Dynamic dysregulation of transcriptomic networks in brainstem autonomic nuclei during hypertension development in the female spontaneously hypertensive rat.

Neurogenic hypertension stems from an imbalance in autonomic function that shifts the central cardiovascular control circuits toward a state of dysfunction. Using the female spontaneously hypertensive rat and the normotensive Wistar-Kyoto rat model, we compared the transcriptomic changes in three autonomic nuclei in the brainstem, nucleus of the solitary tract (NTS), caudal ventrolateral medulla, and rostral ventrolateral medulla (RVLM) in a time series at 8, 10, 12, 16, and 24 wk of age, spanning the prehypertensive stage through extended chronic hypertension. RNA-sequencing data were analyzed using an unbiased, dynamic pattern-based approach that uncovered dominant and several subtle differential gene regulatory signatures. Our results showed a persistent dysregulation across all three autonomic nuclei regardless of the stage of hypertension development as well as a cascade of transient dysregulation beginning in the RVLM at the prehypertensive stage that shifts toward the NTS at the hypertension onset. Genes that were persistently dysregulated were heavily enriched for immunological processes such as antigen processing and presentation, the adaptive immune response, and the complement system. Genes with transient dysregulation were also largely region-specific and were annotated for processes that influence neuronal excitability such as synaptic vesicle release, neurotransmitter transport, and an array of neuropeptides and ion channels. Our results demonstrate that neurogenic hypertension is characterized by brainstem region-specific transcriptomic changes that are highly dynamic with significant gene regulatory changes occurring at the hypertension onset as a key time window for dysregulation of homeostatic processes across the autonomic control circuits.NEW & NOTEWORTHY Hypertension is a major disease and is the primary risk factor for cardiovascular complications and stroke. The gene expression changes in the central nervous system circuits driving hypertension are understudied. Here, we show that coordinated and region-specific gene expression changes occur in the brainstem autonomic circuits over time during the development of a high blood pressure phenotype in a rat model of human essential hypertension.

Closed-loop modeling of central and intrinsic cardiac nervous system circuits underlying cardiovascular control.

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.

Unpacking the multimodal, multi-scale data of the fast and slow lanes of the cardiac vagus through computational modelling.

NEW FINDINGS: 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. ABSTRACT: The vagus nerve is a key mediator of brain-heart signaling, and its activity is necessary for cardiovascular health. Vagal outflow stems from the nucleus ambiguus, responsible primarily for fast, beat-to-beat regulation of heart rate and rhythm, and the dorsal motor nucleus of the vagus, responsible primarily for slow regulation of ventricular contractility. Due to the high-dimensional and multimodal nature of the anatomical, molecular and physiological data on neural regulation of cardiac function, data-derived mechanistic insights have proven elusive. Elucidating insights has been complicated further by the broad distribution of the data across heart, brain and peripheral nervous system circuits. Here we lay out an integrative framework based on computational modelling for combining these disparate and multi-scale data on the two vagal control lanes of the cardiovascular system. Newly available molecular-scale data, particularly single-cell transcriptomic analyses, have augmented our understanding of the heterogeneous neuronal states underlying vagally mediated fast and slow regulation of cardiac physiology. Cellular-scale computational models built from these data sets represent building blocks that can be combined using anatomical and neural circuit connectivity, neuronal electrophysiology, and organ/organismal-scale physiology data to create multi-system, multi-scale models that enable in silico exploration of the fast versus slow lane vagal stimulation. The insights from the computational modelling and analyses will guide new experimental questions on the mechanisms regulating the fast and slow lanes of the cardiac vagus toward exploiting targeted vagal neuromodulatory activity to promote cardiovascular health.

Investigating Drivers of Antireward in Addiction Behavior with Anatomically Specific Single-Cell Gene Expression Methods.

Increasing rates of addiction behavior have motivated mental health researchers and clinicians alike to understand antireward and recovery. This shift away from reward and commencement necessitates novel perspectives, paradigms, and hypotheses along with an expansion of the methods applied to investigate addiction. Here, we provide an example: A systems biology approach to investigate antireward that combines laser capture microdissection (LCM) and high-throughput microfluidic reverse transcription quantitative polymerase chain reactions (RT-qPCR). Gene expression network dynamics were measured and a key driver of neurovisceral dysregulation in alcohol and opioid withdrawal, neuroinflammation, was identified. This combination of technologies provides anatomic and phenotypic specificity at single-cell resolution with high-throughput sensitivity and specific gene expression measures yielding both hypothesis-generating datasets and mechanistic possibilities that generate opportunities for novel insights and treatments.

Single Cell Scale Neuronal and Glial Gene Expression and Putative Cell Phenotypes and Networks in the Nucleus Tractus Solitarius in an Alcohol Withdrawal Time Series.

Alcohol withdrawal syndrome (AWS) is characterized by neuronal hyperexcitability, autonomic dysregulation, and severe negative emotion. The nucleus tractus solitarius (NTS) likely plays a prominent role in the neurological processes underlying these symptoms as it is the main viscerosensory nucleus in the brain. The NTS receives visceral interoceptive inputs, influences autonomic outputs, and has strong connections to the limbic system and hypothalamic-pituitary-adrenal axis to maintain homeostasis. Our prior analysis of single neuronal gene expression data from the NTS shows that neurons exist in heterogeneous transcriptional states that form distinct functional subphenotypes. Our working model conjectures that the allostasis secondary to alcohol dependence causes peripheral and central biological network decompensation in acute abstinence resulting in neurovisceral feedback to the NTS that substantially contributes to the observed AWS. We collected single noradrenergic and glucagon-like peptide-1 (GLP-1) neurons and microglia from rat NTS and measured a subset of their transcriptome as pooled samples in an alcohol withdrawal time series. Inflammatory subphenotypes predominate at certain time points, and GLP-1 subphenotypes demonstrated hyperexcitability post-withdrawal. We hypothesize such inflammatory and anxiogenic signaling contributes to alcohol dependence via negative reinforcement. Targets to mitigate such dysregulation and treat dependence can be identified from this dataset.

A single cell transcriptomics map of paracrine networks in the intrinsic cardiac nervous system.

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.

3D single cell scale anatomical map of sex-dependent variability of the rat intrinsic cardiac nervous system.

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.

Input-output signal processing plasticity of vagal motor neurons in response to cardiac ischemic injury.

Vagal stimulation is emerging as the next frontier in bioelectronic medicine to modulate peripheral organ health and treat disease. The neuronal molecular phenotypes in the dorsal motor nucleus of the vagus (DMV) remain largely unexplored, limiting the potential for harnessing the DMV plasticity for therapeutic interventions. We developed a mesoscale single-cell transcriptomics data from hundreds of DMV neurons under homeostasis and following physiological perturbations. Our results revealed that homeostatic DMV neuronal states can be organized into distinguishable input-output signal processing units. Remote ischemic preconditioning induced a distinctive shift in the neuronal states toward diminishing the role of inhibitory inputs, with concomitant changes in regulatory microRNAs miR-218a and miR-495. Chronic cardiac ischemic injury resulted in a dramatic shift in DMV neuronal states suggestive of enhanced neurosecretory function. We propose a DMV molecular network mechanism that integrates combinatorial neurotransmitter inputs from multiple brain regions and humoral signals to modulate cardiac health.

Systemic Immune Bias Delineates Malignant Astrocytoma Survival Cohorts.

Patients with grade III anaplastic astrocytomas (AA) separate into survival cohorts based on the presence or absence of mutations in isocitrate dehydrogenase (IDH). Progression to glioblastoma (GBM), morphologically distinguishable by elevated microvascular proliferation, necrosis, and cell division in tumor tissues, is considerably more rapid in IDH wild-type tumors such that their diagnosis as AA is relatively rare. More often initially presenting as GBM, these contain higher numbers of tumor-associated macrophages (TAMs) than most AA, and GBM patients also have higher levels of circulating M2 monocytes. TAM and M2 monocytes share functional properties inhibitory for antitumor immunity. Yet, although there is a wealth of data implicating TAM in tumor-immune evasion, there has been limited analysis of the impact of the circulating M2 monocytes. In the current study, immune parameters in sera, circulating cells, and tumor tissues from patients with primary gliomas morphologically diagnosed as AA were assessed. Profound differences in serum cytokines, glioma extracellular vesicle cross-reactive Abs, and gene expression by circulating cells identified two distinct patient cohorts. Evidence of type 2-immune bias was most often seen in patients with IDH wild-type AA, whereas a type 1 bias was common in patients with tumors expressing the IDH1R132H mutation. Nevertheless, a patient's immune profile was better correlated with the extent of tumor vascular enhancement on magnetic resonance imaging than IDH mutational status. Regardless of IDH genotype, AA progression appears to be associated with a switch in systemic immune bias from type 1 to type 2 and the loss of tumor vasculature integrity.

Similarities in alcohol and opioid withdrawal syndromes suggest common negative reinforcement mechanisms involving the interoceptive antireward pathway.

Alcohol and opioids are two major contributors to so-called deaths of despair. Though the effects of these substances on mammalian systems are distinct, commonalities in their withdrawal syndromes suggest a shared pathophysiology. For example, both are characterized by marked autonomic dysregulation and are treated with alpha-2 agonists. Moreover, alcohol and opioids rapidly induce dependence motivated by withdrawal avoidance. Resemblances observed in withdrawal syndromes and abuse behavior may indicate common addiction mechanisms. We argue that neurovisceral feedback influences autonomic and emotional circuits generating antireward similarly for both substances. Amygdala is central to this hypothesis as it is principally responsible for negative emotion, prominent in addiction and motivated behavior, and processes autonomic inputs while generating autonomic outputs. The solitary nucleus (NTS) has strong bidirectional connections to the amygdala and receives interoceptive inputs communicating visceral states via vagal afferents. These visceral-emotional hubs are strongly influenced by the periphery including gut microbiota. We propose that gut dysbiosis contributes to alcohol and opioid withdrawal syndromes by contributing to peripheral and neuroinflammation that stimulates these antireward pathways and motivates substance dependence.

Investigating the Effects of Brainstem Neuronal Adaptation on Cardiovascular Homeostasis.

Central coordination of cardiovascular function is accomplished, in part, by the baroreceptor reflex, a multi-input multi-output physiological control system that regulates the activity of the parasympathetic and sympathetic nervous systems via interactions among multiple brainstem nuclei. Recent single-cell analyses within the brain revealed that individual neurons within and across brain nuclei exhibit distinct transcriptional states contributing to neuronal function. Such transcriptional heterogeneity complicates the task of understanding how neurons within and across brain nuclei organize and function to process multiple inputs and coordinate cardiovascular functions within the larger context of the baroreceptor reflex. However, prior analysis of brainstem neurons revealed that single-neuron transcriptional heterogeneity reflects an adaptive response to synaptic inputs and that neurons organize into distinct subtypes with respect to synaptic inputs received. Based on these results, we hypothesize that adaptation of neuronal subtypes support robust biological function through graded cellular responses. We test this hypothesis by examining the functional impact of neuronal adaptation on parasympathetic activity within the context of short-term baroreceptor reflex regulation. In this work, we extend existing quantitative closed-loop models of the baroreceptor reflex by incorporating into the model distinct input-driven neuronal subtypes and neuroanatomical groups that modulate parasympathetic activity. We then use this extended model to investigate, via simulation, the functional role of neuronal adaptation under conditions of health and systolic heart failure. Simulation results suggest that parasympathetic activity can be modulated appropriately by the coordination of distinct neuronal subtypes to maintain normal cardiovascular functions under systolic heart failure conditions. Moreover, differing degrees of adaptation of these neuronal subtypes contribute to cardiovascular behaviors corresponding to distinct clinical phenotypes of heart failure, such as exercise intolerance. Further, our results suggest that an imbalance between sympathetic and parasympathetic activity regulating ventricular contractility contributes to exercise intolerance in systolic heart failure patients, and restoring this balance can improve the short-term cardiovascular performance of these patients.

Diurnal Patterns of Gene Expression in the Dorsal Vagal Complex and the Central Nucleus of the Amygdala - Non-rhythm-generating Brain Regions.

Genes that establish the circadian clock have differential expression with respect to solar time in central and peripheral tissues. Here, we find circadian-time-induced differential expression in a large number of genes not associated with circadian rhythms in two brain regions lacking overt circadian function: the dorsal vagal complex (DVC) and the central nucleus of the amygdala (CeA). These regions primarily engage in autonomic, homeostatic, and emotional regulation. However, we find striking diurnal shifts in gene expression in these regions of male Sprague Dawley rats with no obvious patterns that could be attributed to function or region. These findings have implications for the design of gene expression studies as well as for the potential effects of xenobiotics on these regions that regulate autonomic and emotional states.

A Comprehensive Integrated Anatomical and Molecular Atlas of Rat Intrinsic Cardiac Nervous System.

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.

Combining Laser Capture Microdissection and Microfluidic qPCR to Analyze Transcriptional Profiles of Single Cells: A Systems Biology Approach to Opioid Dependence.

Profound transcriptional heterogeneity in anatomically adjacent single cells suggests that robust tissue functionality may be achieved by cellular phenotype diversity. Single-cell experiments investigating the network dynamics of biological systems demonstrate cellular and tissue responses to various conditions at biologically meaningful resolution. Herein, we explain our methods for gathering single cells from anatomically specific locations and accurately measuring a subset of their gene expression profiles. We combine laser capture microdissection (LCM) with microfluidic reverse transcription quantitative polymerase chain reactions (RT-qPCR). We also use this microfluidic RT-qPCR platform to measure the microbial abundance of gut contents.

Single-Cell Glia and Neuron Gene Expression in the Central Amygdala in Opioid Withdrawal Suggests Inflammation With Correlated Gut Dysbiosis.

Drug-seeking in opioid dependence is due in part to the severe negative emotion associated with the withdrawal syndrome. It is well-established that negative emotional states emerge from activity in the amygdala. More recently, gut microflora have been shown to contribute substantially to such emotions. We measured gene expression in single glia and neurons gathered from the amygdala using laser capture microdissection and simultaneously measured gut microflora in morphine-dependent and withdrawn rats to investigate drivers of negative emotion in opioid withdrawal. We found that neuroinflammatory genes, notably Tnf, were upregulated in the withdrawal condition and that astrocytes, in particular, were highly active. We also observe a decreased Firmicutes to Bacteroides ratio in opioid withdrawal indicating gut dysbiosis. We speculate that these inflammatory and gut microflora changes contribute to the negative emotion experienced in opioid withdrawal that motivates dependence.

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

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.

Single-Cell Transcriptional Analysis Reveals Novel Neuronal Phenotypes and Interaction Networks Involved in the Central Circadian Clock.

Single-cell heterogeneity confounds efforts to understand how a population of cells organizes into cellular networks that underlie tissue-level function. This complexity is prominent in the mammalian suprachiasmatic nucleus (SCN). Here, individual neurons exhibit a remarkable amount of asynchronous behavior and transcriptional heterogeneity. However, SCN neurons are able to generate precisely coordinated synaptic and molecular outputs that synchronize the body to a common circadian cycle by organizing into cellular networks. To understand this emergent cellular network property, it is important to reconcile single-neuron heterogeneity with network organization. In light of recent studies suggesting that transcriptionally heterogeneous cells organize into distinct cellular phenotypes, we characterized the transcriptional, spatial, and functional organization of 352 SCN neurons from mice experiencing phase-shifts in their circadian cycle. Using the community structure detection method and multivariate analytical techniques, we identified previously undescribed neuronal phenotypes that are likely to participate in regulatory networks with known SCN cell types. Based on the newly discovered neuronal phenotypes, we developed a data-driven neuronal network structure in which multiple cell types interact through known synaptic and paracrine signaling mechanisms. These results provide a basis from which to interpret the functional variability of SCN neurons and describe methodologies toward understanding how a population of heterogeneous single cells organizes into cellular networks that underlie tissue-level function.

Systemic leukotriene B4 receptor antagonism lowers arterial blood pressure and improves autonomic function in the spontaneously hypertensive rat.

KEY POINTS: Evidence indicates an association between hypertension and chronic systemic inflammation in both human hypertension and experimental animal models. Previous studies in the spontaneously hypertensive rat (SHR) support a role for leukotriene B4 (LTB4 ), a potent chemoattractant involved in the inflammatory response, but its mode of action is poorly understood. In the SHR, we observed an increase in T cells and macrophages in the brainstem; in addition, gene expression profiling data showed that LTB4 production, degradation and downstream signalling in the brainstem of the SHR are dynamically regulated during hypertension. When LTB4 receptor 1 (BLT1) receptors were blocked with CP-105,696, arterial pressure was reduced in the SHR compared to the normotensive control and this reduction was associated with a significant decrease in systolic blood pressure (BP) indicators. These data provide new evidence for the role of LTB4 as an important neuro-immune pathway in the development of hypertension and therefore may serve as a novel therapeutic target for the treatment of neurogenic hypertension. ABSTRACT: Accumulating evidence indicates an association between hypertension and chronic systemic inflammation in both human hypertension and experimental animal models. Previous studies in the spontaneously hypertensive rat (SHR) support a role for leukotriene B4 (LTB4 ), a potent chemoattractant involved in the inflammatory response. However, the mechanism for LTB4 -mediated inflammation in hypertension is poorly understood. Here we report in the SHR, increased brainstem infiltration of T cells and macrophages plus gene expression profiling data showing that LTB4 production, degradation and downstream signalling in the brainstem of the SHR are dynamically regulated during hypertension. Chronic blockade of the LTB4 receptor 1 (BLT1) receptor with CP-105,696, reduced arterial pressure in the SHR compared to the normotensive control and this reduction was associated with a significant decrease in low and high frequency spectra of systolic blood pressure, and an increase in spontaneous baroreceptor reflex gain (sBRG). These data provide new evidence for the role of LTB4 as an important neuro-immune pathway in the development of hypertension and therefore may serve as a novel therapeutic target for the treatment of neurogenic hypertension.

Intracellular Information Processing through Encoding and Decoding of Dynamic Signaling Features.

Cell signaling dynamics and transcriptional regulatory activities are variable within specific cell types responding to an identical stimulus. In addition to studying the network interactions, there is much interest in utilizing single cell scale data to elucidate the non-random aspects of the variability involved in cellular decision making. Previous studies have considered the information transfer between the signaling and transcriptional domains based on an instantaneous relationship between the molecular activities. These studies predict a limited binary on/off encoding mechanism which underestimates the complexity of biological information processing, and hence the utility of single cell resolution data. Here we pursue a novel strategy that reformulates the information transfer problem as involving dynamic features of signaling rather than molecular abundances. We pursue a computational approach to test if and how the transcriptional regulatory activity patterns can be informative of the temporal history of signaling. Our analysis reveals (1) the dynamic features of signaling that significantly alter transcriptional regulatory patterns (encoding), and (2) the temporal history of signaling that can be inferred from single cell scale snapshots of transcriptional activity (decoding). Immediate early gene expression patterns were informative of signaling peak retention kinetics, whereas transcription factor activity patterns were informative of activation and deactivation kinetics of signaling. Moreover, the information processing aspects varied across the network, with each component encoding a selective subset of the dynamic signaling features. We developed novel sensitivity and information transfer maps to unravel the dynamic multiplexing of signaling features at each of these network components. Unsupervised clustering of the maps revealed two groups that aligned with network motifs distinguished by transcriptional feedforward vs feedback interactions. Our new computational methodology impacts the single cell scale experiments by identifying downstream snapshot measures required for inferring specific dynamical features of upstream signals involved in the regulation of cellular responses.