Evaluation of the effect of the rSNR-based rank on the classification of experiments from the AtGenExpress dataset.

<p>A) Accuracy was calculated based on gene sets of uniformly decreasing size, selected based on rSNR ranking. B) Accuracy was calculated based on gene sets belonging to different rSNR-based rank sections. Error bars indicate 1 standard deviation away from the mean.</p

Effect of feature selection methods on the classification of time-course expression data.

<p>Accuracy was calculated based on the A) AtGenExpress and B) EDGE datasets. Error bars indicate 1 standard deviation away from the mean.</p

Differential Network Analysis Applied to Preoperative Breast Cancer Chemotherapy Response

<div><p>In silico approaches are increasingly considered to improve breast cancer treatment. One of these treatments, neoadjuvant TFAC chemotherapy, is used in cases where application of preoperative systemic therapy is indicated. Estimating response to treatment allows or improves clinical decision-making and this, in turn, may be based on a good understanding of the underlying molecular mechanisms. Ever increasing amounts of high throughput data become available for integration into functional networks. In this study, we applied our software tool ExprEssence to identify specific mechanisms relevant for TFAC therapy response, from a gene/protein interaction network. We contrasted the resulting active subnetwork to the subnetworks of two other such methods, OptDis and KeyPathwayMiner. We could show that the ExprEssence subnetwork is more related to the mechanistic functional principles of TFAC therapy than the subnetworks of the other two methods despite the simplicity of ExprEssence. We were able to validate our method by recovering known mechanisms and as an application example of our method, we identified a mechanism that may further explain the synergism between paclitaxel and doxorubicin in TFAC treatment: Paclitaxel may attenuate MELK gene expression, resulting in lower levels of its target MYBL2, already associated with doxorubicin synergism in hepatocellular carcinoma cell lines. We tested our hypothesis in three breast cancer cell lines, confirming it in part. In particular, the predicted effect on MYBL2 could be validated, and a synergistic effect of paclitaxel and doxorubicin could be demonstrated in the breast cancer cell lines SKBR3 and MCF-7.</p></div

Accuracy of cross-validations using different parameters.

<p>A) Accuracy computed under scenario 1 (see Methods). B) Accuracy computed under scenario 2 (see Methods). Error bars indicate 1 standard deviation away from the mean.</p

Example showing the calculation of the rSNR for the experimental condition “cold-root” in the AtGenExpress dataset.

<p>A) Division of the training dataset into positive and negative sets. The positive set corresponds to the experimental condition of interest (i.e., “cold-root”, in red). Only some of the remaining experimental conditions in the training set, namely those not involving any of the experimental factors that define the experimental condition of interest, are used to build the negative set (in dark gray). B) Calculation of the mean and standard deviation for the positive and negative sets.<i>T1</i> to <i>T6</i> are time points. Numbers inside the boxes represent the number of replicates for an experimental condition at a given time point. <i>μ</i> and <i>σ</i> represents the mean and standard deviation respectively. For the positive set, we compute a mean and a standard deviation at each time point. For the negative set, we compute a mean at each time point. We then compute the mean and the standard deviation of the negative set as the mean and the standard deviation of the means computed at each time point respectively.</p

Effect of feature selection methods on the classification of the Engreitz dataset.

<p>A) Accuracy. B) Area under the ROC curve (AUC).</p

Selected breast cancer subtypes with their most common marker profile, their overall prevalence and a representative human cell line with these molecular features.

<p>This table was compiled from different sources <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081784#pone.0081784-Carey1" target="_blank">[45]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081784#pone.0081784-Yang1" target="_blank">[48]</a>. ER: Estrogen receptor; PR: Progesterone receptor; HER2: human epidermal growth factor receptor 2; +: positive; −: negative.</p

ExprEssence-condensed network describing the 16 most and 16 least active interactions between the E40 genes/proteins.

<p>For each gene, its mean expression level is visualized for non-responders (left) and responders (right) by color (green for low, white for intermediate, red for high expression). Interactions between the genes/proteins are represented by a line. Stimulations are indicated by an arrow on the target, inhibitions by a t-bar. The up- (red) and down-regulation (green) of interactions are also colorcoded. Full gene names can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081784#pone.0081784.s007" target="_blank">Table S1</a>.</p

Expression analysis of MYBL2 protein after treatment with paclitaxel (Taxol, T) and doxorubicin (Adriamycin, A) in several cell lines by Western blotting (non-tumorigenic cell line MCF-10A and breast cancer cell lines MCF-7, BT-20 and SKBR3).

<p>Single treatment with T or A for 48(T (48 h); A (48 h)), combined treatment for 48 h (T + A (48 h)) or successive treatment for each for 24 h (T (24 h), A (24 h) was applied. Quantification of western blotting results was carried out with individual passaged cells for at least three times. Representative western blots were displayed on top of the graphs. Proliferative alterations were detected against Proliferating Cell Nuclear Antigen (PCNA). Loading controls were labeling of the house keeping protein <i>β</i>-actin and stain-free imaging of the SDS-PAGEs prior blotting procedure. Mean ± SD values (n = 3). * : <i>p</i><0.05; ** : <i>p</i><0.01; * * * : <i>p</i><0.001 as compared to control treatment (unpaired t test).</p

Proteomic Analysis of Mouse Oocytes Identifies PRMT7 as a Reprogramming Factor that Replaces SOX2 in the Induction of Pluripotent Stem Cells

The reprogramming process that leads to induced pluripotent stem cells (iPSCs) may benefit from adding oocyte factors to Yamanaka’s reprogramming cocktail (OCT4, SOX2, KLF4, with or without MYC; OSK­(M)). We previously searched for such facilitators of reprogramming (the reprogrammome) by applying label-free LC–MS/MS analysis to mouse oocytes, producing a catalog of 28 candidates that are (i) able to robustly access the cell nucleus and (ii) shared between mature mouse oocytes and pluripotent embryonic stem cells. In the present study, we hypothesized that our 28 reprogrammome candidates would also be (iii) abundant in mature oocytes, (iv) depleted after the oocyte-to-embryo transition, and (v) able to potentiate or replace the OSKM factors. Using LC–MS/MS and isotopic labeling methods, we found that the abundance profiles of the 28 proteins were below those of known oocyte-specific and housekeeping proteins. Of the 28 proteins, only arginine methyltransferase 7 (PRMT7) changed substantially during mouse embryogenesis and promoted the conversion of mouse fibroblasts into iPSCs. Specifically, PRMT7 replaced SOX2 in a factor-substitution assay, yielding iPSCs. These findings exemplify how proteomics can be used to prioritize the functional analysis of reprogrammome candidates. The LC–MS/MS data are available via ProteomeXchange with identifier PXD003093