1. 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 –blumberg@uci.edublumberg@uci.edu TA – Curtis Daly cdaly@uci.educdaly@uci.edu –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 herehttp://eee.uci.edu/04s/05705/ –http://blumberg-serv.bio.uci.edu/bio145b-sp2004http://blumberg-serv.bio.uci.edu/bio145b-sp2004 –http://blumberg.bio.uci.edu/bio145b-sp2004http://blumberg.bio.uci.edu/bio145b-sp2004

2. BioSci 145B lecture 10 page 2 © copyright Bruce Blumberg 2004. All rights reserved 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

3. BioSci 145B lecture 10 page 3 © copyright Bruce Blumberg 2004. All rights reserved 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

4. BioSci 145B lecture 10 page 4 © copyright Bruce Blumberg 2004. All rights reserved 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

5. BioSci 145B lecture 10 page 5 © copyright Bruce Blumberg 2004. All rights reserved 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 plasmidPrey plasmid If interact, reporter expressed and/or Yeast survive

6. BioSci 145B lecture 10 page 6 © copyright Bruce Blumberg 2004. All rights reserved 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

7. BioSci 145B lecture 10 page 7 © copyright Bruce Blumberg 2004. All rights reserved 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

8. BioSci 145B lecture 10 page 8 © copyright Bruce Blumberg 2004. All rights reserved 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

9. BioSci 145B lecture 10 page 9 © copyright Bruce Blumberg 2004. All rights reserved Genomics - linking biological variation to disease pathophysiology Experimental system DNA Tissues Populations Biological system RNA protein Animal strains Patients Clinical trial volunteers Tissues Resistant / susceptible Cases / controls Responders / non-responders Normal / treated-diseased CellsStimulated / non-stimulated Multivariate! Variant between individuals / populations Variant between tissues Genome sequence Genotyping variation Differential display cDNA sequence (EST) DNA microarrays 2D-electrophoresis / LC Mass spectroscopy ( Yeast 2 hybrid ) ( Yeast 2 hybrid ) What are genomic approaches to aid in these studies?

10. BioSci 145B lecture 10 page 10 © copyright Bruce Blumberg 2004. All rights reserved The rise of -omics The -omics revolution of science –http://www.genomicglossaries.com/content/omes.asphttp://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.

11. BioSci 145B lecture 10 page 11 © copyright Bruce Blumberg 2004. All rights reserved 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)

12. BioSci 145B lecture 10 page 12 © copyright Bruce Blumberg 2004. All rights reserved 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

13. BioSci 145B lecture 10 page 13 © copyright Bruce Blumberg 2004. All rights reserved 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

14. BioSci 145B lecture 10 page 14 © copyright Bruce Blumberg 2004. All rights reserved DNA RNA protein SNP Genotyping Genome data Microarray data EST / cDNA data Proteomics Clinical and experimental material Novel targets Novel pathways Novel diagnostic indicators Novel biomarkers Predictive toxicology Predictive pharmacology Predictive medicine Analysis Mining Modelling Infrastructure function Functional readouts Metabolic space Chemistry space Toxicogenomics (contd) Bioinformatics ties together toxicogenomic studies Overall goal is predictive, personalized medicine –Provide personalized prescriptions to best help each patient Especially cancer therapy

15. BioSci 145B lecture 10 page 15 © copyright Bruce Blumberg 2004. All rights reserved Expression Gene Experiment Rat tissues Normal and treated Timecourses Novelty, mechanism & prediction - toxicogenomics Can we replace animal studies with genomics analyses?

16. BioSci 145B lecture 10 page 16 © copyright Bruce Blumberg 2004. 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?

17. BioSci 145B lecture 10 page 17 © copyright Bruce Blumberg 2004. All rights reserved 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.

18. BioSci 145B lecture 10 page 18 © copyright Bruce Blumberg 2004. 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

19. BioSci 145B lecture 10 page 19 © copyright Bruce Blumberg 2004. 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.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

20. BioSci 145B lecture 10 page 20 © copyright Bruce Blumberg 2004. 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

21. BioSci 145B lecture 10 page 21 © copyright Bruce Blumberg 2004. 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