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. 2012;7(5):e36911.
doi: 10.1371/journal.pone.0036911. Epub 2012 May 14.

VENNTURE--a novel Venn diagram investigational tool for multiple pharmacological dataset analysis

Affiliations

VENNTURE--a novel Venn diagram investigational tool for multiple pharmacological dataset analysis

Bronwen Martin et al. PLoS One. 2012.

Erratum in

  • PLoS One. 2012;7(5): doi/10.1371/annotation/27f1021c-b6f2-4b90-98bc-fcacd2679185

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.

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Conflict of interest statement

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

Figures

Figure 1
Figure 1. Chronic minimal peroxide-mediated alteration of protein expression and ligand responses.
(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)/px2). 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.
Figure 2
Figure 2. MeCh-induced protein phosphorylation in diverse cellular contexts.
(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, ***).
Figure 3
Figure 3. VENNTURE data input and output scenarios.
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
Figure 4. Multiple set VENNTURE data input.
(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™).
Figure 5
Figure 5. VENNTURE analysis of log dose-response ligand data in diverse cellular contexts.
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.
Figure 6
Figure 6. Dose-dependent distribution of MeCh-stimulated signaling activity in diverse cellular contexts.
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
Figure 7. Cellular context modification of downstream receptor signaling activity in SH-SY5Y cells.
(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.

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