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 B(4) (LTB(4)), 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 LTB(4) production, degradation and downstream signalling in the brainstem of the SHR are dynamically regulated during hypertension. When LTB(4) 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 LTB(4) 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 B(4) (LTB(4)), a potent chemoattractant involved in the inflammatory response. However, the mechanism for LTB(4)‐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 LTB(4) production, degradation and downstream signalling in the brainstem of the SHR are dynamically regulated during hypertension. Chronic blockade of the LTB(4) 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 LTB(4) 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

Dynamic microRNA networks in the brainstem underlie the development of hypertension

Essential hypertension is a major disease impacting millions across the globe. Hypertension is often resistant to current therapies, which can result in deadly consequences such as stroke and heart attack. One third of the United States population is hypertensive, and despite 75% using anti-hypertensive medication, only 53% have blood pressure controlled. Hypertensive patients typically exhibit autonomic dysfunction, and it is now compelling that neural contribution to hypertension is a major cause of its development and maintenance. Of the molecular pathways that have been examined in the context of neural contribution to essential hypertension, two have shown to have significant impact in affecting the hypertensive state: Angiotensin II (Ang II) signaling and Leukotriene B4 (LTB4) signaling. However, despite protein signaling pathways being necessary for the development of disease, non-coding RNA, mainly through microRNA, regulation of such pathways has proven to be a key regulatory element in disease. Within this thesis, the first and the only work, on microRNA changes in the brainstem autonomic control circuits that lead to development of hypertension will be presented. Using a systems biology approach integrating high-throughput data, network analysis, and in vivo and in vitro experimental testing, we have identified microRNAs in the brainstem of the Spontaneously Hypertensive Rat (SHR) relative to Wistar-Kyoto (WKY) controls with significantly different expression levels in two key neuroanatomical regions, the nucleus of the solitary tract (NTS) and the rostral ventrolateral medulla (RVLM). Alterations in microRNA expression levels are time and location-dependent, differing at a key period of hypertension onset in NTS, but differing at the prehypertensive stage in RVLM. Using correlational relationships and network identification analysis, between microRNAs and mRNAs measured, we observed a double-negative regulatory motif consisting of a microRNA down-regulating a negative regulator of a pro-hypertensive signaling pathway like Angiotensin II signaling or leukotriene-based inflammation. We demonstrated for the first time that the broad concordance of microRNA dynamics and target gene expression compose a regulatory network in the brainstem underlying hypertension. We then localized the regulatory network to different cell types in these regions in the brain. From the cohort of microRNAs we identified, microRNA-135a and microRNA-376a were previously shown to be enriched in astrocytes, a reactive immune cell in the brain, and neurons, respectively. We examined whether we could localize the tissue-scale network to each cell-type using a novel, optimized technique for measuring microRNA expression and target gene expression in the same 10-cell pool obtained from fresh-frozen slices of NTS from SHR and WKY. Some portions of the network were robust, and did localize to the cell-type level; whereas other portions were only seen at the tissue-level. Having identified two microRNAs, with cell-type specific properties, in SHR at a particular key time point in the development of hypertension, we performed in vivo manipulation studies directing microRNA antagonists directly into the IVth intracerebral ventrical (ICV) in the central nervous system (CNS) to normalize the expression of the disease-associated microRNAs in SHR through stereotaxic surgery. For the first time, our results demonstrate microRNA perturbations in the brain can elicit physiological effects, reducing blood pressure. In this context, disease-associated microRNAs represent a new class of targets for development of microRNA-based therapies, which may yield patient benefits unobtainable by conventional therapeutic approaches

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

<div><p>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 <i>in vivo</i> 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.</p></div

Divergent patterns of dynamic gene expression across organs and genes correspond to autonomic dysfunction.

<p>(A) Expression dynamics of pro-inflammatory gene <i>Il1b</i> across organs and phenotypes. (B) Expression dynamics of angiotensin precursor gene <i>Agt</i>. Error bars indicate standard error of the mean. Smooth curves depict natural cubic splines fit to the data.</p

Data-driven model-based network identification shows that connectivity differences are associated with dynamic differences in autonomic dysfunction.

<p>(A) Phenotype-specific network topology representations of gene-gene interaction coefficients. A given coordinate represents the magnitude of influence of the organ-gene in the corresponding column on the organ-gene in the corresponding row (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005627#pcbi.1005627.g001" target="_blank">Fig 1E</a>). (B) Sample dynamic profiles obtained from the simulation of the mathematical models associated with the interactions described in (A). (C) Degree distributions for both in and out degree. These data show that differences in network topology are associated with differential dynamics in autonomic dysfunction versus control.</p

Divergent dynamics corresponding to differential network structure in autonomic disease.

<p>(A) Dynamic profiles related to gene-gene interactions exclusive to the autonomic dysfunction network. (B) Dynamic profiles related interactions exclusive to the control network. (C) Dynamic profiles related inverted interactions in the autonomic dysfunction phenotype as compared to the control.</p

Conceptual, experimental, and analytic framework for examining multi-organ pathogenesis of autonomic function.

<p>(A, B) The etiology of autonomic dysfunction involves (A) multiple organs in which positive feedback processes involve (B) inflammatory mediators, renin-angiotensin signaling, and sympathetic activity. (C) A number of well studied genes underlie the molecular basis of maladaptive network feedback processes. (D-F) Analysis pipeline for examination of expression patterns of the genes listed in (C) in multiple organs listed in (A). (D) Gene expression measurements were obtained and the data were analyzed using time series statistics and novel network identification approaches. (E) The network identification analysis reconstructed gene expression dynamics and gene regulatory interactions underlying autonomic dysfunction and control phenotypes. (F) Bioinformatic analyses were integrated with analyses of network structure and dynamics to generate novel hypotheses regarding the molecular mechanisms underlying autonomic dysfunction.</p

Dynamic profiles support specific network interactions in the brainstem of the autonomic dysfunction phenotype.

<p>(A) Brainstem subnetwork for the autonomic dysfunction phenotype. (B) Interactions involving <i>Agtr1</i> are highlighted. (C) Example of a three node ‘adaptation motif’ from the autonomic dysfunction phenotype. (D) Dynamic profiles corresponding to the motif from (C) show expected behavior only for the autonomic dysfunction phenotype.</p

Autonomic dysfunction-specific single nucleotide variants in regulatory regions upstream of prominent genes.

<p>(A) Example spatial relations amongst an SNV, TFBS, and transcription start site. (B) Transcription factor binding motif associated with <i>Tfap2a</i> along with the respective sequences in the autonomic dysfunction and control phenotypes. A single nucleotide variation (SNV) occurs in a key element of the putative binding site for the autonomic dysfunction phenotype. (C) Summary of TFs with bioinformatically identified TFBSs in or near autonomic dysfunction-specific SNVs upstream and proximal to genes implicated in physiological homeostasis.</p

Differential network structure in autonomic dysfunction.

<p>(A) Out degree is shown according to the degree order of the autonomic dysfunction network. The control network exhibited a divergent out degree pattern. (B) A highly connected module in the autonomic dysfunction network with genes that have prominent influences (columns, regulators) and their targets (rows). Each row and column of the full network matrix (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005627#pcbi.1005627.g003" target="_blank">Fig 3A</a>) contains more than 15% of non-zero entries. The corresponding control subnetwork is comparatively sparse. (C) Diagram illustrating gene-gene interactions that are present only in the autonomic dysfunction network. (D) Interactions that are only present in the control network. (E) Autonomic dysfunction-specific interactions that have the opposite sign of the corresponding control interactions (e.g., adrenal <i>Ren</i> upregulates brainstem <i>Tnf</i> in the autonomic dysfunction phenotype but downregulates brainstem <i>Tnf</i> in the control phenotype).</p