Abstract

   As pharmacological data sets become increasingly large and complex, new
   visual analysis and filtering programs are needed to aid their
   appreciation. One of the most commonly used methods for visualizing
   biological data is the Venn diagram. Currently used Venn analysis
   software often presents multiple problems to biological scientists, in
   that only a limited number of simultaneous data sets can be analyzed.
   An improved appreciation of the connectivity between multiple,
   highly-complex datasets is crucial for the next generation of data
   analysis of genomic and proteomic data streams. We describe the
   development of VENNTURE, a program that facilitates visualization of up
   to six datasets in a user-friendly manner. This program includes
   versatile output features, where grouped data points can be easily
   exported into a spreadsheet. To demonstrate its unique experimental
   utility we applied VENNTURE to a highly complex parallel paradigm, i.e.
   comparison of multiple G protein-coupled receptor drug dose
   phosphoproteomic data, in multiple cellular physiological contexts.
   VENNTURE was able to reliably and simply dissect six complex data sets
   into easily identifiable groups for straightforward analysis and data
   output. Applied to complex pharmacological datasets, VENNTURE’s
   improved features and ease of analysis are much improved over currently
   available Venn diagram programs. VENNTURE enabled the delineation of
   highly complex patterns of dose-dependent G protein-coupled receptor
   activity and its dependence on physiological cellular contexts. This
   study highlights the potential for such a program in fields such as
   pharmacology, genomics, and bioinformatics.

Introduction

   As our knowledge of the inter-connected complexity of physiological,
   cellular and molecular functions expands, there is an ever increasing
   need for novel methods to study multiple datasets simultaneously. One
   of the most common methods for data set analysis is the Venn diagram.
   These typically consist of multiple intersecting regions (usually
   circles), that allow analysis of all logical relationships between
   different data sets [42][1]. With Venn diagram analysis, the set-unique
   and set-intersection values can be easily illustrated for the viewer.
   For more than three data sets, the structure of the sets and
   intersections can however start to become problematical from a
   visualization standpoint, i.e. usable representation of all overlapping
   regions and Venn symmetry can often be lost. Venn himself generated
   visualization platforms that facilitate simultaneous analysis of higher
   numbers of sets, by using additional successive ellipses that intersect
   with the primary circles. He also gave a construction for Venn diagrams
   for any number of sets, where each successive curve delimiting a set is
   interleaved with previous curves, starting with the 3-circle diagram
   [43][1]. A. W. F. Edwards also developed elegant display methods for
   diagrams composed of higher numbers of sets that feature some
   interesting symmetries [44][2]. The Edwards Venn diagram was achieved
   by projecting the classical circular Venn diagram onto a sphere. In
   this format, three sets are represented by taking three hemispheres at
   right angles to each other, while a fourth set can be represented by
   taking a curve similar to the seam on a tennis ball which undulates
   around the sphere equator. The resulting sets can then be projected
   back to a flat plane, generating ‘cogwheel’ diagrams with increasing
   numbers of teeth.

   Numerous online Venn diagram programs are currently available to create
   a simultaneous visual interpretation of large amounts of biological
   data with up to four independent datasets, examples of such programs
   include Pangloss Venn diagram generator
   ([45]http://www.pangloss.com/seidel/Protocols/venn4.cgi) and Venny
   ([46]http://bioinfogp.cnb.csic.es/tools/venny/index.html). These
   programs provide an invaluable resource, but are often designed
   primarily to generate images rather than assist facile output and
   attachment of additional identifiers to the data. Venn diagram programs
   can require laborious transfer of information from spreadsheets into
   text files before inputting. With the use of ever more complex
   datasets, this data handling can become a serious, time-limiting issue.
   In addition to input limitations, modern Venn diagram programs, such as
   the excellent program Venny, are capable of comparing only a maximum of
   four data sets with symmetrical ellipsoidal regions [47][3]. DrawVenn
   ([48]http://theory.cs.uvic.ca/euler/DrawVenn/) and BioVenn [49][4] are
   also useful programs that instead allow the generation of
   area-proportional venn set analyses. Programs such as GeneVenn
   ([50]http://www.bioinformatics.org/gvenn/) and VennMaster
   ([51]http://www.informatik.uni-ulm.de/ni/staff/HKestler/vennhyp/) have
   the additional feature of linking genes within each group to related
   information in the NCBI Entrez Nucleotide database or the Gene Ontology
   database, but these programs are still limited to the analysis of two
   or three gene lists [52][5], [53][6]. GeneSpring
   ([54]http://www.strandgenomics.com/GeneSpring) and SilicoCyte
   ([55]http://www.cytogenomic.com/silicocyte.htm) are software packages
   specifically designed to analyze large volumes of microarray data with
   several visualizations, including Venn diagrams. However both of these
   programs are also limited in both the number of data sets and the
   variety of studies for which they can be employed. The program we have
   developed, VENNTURE, provides a simple interface for Edwards Venn
   diagram generation. It is designed with the following novel attributes
   in mind, i.e. ease of data importation and exportation, compatibility
   with commonly-used windows-based programs and a sufficient flexibility
   to facilitate the analysis of two to six sets of any length of dataset.

   An ideal practical application for the simultaneous Venn analysis of
   more than four discrete sets is the investigation of
   concentration-dependent effects in log dose-response curve
   relationships generated from drug/ligand-mediated transmembrane
   receptor activation. The ligand-mediated log-dose response curve (LDRC)
   has long been one of the fundamental experimental paradigms in
   pharmacology. Such simple experiments allow the quantitative and
   qualitative investigation of ligand-mediated cell signaling effects.
   For example, a simple LDRC usually consists of a control ligand
   response effect (i.e. no drug/ligand added) and normally five
   logarithmically-increasing ligand/drug doses, each generating
   increasing or decreasing cellular responses. Ideally this drug/ligand
   dose range should encompass both the half-maximal effect of the
   drug/ligand and also the maximal cellular response. In recent times
   however, receptor research especially in the field of G protein-coupled
   receptors (GPCRs), has indicated potential limitations in LDRCs, i.e.
   they only identify the dose-dependent variation of signal generation
   for one output index, e.g. intracellular calcium mobilization. This
   single function output is now considered a serious limitation for GPCR
   analysis, as they are now known to activate many more signaling
   pathways simultaneously than previously thought [56][7]–[57][10].
   Recent evidence has also suggested that distinct isomeric structures of
   the same GPCR, stabilized by differential intracellular protein
   interactions, can mediate qualitatively distinct signaling responses to
   different ligands [58][8], [59][9], [60][11]. It is therefore possible
   that for a given LDRC profile, distinct receptor isomers could
   differentially contribute to the creation of the various dose-related
   responses [61][8], [62][12]. In addition to our current adjustments to
   hypotheses of GPCR function, our historical appreciation of the
   cellular signaling pathways activated by GPCRs as linear and discrete
   entities, has now largely been superseded by a broader posit that
   resultant molecular signaling functions are the products of
   highly-interconnected molecular (gene or protein) network interactions
   [63][7]–[64][9], [65][13]. The functional output therefore of cellular
   signaling cascades may be best appreciated by studying the interaction
   and convergence of multiple pathways and signaling paradigms. Indeed,
   investigation and profiling of GPCR signaling pathways is now often
   performed at both the genomic and proteomic levels. These two
   techniques often can generate large volumes of data, especially with
   the introduction of paradigms involving multiple ligand structures,
   multiple drug doses and stimulation time-points as well as the use of
   diverse cellular receptor contexts [66][11], [67][14], [68][15]. This
   final aspect described, i.e. cellular receptor context, is emerging as
   a critical factor in analyzing receptor signaling function. We have
   recently demonstrated a novel cellular receptor context model,
   generated by molecular adaptive changes to a mild long-term oxidative
   stress (termed ‘chronic minimal peroxide-CMP’), which mimics multiple
   aspects of the neuronal aging process [69][14]. Using this model, we
   demonstrated that multiple receptor-mediated processes are profoundly
   altered at both the cell signaling and the genomic response level. The
   ability to appreciate numerous functional aspects and nuances in GPCR
   signaling is likely to assist with the development of more cellular
   context-specific and signal-selective GPCR-based therapeutics [70][7],
   [71][8]. GPCR-based therapeutic design has been one of the most
   important advances in clinical pharmacology, as nearly 60% of all
   current drugs are GPCR-targeted [72][16]. Therefore the ability to
   analyze multiple (up to 6) complex datasets from LDRC experiments, in
   parallel, may greatly assist our future understanding of the complexity
   of biological receptor signaling systems across multiple cellular
   contexts. In this study, we have employed our newly developed software
   program, to not only identify the multiple subtleties of basic
   dose-dependent GPCR signaling, but also to illuminate the importance of
   considering cellular context with respect to functional GPCR signaling
   outputs.

Methods

Cell Culture

   Human neuroblastoma SH-SY5Y cells were used to generate the biological
   data employed in this study. Passage 5–10 SH-SY5Y cells were obtained
   from the American Type Culture Collection (ATCC:
   [73]http://www.atcc.org/) and cultured according to ATCC protocols.
   SH-SY5Y cells were maintained in medium that was a 1∶1 mixture of
   advanced minimal essential medium (MEM) and F-12+ GlutaMAX-1
   supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin
   (Gibco-BRL), as described previously [74][14].

Cell Stimulation Protocols

   SH-SY5Y cells were allowed to grow in monolayers and were
   serum-deprived for 16 hours prior to experimental protocols. Cell
   monolayers of SH-SY5Y cells, at approximately 75% confluence, were
   stimulated with various doses, 10 nM, 100 nM, 1 µM, 10 µM or 100 µM, of
   the muscarinic G protein-coupled receptor agonist,
   acetyl-β-methylcholine chloride (MeCh: Sigma Aldrich, St Louis MO) for
   fifteen minutes before removal of the stimulating ligand by aspiration.
   Cell monolayers were then washed twice in ice-cold phosphate-buffered
   saline (PBS). Cells were then manually removed from the plates by
   scraping followed by centrifugal pelleting (1000×g, 5 minutes at 4°C).
   Recovered cell pellets were suspended in freshly prepared 8 M urea
   (Invitrogen, Carlsbad CA) with 50 mM NH[4]HCO[3] (Fluka) and were
   completely disrupted using a Sonic Dismembrator (Model 100, Thermo
   Fisher Scientific). Protein lysate concentrations were subsequently
   determined by a BCA (bisinchoninic acid) protein assay kit (Thermo
   Fisher Scientific). Extracted proteins were immediately processed for
   subsequent phosphoprotein isolation and mass spectrometric analysis.

Cellular Context Modification

   To modify the cellular context of the SH-SY5Y cells, experimental
   ligand stimulation with MeCh was performed in both normal (control
   state) serum-deprived SH-SY5Y cells and also SH-SY5Y cells exposed to
   seven days of incubation with hydrogen peroxide (10 nM) [75][14]. This
   form of hydrogen peroxide exposure, simulates in several aspects,
   long-term minimal oxidative cell stress. This peroxide exposure
   paradigm that we have previously described as ‘chronic minimal
   peroxide’ (CMP), can induce multidimensional genomic and proteomic
   alterations that can generate a partial mimetic of aged neuronal
   tissues. In addition, this CMP treatment has also been shown to
   significantly alter ligand-mediated drug effects [76][14]. Whole-cell
   lysates were then produced as described in the previous section and
   phosphoproteins were purified using immobilized metal affinity
   chromatography and identified using a ThermoFinnigan LXQ tandem mass
   spectrometer.

Western Blotting

   To identify specific protein expression alterations in response to the
   CMP treatment, as well as to verify MeCh-induced protein
   phosphorylation targets, SH-SY5Y whole-cell extracts were first
   quantified using BCA reagent (ThermoScientific, Rockford IL) before
   resolution with SDS-PAGE and electrotransfer to PVDF membranes (Perkin
   Elmer, Waltham MA). Membranes were blocked for western blots, as
   described previously [77][13] and primary antibody immune-reactive
   complexes were identified using alkaline phosphatase-conjugated
   secondary antisera (Sigma Aldrich) with enzyme-linked chemifluorescence
   (GE Healthcare, Piscataway NJ) and quantified with a Typhoon 9410
   variable-mode phosphorimager. Anti-lamin-A (LMNA), anti-calmodulin
   (CALM1), anti-calreticulin (CALR) sera were obtained from Santa Cruz
   Biotechnology (Santa Cruz, CA) and anti-G protein-coupled receptor
   kinase interacting ArfGAP 2 (GIT2) sera was obtained from NeuroMab (San
   Jose, CA). Anti-active, and anti-non-active extracellular
   signal-regulated kinase 1/2 (ERK1/2), anti-G protein-coupled receptor
   kinase interacting ArfGAP 1 (GIT1) sera were obtained from Cell
   Signaling Technology (Danvers, MA). ERK1/2 activation log dose-response
   relationships and any subsequent statistical analysis (Student’s
   t-test) were assessed using GraphPad Prism version 5.02.

Immunoprecipitation Protocols

   SH-SY5Y cells, either non-stimulated or MeCh-stimulated, were prepared
   for immunopurification of phosphorylation target proteins as follows.
   MeCh-containing media (post fifteen minute stimulation) was aspirated
   from the stimulated cell monolayers, which were then washed twice in
   ice-cold phosphate-buffered saline before lysis in an NP-40-based lysis
   buffer as described previously [78][17]. Cell lysates were prepared by
   agitation for 20 minutes at 4°C before centrifugal clarification (14000
   rpm/10 minutes/4°C). Normalized supernatant protein lysates were
   pre-cleared using protein A/G pre-conjugated agarose beads (EMD
   Chemicals, Gibbstown NJ). Specific anti-protein sera (10 µg total),
   plus 30 µL of protein A/G pre-conjugated agarose beads, were then added
   to the pre-cleared supernatants with agitation at 4°C for four hours.
   Immunoprecipitates were collected via centrifugation, washed in the
   NP-40-based buffer and proteins were eluted in 30 µL Laemmli (8% SDS)
   buffer. Samples were then processed for western blotting. Specific
   antisera employed for immunoprecipitation studies were obtained from
   the following sources: anti- G protein-coupled receptor kinase
   interacting ArfGAP 1 (GIT1) and anti-p21 protein (Cdc42/Rac)-activated
   kinase 1 (PAK1) was obtained from Cell Signaling Technology (Danvers,
   MA); anti-G protein regulated inducer of neurite outgrowth 1 (GPRIN1)
   and anti-reticulon 4 (RTN4) were obtained from Abcam (Cambridge, MA);
   anti-Grb2-associated binding protein 2 (GAB2) and
   anti-microtubule-associated protein 2 (MAP2) were obtained from Santa
   Cruz Biotechnology (Santa Cruz, CA);
   anti-phosphoserine/phosphothreonine antisera were obtained from
   Millipore (Billerica, MA).

Mass Spectrometry-targeted Primary Protein Extraction

   Proteins were extracted from SH-SY5Y cell pellets for mass spectrometry
   analysis as follows. In brief, samples were initially reduced in 12.5
   mM tris(2-carboxyethyl) phosphine (Sigma) at room temperature (RT) for
   1 h, then alkylated with 25 mM iodoacetamide (Sigma) at RT for 30 min
   with protection from ambient light. The reaction was then quenched with
   incubation in 15 mM dithiothreitol (Sigma) at RT for 15 min. Resultant
   protein extracts were diluted in 50 mM NH[4]HCO[3] to a final
   concentration of 1 M urea and were digested using sequencing-grade
   trypsin (Promega) at a 1∶75 (trypsin/protein, w/w, 37°C) ratio. After a
   sixteen hour digestion, formic acid (Fluka) was added to a final
   concentration of 0.5% (v/v). Peptide samples were then desalted using
   Sep-Pak Vac C18 cartridges (3cc/200 mg, Waters). Sep-Pak cartridges
   were then washed with 3 mL of acetonitrile (ACN, Thermo Fisher
   Scientific) and equilibrated with 3 mL of 0.1% formic acid. Cartridges
   were loaded with peptides (1.5 mg per cartridge), then washed with 6 mL
   of 0.1% formic acid. Peptides were subsequently eluted with 1 mL of 80%
   ACN/0.1% formic acid and dried to below 50 µL using a Savant SpeedVac
   (Thermo Fisher Scientific) before further processing and phosphoprotein
   purification.

Phosphoprotein Extraction and Purification

   All mass spectrometric columns employed were fabricated using 360 µm
   outer-diameter fused-silica capillaries (Polymicro Technologies).
   Phosphoproteins were extracted from general protein extracts using a
   modified immobilized metal affinity chromatography (IMAC) process using
   titanium dioxide (TiO[2]) particles. To create TiO[2] IMAC columns, 150
   µm inner-diameter capillary was cut and one end was placed in Kasil
   solution (Next Advance, Averill NY) to allow the solution to enter the
   capillary to a length of 3–4 cm by capillary action. Frit formation, to
   create a stable compartment to retain the TiO[2] resin, was completed
   using heating of the packed capillary (16 h, 70°C). The excess frit was
   removed (1 cm remaining), then the capillary was packed with TiO[2]
   particles (Titansphere TiO[2], 5 µm, GL Sciences) and suspended in 50%
   methanol (J.T. Baker). Capillary column packing, washing, sample
   loading and elution, were carried out manually using a controllable
   nitrogen pressure loader (Next Advance-model PC 77). The amount of
   packed TiO[2] particles was measured by weighing the column before and
   after packing, using a microbalance (Sartorius Instruments, Goettingen
   Germany) in dry laboratory conditions. Approximately 6 cm packing was
   equal to 2 mg of particles of TiO[2] particles. An analytical C18
   column, with integrated electrospray emitter tip, was prepared on a
   laser puller (P2000, Sutter Instruments, Novato CA) with a 75 µm
   internal-diameter fused-silica capillary, using a previously developed
   protocol [79][18]. C18 resins (Magic C18, 5 µm, 300 Å, Michrom
   Bioresources, Auburn CA) were packed to 8.5 cm of the 10.5-cm frit-less
   capillary column. The resulting C18 capillary column was used for
   standard analysis and as the electrospray interface for liquid
   chromatography-tandem mass spectrometry (LC–MS/MS) [80][19]. To enrich
   primary extracted peptide samples for phosphorylated species, the
   TiO[2]capillary column was first treated with 1% ammonia (Fluka, St.
   Louis MO) and then with a modified wash buffer containing, 80% ACN, 2%
   trifluoroacetic acid and 1 M glycolic acid (‘ATG’ buffer), for 10 min
   each at a 1.5-µL/min flow rate using the nitrogen pressure loader.
   Peptide samples were mixed 1∶1 with the specific ATG buffer and loaded
   onto the column at a flow rate of 0.8 µL/min. The loaded amount of
   total peptide was 150 µg. The TiO[2] column was then washed with ATG
   buffer for 10 min at 1.5 µL/min and then bound peptides were eluted
   with 1% ammonia (pH 11.3) for 5 min at 1.5 µL/min and then immediately
   acidified with 5 µL of 2% trifluoroacetic acid (Fluka). All of the
   samples were prepared as three replicates from tryptic digestions and
   were stored at –80°C until used.

Mass Spectrometric Analysis

   LC–MS/MS was performed using an LXQ linear ion-trap mass spectrometer
   coupled with a Surveyor LC system (ThermoFisher Scientific, West Palm
   Beach FL). Peptide samples were pressure-loaded onto pre-equilibrated
   analytical columns with a flow rate of 200–400 nL/min. Capillary tip
   columns were connected to the LXQ ion source in the MS system, and
   reversed-phase chromatographic separations were carried out using the
   following gradient settings: 100% A (0.1% formic acid in water) for 10
   min, 0–50% B (0.1% formic acid in 100% ACN) for 100 min, 50–70% B for
   10 min, and 70% B for 5 min. Pump flow rates were controlled to deliver
   200 nL/min to the analytical column. This flow rate was consistently
   and reliably achieved by splitting the pump flow at a 1∶1000 ratio. LXQ
   MS settings were as follows: spray voltage of 1.6 kV, 1 microscan for
   MS scans at a maximum inject time of 10 ms with a mass range of m/z
   400–1400, and 3 microscans for MS/MS at a maximum inject time of 100 ms
   with automatic mass range. The LXQ was operated in a data-dependent
   mode, that is, one MS scan for precursor ions followed by four
   data-dependent MS/MS scans for precursor ions above a threshold ion
   count of 500 with a normalized collision energy value of 35%.

   MS/MS data processing and database searches were performed on BioWorks
   Browser 3.3.1 SP1 (Thermo Fisher Scientific). DTA files were generated
   from LC–MS/MS raw files with the following options applied: precursor
   ion tolerance of 1.5 amu, group scan 1, minimum group count 1, minimum
   ion count 20, and filtering through charge state analysis. The
   generated DTA files were searched against a composite target–decoy
   database [81][20]. This database was composed of human complete
   proteome (UniProt release-15.8, with 59,155 protein entries) and its
   reversed sequence version with the following criteria: enzyme, trypsin
   (KR/P); full enzymatic cleavage; missed cleavage sites, 3; peptide
   tolerance, 2.0 amu; fragment ion tolerance, 1.0 amu; variable
   modifications, carbamidomethylation (+57 Da), methionine oxidation (+16
   Da), and STY phosphorylation (+80 Da); modifications per peptide, 4.
   The search result was filtered with Xcorr versus charge state (Xcorr of
   1.5, 2.0, 2.5, and 3.0 for +1, +2, +3, and >+3 charges, respectively)
   and delta CN 0.08. To ensure that the false discovery rate (FDR) of
   peptide–spectral matches (PSMs) remained below 0.5%, the search results
   were further filtered with various Sf (Forial Score) values. An Sf
   value of 0.60 was used for an FDR of 0.49% for the overall results.
   Fragmentation spectra for peptides with only a single observation and
   for multiply phosphorylated peptides were verified through manual
   inspection according to previously published protocols [82][21].

Ligand-mediated Phosphorylation Data Analysis

   The baseline presence of phosphoproteins in the serum-deprived
   non-stimulated SH-SY5Y cells, as well as the induction of protein
   phosphorylation by the fifteen minute stimulation with 10 nM, 100 nM, 1
   µM, 10 µM and 100 µM MeCh doses was assessed in the presence or absence
   of our applied CMP protocol. In addition to the primary protein
   analysis, we performed both Gene Ontology Analysis using WebGestalt
   (p≤0.05 for specific GO term groups)
   ([83]http://bioinfo.vanderbilt.edu/webgestalt/) as well as canonical
   signaling pathway analysis using Ingenuity Pathways Analysis version
   8.5 ([84]http://www.ingenuity.com/). Gene Ontology (GO) term groups are
   curated functional annotations for protein activity
   ([85]http://www.geneontology.org/). Proteins can be associated with
   multiple GO term groups after the generation of empirical data
   concerning the specific activities of that protein. Multifunctional
   proteins such as actin can be associated with many different GO term
   annotations, while more specific proteins are associated with a more
   narrow range of GO terms. Using our novel Venn diagram program,
   VENNTURE, we were then able to separate the multiple phosphoproteins,
   significantly-populated GO terms and significantly-populated canonical
   signaling pathways associated with the differing doses (in control or
   CMP environments) of MeCh stimulation.

VENNTURE Software Development and Application

   VENNTURE was programmed using the Microsoft Foundation Class (MFC)
   library, written in C++. The resulting MFC application can be used on
   any personal computer using any XP or higher Windows™ based system.
   VENNTURE version 1.1.0.2 can be accessed freely at the National
   Institute on Aging ([86]http://www.nia.nih.gov). To upload biological
   data in VENNTURE, the data sets must be organized in an Excel™
   spreadsheet, where each column corresponds to a specific data set. The
   first column in any given spreadsheet represents set 1 and the
   successive columns the additional sets (up to 62 sets for 6-way Edwards
   diagrams). To upload the data into VENNTURE simply requires activation
   of the ‘Load’ window option and browsing for the appropriate Excel™
   spreadsheet file (*.xls or *.xlsx). VENNTURE then automatically
   searches for multiple sets within the first sheet of the Excel™ file.
   One Excel™ file should only contain data columns in the first sheet in
   the file. After the appropriate file is loaded, VENNTURE converts the
   datasets into strings, and uploads the information into internal
   arrays. Once the data has been uploaded, the data strings are compared
   to determine overlap and create the appropriate Edwards Venn diagram
   display categories. VENNTURE then draws the appropriate Venn diagram
   for the number of data sets contained in the uploaded Excel™ file.
   Depending on the number of datasets uploaded, VENNTURE will draw the
   appropriate Edwards Venn diagram variant. The Venn diagram will then be
   populated with the categorized data. In addition to creating the visual
   Edwards Venn image, VENNTURE simultaneously produces an Excel output
   *.xls file. In this Excel™ file, each overlapping category is separated
   into a column directly related to the numerical set order from the
   master Venn diagram output. The ‘Display Mode’ window for the Edwards
   Venn visual display allows the user to replace the numerical categories
   with specific textual representation of the input data points. The
   VENNTURE program also contains a mouse ‘roll-over’ demonstration
   function for demonstration of the contents of each of the specific sets
   in any format of Venn output. The Edwards Venn image can then be simply
   exported graphically in two different ways. One output option is to use
   the ‘Save As’ function to export the Edwards Venn image as an enhanced
   metafile format graphics (*.emf) file. The second option is to use the
   ‘Export’ function which instantly generates a Powerpoint™ file of the
   Edwards Venn. From this Powerpoint™ file a high resolution image (JPG,
   TIFF, PNG etc. ) can be generated using the Powerpoint™ ‘Save As’
   function.

Results

Cellular Context Modification and Ligand Responses

   The proteomic effects, in human SH-SY5Y neural cells, of our
   previously-described chronic minimal peroxide (CMP) protocol was
   assessed, using a panel of divergently CMP-regulated proteins we had
   previously identified as specific markers of this modified cellular
   state [87][14] ([88]Figure 1). Application of the CMP protocol to
   SH-SY5Y cells resulted in a significant upregulation of both lamin
   (LAMN) and GIT2 (G protein-coupled receptor kinase interacting ArfGAP
   2: [89]Figure 1A, B-C) and simultaneous, significant downregulation of
   calmodulin (CALM) and calreticulin (CALR) ([90]Figure 1A, D-E). To
   assess the ability of the stable acetylcholine analog
   (acetyl-β-methylcholine: MeCh) to stimulate signaling activity in the
   SH-SY5Y cells, MeCh-mediated extracellular signal-regulated kinase
   (ERK1/2) activation was assessed. In both control-state and CMP-treated
   SH-SY5Y cells MeCh application resulted in a dose-dependent increase in
   the activity status of ERK1/2, as we have previously reported [91][14].
   The basal level of non-phosphorylated as well as phosphorylated ERK1/2
   was not significantly altered by the CMP protocol. From log
   dose-response analysis, it was apparent that the alteration in cellular
   context induced by CMP, caused an increase in the maximal response
   (R[max]) to MeCh ([92]Figure 1F). ERK1/2 activation responses to each
   of the doses of MeCh tested (in both control- and CMP-state cells) were
   effectively blocked by the pre-exposure of the SH-SY5Y cells to the
   selective muscarinic G protein-coupled receptor antagonist pirenzipine
   (10 µM, 30 minutes incubation prior to MeCh exposure). In addition, no
   significant alteration of plasma membrane muscarinic receptor
   expression was noted between control and CMP conditions ([93]Figure S1:
   [94][14]).

Figure 1. Chronic minimal peroxide-mediated alteration of protein expression
and ligand responses.

   [95]Figure 1
   [96]Open in a new tab

   (A) Representative western immunoblot (IB) analysis of control
   (PBS-vehicle treated) or CMP treatment (7 days, 10 nM hydrogen peroxide
   treatment) effects upon cellular expression of lamin A (LMNA), G
   protein-coupled receptor kinase interacting ArfGAP 2 (GIT2),
   calreticulin (CALR) and calmodulin (CALM1). Quantification of
   CMP-mediated expression changes of LMNA (B), GIT2 (C), CALM1 (D) and
   CALR (E). Quantification was achieved as follows: western blot relative
   absorbance units (x1000) minus background absorbance subtraction per
   square pixel: ((AU-B)/px^2). Quantification was performed using
   ImageQuant 5.2 and statistical analysis was performed on three
   independent experiments using GraphPad Prism version 5.02 with a
   Student’s t-test (p<0.05, *; p<0.01, **; p<0.001, ***). Data is
   represented as mean ± standard error of mean (SEM). (F) Log
   dose-response curves for acetyl-β-methycholine (MeCh)-mediated ERK1/2
   activation in control (black circles) or CMP-treated (white square)
   cells. (G) Response-normalized MeCh ERK1/2 log dose-response curves.
   The maximal response for MeCh in each cellular context (control versus
   CMP) was considered to be 100% in each circumstance. Each experimental
   point on the curves (fitted using sigmoidal dose-response functions in
   GraphPad Prism) represents the mean ± SEM of three independent
   experiments.

Phosphoprotein Extraction and Enrichment

   Using a titanium dioxide-mediated phosphoprotein extraction process
   [97][22], we were able to identify in SH-SY5Y cells the presence of
   multiple phosphoproteins in the non-stimulated control and
   peroxide-treated (CMP) state, as well as after the application of 10
   nM, 100 nM, 1 µM, 10 µM and 100 µM acetyl-β-methylcholine (MeCh) in
   both cellular conditions (Protein identifications listed in
   Supplementary Tables as follows: control SH-SY5Y cells,
   non-stimulated-[98]Table S1, 10 nM, 100 nM, 1 µM, 10 µM, 100µM
   MeCh-[99]Tables S2, [100]S3, [101]S4, [102]S5, [103]S6 respectively;
   CMP SH-SY5Y cells, non-stimulated-[104]Table S7, 10 nM, 100 nM, 1 µM,
   10 µM, 100 µM MeCh-[105]Tables S8, [106]S9, [107]S10, [108]S11, 12
   respectively). Therefore, in our paradigm of ligand dose-responses in
   diverse cellular contexts, we were able to generate six different
   phosphoprotein datasets for each cellular context, i.e. control or
   CMP-treated. The total number of phosphoproteins specifically enriched
   and identified (≥2 peptides required for identification of each
   specific protein) for each of the MeCh doses applied, as well as the
   proteins identified in the non-stimulated conditions for each cell
   context (control or CMP) are depicted in [109]Figure 2A. We validated
   the presence of several protein serine or threonine phosphorylations,
   stimulated by MeCh at multiple doses (10 nM, 100 nM, 1 µM), in both
   cell contexts (control, [110]Figure 2B–D; CMP, [111]Figure 2E–G) using
   selective immunoprecipitation of the target protein. The isolated
   target protein was then immunoblotted with specific anti-phosphoserine
   and anti-phosphothreonine antisera. We did not assess the
   anti-phosphotyrosine content of the selected proteins due to the
   considerably lower extent of this form of protein phosphorylation
   typically observed with TiO[2]-mediated enrichment techniques
   [112][22]. Using immunoblotting, we verified our proteomic
   identification of the phosphorylation of G protein-coupled receptor
   kinase interacting ArfGAP 1 (GIT1: 10 nM MeCh), G protein-regulated
   inducer of neurite outgrowth 1 (GPRIN1: 100 nM MeCh) and p21 protein
   (Cdc42/Rac)-activated kinase 1 (PAK1: 1 µM MeCh) in control SH-SY5Y
   cells. We also verified the MeCh-induced increase in phosphorylation,
   identified with our TiO[2]-enrichment, of microtubule-associated
   protein 2 (MAP2: 10 nM MeCh), reticulon-4 (RTN4: 100 nM MeCh) and
   Grb2-associated binding protein 2 (GAB2: 1 µM MeCh) in the CMP-treated
   SH-SY5Y cells.

Figure 2. MeCh-induced protein phosphorylation in diverse cellular contexts.

   [113]Figure 2
   [114]Open in a new tab

   (A) Proportional diagrams illustrating the number of specifically
   enriched and identified phosphoproteins, basally occurring, and induced
   by MeCh stimulation (10 nM to 100 µM) of control or CMP-treated cells.
   (B) Selective protein immunoprecipitation (IP) and determination of
   MeCh-induced G protein-coupled receptor kinase interacting ArfGAP 1
   (GIT1) serine and threonine phosphorylation, with antisera immunoblot
   (IB), in MeCh-stimulated control-state SH-SY5Y cells. The associated
   histogram (representing mean ± SEM data from three independent
   immunoprecipitations) indicates the extent of immunoprecipitated
   protein serine and threonine phosphorylation. Panels (C) and (D)
   indicate similar data from control-state cells for the MeCh-induced
   phosphorylation of G protein regulated inducer of neurite outgrowth 1
   (GPRIN1) and p21 protein (Cdc42/Rac)-activated kinase 1 (PAK1). (E)
   Selective protein immunoprecipitation and determination of MeCh-induced
   microtubule-associated protein 2 (MAP2) serine and threonine
   phosphorylation, with antisera immunoblot, in MeCh-stimulated CMP-state
   SH-SY5Y cells. Panels (F) and (G) indicate similar data from CMP-state
   cells for the MeCh-induced phosphorylation of reticulon-4 (RTN4) and
   GRB2-associated binding protein 2 (GAB2). Statistical analysis was
   performed on three independent experiments using GraphPad Prism version
   5.02 with a Student’s t-test (p<0.05, *; p<0.01, **; p<0.001, ***).

VENNTURE Biological Data Analysis and Implementation

   Our novel software program, VENNTURE, is constructed to allow simple
   data (e.g. two to six datasets of any length) uploading from commonly
   used software (Excel™), facilitate various forms of dataset annotation
   display and allow simple exportation of results to commonly used data
   and visual analytical tools (Excel™, Powerpoint™) ([115]Figure 3).
   Employing a classical Edwards ‘cogwheel’ approach, VENNTURE is able to
   accommodate between 2 and 6 individual datasets that generate an
   ever-increasing series of Excel™ spreadsheet data output, due to a
   rapid increase in Venn diagram intersection complexity ([116]Figure
   4A). For our primary application in this study, we have generated two
   groups (control or CMP) of six separate datasets of proteins displaying
   a ligand-mediated phosphorylated status which we then uploaded into
   VENNTURE. We then employed VENNTURE for spreadsheet analysis, visual
   analytical output and also for direct visual appreciation of protein
   identification in each intersection using VENNTURE’s ‘roll-over’
   annotation option ([117]Figure 4B).

Figure 3. VENNTURE data input and output scenarios.

   [118]Figure 3
   [119]Open in a new tab

   Simplistic data (two set analysis) input into VENNTURE is achieved
   using an Excel™ input file. The data representation type can be
   selected using a drop-down menu for numerical set analysis, set order
   demonstration for eventual output and also textual set content. The
   resultant set data output can be directly generated for PowerPoint™
   (Venn diagram) and Excel™ (dataset descriptions).

Figure 4. Multiple set VENNTURE data input.

   [120]Figure 4
   [121]Open in a new tab

   (A) In addition to simplistic data (two set analysis) input into
   VENNTURE, the program is flexible in that it can accept up to 6 input
   sets. With increasing set input, from Excel™ files, is a concomitant
   elevation in diagram complexity along with the eventual dataset output
   in column format in an Excel™ spreadsheet. With increasing numbers of
   input sets VENNTURE automatically alters its Venn output and organizes
   the eventual column display in Excel™. (B) Basic flow-chart for use of
   VENNTURE in the experimental investigation of the log dose-response of
   MeCh-induced phosphoproteomic changes in multiple cellular contexts.
   VENNTURE data output can be used for diagram generation (PowerPoint™
   output), direct inspection Venn intersection contents (direct roll-over
   reveal option) and output for mathematical calculation (Excel™).

VENNTURE-based Dose-specific Data Analysis

   VENNTURE was used to rapidly separate and identify the multiple
   subsections of the TiO[2]-enriched phosphoproteomic data. The two input
   six-set phosphoprotein lists that we employed for this assessment
   (using official human protein symbols) are represented in [122]Table
   S13 (control) and [123]Table S14 (CMP). Using VENNTURE we were able to
   differentiate the highly-complex, dose-dependent phosphoproteomic
   effects of MeCh treatment, in both the control- and peroxide-treated
   (CMP) state cells ([124]Figure 5). In both cases when using VENNTURE to
   separate out the specific subsets of phosphoproteins, it became evident
   that different MeCh ligand doses, in both cellular contexts employed
   (control- or CMP-state), produced complex and largely ‘dose-unique’
   phosphoproteomic lists ([125]Figure 5, control-state 5A, CMP-state 5B).
   Hence, many of the MeCh-stimulated phosphoproteins were unique to a
   specific log dose of the ligand (‘dose-unique’ protein sets are listed
   in [126]Table S15 (control) and [127]Table S16 (CMP)). Such
   ‘dose-unique’ activity may suggest that these large differences in
   applied MeCh dose interact preferentially with different stable
   ‘sub-states’ of the same receptor [128][8], [129][23]–[130][26].
   However it is also possible that these specific signaling profiles may
   also be influenced by the differential availability of downstream
   signaling molecules, the progressive activation of distinct stimulatory
   and autoregulatory signaling events as well as micro temporal or
   kinetic variances in ligand-receptor interactions induced by the
   different ligand doses. Even with these potential complexities of
   receptor signaling, using the unique capacities of VENNTURE, we have
   shown that different doses of a single ligand, stimulating a unitary
   target receptor, can activate largely distinct pools of phosphoproteins
   across a log-dose response series. While phosphorylation is potentially
   an important process in the alteration of individual protein function,
   this information in itself may not be specifically indicative of a
   given functional activity, as most physiological actions involve
   interactions between multiple associated proteins. We therefore applied
   Gene Ontology (GO) term analysis (WebGestalt:
   [131]http://bioinfo.vanderbilt.edu/webgestalt/ [132][27], [133][28])
   and canonical signaling pathway analysis (Ingenuity Pathway Analysis:
   [134]http://www.ingenuity.com/) to generate a higher order of
   appreciation of the functional connections between our protein sets,
   allowing us to further investigate the ‘dose-unique’ behavior observed
   at the phosphoprotein level ([135]Figure 5A, B). Data lists of
   significantly populated GO term groups (n≥2 proteins per group,
   probability (p)≤0.05) were created for each non-stimulated, or
   MeCh-stimulated dose state ([136]Tables S17, [137]S18, [138]S19,
   [139]S20, [140]S21, [141]S22 (control state) and [142]Tables S23,
   [143]S24, [144]S25, [145]S26, [146]S27, [147]S28 (CMP state)). For
   simple VENNTURE input, these dose-dependent tables were then summarized
   ([148]Table S29 (control) and [149]Table S30 (CMP)), and then subjected
   to six-way VENNTURE separation ([150]Figure 5C-control state, 5D-CMP
   state). In an analogous manner to the phosphoprotein VENNTURE
   separations, we noticed a strong ‘dose-specific’ allocation of the
   different GO-term groups for MeCh treatment of control and
   peroxide-treated cells. This may suggest that with the relatively
   specific dose-dependent protein phosphorylation, there is also a
   dose-dependent variation in the activation of various functional groups
   of proteins. The ‘dose-unique’ groups of significantly enriched GO
   terms, for each cellular context, are listed in [151]Table S31 (control
   state) and [152]Table S32 (CMP-state). Significantly populated
   canonical signaling pathway matrices (n≥2 proteins per signaling
   pathway, p≤0.05) were next created for each non-stimulated, or
   MeCh-stimulated dose state in both cellular contexts ([153]Table S33
   (control state) and [154]Table S34 (CMP-state)). The signaling pathway
   analysis was then subjected to six-way VENNTURE separation. In a manner
   reminiscent to the phosphoprotein and GO-term VENNTURE separations, we
   noticed a strong dose-unique allocation of the different canonical
   signaling pathways for MeCh treatment of control or peroxide-treated
   CMP cells ([155]Figure 5E and 5F respectively). The ‘dose-unique’
   groups of significantly enriched canonical signaling pathways for each
   cellular context are listed in [156]Table S35 (control-state) and
   [157]Table S36 (CMP-state).

Figure 5. VENNTURE analysis of log dose-response ligand data in diverse
cellular contexts.

   [158]Figure 5
   [159]Open in a new tab

   VENNTURE Venn diagram set distribution analysis (1–63 set
   intersections) of extracted phosphoproteins in the stimulated or
   non-stimulated SH-SY5Y cells in the control state (‘Control’ - A) or
   peroxide-treated state (‘CMP’ - B). Specific column color coding is as
   follows: blue - phosphoproteins unique to non-stimulated state only,
   red – phosphoproteins unique to a specific MeCh stimulation dose only;
   grey – phosphoproteins common to multiple MeCh doses but not present in
   the non-stimulated set; black – phosphoproteins common to multiple MeCh
   doses also present in the non-stimulated set; green – phosphoproteins
   common to all doses and the non-stimulated set. GO term enrichment
   analysis was performed using the initial phosphoprotein sets from cells
   in the control or CMP-treated states. The significantly enriched
   (p≤0.05) GO term groups for each non-stimulated or MeCh-stimulated set
   were then separated using VENNTURE in a similar manner to the actual
   phosphoprotein identifications. VENNTURE Venn diagram set distribution
   analysis of GO term groups significantly populated by the extracted
   phosphoproteins in the control state (C) or CMP-state (D) cells is
   depicted. Color-coding of the histogram is as described previously.
   Canonical signaling pathway enrichment analysis was also performed
   using the initial phosphoprotein sets from cells in the control or
   CMP-treated states. The significantly enriched (p≤0.05) canonical
   signaling pathways for each non-stimulated or MeCh-stimulated set were
   then separated using VENNTURE in a similar manner to the actual
   phosphoprotein identifications. VENNTURE Venn diagram set distribution
   analysis of the canonical signaling pathways significantly populated by
   the extracted phosphoproteins in the control state (E) or CMP-state (F)
   cells is depicted.

Dose-dependent Receptor Signaling Variation

   Using VENNTURE, we were able to discern that many of the functional
   outputs of MeCh-mediated muscarinic receptor stimulation were strongly
   dose-dependent and also largely distinct at each different dose. We
   further investigated how this signaling diversity was specifically
   affected by dose between our two experimental cellular contexts, i.e.
   control state versus CMP state. We found that for phosphoproteins that
   were specific to MeCh-stimulated VENNTURE sets ([160]Figure 5A–B, Venn
   sets 2–62) a similar percentage, in both cellular contexts, of these
   proteins were found in ‘dose-unique’ sets only (36% control-state, 38%
   CMP-sate). When analyzing the VENNTURE distribution of the GO terms and
   canonical signaling pathways, we found a largely similar distribution
   of factors to these ‘dose-unique’ Venn sets again for both cellular
   contexts, i.e. approximately 36–39% of the MeCh-stimulated GO
   term/signaling pathways (excluding phosphoproteins common to
   non-stimulated samples) were ‘dose-unique’ and in the CMP groups 35–38%
   were ‘dose-unique’ (data not shown). We then sub-divided these
   ‘dose-unique’ sectors into the respective dose-specific proportions of
   phosphoproteins/GO terms/signaling pathways ([161]Figure 6). At the
   phosphoprotein level the percentage bias of each dose within the total
   number of ‘dose-unique’ phosphoproteins was generally similar in the
   control state cells (10 nM-15%, 100 nM-15%, 1 µM-21%, 10 µM-22%, 100
   µM-27%). In contrast, for the CMP-state cells, a reduction in the
   percentage of phosphoproteins unique to the lower doses was observed
   (10 nM-7%, 100 nM-11%: [162]Figure 6-Protein) compared to control-state
   cells. As proteins can possess multiple functions, and therefore could
   be grouped into several GO term groups or signaling pathways, it would
   be expected that a divergence in relative dose-specific proportions in
   these functional groups would occur. A stark contrast was observed with
   respect to the ‘dose-unique’ profile of GO term groups between the
   control and CMP-state cells. The relative proportions of ‘dose-unique’
   GO term groups were roughly similar in control-state cells ([163]Figure
   6-GO term). However in CMP cells more than half (54%) of the total
   number of ‘dose-unique’ significantly-populated GO term groups were
   found at the 100 µM dose. Concomitantly, a large reduction of the
   low-MeCh dose-specific GO term groups was observed (10 nM-4% in CMP
   cells versus 24% in control cells: [164]Figure 6-GO term). At the
   signaling pathway level, the dose-related functional output differences
   were even more diverse between control and CMP-state cells. In each
   cell context, one specific MeCh dose appeared to generate the most
   extensive signaling repertoire, i.e. ability to stimulate many forms of
   potential signaling cascades. Such activity is indicated by the largest
   numbers of significantly-populated signaling pathways populated by
   protein phosphorylation events induced by a specific ligand dose. In
   the control state cells this dose was 100 nM MeCh, while in the
   CMP-state cells this dose was 10 µM MeCh ([165]Figure 6-Signaling
   pathway). No significant signaling pathways were populated by the
   lowest MeCh dose (10 nM) in the CMP-state cells, while 11% of total
   ‘dose-unique’ signaling pathways were stimulated specifically by 10 nM
   MeCh in the control-state cells. Such ligand dose- and cellular
   context-dependent alteration of receptor signaling profiles may be
   suggestive of altered populations of muscarinic receptors in stable
   isomeric conformations that are ‘hard-wired’ into specific signaling
   paradigms [166][8], [167][9], [168][13], [169][29], [170][30].
   Reinforcing the considerable differences observed between ‘dose-unique’
   proportions of phosphoprotein/GO term/signaling pathways in the
   control- or CMP-state cells, we found that in a dose-by-dose comparison
   there was minimal cross-over between proteins/GO terms/signaling
   pathways between these two cell contexts ([171]Figure 7A). Combining
   and summarizing the functional annotation information (both GO term and
   canonical signaling pathway enrichment) for each dose of MeCh, it was
   apparent that distinct potential functional outcomes were generated by
   different MeCh doses within a single cellular context ([172]Figure 7B).
   Moreover, in a dose-to-dose comparison, between cellular context
   models, profoundly distinct signaling outcomes were generated between
   these two cellular states (control versus CMP: [173]Figure 7B). This
   may suggest that the functional outcomes of receptor stimulation may be
   considerably disrupted by the multiple proteomic/genomic alterations
   induced by CMP treatment which, in-part, mimics cell aging [174][14].
   While the CMP treatment may affect dynamic responses to receptor
   stimulation, it clearly also exerts multiple and differential effects
   upon steady state (non-stimulated experiments) protein expression as
   well ([175]Figure 7B: [176]Tables S17, [177]S23, [178]S33, [179]S34).
   Hence in non-stimulated control-state cells cyclin-dependent kinases
   signaling was evident, while fatty acid synthesis and apoptotic
   mechanisms were evident in non-stimulated CMP-state cells ([180]Figure
   7B). With respect to the dynamic signaling responses with 10 nM MeCh
   stimulation of control cells, an output related to spindle kinetics,
   molecular motor function and energy regulatory pathways was predicted,
   while similar MeCh-stimulation of CMP-treated SH-SY5Y cells suggested
   only effects upon cell motility. With 100 nM MeCh stimulation of
   control state cells, stimulation of microtubule-associated protein
   kinases, DNA regulatory behavior and neuroprotective kinase
   (phosphoinositide-3-kinase) signaling was predicted, while a distinct
   output phenotype was present in CMP cells, i.e. stimulation of synaptic
   signaling and endocytic activity was predicted. With 1 µM stimulation,
   again a strong divergence of signaling activity for MeCh was seen
   between control and CMP cellular states. In control cells, at a 1 µM
   dose, stimulation of histone modifying activity and mRNA processing was
   observed, while in CMP-cells signaling activity appeared to center more
   upon neuronal filament/stress fiber modification, amine metabolism and
   neurotrophic signaling. With 10 µM stimulation, MeCh-stimulation of
   control-state cells regulated nucleotide-dependent kinase activity as
   well as signaling involved in pro-excitatory neurotransmission. The 10
   µM stimulation of CMP-state cells however was less phenotypically
   robust and was instead linked to cation binding, genomic stabilization
   and lipid-mediated stress signaling. At the highest dose of MeCh
   employed (100 µM), stimulation of stress fiber, proteasomal and gene
   regulatory activity was observed in control-state cells, while in
   CMP-state cells we noted a strong neuronal differentiation, metabolic
   and biosynthetic bias of signaling ([181]Figure 7B: [182]Tables S31,
   [183]S32-GO terms; [184]S35, [185]S36-canonical signaling pathways).

Figure 6. Dose-dependent distribution of MeCh-stimulated signaling activity
in diverse cellular contexts.

   [186]Figure 6
   [187]Open in a new tab

   Proportional diagrams representing the relative distribution of
   phosphoproteins (Proteins)/GO term groups (GO term)/signaling pathways
   (Signaling Pathway) unique to an individual stimulating MeCh dose
   (color coded) in either cellular context (control or CMP).

Figure 7. Cellular context modification of downstream receptor signaling
activity in SH-SY5Y cells.

   [188]Figure 7
   [189]Open in a new tab

   (A) Comparison of phosphoprotein/GO term group/canonical signaling
   pathway activation in the non-stimulated (blue circle) and multiple
   MeCh-stimulated conditions (red circles) in either control-state (solid
   line) or CMP-state (dashed lines) SH-SY5Y cells. (B) Summary of
   potential non-stimulated and MeCh-induced signaling functionality
   (aggregate of highest-scoring GO-term group and canonical signaling
   pathway enrichments) in a dose-dependent manner compared between
   control-state or CMP-state cells.

Discussion

   Our novel program, VENNTURE, allows the facile simultaneous analysis of
   multiple large and complex pharmacological datasets. Using this program
   we have shown, with a high volume of complex input data, that VENNTURE
   is able to generate biological datasets that have aided the
   comprehension of higher orders of signaling behavior in ligand-receptor
   systems. Ligand-receptor interactions at GPCRs are perhaps one of the
   most important events in clinical and molecular pharmacology, as more
   than half of the current pharmacopeia is populated by GPCR-targeted
   drugs [190][8], [191][16]. For many years these molecular interactions
   were considered to be mechanistically relatively simple, e.g. the
   agonist-driven receptor structural change concept was described as the
   ‘two-state’ model of receptor activation. This model eventually was
   superseded by the ‘ternary complex model’ [192][31] that identified a
   third stable receptor conformation that was a ‘transition state’
   between R and R* [193][32]. With experimental advances it has become
   clear that GPCR activity is even more complicated then ever imagined.
   i.e. GPCRs can demonstrate higher-order signaling complex assembly,
   downstream signaling promiscuity and signal-selective ligand behavior
   [194][33]–[195][41]. As both ligand interaction with GPCRs
   [196][23]–[197][25], [198][42]–[199][44] and the eventual downstream
   signaling induced by receptor activation [200][26],
   [201][45]–[202][49], are likely to be controlled by multiple cellular
   factors, such as stress-state or the presence of intracellular
   scaffolding proteins, it is imperative that our understanding of how
   these changes affect dose-dependent GPCR pharmacology be included in
   our drug discovery methodologies. Using VENNTURE we were able to
   distinguish and investigate highly nuanced dose- and context-specific
   ligand-receptor behavior. The ability to generate such an in-depth
   appreciation of the ligand-receptor signaling profile, in multiple
   cellular states, may be initially employed to greatly enhance the
   efficiency and accuracy of drug design. The ability to understand the
   changes in drug response profiles in different pharmacogenomic
   backgrounds, e.g. control and peroxide-treated states [203][14], may
   greatly assist in the generation of contextually-specific
   pharmacological agents. The functional experimental example
   demonstrated in this study outlines one potential use of VENNTURE,
   however, this novel program can be utilized for any research purpose
   that requires simultaneous comparison of multiple large data sets.

   The specific application of our novel VENNTURE program to these
   extremely complex sets of signaling data allowed the facile elucidation
   of the subtle effects that environmental perturbations can exert upon
   dose-dependent ligand actions. Such findings may have profound
   implications for novel therapeutic design, as both the dose-specificity
   of signaling quality and its modulation by altered cellular context may
   be extremely important factors to consider for future GPCR drug
   discovery [204][8], [205][9], [206][14], [207][50].

   The VENNTURE application can be used on any personal computer using any
   Windows™ XP or higher Windows™-based system. To make VENNTURE as
   accessible to as many potential users we have specifically tailored the
   program to employ the most commonly used MS-Office platforms, e.g.
   Excel™ and Powerpoint™. While the MS-Office suite is the most
   widely-used platform in the world, we appreciate that non-MS Office
   users also present an important user-base, therefore we intend to
   develop VENNTURE for use with non-MS-Office or OpenSource (e.g.
   OpenOffice) software platforms. Presently VENNTURE version 1.1.0.2 can
   be accessed freely at the National Institute on Aging
   ([208]http://www.nia.nih.gov). There are some potential aspects to
   VENNTURE that can be expanded upon in the future, such as an elevation
   of dataset handling beyond only six sets. However, while VENNTURE is
   able to theoretically model an infinite number of data sets, the visual
   presentation of more than eight data sets ultimately becomes too
   visually complicated to extract actionable data. VENNTURE presents
   significant advancements in visual presentation, data analysis, and
   user-friendliness over currently available Venn diagram programs and it
   will be a useful tool for the analysis of complex datasets in
   pharmacology, genomics, and bioinformatics research fields.

Supporting Information

   Figure S1

   Effects of chronic minimal peroxide (CMP) exposure upon muscarinic
   receptor activity. (A) Twenty micrograms of SH-SY5Y whole-cell lysate
   resolved using an SDS-polyacrylamide gel and then stained with
   coomassie for total protein detection (MW-molecular weight markers).
   Associated histogram and representative western blot indicate minimal
   alteration of m1-muscarinic acetylcholine receptor (m1AchR: antisera
   obtained from Sigma-Aldrich, St. Louis MO) expression (quantified as
   1000× arbitrary intensity units (AU) minus background (B) intensity per
   square pixel (1000×(AU-B)/px^2)) with application of CMP protocol
   (expression controlled using whole-cell coomassie stain). (B) Blockade
   of acetyl-β-methylcholine (MeCh)-mediated ERK1/2 activation in
   control-state cells with a 30 minute pre-exposure of cells to the
   muscarinic receptor antagonist pirenzipine (10 µM: gray shaded bars).
   (C) Blockade of MeCh-mediated ERK1/2 activation in CMP-state cells with
   a 30 minute pre-exposure of cells to the muscarinic receptor antagonist
   pirenzipine (10 µM: shaded bars).

   (TIF)
   [209]Click here for additional data file.^ (1.4MB, tif)
   Table S1

   Phosphoproteins extracted from un-stimulated control-state human
   neuroblastoma SH-SY5Y cells. For each successfully identified protein
   official symbol, Uniprot accession code and number of peptides
   recovered are indicated.

   (DOC)
   [210]Click here for additional data file.^ (293.5KB, doc)
   Table S2

   Phosphoproteins extracted from 10 nM MeCh-treated control-state human
   neuroblastoma SH-SY5Y cells. For each successfully identified protein
   official symbol, Uniprot accession code and number of peptides
   recovered are indicated.

   (DOC)
   [211]Click here for additional data file.^ (233KB, doc)
   Table S3

   Phosphoproteins extracted from 100 nM MeCh-treated control-state human
   neuroblastoma SH-SY5Y cells. For each successfully identified protein
   official symbol, Uniprot accession code and number of peptides
   recovered are indicated.

   (DOC)
   [212]Click here for additional data file.^ (197.5KB, doc)
   Table S4

   Phosphoproteins extracted from 1 µM MeCh-treated control-state human
   neuroblastoma SH-SY5Y cells. For each successfully identified protein
   official symbol, Uniprot accession code and number of peptides
   recovered are indicated.

   (DOC)
   [213]Click here for additional data file.^ (253.5KB, doc)
   Table S5

   Phosphoproteins extracted from 10 µM MeCh-treated control-state human
   neuroblastoma SH-SY5Y cells. For each successfully identified protein
   official symbol, Uniprot accession code and number of peptides
   recovered are indicated.

   (DOC)
   [214]Click here for additional data file.^ (231.5KB, doc)
   Table S6

   Phosphoproteins extracted from 100 µM MeCh-treated control-state human
   neuroblastoma SH-SY5Y cells. For each successfully identified protein
   official symbol, Uniprot accession code and number of peptides
   recovered are indicated.

   (DOC)
   [215]Click here for additional data file.^ (284KB, doc)
   Table S7

   Phosphoproteins extracted from untreated chronic minimal peroxide
   (CMP)-state human neuroblastoma SH-SY5Y cells. For each successfully
   identified protein official symbol, Uniprot accession code and number
   of peptides recovered are indicated.

   (DOC)
   [216]Click here for additional data file.^ (537.5KB, doc)
   Table S8

   Phosphoproteins extracted from 10 nM MeCh-stimulated chronic minimal
   peroxide (CMP)-state human neuroblastoma SH-SY5Y cells. For each
   successfully identified protein official symbol, Uniprot accession code
   and number of peptides recovered are indicated.

   (DOC)
   [217]Click here for additional data file.^ (294.5KB, doc)
   Table S9

   Phosphoproteins extracted from 100 nM MeCh-stimulated chronic minimal
   peroxide (CMP)-state human neuroblastoma SH-SY5Y cells. For each
   successfully identified protein official symbol, Uniprot accession code
   and number of peptides recovered are indicated.

   (DOC)
   [218]Click here for additional data file.^ (280KB, doc)
   Table S10

   Phosphoproteins extracted from 1 µM MeCh-stimulated chronic minimal
   peroxide (CMP)-state human neuroblastoma SH-SY5Y cells. For each
   successfully identified protein official symbol, Uniprot accession code
   and number of peptides recovered are indicated.

   (DOC)
   [219]Click here for additional data file.^ (488KB, doc)
   Table S11

   Phosphoproteins extracted from 10 µM MeCh-stimulated chronic minimal
   peroxide (CMP)-state human neuroblastoma SH-SY5Y cells. For each
   successfully identified protein official symbol, Uniprot accession code
   and number of peptides recovered are indicated.

   (DOC)
   [220]Click here for additional data file.^ (544.5KB, doc)
   Table S12

   Phosphoproteins extracted from 100 µM MeCh-stimulated chronic minimal
   peroxide (CMP)-state human neuroblastoma SH-SY5Y cells. For each
   successfully identified protein official symbol, Uniprot accession code
   and number of peptides recovered are indicated.

   (DOC)
   [221]Click here for additional data file.^ (595.5KB, doc)
   Table S13

   Dose-dependent acetyl-β-methylcholine-stimulated phosphoproteins in
   control-state human neuroblastoma SH-SY5Y cells. Ligand stimulation
   with acetyl-β-methylcholine (MeCh) was for 15 minutes before cell
   lysate protein extraction and titanium dioxide-mediated purification.

   (DOC)
   [222]Click here for additional data file.^ (514.5KB, doc)
   Table S14

   Dose-dependent acetyl-β-methylcholine-stimulated phosphoproteins in
   CMP-state human neuroblastoma SH-SY5Y cells. Ligand stimulation,
   performed after pre-treatment for seven days with 10 nM continuous
   hydrogen peroxide treatment (CMP protocol), with acetyl-β-methylcholine
   (MeCh) was for 15 minutes before cell lysate protein extraction and
   titanium dioxide-mediated purification.

   (DOC)
   [223]Click here for additional data file.^ (1,002.5KB, doc)
   Table S15

   Dose-unique MeCh-stimulated protein phosphorylation in control-state
   SH-SY5Y cells. The proteins uniquely phosphorylated at the specified
   MeCh dose only in control-state SH-SY5Y cells are indicated by their
   protein symbol as well as Uniprot accession number.

   (DOC)
   [224]Click here for additional data file.^ (281.5KB, doc)
   Table S16

   Dose-unique MeCh-stimulated protein phosphorylation in CMP-state
   SH-SY5Y cells. The proteins uniquely phosphorylated at the specified
   MeCh dose only in CMP-state SH-SY5Y cells are indicated by their
   protein symbol as well as Uniprot accession number.

   (DOC)
   [225]Click here for additional data file.^ (603KB, doc)
   Table S17

   GO term groups populated by extracted phosphoproteins in non-stimulated
   control-state SH-SY5Y cells. GO term groups were considered enriched
   only if at least two proteins were present in each group and with a
   probability of ≤0.05. Hybrid GO term group scores were generated by
   multiplication of the GO term group enrichment score with the negative
   log[10] of the probability result.

   (DOC)
   [226]Click here for additional data file.^ (190KB, doc)
   Table S18

   GO term groups populated by extracted phosphoproteins in 10 nM
   MeCh-stimulated control-state SH-SY5Y cells. GO term groups were
   considered enriched only if at least two proteins were present in each
   group and with a probability of ≤0.05. Hybrid GO term group scores were
   generated by multiplication of the GO term group enrichment score with
   the negative log[10] of the probability result.

   (DOC)
   [227]Click here for additional data file.^ (150KB, doc)
   Table S19

   GO term groups populated by extracted phosphoproteins in 100 nM
   MeCh-stimulated control-state SH-SY5Y cells. GO term groups were
   considered enriched only if at least two proteins were present in each
   group and with a probability of ≤0.05. Hybrid GO term group scores were
   generated by multiplication of the GO term group enrichment score with
   the negative log[10] of the probability result.

   (DOC)
   [228]Click here for additional data file.^ (173KB, doc)
   Table S20

   GO term groups populated by extracted phosphoproteins in 1 µM
   MeCh-stimulated control-state SH-SY5Y cells. GO term groups were
   considered enriched only if at least two proteins were present in each
   group and with a probability of ≤0.05. Hybrid GO term group scores were
   generated by multiplication of the GO term group enrichment score with
   the negative log[10] of the probability result.

   (DOC)
   [229]Click here for additional data file.^ (96KB, doc)
   Table S21

   GO term groups populated by extracted phosphoproteins in 10 µM
   MeCh-stimulated control-state SH-SY5Y cells. GO term groups were
   considered enriched only if at least two proteins were present in each
   group and with a probability of ≤0.05. Hybrid GO term group scores were
   generated by multiplication of the GO term group enrichment score with
   the negative log[10] of the probability result.

   (DOC)
   [230]Click here for additional data file.^ (169.5KB, doc)
   Table S22

   GO term groups populated by extracted phosphoproteins in 100 µM
   MeCh-stimulated control-state SH-SY5Y cells. GO term groups were
   considered enriched only if at least two proteins were present in each
   group and with a probability of ≤0.05. Hybrid GO term group scores were
   generated by multiplication of the GO term group enrichment score with
   the negative log[10] of the probability result.

   (DOC)
   [231]Click here for additional data file.^ (82.5KB, doc)
   Table S23

   GO term groups populated by extracted phosphoproteins in non-stimulated
   CMP-state SH-SY5Y cells. GO term groups were considered enriched only
   if at least two proteins were present in each group and with a
   probability of ≤0.05. Hybrid GO term group scores were generated by
   multiplication of the GO term group enrichment score with the negative
   log[10] of the probability result.

   (DOC)
   [232]Click here for additional data file.^ (206.5KB, doc)
   Table S24

   GO term groups populated by extracted phosphoproteins in 10 nM
   MeCh-stimulated CMP-state SH-SY5Y cells. GO term groups were considered
   enriched only if at least two proteins were present in each group and
   with a probability of ≤0.05. Hybrid GO term group scores were generated
   by multiplication of the GO term group enrichment score with the
   negative log[10] of the probability result.

   (DOC)
   [233]Click here for additional data file.^ (61KB, doc)
   Table S25

   GO term groups populated by extracted phosphoproteins in 100 nM
   MeCh-stimulated CMP-state SH-SY5Y cells. GO term groups were considered
   enriched only if at least two proteins were present in each group and
   with a probability of ≤0.05. Hybrid GO term group scores were generated
   by multiplication of the GO term group enrichment score with the
   negative log[10] of the probability result.

   (DOC)
   [234]Click here for additional data file.^ (84KB, doc)
   Table S26

   GO term groups populated by extracted phosphoproteins in 1 µM
   MeCh-stimulated CMP-state SH-SY5Y cells. GO term groups were considered
   enriched only if at least two proteins were present in each group and
   with a probability of ≤0.05. Hybrid GO term group scores were generated
   by multiplication of the GO term group enrichment score with the
   negative log[10] of the probability result.

   (DOC)
   [235]Click here for additional data file.^ (45KB, doc)
   Table S27

   GO term groups populated by extracted phosphoproteins in 10 µM
   MeCh-stimulated CMP-state SH-SY5Y cells. GO term groups were considered
   enriched only if at least two proteins were present in each group and
   with a probability of ≤0.05. Hybrid GO term group scores were generated
   by multiplication of the GO term group enrichment score with the
   negative log[10] of the probability result.

   (DOC)
   [236]Click here for additional data file.^ (105.5KB, doc)
   Table S28

   GO term groups populated by extracted phosphoproteins in 100 µM
   MeCh-stimulated CMP-state SH-SY5Y cells. GO term groups were considered
   enriched only if at least two proteins were present in each group and
   with a probability of ≤0.05. Hybrid GO term group scores were generated
   by multiplication of the GO term group enrichment score with the
   negative log[10] of the probability result.

   (DOC)
   [237]Click here for additional data file.^ (155.5KB, doc)
   Table S29

   Cumulated significantly populated Gene Ontology term groups generated
   from dose-dependent acetyl-β-methylcholine-stimulated phosphoproteins
   in control-state human neuroblastoma SH-SY5Y cells. Ligand stimulation
   with acetyl-β-methylcholine (MeCh: 10 nM–100 µM) was for 15 minutes
   before cell lysate protein extraction and titanium dioxide-mediated
   purification.

   (DOC)
   [238]Click here for additional data file.^ (226.5KB, doc)
   Table S30

   Cumulated significantly populated Gene Ontology term groups generated
   from dose-dependent acetyl-β-methylcholine-stimulated phosphoproteins
   in peroxide (CMP)-treated-state human neuroblastoma SH-SY5Y cells.
   Ligand stimulation with acetyl-β-methylcholine (MeCh: 10 nM–100 µM) was
   for 15 minutes before cell lysate protein extraction and titanium
   dioxide-mediated purification.

   (DOC)
   [239]Click here for additional data file.^ (243KB, doc)
   Table S31

   MeCh dose-unique GO term population in control-state SH-SY5Y cells. The
   GO term groups uniquely and significantly (p≤0.05, n>2 proteins per
   group) at the specified MeCh dose only, in control-state SH-SY5Y cells
   are indicated. Hybrid scores for GO term population were generated by
   multiplication of the GO term enrichment ratio with the –log[10] of the
   enrichment probability.

   (DOC)
   [240]Click here for additional data file.^ (148KB, doc)
   Table S32

   MeCh dose-unique GO term population in CMP-state SH-SY5Y cells. The GO
   term groups uniquely and significantly (p≤0.05, n>2 proteins per group)
   at the specified MeCh dose only, in CMP-state SH-SY5Y cells are
   indicated. Hybrid scores for GO term population were generated by
   multiplication of the GO term enrichment ratio with the –log[10] of the
   enrichment probability.

   (DOC)
   [241]Click here for additional data file.^ (106.5KB, doc)
   Table S33

   Canonical signaling pathways populated by extracted phosphoproteins in
   control-state SH-SY5Y cells. Canonical signaling pathways enriched by
   phosphoproteins extracted from non-stimulated and MeCh-stimulated
   control-state SH-SY5Y cells are represented in a simple matrix format.
   Canonical signaling pathways were considered enriched only if at least
   two proteins were present in each signaling pathway and with a
   probability of ≤0.05. Hybrid signaling pathway scores, indicated in
   specific cells of the matrix, were generated by multiplication of the
   pathway enrichment ratio with the negative log[10] of the probability
   result. Cells in the matrix not possessing a hybrid score were not
   significantly populated by proteins in that specific stimulation
   condition.

   (DOC)
   [242]Click here for additional data file.^ (211.5KB, doc)
   Table S34

   Canonical signaling pathways populated by extracted phosphoproteins in
   CMP-state SH-SY5Y cells. Canonical signaling pathways enriched by
   phosphoproteins extracted from non-stimulated and MeCh-stimulated
   CMP-state SH-SY5Y cells are represented in a simple matrix format.
   Canonical signaling pathways were considered enriched only if at least
   two proteins were present in each signaling pathway and with a
   probability of ≤0.05. Hybrid signaling pathway scores, indicated in
   specific cells of the matrix, were generated by multiplication of the
   pathway enrichment ratio with the negative log[10] of the probability
   result. Cells in the matrix not possessing a hybrid score were not
   significantly populated by proteins in that specific stimulation
   condition.

   (DOC)
   [243]Click here for additional data file.^ (270KB, doc)
   Table S35

   Canonical signaling pathways populated by extracted phosphoproteins
   unique to non-stimulated or MeCh-stimulated control-state SH-SY5Y
   cells. Significantly populated canonical signaling pathways, unique to
   a specific stimulation condition (non-stimulated or with a specific
   MeCh dose) are listed. Canonical signaling pathways were considered
   enriched only if at least two proteins were present in each signaling
   pathway and with a probability of ≤0.05. Hybrid signaling pathway
   scores indicated were generated by multiplication of the pathway
   enrichment ratio with the negative log[10] of the probability result.

   (DOC)
   [244]Click here for additional data file.^ (90KB, doc)
   Table S36

   Canonical signaling pathways populated by extracted phosphoproteins
   unique to non-stimulated or MeCh-stimulated CMP-state SH-SY5Y cells.
   Significantly populated canonical signaling pathways, unique to a
   specific stimulation condition (non-stimulated or with a specific MeCh
   dose) are listed. Canonical signaling pathways were considered enriched
   only if at least two proteins were present in each signaling pathway
   and with a probability of ≤0.05. Hybrid signaling pathway scores
   indicated were generated by multiplication of the pathway enrichment
   ratio with the negative log[10] of the probability result.

   (DOC)
   [245]Click here for additional data file.^ (107KB, doc)

Footnotes

   Competing Interests: The authors have declared that no competing
   interests exist.

   Funding: This work was supported entirely by the Intramural Program of
   the National Institute on Aging, National Institutes of Health. The
   funders had no role in study design, data collection and analysis,
   decision to publish, or preparation of the manuscript.

References