A crowd-sourcing approach for the construction of species-specific cell signaling networks

Abstract

Motivation: Animal models are important tools in drug discovery and for understanding human biology in general. However, many drugs that initially show promising results in rodents fail in later stages of clinical trials. Understanding the commonalities and differences between human and rat cell signaling networks can lead to better experimental designs, improved allocation of resources and ultimately better drugs.

Results: The sbv IMPROVER Species-Specific Network Inference challenge was designed to use the power of the crowds to build two species-specific cell signaling networks given phosphoproteomics, transcriptomics and cytokine data generated from NHBE and NRBE cells exposed to various stimuli. A common literature-inspired reference network with 220 nodes and 501 edges was also provided as prior knowledge from which challenge participants could add or remove edges but not nodes. Such a large network inference challenge not based on synthetic simulations but on real data presented unique difficulties in scoring and interpreting the results. Because any prior knowledge about the networks was already provided to the participants for reference, novel ways for scoring and aggregating the results were developed. Two human and rat consensus networks were obtained by combining all the inferred networks. Further analysis showed that major signaling pathways were conserved between the two species with only isolated components diverging, as in the case of ribosomal S6 kinase RPS6KA1. Overall, the consensus between inferred edges was relatively high with the exception of the downstream targets of transcription factors, which seemed more difficult to predict.

Contact: ebilal@us.ibm.com or gustavo@us.ibm.com.

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

Overview of the Network Inference Challenge. Participants are provided with a reference network together with Affymetrix gene expression and Luminex phosphoproteomics and cytokine data derived from human and rat bronchial epithelial cells. The goal is to generate two separate networks for human and rat by adding and removing edges from the reference network using the data provided

Fig. 2.

The top 10 canonical pathways represented in the reference network. The pathways are ordered by the proportion of genes present in the reference network

Fig. 3.

The predicted networks for human (A) and rat (B) were compared with the silver standard and against each other using MCC. Only edges present in the reference network were considered

Fig. 4.

(A) The beta-binomial mixture weight can be calculated by maximizing the log-likelihood function. (B) Using this value, the fitted mixture is shown in red together with the individual-weighted components in black. Only edges present in the reference network were used in this case

Fig. 5.

Panels A and B show two example subnetworks of the consensus network where in blue are human-specific edges, in red rat-specific edges and in black edges common to both species. Depicted in gray are edges from the original reference network that did not gather sufficient consensus between participants. Panel C shows the average consensus score of the edges between a layer and the next one downstream from it for human and rat networks