Release date: 2016-12-02
Increase data interpretation power and simplify pathway modeling by adding interactive nodes representing Canonical Pathways to networks or pathways. These newly available nodes correspond 1:1 with the Canonical Pathways that have always been in IPA and behave similarly to disease or function nodes. You can connect them to molecules that are part of each particular Canonical Pathway and simulate the effect of activating or inhibiting these molecules on the pathway as a whole. The activity simulation is available only for pathways that have a Pathway Activity Pattern.
Figure 1 shows the TNFR1 signaling pathway as a node on a ‘My Pathway’ connected to its primary activating ligand TNF. IPA’s MAP tool was used to “activate” TNF (shown in red), predicting this would lead to activation of the TNFR1 pathway (shown in orange).
Figure 1. An example of a Canonical Pathway displayed as an interactive node in IPA (connected to its principal activating ligand for purposes of illustration). Each pathway can be linked to the full collection of genes that make up that pathway by using the Build > Grow tool, starting with the pathway node. The interactive pathway diagram that accompanies each Canonical Pathway can be visualized by double-clicking the pathway icon. Note that when using the Grow tool to go from a pathway node to genes, all genes that are part of that pathway are added to the pathway, including those that are members of groups and complexes. If you wish to find all the genes that are included in a pathway for scoring against your dataset, it is best to use the search engine to search for that pathway, and add both the pathway and the nodes to a new ‘My Pathway’. This method will show the groups and complexes that belong to the pathway but are not included in scoring. These nodes can be removed with the Build > Trim tool.
Canonical Pathway nodes can be added to any network to increase interpretability. Figure 2 shows an example of adding Canonical Pathway nodes to an interaction network from a Core Analysis of stem cells differentiating to cardiomyocytes, indicating that several of the molecules in the network are activators of the apelin endothelial signaling and paxillin signaling pathways.
Figure 2. Two Canonical Pathways manually added to an interaction network. Using Build > Grow, Canonical Pathways were added to a pre-existing interaction network from a Core Analysis. You can also add Canonical Pathways to Regulator Effects networks or include them inside other Canonical Pathways, as shown in Figures 3 and 4, respectively.
Figure 3. Two Canonical Pathways manually added to a Regulator Effects network. Using Build > Grow, Canonical Pathways were added to a pre-existing Regulator Effects network from a Core Analysis. These pathways are predicted to be activated due to the increased activity of the molecules colored in red in the network.
Figure 4. Canonical Pathway manually added inside another Canonical Pathway. Using Build > Grow, Canonical Pathways can be added inside another Canonical Pathway. The MAP tool coloring indicates the added pathway is inhibited (blue color) with this overlaid dataset. Over 90 pathways in IPA have an existing pathway embedded within them, represented as a single node. Previously, these were shown using a non-interactive node. Now, these “pathways on pathways” are interactive and their activity can be predicted. Figure 5 shows a portion of the CDC42 signaling pathway that embeds two Canonical Pathways (ERK/MAPK signaling and SAPK/JNK signaling) which are predicted to be activated downstream of the CDC42 pathway.
Figure 5. Canonical Pathways already existing inside another Canonical Pathway. Over 90 pathways in IPA already have one or more Canonical Pathways embedded within them. The MAP tool in IPA was turned on to predict the effect of activating c-RAF and the JNK protein family on each of the connected pathways.
When using Build tools such as Grow and Connect, sometimes you need to repeatedly perform the same operations on every network or pathway that you open. For example, you might need to always Grow upstream to transcription regulators. Now, you can make the appropriate selections in the various Build filters and save them as defaults. From that moment on, each new Build tool you use will remember your saved settings. You can always reset your custom settings back to “factory defaults” when needed.
Figure 6. The new “save as preferences” in the context of the Grow tool. The node types of ligand-dependent nuclear receptor and transcription regulator have been saved as defaults. Now, whenever a pathway or network is opened, the Grow tool will add molecules only of that type. The Build Preferences panel in IPA’s Application Preferences will show your saved settings as shown in Figure 7.
Figure 7. The new Build Filters preferences. The panel is located in File > Preferences > Application Preferences.