Quantitative proteomics of TRAMP mice combined with bioinformatics

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Quantitative proteomics of TRAMP mice combined with bioinformatics analysis reveals that PDGF-B regulatory network plays a key role in prostate cancer progression Yuan Zhang, Dan Wang, Min Li, Xiaodan Wei, Shuang Liu, Miaoqing Zhao, Chu Liu, Xizhen Wang, Xingyue Jiang, Xuri Li, Shuping Zhang, Jonas Bergquist, Bin Wang, Chunhua Yang, Jia Mi, and Geng Tian J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.8b00158 • Publication Date (Web): 04 Jun 2018 Downloaded from http://pubs.acs.org on June 4, 2018

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Journal of Proteome Research

Transgenic adenocarcinoma of the mouse prostate (TRAMP) mice is a widely used transgenic animal model of prostate cancer (PCa). We performed a label free quantitative proteomics analysis combined with a bioinformatics analysis on the entire prostate protein extraction from TRAMP mice and compared with WT littermates. From totally 2379 identified proteins, we presented a modest mice prostate reference proteome containing 919 proteins. 61 proteins presented a significant expression difference between two groups. The integrative bioinformatics analysis predicted the overexpression of platelet-derived growth factor B (PDGF-B) in tumor tissue and supports the hypothesis of the PDGF-B signaling network as a key upstream regulator in PCa progression. Furthermore, we demonstrated that Crenolanib, a novel PDGF receptor inhibitor, inhibited PCa cell proliferation in a dose-dependent manner. Finally, we revealed the importance of PDGF-B regulatory network in PCa progression, which will assist to understand the role and mechanisms of PDGF-B in promoting the cancer growth and provide valuable knowledge reference in the future research on anti-PDGF therapy. 529x211mm (300 x 300 DPI)

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Quantitative proteomics of TRAMP mice combined with bioinformatics analysis reveals

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that PDGF-B regulatory network plays a key role in prostate cancer progression

3

Running title: Quantitative proteomics analysis of prostate cancer mice

4 5

Yuan Zhang1,#, Dan Wang1,2,#, Min Li1 , Xiaodan Wei1, Shuang Liu3, Miaoqing Zhao4, Chu Liu5,

6

Xizhen Wang6, Xingyue Jiang2, Xuri Li1, Shuping Zhang1, Jonas Bergquist1,7, Bin Wang1,

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Chunhua Yang1,*, Jia Mi1,7,*, Geng Tian1,*

8 9 10 11 12 13 14 15 16 17 18 19 20 21

1. Medicine and Pharmacy Research Center, Binzhou Medical University, Laishan District,

No.346, Guanhai Road, Yantai, Shandong Province, 264003 China 2. Department of Radiology, Afﬁliated Hospital of Binzhou Medical University, 661 Second

Huanghe Rd, Binzhou, Shandong Province,256603 China 3. College of Enology, Binzhou Medical University, Laishan District, No.346,

Guanhai

Road, Yantai, Shandong Province, 264003 China 4. Department of Pathology, Provincial Hospital Affiliated to Shandong University, No. 324

Jingwu Weiqi Road, 250021, Jinan, Shandong Province, China. 5. Department

of Urology, Yantai Yuhuangding Hospital, Zhifu District, No.20,

Yuhuangding East Road, Yantai, Shandong Province, 264000 China 6. Imaging Center, Affiliated Hospital of Weifang Medical University, Kuiwen District,

NO.465, Yuhe Road, Weifang, Shandong Province, 256603 China 7. Department of Chemistry – BMC, Uppsala University, PO Box 599, Husargatan 3, 1


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Uppsala, 75124

22

Sweden

23

#

24

*

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Geng Tian; Phone: +86-535-6913395; Fax: +86-535-6913034

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Email: [email protected]

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Or

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Jia Mi; Phone: +86-535-6913395; Fax: +86-535-6913034

29


30

Or

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Chunhua Yang; Phone: +86-535-6913395; Fax: +86-535-6913034

32


These authors have contributed equally to this work.

corresponding authors to

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Word count: 5000

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Figures: 5

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Table:1

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Supporting Information :11

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2



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Abstract

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Transgenic adenocarcinoma of the mouse prostate (TRAMP) mice is a widely used transgenic

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animal model of prostate cancer (PCa). We performed a label free quantitative proteomics

43

analysis combined with a bioinformatics analysis on the entire prostate protein extraction from

44

TRAMP mice and compared with WT littermates. From totally 2379 identified proteins, we

45

presented a modest mice prostate reference proteome containing 919 proteins. 61 proteins

46

presented a significant expression difference between two groups. The integrative bioinformatics

47

analysis predicted the overexpression of platelet-derived growth factor B (PDGF-B) in tumor

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tissues and supports the hypothesis of the PDGF-B signaling network as a key upstream

49

regulator in PCa progression. Furthermore, we demonstrated that Crenolanib, a novel PDGF

50

receptor inhibitor, inhibited PCa cell proliferation in a dose-dependent manner. Finally, we

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revealed the importance of PDGF-B regulatory network in PCa progression, which will assist to

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understand the role and mechanisms of PDGF-B in promoting the cancer growth and provide

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valuable knowledge reference in the future research on anti-PDGF therapy.

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Key words:

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signal pathway

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Introduction

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Prostate cancer (PCa) remains the second most frequent cancer and the second cause of

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cancer-related death in men1, 2. Although several effective therapy options are available, PCa is

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still one of the most intriguing challenge in oncology due to the lack of knowledge of disease

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progression mechanisms on the molecular and cellular levels3. Transgenic cancer animal models

PDGF-B; proteomics; prostate cancer; bioinformatics analysis; TRAMP mice;

3


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have provided a fundamental contribution to the investigation and understanding tumor growth,

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and have been widely used in investigating multiple aspects of cancer progression.

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The TRAMP model is one of the most well-known prostate cancer mouse models, which was

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generated and characterized during 1995–19974, 5. All the male TRAMP mice develop prostatic

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intraepithelial neoplasia (PIN) by 18 weeks of age, and display distant organ metastasis in lymph

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nodes, adrenal glands, and the lungs. Two major advantages of the TRAMP mice are that 1) the

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oncoprotein is specifically expressed in the prostate epithelial cells, and 2) the tumor tissue

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resembles human prostate cancer histologically and biochemically4, 6, 7.

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Several proteomics studies have already been conducted using the TRAMP mice. The effect of

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methyl-Selenium compounds on TRAMP proteomic profiling was evaluated on a

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MALDI-TOF/TOF MS platform with iTRAQ labeling8. Furthermore, the proteome difference of

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dorsal-lateral (DLP) and ventral (VP) prostate was reported using the same platform9. For mouse

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prostate proteome profiling, the most comprehensive study reported 619 distinct prostate proteins

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from 1D-SDS gel coupled with LC-MS/MS identification10. However, a more comprehensive

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and in-depth analysis of prostate proteome from TRAMP mice are still in need, and the

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development of high resolution MS techniques has made this possible.

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In the current study, we presented a comprehensive prostate gland proteomics comparison

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between TRAMP mice and WT mice. A label free quantitative mass spectrometry-based analysis

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was performed on prostate tissue proteins from TRAMP mice and WT mice. Using a

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bioinformatics approach, we predicted the overexpression of PDGF-B in PCa tissues and

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hypothesized a novel PDGF-B regulatory network. The overexpression of PDGF-B and 4



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associated regulatory network were experimentally validated in animal tissues and clinical

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human samples. Furthermore, we reveal that inhibiting the PDGF signaling pathway using PDGF

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receptor inhibitors can significantly inhibit prostate cancer cell growth both in vivo and in vitro.

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Materials and Methods

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Reagents

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Anti-RAF-1 antibody (R&D Systems: MAB4540-SP), Anti-MAPK3 antibody (ZSGB-BIO;

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sc-94), Anti-MAPK antibody (ZSGB-BIO; SC-7149), Anti-MAPK1 antibody (BIOSS;

90

bs-0022R), Anti-P85 antibody (BIOSS; bs-0128R), Anti-PRDX2 antibody (Abcam; ab109367),

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Anti-PDIA3 antibody (Abcam; ab154197), Anti-GAPDH antibody (Santa Cruz; sc-32233), Anti-

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HNRNPL antibody (Cell Signaling Technology; 4783), PDGF-BB (Prospec; cyt-501-b),

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Anti-CD31 antibody (Cell Signaling Technology, 77699T)，Anti-α-SMA antibody (CusAb,

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CSB-MA080159), Anti-PCNA antibody (Abcam, EPR3821), Anti-Ki67 antibody (Proteintech,

95

27309-1-AP),

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oligonucleotide

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GCAAGCACCGGAAATTCAAGC; PGPU6/Neo-shNC: GTTCTCCGAACGTGTCACGT.

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Mouse Strains TRAMP mice were purchased from Jackson Laboratory (www.jax.org). WT and

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TRAMP mice were both from a C57BL/6 origin and obtained from C57- x C57-matings. The

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genotype of animals was confirmed by PCR-based assay from tail biopsies DNA (Fig. S1A), and

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the tumorigenesis was confirmed by high field (7T) small animal magnetic resonance imaging

The following reagents were used: Anti-PDGF-B antibody (Abcam; ab178409),

Crenolanib sequences

(Selleck

Chemicals

of

shRNAs

the

, used

CP-868596). are:

5


PDGF-B

and

PDGF-B-homo-1651

control shRNA:

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(MRI) (Bruker BioSpin, Germany) (Fig. S1B). The detailed method was described previously5, 11.

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The PCR primers were listed in Supplement materials (Supplemental Table S1).

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Tissue Collection Both WT mice and TRAMP mice were sacrificed at the age of 18 weeks. The

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entire prostate gland was isolated from WT mice. In TRAMP mice, the entire prostate gland

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together with associated cancer tissue were collected. The samples were rinsed with PBS buffer

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and snap frozen in liquid nitrogen and stored at -80°C for further analysis. Four pairs of prostate

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cancer and adjacent normal prostate tissues were obtained from patients undergoing radical

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prostatectomy in Yuhuangding Hospital, Yantai, China.

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Protein Extraction Prostate tissue samples from TRAMP and WT mice were suspended and

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homogenized in lysis buffer (9 M Urea, 20mM HEPES, and proteinase inhibitor)). Samples were

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sonicated shortly followed by centrifugation at 12,000 g at 4 °C for 10 min. The lysates were

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stored at −80 °C.

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In-solution Digestion and Peptide Purification In-solution digestion was performed prior to the

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MS analysis. 35µg proteins were diluted in 100µL digestion buffer (6 M urea, 100mM TEAB).

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Then 10µL of 45mM DTT solution was added and incubated at 50°C for 15min, followed by the

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addition of 10µL 100mM IAA solution and incubated in darkness at room temperature for 15

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min. Trypsin/Lys-C (Wako Chemicals, Osaka, Japan) was dissolved in digestion buffer and

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added in each sample to reach a final trypsin/protein concentration of 5% (w/w) and followed by

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incubation overnight at 37°C. The digestion was stopped by diluting the sample 1:1 with

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trifluoroacetic acid (TFA) in acetonitrile (ACN) and MilliQ water (1/5/94, v/v). A sample 6



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corresponding to 20µg digested proteins was desalted using C18 Stage-tips with

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EmporeDisksC18 from Varian (Palo Alto, CA, USA), and dried completely in a vacuum

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centrifuge.

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Mass Spectrometry all analyses were performed using a QExactive plus Orbitrap mass

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spectrometer (Thermo Fisher Scientific, Bremen, Germany) equipped with a nano electrospray

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ion source as described. Dried Samples were dissolved with 0.1% formic acid. Peptides were

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separated by reversed phase liquid chromatography using an EASY-nLC 1000 system (Thermo

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Fisher Scientific, Bremen, Germany). A set-up of a two-step column separation was used. The

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pre-column was a 2 cm EASY-column (1D 100 µm, 5 µm, C18) (Thermo Fisher Scientific),

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while the analytical column was a 10 cm EASY-column (1D 75 µm, 3 µm, C18) (Thermo Fisher

132

Scientific). Peptides were eluted with a 90-min long linear gradient from 4% to 100% ACN at

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250nl/min. The mass spectrometer was operated in positive ion mode acquiring a survey mass

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spectrum with resolving power 70 000 and consecutive high collision dissociation (HCD)

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fragmentation spectra of the 10 most abundant ions.

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Data Analysis Acquired data (RAW-files) were processed by MaxQuant (version 1.4.0.1).

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Tandem mass spectra were searched with Andromeda against the UniProt mouse database

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(release 2015–05, with 76089 protein entries). The searching settings were set as:

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ppm and 5 ppm error tolerance for the survey scan and MS/MS analysis respectively; enzyme

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specificity was trypsin/Lys-C; maximum two missed cleavage sites allowed; cysteine

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carbamidomethylation was set as static modification; Oxidation (M) was set as dynamic

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modification. A maximum false discovery rate (FDR) of 1% for peptides and proteins was 7


maximum 10

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selected. The protein identifications were based on at least two matched peptides. Feature

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matching between raw files was enabled, using a retention time window of 2 min. Both razor and

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unique peptides were used for Label free quantification (LFQ). A decoy sequence database was

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constructed by reversing the target sequence database. A list of known contamination was also

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included in the identification. Averaged LFQ intensity values were used to for further data

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analysis. The mass spectrometry proteomics data have been deposited to the ProteomeXchange

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Consortium12

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PXD003749(http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD003749).

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Username: [email protected]

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Password: QaWULAUh

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Pathway Analysis and Network Analysis The Pathway analysis was performed through the use

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of

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www.qiagen.com/ingenuity). Differential expressed proteins were uploaded to IPA 2015 winter

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release. A core analysis was performed including canonical pathway analysis, upstream regulator

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analysis, casual network analysis, and network analysis.

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Immunoblot Collected cells and tissues were suspended in lysis buffer (50mM Tris, pH 7.4,

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100mM NaCl2, 1mM MgCl2, 2.5mM Na3VO4,1mM PMSF, 2.5mM EDTA, 0.5% Triton X-100,

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0.5% NP-40, and proteinase inhibitor). The lysates were centrifuged at 12,000 g for 10 min at

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4°C. 20 µg of proteins was used for immunoblot analysis. All the analyses were replicated for at

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least three times and representative blots are presented.

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Immunohistochemistry Formalin-fixed, paraffin-embedded prostate cancer tissues were used for

QIAGEN’s

via

the

PRIDE

Ingenuity

partner

Pathway

repository

Analysis

(IPA®,

8


with

the

QIAGEN

dataset

identifier

Redwood

City,


164

selected proteins immunohistochemical staining. After deparaffinage and blocking, the

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antigen–antibody reaction was incubated overnight at 4°C. Diaminobenzidine (DAB) reagents

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were applied to detect the signal from the antigen-antibody reaction. All sections were

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counterstained with hema-toxylin. IHC was scored independently using software Image Pro-plus.

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RNA Isolation and Quantitative Real-Time PCR Total RNA were extracted with the TRIzol

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reagent kit (Invitrogen, Carlsbad, CA, USA). Reverse transcription was performed using

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M-MLV reverse transcriptase cDNA Synthesis Kit (Takara Bio, Otsu, Shiga, Japan). qRT-PCR

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was carried out on an ABI 7900HT Fast Real-Time PCR System (Foster City, CA, USA) with

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SYBR-Green PCR Master Mix (Toyobo, Kita-ku, Osaka , Japan).

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Human Prostate Cancer Cell Lines Human LNCaP cell line was obtained from China

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Infrastructure of Cell Line Resource. Cells were grown in RPMI-1640 medium supplemented

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with 10% FBS. All cells were maintained at 37°C in 5% CO2 until treatment. Routine

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mycoplasma testing was performed regularly.

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Cell Culture and Transfection Approximately 4x105 cells were seeded on 6-well plates for

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qRT-PCR and Western Blot validation. The cells were cultured until reaching 50–60%

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confluence prior to transfection. Transfection was performed with 2µg shRNA-PDGF-B

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plasmids and 1-2µl LipofectamineTM 2000 in 200µL OptiMEM®-I per well for 3 hours. For cell

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proliferation assay, 5x103 cells were seeded on 96-well plates. 0.2µg shRNA-PDGF-B plasmids

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and 0.1-0.2µl LipofectamineTM 2000 in 200µL OptiMEM®-I per well were added to plate and

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incubated for 3 hours. The transfected cells were cultured in RPMI 1640 medium supplemented

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with 10% FBS for 48 or 72 hours before analysis. 9


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Cell Proliferation Assay 5 mg/mL MTT in PBS was added to each well to a final concentration

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of 0.5 mg/mL. The mixture was incubated for 4 hour at 37°C. The supernatant was removed and

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150µL DMSO was added. The absorbance was measured at the wavelength of 450nm. All

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experiments were performed in triplicates for each treatment.

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In vivo Animal model treatment TRAMP and WT mice at 10 weeks age were randomized into

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control and treated cohort and treated i.p. either with normal saline (NaCl 0.9%) , or Crenolanib

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15 mg/kg twice daily (n=6 per group). All the animals were sacrificed after follow-up of 56 days.

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The prostate glands were collected and fixed. Microvessel density of tumor was analyzed by

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anti-CD31 and anti α-SMA immunostaining for assessing the compound effects. Cell

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proliferation was analyzed by anti-Ki67 and anti-PCNA immunostaining

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Statistics — Data were presented as mean ± SE, and analyzed with the two-tailed Student’s t-test

196

between two groups. It was considered statistically significant if the P-value was lower than 0.05.

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Principle component analysis (PCA) was performed using the Excel add-in Multibase package

198

(Numerical Dynamics, Japan). RNA binding sites were predicted with web server RBPmap

199

(http://rbpmap.technion.ac.il/index.html)13.

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Results

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Reference Map of the Mouse prostate Proteome

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In this study, we extended the coverage of the mouse prostate proteome with state-of-the-art

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proteomics and performed an in-depth analysis of proteins present in the entire mouse prostate 10



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gland (Fig 1A). In total, 2379 non-redundant proteins were identified across eight individual

206

animals after removal of known contaminates and proteins found from the decoy database. For

207

higher confidence, only proteins with identification in at least two individual animals were

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included in the reference proteome (Table S2). The Gene Ontology analysis of reference proteins

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indicated that prostate proteins are mostly involved in catalytic activities (40.7%). The detailed

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protein distribution presented as molecular function, cellular component content, biological

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process and protein class is found in Fig. S2. In TRAMP mice, 1086 proteins were identified in

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at least two TRAMP mice. The overlap of proteins can be seen in Fig. 1B. For the subsequent

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bioinformatics analysis, we focused on 11 proteins that were quantitated in all four biological

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replicates in TRAMP mice but never identified in WT mice, as well as one protein was identified

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in all four biological replicates in WT mice but never identified in TRAMP mice (Table 1a and

216

1b).

217

218

219

220

221 222 11


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Figure 1. Differential proteomics analysis of TRAMP and WT mice. A. Workflow for the analysis of TRAMP and WT mouse proteomes. B. Venn diagram showing the overlap between the proteins identified in TRAMP mice and WT mice. C. Volcano plot illustrating significantly differential abundant proteins in quantitative analysis. The -log10 (Pvalue) is plotted against the log2(Ratio TRAMP/WT). The red dots indicate proteins with significantly different expression between TRAMP and WT mice, the black dots indicate proteins not significantly different in the TRAMP and WT mice. D. Principal component analysis of quantitative proteome profiles of TRAMP and WT mice. The first and second principal components of each analysis were calculated and plotted. The relative distance between points is a measure of similarity or difference. The clustering shows clear differentiation of animal groups. E. PDGFB regulatory network was predicted based on experimental evidences in IPA knowledgebase. The key of figure legend is shown at right side.

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Table 1a. List of 11 proteins that uniquely identified in TRAMP mice prostate IDs

Symbol

Full names

Q9EPV7 Q99KR7 G5E8K5-2 Q9DBE0 Q62318 Q922B1 A2ADY9 P62965 P50544 E9PWU4 Q62446

9530002B09Rik Ppif Ank3 Csad Trim28 Macrod1 Ddi2 Crabp1 Acadvl Adipoq Fkbp3

AUMP Peptidyl-prolyl cis-trans isomerase F, mitochondrial Ankyrin-3 Cysteine sulfinic acid decarboxylase Transcription intermediary factor 1-beta O-acetyl-ADP-ribose deacetylase MACROD1 Protein DDI1 homolog 2 Cellular retinoic acid-binding protein 1 Very long-chain specific acyl-CoA dehydrogenase, mitochondrial Adiponectin Peptidyl-prolyl cis-trans isomerase FKBP3

236

Table 1b. List of 1 proteins that uniquely identified in WT mice prostate

237 IDs

Symbol

Full names

Q9JLV1

Bag3

BAG family molecular chaperone regulator 3

238

239 240 241 242 243 12



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Quantitative proteomics analysis of the entire prostate glands from WT and TRAMP mice

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In the quantitative proteomics analysis, we focused on 515 proteins that were identified in all 8

246

individual animals. Biostatistics analysis reveals 61 proteins presented significantly between WT

247

and TRAMP group (p

Quantitative proteomics of TRAMP mice combined with bioinformatics

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