg 2009. All rights reserved
Genomic technology – implications (contd)
• Clinical genetics
– Molecular diagnostics are becoming very widespread as genes are
matched with diseases
• huge growth area for the future
• big pharma is dumping billions into diagnostics
– room for great benefit and widespread abuse
• diagnostics will enable early identification and treatment of diseases
• but insurance companies will want access to these data to maximize
profits
– The solution?
• Personalized testing – no doctors or insurance involved
• But how good are the tests?
– Appropriate counseling available
– Cost effective?
– Predictive?
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©copyright
Bruce Blumberg 2009. All rights reserved
BioSci D145 lecture 10
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Bruce Blumberg 2009. All rights reserved
BioSci D145 lecture 10
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Bruce Blumberg 2009. All rights reserved
23 and me is a new company in this area (Anne Wojcicki is founder)
• What is her claim to fame (beside this company?)
• https://www.23andme.com/
BioSci D145 lecture 10
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©copyright
Bruce Blumberg 2009. All rights reserved
BioSci D145 lecture 10
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©copyright
Bruce Blumberg 2009. All rights reserved
23 and me is a new company in this area (Anne Wojcicki is founder)
BioSci D145 lecture 10
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©copyright
Bruce Blumberg 2009. All rights reserved
23 and me is a new company in this area (Anne Wojcicki is founder)
BioSci D145 lecture 10
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©copyright
Bruce Blumberg 2009. All rights reserved
BioSci D145 lecture 10
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©copyright
Bruce Blumberg 2009. All rights reserved
Genomic technology – implications (contd)
• gene therapy
– new viral vector technology is making this a reality
• efficient transfer and reasonable regulation possible
– long lag time from laboratory to clinic, still working with old technology
in many cases
– The Biotech Death of Jesse Gelsinger. Sheryl Gay Stolberg, NY Times,
Sunday Magazine, 28 Nov 99
• http://www.nytimes.com/library/magazine/home/19991128magstolberg.html
• protein engineering
– not as widely appreciated as more glamorous techniques such as gene
therapy and transgenic crops
– better drugs, e.g., more stable insulin, TPA for heart attacks and
strokes, etc.
– more efficient enzymes (e.g. subtilisin in detergents)
– safe and effective vaccines
• just produce antigenic proteins rather than using inactivated or
attenuated organisms to reduce undesirable side effects
BioSci D145 lecture 10
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©copyright
Bruce Blumberg 2009. All rights reserved
Genomic technology – implications (contd)
• metabolite engineering
– enhanced microbial synthesis of valuable products
• eg indigo (jeans)
• vitamin C
– generation of entirely new small molecules
• transfer of antibiotic producing genes to related species yields new
antibiotics (badly needed)
– reduction of undesirable side reactions
• faster more efficient production of beer
• plants as producers of specialty chemicals
– underutilized because plant technology lags behind techniques in animals
• But regulations are strict (Monsanto)
– plants as factories to produce materials more cheaply and efficiently
• especially replacements for petrochemicals
– plants and herbs are the original source of many pharmaceutical products
• engineer them to overproduce desirable substances
BioSci D145 lecture 10
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©copyright
Bruce Blumberg 2009. All rights reserved
Genomic technology – implications (contd)
• transgenic food
– gene transfer techniques have allowed the creation of desirable
mutations into animals and crops of commercial value
• disease resistance (various viruses)
• pest resistance (Bt cotton)
• Pesticide, herbicide and fungicide resistance
• growth hormone and milk production
– effective but necessary?
– negative implications – “Frankenfoods”
• pesticide and herbicide resistance lead to much higher use of toxic
compounds
• results are not predictable due to small datasets
• at least one herbicide (bromoxynil) for which resistance was
engineered has since been banned
• Atrazine is becoming highly controversial
• Monsanto wants to make 2,4-D (Agent Orange) resistant plants
• Roundup-ready corn and soy associated with health problems
BioSci D145 lecture 10
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©copyright
Bruce Blumberg 2009. All rights reserved
Genomic technology – implications (contd)
• Cradle-grave care (vertical integration in agriculture)
– Seed companies purchased by pesticide and biotech companies
– These purchased (or divested by) by pharmaceutical companies
– Seeds -> crops resistant to parent company’s pesticides and herbicides ->
increased chemical use -> adverse health consequences ->
pharmaceutical parent company’s drugs to treat diseases
glyphosate
atrazine
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©copyright
Bruce Blumberg 2009. All rights reserved