Bayesian Multi-Species Approaches for Gene Regulatory Network Inference in Halophilic Archaea

Abstract

Control of gene expression as a result of regulation by transcription factors is a critical mechanism for cells to maintain homeostasis and identity, as well as respond to environmental fluctuations and external signals. To aid in understanding these complex processes, biologists employ gene regulatory networks to model the regulatory interactions between transcription factors and the genes they regulate. These models are useful as they provide testable hypotheses of regulatory interactions, transcription factor function, and accelerate the study of uncharacterized transcription factors. However, inference of these models is computationally challenging due to the vast quantity of data required given that microbial genomes contain hundreds of transcription factors to model. This problem is accentuated in understudied organisms, species that would greatly benefit from an inferred network for biological discovery, where the lack of available data is particularly constraining for effective inference. To address this problem, we have developed GRN-BMuSeR (Gene Regulatory Networks from Bayesian MUlti-SpEcies Regression), a novel approach to gene regulatory network inference that leverages gene orthology between closely related species to improve inference performance. We first present the theory behind the model and evaluate its performance on a dataset from Bacillus subtillis as well as simulated data. Next, we explore the genetic background of two model halophilic archaeal species, Halobacterium salinarum and Haloferax volcanii, and validate the data that will be used to drive model inference. Finally, we apply our approach to generate gene regulatory network models for each species and explore the network predictions. Moving forward, our results provide a framework and collection of testable hypotheses that will serve to guide experimental work and accelerate discovery in Haloferax volcanii.