A Differential Wiring Analysis of Expression Data Correctly Identifies the Gene Containing the Causal Mutation

Abstract

Transcription factor (TF) regulation is often post-translational. TF modifications such as reversible phosphorylation and missense mutations, which can act independent of TF expression level, are overlooked by differential expression analysis. Using bovine Piedmontese myostatin mutants as proof-of-concept, we propose a new algorithm that correctly identifies the gene containing the causal mutation from microarray data alone. The myostatin mutation releases the brakes on Piedmontese muscle growth by translating a dysfunctional protein. Compared to a less muscular non-mutant breed we find that myostatin is not differentially expressed at any of ten developmental time points. Despite this challenge, the algorithm identifies the myostatin ‘smoking gun’ through a coordinated, simultaneous, weighted integration of three sources of microarray information: transcript abundance, differential expression, and differential wiring. By asking the novel question “which regulator is cumulatively most differentially wired to the abundant most differentially expressed genes?” it yields the correct answer, “myostatin”. Our new approach identifies causal regulatory changes by globally contrasting co-expression network dynamics. The entirely data-driven ‘weighting’ procedure emphasises regulatory movement relative to the phenotypically relevant part of the network. In contrast to other published methods that compare co-expression networks, significance testing is not used to eliminate connections.

Author Summary

Evolution, development, and cancer are governed by regulatory circuits where the central nodes are transcription factors. Consequently, there is great interest in methods that can identify the causal mutation/perturbation responsible for any circuit rewiring. The most widely available high-throughput technology, the microarray, assays the transcriptome. However, many regulatory perturbations are post-transcriptional. This means that they are overlooked by traditional differential gene expression analysis. We hypothesised that by viewing biological systems as networks one could identify causal mutations and perturbations by examining those regulators whose position in the network changes the most. Using muscular myostatin mutant cattle as a proof-of-concept, we propose an analysis that succeeds based solely on microarray expression data from just 27 animals. Our analysis differs from competing network approaches in that we do not use significance testing to eliminate connections. All connections are contrasted, no matter how weak. Further, the identity of target genes is maintained throughout the analysis. Finally, the analysis is ‘weighted’ such that movement relative to the phenotypically most relevant part of the network is emphasised. By identifying the question to which myostatin is the answer, we present a comparison of network connectivity that is potentially generalisable.