1. BioSci D145 lecture 1 page 1 © copyright Bruce Blumberg 2010. All rights reserved BioSci D145 Lecture #10 Bruce Blumberg (blumberg@uci.edu) –4103 Nat Sci 2 - office hours Tu, Th 3:30-5:00 (or by appointment) –phone 824-8573 TA – Bassem Shoucri (bshoucri@uci.edu) –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-w2015http://blumberg.bio.uci.edu/biod145-w2015 –http://blumberg-lab.bio.uci.edu/biod145-w2015http://blumberg-lab.bio.uci.edu/biod145-w2015 Please work through the posted final exam

2. BioSci D145 lecture 10 page 2 © copyright Bruce Blumberg 2009. 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 Epigenomic analysis RNA seq 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?

3. BioSci D145 lecture 10 page 3 © copyright Bruce Blumberg 2009. 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 – 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!

4. BioSci D145 lecture 10 page 4 © copyright Bruce Blumberg 2009. All rights reserved Protein Assay Compound library Hit Target identification Target validation Hit identification (HTS) Hit to lead (Lead identification) Lead optimization 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 (or toxic effects of drugs) - toxicogenomics The rise of –omics (contd)

5. BioSci D145 lecture 10 page 5 © copyright Bruce Blumberg 2009. 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 Non-monotonic dose responses, Barney? I don't want to hear any more about it!

6. BioSci D145 lecture 10 page 6 © 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

7. BioSci D145 lecture 10 page 7 © copyright Bruce Blumberg 2009. 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 metabolism – ~10 years

8. BioSci D145 lecture 10 page 8 © copyright Bruce Blumberg 2009. 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

9. BioSci D145 lecture 10 page 9 © copyright Bruce Blumberg 2009. All rights reserved Expression Gene Experiment Rat tissues Normal and treated Timecourses Novelty, mechanism & prediction - toxicogenomics Can we replace animal studies with genomics analyses?

10. BioSci D145 lecture 10 page 10 © 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?

11. BioSci D145 lecture 10 page 11 © copyright Bruce Blumberg 2009. All rights reserved Aim - associate genetics with susceptibility to environmental agents (loosely defined)

12. BioSci D145 lecture 10 page 12 © copyright 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

13. BioSci D145 lecture 10 page 13 © 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

14. BioSci D145 lecture 10 page 14 © 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)

15. Figure 6.3 Integration of genomic, transcriptomic, and metabolomic data

16. Figure 6.3 Integration of genomic, transcriptomic, and metabolomic data (Part 1)

17. Figure 6.3 Integration of genomic, transcriptomic, and metabolomic data (Part 2)

18. Figure 6.3 Integration of genomic, transcriptomic, and metabolomic data (Part 3)

19. Figure 6.4 Visualization of metabolic pathways

20. BioSci D145 lecture 10 page 20 © copyright Bruce Blumberg 2009. 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.

21. BioSci D145 lecture 10 page 21 © 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?

22. BioSci D145 lecture 10 page 22 © copyright Bruce Blumberg 2009. All rights reserved

23. BioSci D145 lecture 10 page 23 © copyright Bruce Blumberg 2009. All rights reserved

24. BioSci D145 lecture 10 page 24 © copyright 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/

25. BioSci D145 lecture 10 page 25 © copyright Bruce Blumberg 2009. All rights reserved

26. BioSci D145 lecture 10 page 26 © copyright Bruce Blumberg 2009. All rights reserved 23 and me is a new company in this area (Anne Wojcicki is founder)

27. BioSci D145 lecture 10 page 27 © copyright Bruce Blumberg 2009. All rights reserved 23 and me is a new company in this area (Anne Wojcicki is founder)

28. BioSci D145 lecture 10 page 28 © copyright Bruce Blumberg 2009. All rights reserved

29. BioSci D145 lecture 10 page 29 © 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/19991128mag- stolberg.htmlhttp://www.nytimes.com/library/magazine/home/19991128mag- stolberg.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

30. BioSci D145 lecture 10 page 30 © 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

31. BioSci D145 lecture 10 page 31 © 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

32. BioSci D145 lecture 10 page 32 © 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