BioSci 145B lecture 10 page 1 © copyright Bruce Blumberg 2004. All rights reserved BioSci 145B Lecture #10 6/8/2004 Bruce Blumberg –2113E McGaugh Hall

BioSci 145B lecture 10 page 1 ©copyright Bruce Blumberg 2004. All rights reserved

BioSci 145B Lecture #10 6/8/2004

• Bruce Blumberg– 2113E McGaugh Hall - office hours Wed 12-1 PM (or by

appointment)– phone 824-8573– [email protected]

• TA – Curtis Daly [email protected]– 2113 McGaugh Hall, 924-6873, 3116– Office hours Tuesday 11-12

• lectures will be posted on web pages after lecture – http://eee.uci.edu/04s/05705/ - link only here– http://blumberg-serv.bio.uci.edu/bio145b-sp2004– http://blumberg.bio.uci.edu/bio145b-sp2004

http://blumberg.bio.uci.edu/bio145b-sp2004


Library-based methods to map protein-protein interactions (contd)

• Phage display screening (a.k.a. panning)– requires a library that expresses

inserts as fusion proteins with a phage capsid protein

• most are M13 based• some lambda phages used

– prepare target protein• as affinity matrix• or as radiolabeled probe

– test for interaction with library members• if using affinity matrix you purify phages from a mixture• if labeling protein one plates fusion protein library and

probes with the protein– called receptor panning based on similarity with panning

for gold


Library-based methods to map protein-protein interactions (contd)• Phage display screening (a.k.a. panning) (contd)

– advantages• stringency can be manipulated• if the affinity matrix approach works the cloning could go

rapidly– disadvantages

• Fusion proteins bias the screen against full-length cDNAs• Multiple attempts required to optimize binding• Limited targets possible• may not work for heterodimers• unlikely to work for complexes• panning can take many months for each screen

– Greg Weiss in Chemistry is local expert


Mapping protein-protein interactions (contd)

• Two hybrid screening– originally used in yeast, now

other systems possible– prepare bait - target protein

fused to DBD (GAL4) usual• stable cell line is commonly

used– prepare fusion protein library

with an activation domain - prey– Key factor required for success is

no activation domain in bait!

– approach• transfect library into cells and

either select for survival or activation of reporter gene

• purify and characterize positive clones


Mapping protein-protein interactions (contd)

• Two-hybrid screening (contd)– Can be easily converted to

genome wide searching by making haploid strains, each containing one candidate interactor

– Mate these and check for growth or expression of reporter gene

Bait plasmid Prey plasmid

If interact, reporter expressed and/or

Yeast survive


Molecular Interaction Screening - A New Approach to Protein Function

• Principle– small pools of cDNAs are transcribed and translated in vitro to

produce pools of proteins that may be assayed in a variety of ways

• EMSA, co-ip, FRET, SPA– cDNAs identified by protein function

• Starting material arrayed in 384-well plates

– Robotically pool source plates into daughter 96/384-well plates

• Pool size is optimizable - 96 works well

• Grow bacteria, prepare DNA, TNT -> labeled protein

• Perform functional assay (SPA)

• Unpool positive wells into components and rescreen

– Positive pools have known composition

• only one second level screen is required


Automated Molecular Interaction Screening

• Why do it this way?– arbitrary size and complexity of target is possible– Normalized cDNA pool -> representation of rare messages– numerous possible endpoint assays

• radioactive, fluorescent, luminescent– saturation screening of genome is feasible– two screening steps to pure cDNA of interest in ~2 weeks


Large scale mapping of protein-protein interactions

• GST (glutathione-S-transferase) pulldown assay– Or other purification wherein

one protein is tagged and complex of proteins binding to it is recovered

– Purify complexes from cells– Characterize complexes by

mass-spectrometry– Iteratively build up a map of

protein interactions from such complexes


Genomics - linking biological variation to disease pathophysiology

Experimental system

DNA

TissuesPopulations

Biological system

RNA

protein

Animal strains Patients

Clinical trial volunteersTissues

Resistant / susceptibleCases / controls

Responders / non-respondersNormal / treated-diseased

Cells Stimulated / non-stimulatedMultivariate!

Variant between individuals / populations

Variant between tissues

Variant between tissues

Genome sequenceGenome sequenceGenotyping variationGenotyping variation

Differential displayDifferential displaycDNA sequence (EST)cDNA sequence (EST)DNA microarraysDNA microarrays

2D-electrophoresis / LC2D-electrophoresis / LCMass spectroscopyMass spectroscopy ( Yeast 2 hybrid )( Yeast 2 hybrid )

What are genomic approaches to aid in these studies?


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 – very vague term, typically encompasses

many others– Structural genomics – prediction of structure and interactions

from sequence– 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.


Protein

Assay

Compound library

Hit

Target identification

Target validation

Hit identification (HTS)

Hit to lead (Lead identification)

Lead optimisation

Candidate drug

Clinical trials

Genes

Effort

All of them!!

• What do we want to know for drug development?– How do individuals respond to drugs differently – pharmacogenomics– How do individuals respond differently to toxicants - toxicogenomics

The rise of –omics (contd)


Toxicogenomics

• Lump pharmacogenomics and toxicogenomics together in the context of drug development

• Toxicology is the study of effects of toxicant exposure– Traditional toxicology focuses on exposure, dose, effect– “dose makes the poison” – overly simplistic and probably incorrect

• 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)– Compound selection

• Select compound with least toxic liability from a series (pharma)– Compound management

• Tailor conventional studies and perform timely investigational toxicology studies


Drug Discovery

PreClinical Testing

Clinical Development

Phase IV

FDA

Mechanism-based

Mechanistic studies Pattern-based

Predictive screens

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 profile

of metabolism


DNA

RNA

protein

SNP GenotypingGenome data

Microarray dataEST / cDNA data

Proteomics

Clinical and experimental material

Novel targetsNovel pathways

Novel diagnostic indicatorsNovel biomarkers

Predictive toxicologyPredictive pharmacology

Predictive medicine

Analysis

Mining

Modelling

Infrastructure

functionFunctional readouts

Metabolic spaceChemistry space


• Bioinformatics ties together toxicogenomic studies• Overall goal is predictive, personalized medicine

– Provide personalized prescriptions to best help each patient• Especially cancer therapy


Expression

Gen

eExperiment

Rat tissuesNormal and treated

Timecourses

Novelty, mechanism & prediction - toxicogenomics

Can we replaceanimal studies withgenomics analyses?



• 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?


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.


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



• 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.frenchanderson.org/history/biotech.pdf

• 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



• 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



• 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

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BioSci 145B lecture 10 page 1 © copyright Bruce Blumberg 2004. All rights reserved BioSci 145B Lecture #10 6/8/2004 Bruce Blumberg –2113E McGaugh Hall