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. 2020 Sep 15;36(18):4781-4788.
doi: 10.1093/bioinformatics/btaa590.

Predicting mechanism of action of cellular perturbations with pathway activity signatures

Yan Ren  1 , Siva Sivaganesan  2 , Nicholas A Clark  1 , Lixia Zhang  1 , Jacek Biesiada  1 , Wen Niu  1 , David R Plas  3 , Mario Medvedovic  1
Affiliations

Predicting mechanism of action of cellular perturbations with pathway activity signatures

Yan Ren et al. Bioinformatics. .

Abstract

Motivation: Misregulation of signaling pathway activity is etiologic for many human diseases, and modulating activity of signaling pathways is often the preferred therapeutic strategy. Understanding the mechanism of action (MOA) of bioactive chemicals in terms of targeted signaling pathways is the essential first step in evaluating their therapeutic potential. Changes in signaling pathway activity are often not reflected in changes in expression of pathway genes which makes MOA inferences from transcriptional signatures (TSeses) a difficult problem.

Results: We developed a new computational method for implicating pathway targets of bioactive chemicals and other cellular perturbations by integrated analysis of pathway network topology, the Library of Integrated Network-based Cellular Signature TSes of genetic perturbations of pathway genes and the TS of the perturbation. Our methodology accurately predicts signaling pathways targeted by the perturbation when current pathway analysis approaches utilizing only the TS of the perturbation fail.

Availability and implementation: Open source R package paslincs is available at https://github.com/uc-bd2k/paslincs.

Supplementary information: Supplementary data are available at Bioinformatics online.

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Figures

Fig. 1.
Fig. 1.
Integrating signaling pathway topology and LINCS consensus gene signatures to construct transcriptional PASs. In all panels, shades of red indicate different levels of positive and shades of blue indicate different levels of negative numbers. (A) A chemical or GP affects the activity of a protein in a signaling pathway and dysregulates the activity of the overall pathway. The pathway dysregulation results in downstream changes in gene expression levels of measured genes which is captured by the TS. (B) Expression profiles (y) of measured genes (rows) that are consistent with pathway topology. An expression profile of a gene is consistent with the pathway topology if an activation interaction between two nodes results in an expression change in the same direction and an inhibition interaction results in a change in the opposite direction. (C) The topology of the pathway protein interactions is summarized with the signed adjacency matrix (A) and the signed Laplacian (L). (D) Integration of the pathway topology and data with the Bayesian hierarchical model. (D1) The mean prior expression profile (µ), all nodes equal to zero; (D2) Observed expression profile (y), with AKT1 value equal to 1 and all other nodes zero; (D3) Posterior profile integrating topology and data. (E) PAS is constructed using expression profiles of top 100 signature genes most consistent with the pathway topology
Fig. 2.
Fig. 2.
PAS of the mTOR signaling pathway. (A) mTOR signaling pathway constructed from literature consisting of four key modules; (B) PAS constructed using the methods in Figure 1 and the LINCS CP signatures for the pathway inhibitors and vehicle treatment; (C) distribution of concordance scores for pathway inhibitors and vehicle treatment; (D) statistical significance (-log10(empirical P-value)) of the concordance scores for PI3K inhibition signatures (first two heatmaps) in two glioblastoma cell lines (U87MG and SF268), and amino acid starvation (the third heatmap) in MCF7 cell line; (E) using node contribution scores to assess the role of S6K kinase in regulating mTOR pathway activity in the MCF7 PAS
Fig. 3.
Fig. 3.
Predicting pathways perturbed by LINCS CPs. (A) ROC curves for predicting correctly mTOR signaling pathways for CP’s known to target proteins in the pathway using seven different methods: PASs = pathway activity signatures using our new method; KD = pathway signatures constructed using only CGS data, but not utilizing the pathway topology; TP = using only CP TSes and the pathway topology, but not using CGSs; RS = classical enrichment analysis not utilizing GP signatures or the pathway topology; (B) percentage of pathways predicted with the highest AUC for seven different methods across four different types of KEGG pathways; (C) heatmap of AUC’s for predicting affected KEGG signaling pathways for all seven methods
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