DOI: 10.5061/dryad.63xsj3vbb
title: Data from: Identification of druggable targets from the interactome of the Androgen Receptor and Serum Response Factor pathways in prostate cancer

description: Background: The Androgen Receptor (AR) pathway is crucial in driving the
 progression of prostate cancer (PCa) to an advanced state. Despite the
 introduction of second-generation AR antagonists, such as enzalutamide,
 majority of patients develop resistance. Several mechanisms of resistance
 have been identified, including the constitutive activation of the AR
 pathway, the emergence of AR spliced variants, and the influence of other
 signalling pathways. The Serum Response Factor (SRF) was previously
 identified as a possible player of resistance involved in a crosstalk with
 the AR signalling pathway. Elevated SRF levels in PCa patients were
 associated with disease progression and resistance to enzalutamide.
 However, the molecular mediators of the crosstalk between SRF and AR still
 need to be elucidated. The objective of this study was to identify common
 interactors of the AR/SRF crosstalk as therapeutic targets. Methods: Here
 we used affinity purification mass spectrometry (MS) following
 immunoprecipitation of SRF and AR, to identify proteins that interact with
 both SRF and AR. The list of common interactors was expanded using STRING.
 Four common interactors were functionally validated using MTT assays.
 Results: Seven common interactors were identified, including HSP70,
 HSP0AA1, HSP90AB1, HSAP5, PRDX1, and GAPDH. Pathway analysis revealed that
 the PI3k/AKT pathway was the most enriched in the AR/SRF network.
 Moreover, pharmacological inhibition of several proteins in this network,
 including HSP70, HSP90, PI3k, and AKT, significantly decreased cellular
 viability of PCa cells. Conclusions: This study identified a list of
 AR/SRF common interactors that represent a pipeline of druggable targets
 for the treatment of PCa.
description: <strong><em>Co-Immunoprecipitation
 Assay</em></strong> Prior to cellular
 lysis, the antibody solution was prepared adding 2μg of either SRF (Novus,
 Biotechne), AR (Santa Cruz, California, US), Ms IgG or Rb IgG to 20μL A/G
 protein beads (Pierce) and 300μL PBS (Gibco). The beads/antibody mixture
 was incubated for an hour on a rotator at 4°C. Following incubation, the
 mixture was washed with ice-cold lysis buffer 3 times.  Cells were scraped
 with 500μL of lysis buffer (500μL of 1% Triton x100, 1mL of 20mM Tris-HCl
 pH7.5, 1.5mL of 150mM NaCl and 50μL of 1mM MgCl<sub>2</sub>)
 and incubated for 10 mins on ice. 1mg of protein was added to the
 beads/antibody mix and incubated for 1 hour on a rotator at 4°C. Samples
 were washed 3 times in ice-cold lysis buffer.
 <strong><em>Peptide elution and
 digestion</em></strong> Following Co-IP,
 the peptides in each sample were eluted with 60μL of ice-cold Elution
 Buffer I (0.012g Urea, 50μL of 1M Tris-HCl pH7.5) and 5μg/mL Trypsin
 (Promega, Seq Grade Modified) for 30 mins at RT. Samples were then
 centrifuged at 3000rpm for 30s. The supernatant was collected into a new
 Eppendorf tube, and 20μL of Elution Buffer II was added to each sample.
 This step was repeated twice. The supernatant was collected into a new
 centrifuge tube with a total volume of 110μL. Samples were left to digest
 overnight at 37°C at 300rpm.
 <strong><em>Liquid Mass spectrometry (Bruker timsTOF
 Pro) and data analysis with MaxQuant</em></strong>
 A Bruker timsTOF Pro MS (Bruker Daltonics, Bremen, Germany)
 connected to an EvoSep One chromatography system was used. The timsTOF Pro
 MS was run using positive ion polarity with TIMS (Trapped Ion Mobility
 Spectrometry) and PASEF (Parallel Accumulation Serial Fragmentation)
 modes. Accumulating ramp times for the TIMS were set at 100ms, with the
 ion mobility ranging from 0.6 to 1.6 Vs/cm. A mass range from 100 to 1,700
 m/z was the set range to record the spectra of ions. The precursor MS
 Intensity Threshold was set to 2,500 and the precursor Target Intensity
 was set to 20,000. Each PASEF cycle included one MS ramp for precursor
 detection, accompanied by 5 PASEF MS/MS ramps and a total cycle time of
 1.03s. Peptides were separated using reverse-phase
 C<sub>18</sub> Endurance column (15cm x 150µm ID, C18, 1.9µm)
 using the Evosep pre-set 30 SPD method. Mobile phase A consisted of 0.1%
 (v/v) formic acid in water and phase B included 0.1% (v/v) formic acid in
 acetonitrile. Peptides were separated by increasing gradient of solvent B
 for 44 minutes with a flow rate of 0.5µL/min. MaxQuant v1.6.17.0 was used
 by applying the <em>Homo sapiens</em> subset of the Uniprot
 Swissprot database against the raw data. A contaminants database was
 included in the search and the ‘Match Between Runs’ and ‘Label free
 quantification’ were selected. The minimum peptide length allowed was 7
 amino acids.  False discovery rate (FDR) for peptides was set at 0.01.
 Protein intensity of each identified protein was normalised to obtain the
 label free quantification intensity (LFQi) value. A ProteinGroups.txt
 output file generated by MaxQuant was used for subsequent data
 analysis. 
description: # List of peptides precipitated with AR and SRF
 [https://doi.org/10.5061/dryad.63xsj3vbb](https://doi.org/10.5061/dryad.63xsj3vbb) ## Description of the data and file structure This dataset was created to identify common interactors of the androgen receptor (AR) and serum response factor (SRF) in prostate cancer. AR and SRF were precipitated using specific antibodies followed by affinity purification mass spectrometry. ### Files and variables #### File: 220209kw\_HaleemaIP\_1\_S1-A2\_1\_8007.d\_Caitriona\_Scaife.zip **Description:** AR co-IP of ARsiRNA N1 mass spec row data #### File: 220209kw\_HaleemaIP\_2\_S1-A3\_1\_8008.d\_Caitriona\_Scaife.zip **Description:** AR co-IP of endogenous AR N1 mass spec row data #### File: 220209kw\_HaleemaIP\_3\_S1-A4\_1\_8009.d\_Caitriona\_Scaife.zip **Description:** AR co-IP of endogenous AR post DHT stimulation N1 mass spec row data #### File: 220209kw\_HaleemaIP\_4\_S1-A5\_1\_8010.d\_Caitriona\_Scaife.zip **Description:** AR co-IP of ARsiRNA N2 mass spec row data #### File: 220209kw\_HaleemaIP\_5\_S1-A6\_1\_8011.d\_Caitriona\_Scaife.zip **Description:** AR co-IP of endogenous AR N2 mass spec row data #### File: 220209kw\_HaleemaIP\_6\_S1-A7\_1\_8012.d\_Caitriona\_Scaife.zip **Description:**  AR co-IP of endogenous AR post DHT stimulation N2 mass spec row data #### File: 220209kw\_HaleemaIP\_7\_S1-A8\_1\_8013.d\_Caitriona\_Scaife.zip **Description:** AR co-IP of ARsiRNA N3 mass spec row data #### File: 220209kw\_HaleemaIP\_8\_S1-A9\_1\_8014.d\_Caitriona\_Scaife.zip **Description:** AR co-IP of endogenous AR N3 mass spec row data #### File: 220209kw\_HaleemaIP\_9\_S1-A10\_1\_8015.d\_Caitriona\_Scaife.zip **Description:** AR co-IP of endogenous AR post DHT stimulation N3 mass spec row data #### File: 220209kw\_HaleemaIP\_10\_S1-A11\_1\_8016.d\_Caitriona\_Scaife.zip **Description:** AR co-IP of ARsiRNA N4 mass spec row data #### File: 220209kw\_HaleemaIP\_11\_S1-A12\_1\_8017.d\_Caitriona\_Scaife.zip **Description:** AR co-IP of endogenous AR N4 mass spec row data #### File: 220209kw\_HaleemaIP\_12\_S1-B1\_1\_8018.d\_Caitriona\_Scaife.zip **Description:** AR co-IP of endogenous AR post DHT stimulation N4 mass spec row data #### File: 220209kw\_HaleemaIP\_13\_S1-B2\_1\_8019.d\_Caitriona\_Scaife.zip **Description:** IgG control (mouse) mass spec row data #### File: 220209kw\_HaleemaIP\_14\_S1-B3\_1\_8020.d\_Caitriona\_Scaife.zip **Description:** SRF co-IP of SRFsiRNA N1 mass spec row data #### File: 220209kw\_HaleemaIP\_15\_S1-B4\_1\_8021.d\_Caitriona\_Scaife.zip **Description:** SRF co-IP of SRF overexpression N1 mass spec row data #### File: 220209kw\_HaleemaIP\_16\_S1-B5\_1\_8022.d\_Caitriona\_Scaife.zip **Description:** SRF co-IP of SRF overexpression + DHT N1 mass spec row data #### File: 220209kw\_HaleemaIP\_17\_S1-B6\_1\_8023.d\_Caitriona\_Scaife.zip **Description:** IgG control (rabbit) mass spec row data #### File: 220209kw\_HaleemaIP\_18\_S1-B7\_1\_8024.d\_Caitriona\_Scaife.zip **Description:** SRF co-IP of SRFsiRNA N2 mass spec row data #### File: 220209kw\_HaleemaIP\_19\_S1-B8\_1\_8025.d\_Caitriona\_Scaife.zip **Description:**  SRF co-IP of SRF overexpression N2 mass spec row data #### File: 220209kw\_HaleemaIP\_20\_S1-B9\_1\_8026.d\_Caitriona\_Scaife.zip **Description:** SRF co-IP of SRF overexpression + DHT N2 mass spec row data #### File: 220209kw\_HaleemaIP\_21\_S1-B10\_1\_8027.d\_Caitriona\_Scaife.zip **Description:** SRF co-IP of SRFsiRNA N3 mass spec row data #### File: 220209kw\_HaleemaIP\_22\_S1-B11\_1\_8028.d\_Caitriona\_Scaife.zip **Description:** SRF co-IP of SRF overexpression N3 mass spec row data #### File: 220209kw\_HaleemaIP\_23\_S1-B12\_1\_8029.d\_Caitriona\_Scaife.zip **Description:** SRF co-IP of SRF overexpression + DHT N3 mass spec row data #### File: 220209kw\_HaleemaIP\_24\_S1-C1\_1\_8030.d\_Caitriona\_Scaife.zip **Description:** SRF co-IP of SRFsiRNA N4 mass spec row data #### File: 220209kw\_HaleemaIP\_25\_S1-C2\_1\_8031.d\_Caitriona\_Scaife.zip **Description:** SRF co-IP of SRF overexpression N4 mass spec row data #### File: 220209kw\_HaleemaIP\_26\_S1-C3\_1\_8032.d\_Caitriona\_Scaife.zip **Description:** SRF co-IP of SRF overexpression + DHT N4 mass spec row data ## Code/software The dataset uploaded here includes row data from Bruker timsTOF Pro MS (Bruker Daltonics, Bremen, Germany). Bruker Compass DataAnalysis (version 6.1) is needed to open the row data. Alternatively, the free software Alphatims available at the link in brackets  ([https://github.com/MannLabs/alphatims#one-click-gui](https://github.com/MannLabs/alphatims#one-click-gui))  can be used to visualize and manipulate the raw data file from the Bruker timsTOF. "_Caitriona_Scaife" needs to be removed from the file name before opening the file as Alphatims only recognizes a folder that ends in ".d". The data uploaded here came straight from the mass spec runs with no further analysis.   A Bruker timsTOF Pro MS (Bruker Daltonics, Bremen, Germany) connected to an EvoSep One chromatography system was used to run each proteomic sample. The timsTOF Pro MS was run using positive ion polarity with TIMS (Trapped Ion Mobility Spectrometry) and PASEF (Parallel Accumulation Serial Fragmentation) modes. Accumulating ramp times for the TIMS were set at 100ms, with the ion mobility ranging from 0.6 to 1.6 Vs/cm. A mass range from 100 to 1,700 m/z was the set range to record the spectra of ions. The precursor (MS) Intensity Threshold was set to 2,500 and the precursor Target Intensity was set to 20,000. Each PASEF cycle included one MS ramp for precursor detection, accompanied by 5 PASEF MS/MS ramps and a total cycle time of 1.03s. Peptides were separated using reverse-phase C18 Endurance column (15cm x 150µm ID, C18, 1.9µm) using the Evosep pre-set 30 SPD method. Mobile phase A consisted of 0.1% (v/v) formic acid in water and phase B included 0.1% (v/v) formic acid in acetonitrile. Peptides were separated by increasing gradient of solvent B for 44 minutes with a flow rate of 0.5µL/min. MaxQuant v1.6.17.0 was used by applying the Homo sapiens subset of the Uniprot Swissprot database against the raw data (date downloaded 11.02.22). Trypsin was selected as the digesting enzyme and up to two missed cleavages were allowed. Oxidation of Methionine and N-terminal acetylation were selected as variable modifications while Carbamiodomethylation of cysteine was selected as a fixed modification. A contaminants database was included in the search and the ‘Match Between Runs’ and ‘Label free quantification’ were selected(157). The minimum peptide length allowed was 7 amino acids. False discovery rate (FDR) for peptides was set at 0.01. Protein intensity of each identified protein was normalised to obtain the label free quantification intensity (LFQi) value. A ProteinGroups.txt output file generated by MaxQuant was used for subsequent data analysis analysis.