Dynamic responses of the adrenal steroidogenic regulatory network.

The hypothalamic-pituitary-adrenal axis is a dynamic system regulating glucocorticoid hormone synthesis in the adrenal glands. Many key factors within the adrenal steroidogenic pathway have been identified and studied, but little is known about how these factors function collectively as a dynamic network of interacting components. To investigate this, we developed a mathematical model of the adrenal steroidogenic regulatory network that accounts for key regulatory processes occurring at different timescales. We used our model to predict the time evolution of steroidogenesis in response to physiological adrenocorticotropic hormone (ACTH) perturbations, ranging from basal pulses to larger stress-like stimulations (e.g., inflammatory stress). Testing these predictions experimentally in the rat, our results show that the steroidogenic regulatory network architecture is sufficient to respond to both small and large ACTH perturbations, but coupling this regulatory network with the immune pathway is necessary to explain the dissociated dynamics between ACTH and glucocorticoids observed under conditions of inflammatory stress.

The long and viscous road: uncovering nuclear diffusion barriers in closed mitosis.

Diffusion barriers are effective means for constraining protein lateral exchange in cellular membranes. In Saccharomyces cerevisiae, they have been shown to sustain parental identity through asymmetric segregation of ageing factors during closed mitosis. Even though barriers have been extensively studied in the plasma membrane, their identity and organization within the nucleus remains poorly understood. Based on different lines of experimental evidence, we present a model of the composition and structural organization of a nuclear diffusion barrier during anaphase. By means of spatial stochastic simulations, we propose how specialised lipid domains, protein rings, and morphological changes of the nucleus may coordinate to restrict protein exchange between mother and daughter nuclear lobes. We explore distinct, plausible configurations of these diffusion barriers and offer testable predictions regarding their protein exclusion properties and the diffusion regimes they generate. Our model predicts that, while a specialised lipid domain and an immobile protein ring at the bud neck can compartmentalize the nucleus during early anaphase; a specialised lipid domain spanning the elongated bridge between lobes would be entirely sufficient during late anaphase. Our work shows how complex nuclear diffusion barriers in closed mitosis may arise from simple nanoscale biophysical interactions.

Delays induce novel stochastic effects in negative feedback gene circuits.

Stochastic models of reaction networks are widely used to depict gene expression dynamics. However, stochastic does not necessarily imply accurate, as subtle assumptions can yield erroneous results, masking key discrete effects. For instance, transcription and translation are not instantaneous processes-explicit delays separate their initiation from the appearance of their functional products. However, delays are often ignored in stochastic, single-gene expression models. By consequence, effects such as delay-induced stochastic oscillations at the single-cell level have remained relatively unexplored. Here, we present a systematic study of periodicity and multimodality in a simple gene circuit with negative feedback, analyzing the influence of negative feedback strength and transcriptional/translational delays on expression dynamics. We demonstrate that an oscillatory regime emerges through a Hopf bifurcation in both deterministic and stochastic frameworks. Of importance, a shift in the stochastic Hopf bifurcation evidences inaccuracies of the deterministic bifurcation analysis. Furthermore, noise fluctuations within stochastic oscillations decrease alongside increasing values of transcriptional delays and within a specific range of negative feedback strengths, whereas a strong feedback is associated with oscillations triggered by bursts. Finally, we demonstrate that explicitly accounting for delays increases the number of accessible states in the multimodal regime, and also introduces features typical of excitable systems.

Chemogenetic activation of PVN CRH neurons disrupts the estrous cycle and LH dynamics in female mice.

Introduction: The impact of stress on reproductive function is significant. Hypothalamic paraventricular nucleus (PVN) corticotrophin-releasing hormone (CRH) plays a major role in regulating the stress response. Understanding how the hypothalamic-pituitary-adrenal (HPA) axis and the hypothalamic-pituitary-gonadal (HPG) axis interact is crucial for comprehending how stress can lead to reproductive dysfunction. However, whether stress influences reproductive function via modulating PVN CRH or HPA sequelae is not fully elucidated. Methods: In this study, we investigated the impact of chemogenetic activation of PVN CRH neurons on reproductive function. We chronically and selectively stimulated PVN CRH neurons in female CRH-Cre mice using excitatory designer receptor exclusively activated by designer drugs (DREADDs) viral constructs, which were bilaterally injected into the PVN. The agonist compound-21 (C21) was delivered through the drinking water. We determined the effects of DREADDs activation of PVN CRH neurons on the estrous cycles, LH pulse frequency in diestrus and metestrus and LH surge in proestrus mice. The effect of long-term C21 administration on basal corticosterone secretion and the response to acute restraint stress during metestrus was also examined. Additionally, computer simulations of a mathematical model were used to determine the effects of DREADDs activation of PVN CRH neurons, simulating chronic stress, on the physiological parameters examined experimentally. Results: As a result, and consistent with our mathematical model predictions, the length of the estrous cycle was extended, with an increase in the time spent in estrus and metestrus, and a decrease in proestrus and diestrus. Additionally, the frequency of LH pulses during metestrus was decreased, but unaffected during diestrus. The occurrence of the preovulatory LH surge during proestrus was disrupted. The basal level of corticosterone during metestrus was not affected, but the response to acute restraint stress was diminished after long-term C21 application. Discussion: These data suggest that PVN CRH neurons play a functional role in disrupting ovarian cyclicity and the preovulatory LH surge, and that the activity of the GnRH pulse generator remains relatively robust during diestrus but not during metestrus under chronic stress exposure in accordance with our mathematical model predictions.

High-resolution daily profiles of tissue adrenal steroids by portable automated collection.

Rhythms are intrinsic to endocrine systems, and disruption of these hormone oscillations occurs at very early stages of the disease. Because adrenal hormones are secreted with both circadian and ultradian periods, conventional single-time point measurements provide limited information about rhythmicity and, crucially, do not provide information during sleep, when many hormones fluctuate from nadir to peak concentrations. If blood sampling is attempted overnight, then this necessitates admission to a clinical research unit, can be stressful, and disturbs sleep. To overcome this problem and to measure free hormones within their target tissues, we used microdialysis, an ambulatory fraction collector, and liquid chromatography-tandem mass spectrometry to obtain high-resolution profiles of tissue adrenal steroids over 24 hours in 214 healthy volunteers. For validation, we compared tissue against plasma measurements in a further seven healthy volunteers. Sample collection from subcutaneous tissue was safe, well tolerated, and allowed most normal activities to continue. In addition to cortisol, we identified daily and ultradian variation in free cortisone, corticosterone, 18-hydroxycortisol, aldosterone, tetrahydrocortisol and allo-tetrahydrocortisol, and the presence of dehydroepiandrosterone sulfate. We used mathematical and computational methods to quantify the interindividual variability of hormones at different times of the day and develop "dynamic markers" of normality in healthy individuals stratified by sex, age, and body mass index. Our results provide insight into the dynamics of adrenal steroids in tissue in real-world settings and may serve as a normative reference for biomarkers of endocrine disorders (ULTRADIAN, NCT02934399).

Misaligned hormonal rhythmicity: Mechanisms of origin and their clinical significance.

Rhythmic hormonal secretion is key for sustaining health. While a central pacemaker in the hypothalamus is the main driver of circadian periodicity, many hormones oscillate with different frequencies and amplitudes. These rhythms carry information about healthy physiological functions, while at the same time they must be able to respond to external cues and maintain their robustness against severe perturbations. Since endocrine disruptions can lead to hormonal misalignment and disease, understanding the clinical significance of these rhythms can help support diagnosis and disease management. While the misalignment of dynamic hormone profiles can be quantitatively analysed though statistical and computational techniques, mathematical modelling can provide fundamental understanding about the mechanisms underpinning endocrine rhythms, particularly around the question of what makes them robust to some perturbations but fragile to others. In this study, I will review the key challenges of understanding hormonal rhythm misalignment from a mathematical perspective, including their causes and clinical significance. By reviewing modelling examples of coupled endocrine axes, I will address the question of how perturbations in one endocrine axis propagate to another, leading to the more complex issue of disentangling the contribution of each endocrine system to a robust dynamic environment.

Analysis of wearable time series data in endocrine and metabolic research.

Many hormones in the body oscillate with different frequencies and amplitudes, creating a dynamic environment that is essential to maintain health. In humans, disruptions to these rhythms are strongly associated with increased morbidity and mortality. While mathematical models can help us understand rhythm misalignment, translating this insight into personalised healthcare technologies requires solving additional challenges. Here, we discuss how combining minimally invasive, high-frequency biosampling technologies with wearable devices can assist the development of hormonal surrogates. We review bespoke algorithms that can help analyse multidimensional, noisy, time series data and identify wearable signals that could constitute clinical proxies of endocrine rhythms. These techniques can support the development of computational biomarkers to support the diagnosis and management of endocrine and metabolic conditions.

Modelling the dynamic interaction of systemic inflammation and the hypothalamic-pituitary-adrenal (HPA) axis during and after cardiac surgery.

Major surgery and critical illness produce a potentially life-threatening systemic inflammatory response. The hypothalamic-pituitary-adrenal (HPA) axis is one of the key physiological systems that counterbalances this systemic inflammation through changes in adrenocorticotrophic hormone (ACTH) and cortisol. These hormones normally exhibit highly correlated ultradian pulsatility with an amplitude modulated by circadian processes. However, these dynamics are disrupted by major surgery and critical illness. In this work, we characterize the inflammatory, ACTH and cortisol responses of patients undergoing cardiac surgery and show that the HPA axis response can be classified into one of three phenotypes: single-pulse, two-pulse and multiple-pulse dynamics. We develop a mathematical model of cortisol secretion and metabolism that predicts the physiological mechanisms responsible for these different phenotypes. We show that the effects of inflammatory mediators are important only in the single-pulse pattern in which normal pulsatility is lost-suggesting that this phenotype could be indicative of the greatest inflammatory response. Investigating whether and how these phenotypes are correlated with clinical outcomes will be critical to patient prognosis and designing interventions to improve recovery.

Modelling Hydrocortisone Pharmacokinetics on a Subcutaneous Pulsatile Infusion Replacement Strategy in Patients with Adrenocortical Insufficiency.

In the context of glucocorticoid (GC) therapeutics, recent studies have utilised a subcutaneous hydrocortisone (HC) infusion pump programmed to deliver multiple HC pulses throughout the day, with the purpose of restoring normal circadian and ultradian GC rhythmicity. A key challenge for the advancement of novel HC replacement therapies is the calibration of infusion pumps against cortisol levels measured in blood. However, repeated blood sampling sessions are enormously labour-intensive for both examiners and examinees. These sessions also have a cost, are time consuming and are occasionally unfeasible. To address this, we developed a pharmacokinetic model approximating the values of plasma cortisol levels at any point of the day from a limited number of plasma cortisol measurements. The model was validated using the plasma cortisol profiles of 9 subjects with disrupted endogenous GC synthetic capacity. The model accurately predicted plasma cortisol levels (mean absolute percentage error of 14%) when only four plasma cortisol measurements were provided. Although our model did not predict GC dynamics when HC was administered in a way other than subcutaneously or in individuals whose endogenous capacity to produce GCs is intact, it was found to successfully be used to support clinical trials (or practice) involving subcutaneous HC delivery in patients with reduced endogenous capacity to synthesize GCs.

Dynamic Hormone Control of Stress and Fertility.

Neuroendocrine axes display a remarkable diversity of dynamic signaling processes relaying information between the brain, endocrine glands, and peripheral target tissues. These dynamic processes include oscillations, elastic responses to perturbations, and plastic long term changes observed from the cellular to the systems level. While small transient dynamic changes can be considered physiological, larger and longer disruptions are common in pathological scenarios involving more than one neuroendocrine axes, suggesting that a robust control of hormone dynamics would require the coordination of multiple neuroendocrine clocks. The idea of apparently different axes being in fact exquisitely intertwined through neuroendocrine signals can be investigated in the regulation of stress and fertility. The stress response and the reproductive cycle are controlled by the Hypothalamic-Pituitary-Adrenal (HPA) axis and the Hypothalamic-Pituitary-Gonadal (HPG) axis, respectively. Despite the evidence surrounding the effects of stress on fertility, as well as of the reproductive cycle on stress hormone dynamics, there is a limited understanding on how perturbations in one neuroendocrine axis propagate to the other. We hypothesize that the links between stress and fertility can be better understood by considering the HPA and HPG axes as coupled systems. In this manuscript, we investigate neuroendocrine rhythms associated to the stress response and reproduction by mathematically modeling the HPA and HPG axes as a network of interlocked oscillators. We postulate a network architecture based on physiological data and use the model to predict responses to stress perturbations under different hormonal contexts: normal physiological, gonadectomy, hormone replacement with estradiol or corticosterone (CORT), and high excess CORT (hiCORT) similar to hypercortisolism in humans. We validate our model predictions against experiments in rodents, and show how the dynamic responses of these endocrine axes are consistent with our postulated network architecture. Importantly, our model also predicts the conditions that ensure robustness of fertility to stress perturbations, and how chronodisruptions in glucocorticoid hormones can affect the reproductive axis' ability to withstand stress. This insight is key to understand how chronodisruption leads to disease, and to design interventions to restore normal rhythmicity and health.

Are emotional states based in the brain? A critique of affective brainocentrism from a physiological perspective.

We call affective brainocentrism the tendency to privilege the brain over other parts of the organism when defining or explaining emotions. We distinguish two versions of this tendency. According to brain-sufficient, emotional states are entirely realized by brain processes. According to brain-master, emotional states are realized by both brain and bodily processes, but the latter are entirely driven by the brain: the brain is the master regulator of bodily processes. We argue that both these claims are problematic, and we draw on physiological accounts of stress to make our main case. These accounts illustrate the existence of complex interactions between the brain and endocrine systems, the immune system, the enteric nervous system, and even gut microbiota. We argue that, because of these complex brain-body interactions, the brain cannot be isolated and identified as the basis of stress. We also mention recent evidence suggesting that complex brain-body interactions characterize the physiology of depression and anxiety. Finally, we call for an alternative dynamical, systemic, and embodied approach to the study of the physiology of emotions that does not privilege the brain, but rather aims at understanding how mutually regulating brain and bodily processes jointly realize a variety of emotional states.

Mathematical Modelling of Endocrine Systems.

Hormone rhythms are ubiquitous and essential to sustain normal physiological functions. Combined mathematical modelling and experimental approaches have shown that these rhythms result from regulatory processes occurring at multiple levels of organisation and require continuous dynamic equilibration, particularly in response to stimuli. We review how such an interdisciplinary approach has been successfully applied to unravel complex regulatory mechanisms in the metabolic, stress, and reproductive axes. We discuss how this strategy is likely to be instrumental for making progress in emerging areas such as chronobiology and network physiology. Ultimately, we envisage that the insight provided by mathematical models could lead to novel experimental tools able to continuously adapt parameters to gradual physiological changes and the design of clinical interventions to restore normal endocrine function.