The baroreflex is a multi-input, multi-output control physiological system that regulates blood pressure by modulating nerve activity between the brainstem and the heart. Existing computational models of the baroreflex do not explictly incorporate the intrinsic cardiac nervous system (ICN), which mediates central control of the heart function. We developed a computational model of closed-loop cardiovascular control by integrating a network representation of the ICN within central control reflex circuits. We examined central and local contributions to the control of heart rate, ventricular functions, and respiratory sinus arrhythmia (RSA). Our simulations match the experimentally observed relationship between RSA and lung tidal volume. Our simulations predicted the relative contributions of the sensory and the motor neuron pathways to the experimentally observed changes in the heart rate. Our closed-loop cardiovascular control model is primed for evaluating bioelectronic interventions to treat heart failure and renormalize cardiovascular physiology.

We have developed and integrated several technologies including whole-organ imaging and software development to support an initial precise 3D neuroanatomical mapping and molecular phenotyping of the intracardiac nervous system (ICN). While qualitative and gross anatomical descriptions of the anatomy of the ICN have each been pursued, we here bring forth a comprehensive atlas of the entire rat ICN at single-cell resolution. Our work precisely integrates anatomical and molecular data in the 3D digitally reconstructed whole heart with resolution at the micron scale. We now display the full extent and the position of neuronal clusters on the base and posterior left atrium of the rat heart, and the distribution of molecular phenotypes that are defined along the base-to-apex axis, which had not been previously described. The development of these approaches needed for this work has produced method pipelines that provide the means for mapping other organs.

We developed a spatially-tracked single neuron transcriptomics map of an intrinsic cardiac ganglion, the right atrial ganglionic plexus (RAGP) that is a critical mediator of sinoatrial node (SAN) activity. This 3D representation of RAGP used neuronal tracing to extensively map the spatial distribution of the subset of neurons that project to the SAN. RNA-seq of laser capture microdissected neurons revealed a distinct composition of RAGP neurons compared to the central nervous system and a surprising finding that cholinergic and catecholaminergic markers are coexpressed, suggesting multipotential phenotypes that can drive neuroplasticity within RAGP. High-throughput qPCR of hundreds of laser capture microdissected single neurons confirmed these findings and revealed a high dimensionality of neuromodulatory factors that contribute to dynamic control of the heart. Neuropeptide-receptor coexpression analysis revealed a combinatorial paracrine neuromodulatory network within RAGP informing follow-on studies on the vagal control of RAGP to regulate cardiac function in health and disease.

We developed and analyzed a single cell scale anatomical map of the rat intrinsic cardiac nervous system (ICNS) across four male and three female hearts. We find the ICNS has a reliable structural organizational plan across individuals that provide the foundation for further analyses of the ICNS in cardiac function and disease. The distribution of the ICNS was evaluated by 3D visualization and data-driven clustering. The pattern, distribution, and clustering of ICNS neurons across all male and female rat hearts is highly conserved, demonstrating a coherent organizational plan where distinct clusters of neurons are consistently localized. Female hearts had fewer neurons, lower packing density, and slightly reduced distribution, but with identical localization. We registered the anatomical data from each heart to a geometric scaffold, normalizing their 3D coordinates for standardization of common anatomical planes and providing a path where multiple experimental results and data types can be integrated and compared.

What is the topic of this review? The vagus nerve is a crucial regulator of cardiovascular homeostasis, and its activity is linked to heart health. Vagal activity originates from two brainstem nuclei: the nucleus ambiguus (fast lane) and the dorsal motor nucleus of the vagus (slow lane), nicknamed for the time scales that they require to transmit signals. What advances does it highlight? Computational models are powerful tools for organizing multi-scale, multimodal data on the fast and slow lanes in a physiologically meaningful way. A strategy is laid out for how these models can guide experiments aimed at harnessing the cardiovascular health benefits of differential activation of the fast and slow lanes.

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.