A data-driven modeling approach to identify disease-specific multi-organ networks driving physiological dysregulation

Abstract

Multiple physiological systems interact throughout the development of a complex disease. Knowledge of the dynamics and connectivity of interactions across physiological systems could facilitate the prevention or mitigation of organ damage underlying complex diseases, many of which are currently refractory to available therapeutics (e.g., hypertension). We studied the regulatory interactions operating within and across organs throughout disease development by integrating in vivo analysis of gene expression dynamics with a reverse engineering approach to infer data-driven dynamic network models of multi-organ gene regulatory influences. We obtained experimental data on the expression of 22 genes across five organs, over a time span that encompassed the development of autonomic nervous system dysfunction and hypertension. We pursued a unique approach for identification of continuous-time models that jointly described the dynamics and structure of multi-organ networks by estimating a sparse subset of ∼12,000 possible gene regulatory interactions. Our analyses revealed that an autonomic dysfunction-specific multi-organ sequence of gene expression activation patterns was associated with a distinct gene regulatory network. We analyzed the model structures for adaptation motifs, and identified disease-specific network motifs involving genes that exhibited aberrant temporal dynamics. Bioinformatic analyses identified disease-specific single nucleotide variants within or near transcription factor binding sites upstream of key genes implicated in maintaining physiological homeostasis. Our approach illustrates a novel framework for investigating the pathogenesis through model-based analysis of multi-organ system dynamics and network properties. Our results yielded novel candidate molecular targets driving the development of cardiovascular disease, metabolic syndrome, and immune dysfunction.

Novel Influences of IL-10 on CNS Inflammation Revealed by Integrated Analyses of Cytokine Networks and Microglial Morphology

“…Our general approach to statistically evaluating differences between the WT and KO conditions entailed comparing temporal profiles rather than individual data points (Storey et al, 2005 ; Anderson et al, 2017 ). We used kernel density plots visualize the distributions of morphological variables using the beanplot package in R (Kampstra, 2008 ).…”

“…We used kernel density plots visualize the distributions of morphological variables using the beanplot package in R (Kampstra, 2008 ). To determine whether particular morphological features exhibited differential dynamic profiles in WT versus IL-10 KO microglia, we implemented the optimal discovery procedure (Storey et al, 2007 ), as documented in our recent work (Anderson et al, 2017 ). According to this method, we fitted natural cubic splines to a given feature's temporal profile for each genotype and compared the computed error (i.e., sum of squared error, SS) to the error obtained if a single spline was fitted to the entire data set without regard for genotype.…”

Neuroinflammation, Glia, and Cytokines: Networks of Networks

“…Therefore, circulating cytokines from multiple sources can access the CNS. The inevitable conclusion is that CNS inflammation is coupled to the organismal physiological state, the study of which necessitates a network of networks approach to probe the multiorgan interaction dynamics (Anderson et al 2017b; Fig. 1).…”

“…An important avenue for future research would be to integrate computational models of intra-and inter-cellular cytokine networks with multiscale models of cell signaling influences on ion channel function and excitability (Makadia et al 2015). Moreover, considering neuroinflammation in the broader context of multiorgan networks will enhance our understanding of CNS diseases and their potential treatments (Anderson et al 2017b). In conclusion, the mechanistic and functional relationships connecting cytokines, glia, and neuroinflammation represent networks within networks of signaling and functional mechanisms interacting across multiple spatial and temporal scales.…”

Single cell high-throughput qRT-PCR protocol

“…This protocol has been developed over the past decade in our lab to enable reliable high-throughput detection of gene expression by qRT-PCR in low input samples starting from 10 pg total RNA (single cells). We have used this protocol to measure gene expression in a wide variety of samples types, tissue treatments and disease contexts (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12). The present version of this protocol was developed as part of the National Institutes of Health SPARC project efforts to construct a comprehensive anatomical, molecular and functional map of the peripheral nervous system at the visceral organs.…”