Dynamics of Degradation:

Monitoring Protein Turnover Rates in Response to Chemoresistance

Juan D. Chavez1; Michael R. Hoopmann 2; Chad R. Weisbrod 1; Jimmy K. Eng1; James E. Bruce1 University of Washington1, Institute for Systems Biology2 ASMS 2012 1 Problem of drug resistance

• Multi-drug resistance – Failure of Chemotherapy

• Model system – 3 drug resistant HeLa sublines – Cisplatin resistant: HeLa:CDDP – 1 M cisplatin (DNA damaging agent) – Paclitaxel resistant: HeLa:TXL – 20nM paclitaxel (Mitotic inhibitor) – SN-38 resistant: HeLa:SN100 – 100nM SN-38 (topoisomerase I inhibitor)

K. Takara et al.; Chemother Pharmacol. 2006 Dec;58(6):785-93. K. Takara et al.; Cancer Lett. 2009 Jun 8;278(1):88-96. 2

Overview

• Proteins display altered degradation rates with drug resistance?

• Stable isotope labeling by amino acids in cell culture (SILAC1): pulse- chase experiment – Monitor protein degradation rates

• Examine changes in degradation rates between drug sensitive and three resistant HeLa cell lines

1Ong SE, et al. Mol Cell Proteomics. 2002 May;1(5):376-86(2002). 3 Protein Turnover

Degradation Synthesis 1st order 0th order [protein] [mRNA] / translation/ Lysosome activity transcription

4 Importance of Protein Turnover to Drug Resistance Problem

• -proteasome system control of and tumorigenesis

• Proteasome inhibitors – combination anti- cancer therapies – Velcade (bortezomib) – salinosporamide A, carfilzomib...

5 Protein Turnover by SILAC

Light HeLa Cells 13 15 13 Heavy HeLa Cells Lys- C6 N2 & Arg- C6 incorporation Time

m/z m/z m/z

Time-point sampling RIA= area light LC-MS/MS analysis area light+area heavy

0

Velos-FTICR

VI

nanoAcquity

XVIII UPLC

XII

6 Peak list (Hardklör) & Peptide ID(Mascot)

SILACtor Peptide Mass RT GNVGFVFTK 967.5127 31.2 IIQLLDDYPK. 1216.6703 . 40.6 . 1) Accurate Mass & Retention . . . Time Peptide Database #1 . . .

#2 2) Combine Replicates Intensity } Peptide distribution Time

3) Target Peptides of Interest frequency 0 RIA 1

n Protein RIA = 1- ∑ peptide RIA i n i=1

4) Protein Quantitative Analysis time point analysis

5) Multiple Time Point Analysis 0 0 RIA 1 time

Results Output 7 Hoopmann et al. Anal. Chem., 2011, 83 (22), pp 8403–8410 Exponential Decay Curve 1 kloss = kdeg + kdil 0.9 =ln(2)/kdeg y = e-0.027x 0.8 R² = 0.9974

0.7

0.6 Expon.G3P_Human (G3P_Human)

0.5 Expon.K.LISVWYDNEFGYSNR.V (K.LISWYDNEFGYSNR.V)

0.4 Expon.K.QASEGPLK.G (K.QASEGPLK.G)

Expon.K.RVIISAPSADAPMFVMGVNHEK.Y (K.RVIISAPSADAPMFVMGVNHEK.Y)

Relative Isotope Abundance Isotope Relative 0.3

Expon.R.GALQNIIPASTGAAK.A (R.GALQNIIPASTGAAK.A) 0.2

Expon.R.VIISAPSADAPMFVMGVNHEK.Y (R.VIISAPSADAPMFVMGVNHEK.Y) 0.1 Expon.R.VVDLMAHMASK.E (R.VVDLMAHMASK.E) 0 0 5 10 15 20 25 30 35 40 45 50

Time (hr) 8 Exponential Decay Curve

1 G3P S38A2 0.9 H3 0.8 PDCD4

y = e-0.019x

0.7 R² = 0.9599

0.6

0.5 y = e-0.027x R² = 0.9963 0.4

-0.075x 0.3 y = e Relative Isotope Abundance Isotope Relative R² = 0.9131 0.2 y = e-0.128x R² = 0.9748 0.1

0 0 5 10 15 20 25 30 35 40 45 50 Time (hr) 9 Protein overlap Venn diagram CP SN TX

HeLa:TXL HeLa:SN100 HeLa HeLa:CDDP

PTPA RBBP7 H90B4 TNPO1 FKB10 GNA1 TOPK AXA2L TLN1

kdiff = kdeg,Resistant – kdeg,Sensitive

10 Oliveros, J.C. (2007) VENNY http://bioinfogp.cnb.csic.es/tools/venny/index.html kdiff Histograms 300 Q1-1.5*IQR Q3+1.5*IQR TXL 10 common outliers 75 Decreased k Increased k deg deg AT1A2_HUMAN BAF_HUMAN Increased 0 DRG1_HUMAN -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 HN1_HUMAN kdeg

250 TOPK_HUMAN

SN100 67 CATD_HUMAN

Decreased BUB3_HUMAN 0 ITB1_HUMAN

-0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 KINH_HUMAN kdeg 250 PTPA_HUMAN CDDP

56

0 11 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 TXL resistant altered k deg Cathepsin D (breast cancer VDAC1 biomarker) 2AAB PSMD6 CATD Sensitive = 18.3 hr ATX10 L1CAM Resistant = 9.8 hr Apoptotis/cell death RL40 ANXA4 STAT1 Cullin-4a ITB1 PTPA (prostate cancer CUL4A DDX47 26S proteasome non- biomarker) increased ATPase regulatory levels ->drug resistance Signal Transducer and subunit 6 Sensitive = 15.5 hr Sensitive = 34.3 hr Resistant = 21.7 hr Activator of Transcription 1 Resistant = 23.4 hr (tumor supressor activity) Sensitive = 17.8 hr Resistant = 27.4hr

MCM7 TOPK SSBP PSMD6 ERF3A BAF PSMD6 H14 CDK3 DLG1 ZFR TYSY TYSY MCM7 DNA damage Mitosis/ response/ KINH remodeling CUL4A RL40 MCM5 BUB3 CUL4A RL40 Mitotic checkpoint protein BUB3 H13 MCM5 Sensitive = 13 hr Resistant = 19 hr STAT1 SN38 resistant altered kdeg Cathepsin D (breast cancer ASNS NDUAD biomarker) PSD7 PSMD4 Sensitive = 18.3 hr CATD BAP31 Resistant = 12.4 hr Apoptotis/cell death PAK2 PP2AA PTPA ITB1 MCA3 ANXA4 DDX5 Lymphokine- GBLP activated killer T- 26S cell-originated proteasome protein kinase non-ATPase MCM3 MCM7 (colorectal cancer regulatory biomarker) GRB2 PSMD4 CD123 subunits Sensitive =ASNS 21.5 hr PSD7 PSD7 Resistant = 16.2 hr Sensitive = 29.5hr NDUAD MCM3 MCM7 TOPK ERF3A Resistant = 13.5 hr DNA damage NOLC1 PSMD4 SKP1 response/ Mitosis/ cell cycle remodeling DDX5 MCA3 BUB3 PAK2 PP2AA DDX5 MCA3 GOLP3 MCM5 PP2AA MCM5 GBLP

Mitotic checkpoint protein BUB3 13 Sensitive = 13 hr Resistant = 17 hr CDDP resistant altered kdeg

MSH6 CDK1 PSMD4 Apoptotis/cell death PTPA ITB1 NICA CATD PAK2 DNA mismatch repair protein Cathepsin D Sensitive = 18 hr (breast cancer Resistant = 15hr biomarker) Sensitive = 18 hr Resistant = 28hr Cyclin dependent kinases CHD4 TYSY Sensitive = 18 hr CD123 CDK3 CDK1 HAT1 PSMD4 Resistant = 14hr H14 TYSY DLG1 MSH6 PSMD4 TOPK DNA damage Mitosis/ cell cycle response/ remodeling BUB3 PAK2 STAT3 H13 SUMO2 Mitotic checkpoint protein BUB3 14 Sensitive = 13 hr Resistant = 17 hr Summary

• Dynamic proteome measurements provide insight into mechanism of drug resistance

• Several proteins with altered kdeg values related to mechanism of drug action • Altered protein turnover rates may be effective biomarkers of drug resistance • Future work: Relate changes in protein half- life to protein abundance level changes and protein-protein interactions

15 Acknowledgements

bromine uranium cerium lanthanum boron 35 92 58 57 5 Br U Ce La B 79.904 238.03 140.12 138.91 10.811 Jimmy Eng Jim Bruce Priska von Haller Chad Weisbrod Eva Jahan Jake Zheng Vagisha Sharma Xia Wu Rick Harkewicz Collaborators Mike Hoopmann – Institute of Systems Biology

ASMS presentations Kohji Takara – Chad - Poster: MP17 #398 Himeji Dokkyo University Mike - Poster: MP28 #644 Michelle - Oral: TOD pm - Plant "omics"Time: 2:50 Funding Jake - Poster: TP03 #61 2R01GM086688 Xia - Poster: WP09 #226 5R01GM097112 Jim - Oral: ThOE am - Biomolecular Structure Analysis 5R01RR023334 by Covalent Labeling: Future DirectionsTime: 08:30 7S10RR025107 16 Xia - Poster: ThP12 #280