Enzyme Response Profiling: Integrating proteomics and genomics with xenobiotic metabolism and cytotoxicity

Transcription

1 Enzyme Response Profiling: Integrating proteomics and genomics with xenobiotic metabolism and cytotoxicity

2 Proteomics high throughput separation and characterization of expressed proteins in tissue and biofluids Identities: ex. Human Proteome Project launched 2011 Goal: characterize at least one protein product from each of the 20,300 protein-coding genes Timing reflects dramatic advances in proteomics technologies: mass spectrometry platforms and antibody-based hprotein data Quantities: How much of what gene products are being made where, under what conditions? Image: 2

3 Goals of the presentation 1. Overview of proteomics as a SysTox Enabler 2. Systems-wide Responses in Food-Drug interactions Discovery-driven proteomics Validation-oriented proteomics Application to bioactive food components Comparison with enzyme activity and transcriptome data 3. Further sub-proteome technologies 3

4 Applying Proteomics to Toxicology Characterize changes in expressed protein profiles as a result of an exposure Pathways: define molecular steps and sequence of events to gain insight into mechanisms Direct Link: Toxins interact with expressed proteins and proteins mediate toxic responses Detection of novel molecular targets and diagnostic markers 4

5 Advantages Increase speed and sensitivity of toxicological screening by identifying protein markers of toxicity Potentially unanticipated responses and novel endogenous biomarkers more sensitive and earlier detection of adverse events in vivo? 5

6 Technological Advances in Proteomics Mass spectrometry & isotope-coded affinity tags Relative expression levels of individual proteins with high precision coefficients of variation less than 10% Availability of data across cell lines and tissues 6

7 Current Toxicology Applications of Proteomics 7

8 ETH-UZH Sinergia Collaboration: Systems Wide Responses to Bioactive Food Components Marra 8

9 ETH-UZH Sinergia Collaboration: Systems Wide Responses to Bioactive Food Components (BFC) BFC Drug Toxicity (i.e. Drug Sensitivity) Altered Toxicity.and impact on drug toxicity 9

10 Critical Protein Mediators of Toxicity Cell surface receptors and transporters Xenobiotic biotransformation Redox-regulating proteins Interaction partners/regulators of these enzymes 10

11 Cellular sensors and transcriptional activation Relevance of lowdose chemical stimuli Sulforaphane as a BFC trigger Sykiotis G.P. and Bohmann D., Sci. Signal., 3, p. re3 (2010) 11

12 Developing The Food-Drug Interaction Hypothesis: (1) PTGR1 & Acylfulvenes Acylfulvenes PTGR1 is inducible by bioactive food components Impact of levated Prostaglandin Reductase 1 Acylfulvene-DNA adducts Acylfulvene Cytotoxicity Published in: Kathryn E. Pietsch; Paul M. van Midwoud; Peter W. Villalta; Shana J. Sturla; Chem. Res. Toxicol. 2013, 26, DOI: /tx300430r Copyright 2012 American Chemical Society 12

13 Developing The Food-Drug Interaction Hypothesis: (2) Redox-regulating enzymes & acylfulvenes Acylfulvenes interfere with structure and function of redox-regulating enzymes Acylfulvenes are more toxic in pre-conditioned cells that overexpress these enzymes Liu, X. and Sturla, S. J. Mol. Biosys. 2009, Liu, X; Pietsch, K. E.; Sturla, S. J.; Chem. Res. Toxicol. 2011, 24, DOI: /tx Chemistry and Biology of Acylfulvenes: Sesquiterpene-Derived Antitumor Agents. M. Tanasova and S. J. Sturla, Chem. Rev., 2012, 112 (6), pp

14 The Food-Drug Interaction Hypothesis: Systems-wide responses and the reductome Prof. Niko Beerenwinkel 14

15 Two basic proteomic strategies to characterize protein responses Discovery (untargeted) Validation (targeted) SILAC ~3000 proteins/sample 8 samples/ week Standard assay SRM ~100 proteins/sample 40 samples/week Specific assay 15

16 Proteome-wide Discovery: SILAC approach Light (L) [ 12 C 6 14 N 2 ]-Lys [ 12 C 6 14 N 4 ]-Arg Extraction Mix 1:1 Digestion Fractionation LC-MS/MS [ 13 C 6 15 N 2 ]-Lys [ 13 C 6 15 N 4 ]-Arg Up- 8 Da Down- 8 Da 8 Da Heavy (H) SILAC = Stable Isotope Labeling with Amino acids in Cell culture Intensity m/z 16

17 Targeted Measurement LC-MS/MS Selected Reaction Monitoring Specificity Sensitivity Adapted from A. Schimdt et al. Proteomeanalyse und systembiologie BIOspektrum, 1/2008, S. 44 Endogenous Extraction Digestion Internal Std LC-MS/MS (QQQ) Exogenous 17

18 Goals of the presentation Overview of proteomics as a SysTox Enabler Systems-wide Responses in Food-Drug interactions Discovery-driven proteomics Validation-oriented proteomics Application to bioactive food components Comparison with enzyme activity and transcriptome data Further sub-proteome technologies 18

19 Reductase Interaction Network Determination of protein interaction partners for 12- enzyme reductome Validation of functional network (sirna) Investigation of network perturbation by BFC treatment 19

20 Representative Protein Interaction Network: hpp2a Protein phosphatase 197 Protein interactions Triangles: Bait proteins Circles: Prey proteins Node Color: function classified (i.e. scaffold, regulatory, catalytic) Edge color indicates interaction Blue: anticipated from literature Orange: novel interactions Thickness proportional to identified unique peptides Glatter et al. 2009, Mol Syst Biol 20

21 Decoding ligand-receptor interactions: Cell Surface Capture (CSC) technology as surfaceome discovery platform Biocytin Hydrazide Cell surface protein Oxidized carbohydrate Mass-spectrometric identification and relative quantification of N-linked cell surface glycoproteins. Nat Biotechnol 27, (2009). 21

22 Chemoproteomic Ligand-based Receptor Capture (LRC) technology Ligand-based receptor identification platform Direct identification of interactions on cells & tissue Ligand of interest Trifunctional molecule Receptor Frei, A. P. et al. Direct identification of ligand-receptor interactions on living cells and tissues. Nat Biotechnol 30, (2012) 22

23 Thank you! Nadine Sobotzki Barbara Melanie Nina Cedric Katrin Simona Haldiman Erzinger Kraus Bovet Hecht Constantenescue Pascale Winiker Jerry Shay, UT Southwestern, HCEC Cells Dalibor Sames, Columbia University, AKR1C3 Activity probe 23