Computational Modeling of the Synergistic Role of GCN2 and HPA Axis in Regulating the Integrated Stress Response in the Central Circadian Timing System.

The circadian timing system and integrated stress response (ISR) systems are fundamental regulatory mechanisms that maintain body homeostasis. The central circadian pacemaker in the suprachiasmatic nucleus (SCN) governs daily rhythms through interactions with peripheral oscillators via the hypothalamus-pituitary-adrenal (HPA) axis. On the other hand, ISR signaling is pivotal for preserving cellular homeostasis in response to physiological changes. Notably, disrupted circadian rhythms are observed in cases of impaired ISR signaling. In this work, we examine the potential interplay between the central circadian system and the ISR, mainly through the SCN and HPA axis. We introduce a semi-mechanistic mathematical model to delineate the suprachiasmatic nucleus (SCN)'s capacity for indirectly perceiving physiological stress through glucocorticoid-mediated feedback from the HPA axis and orchestrating a cellular response via the ISR mechanism. Key components of our investigation include evaluating general control nonderepressible 2 (GCN2) expression in the SCN, the effect of physiological stress stimuli on the HPA axis, and the interconnected feedback between the HPA and SCN. Simulation reveals a critical role for GCN2 in linking ISR with circadian rhythms. Experimental findings have demonstrated that a Gcn2 deletion in mice leads to rapid re-entrainment of the circadian clock following jetlag, as well as to an elongation of the circadian period. These.

The Physiological and Pharmacological Significance of the Circadian Timing of the HPA Axis: A Mathematical Modeling Approach.

As a potent endogenous regulator of homeostasis, the circadian time-keeping system synchronizes internal physiology to periodic changes in the external environment to enhance survival. Adapting endogenous rhythms to the external time is accomplished hierarchically with the central pacemaker located in the suprachiasmatic nucleus (SCN) signaling the hypothalamus-pituitary-adrenal (HPA) axis to release hormones, notably cortisol, which help maintain the body's circadian rhythm. Given the essential role of HPA-releasing hormones in regulating physiological functions, including immune response, cell cycle, and energy metabolism, their daily variation is critical for the proper function of the circadian timing system. In this review, we focus on cortisol and key fundamental properties of the HPA axis and highlight their importance in controlling circadian dynamics. We demonstrate how systems-driven, mathematical modeling of the HPA axis complements experimental findings, enhances our understanding of complex physiological systems, helps predict potential mechanisms of action, and elucidates the consequences of circadian disruption. Finally, we outline the implications of circadian regulation in the context of personalized chronotherapy. Focusing on the chrono-pharmacology of synthetic glucocorticoids, we review the challenges and opportunities associated with moving toward personalized therapies that capitalize on circadian rhythms.

The SCN-HPA-Periphery Circadian Timing System: Mathematical Modeling of Clock Synchronization and the Effects of Photoperiod on Jetlag Adaptation.

Synchronizing the circadian timing system (CTS) to external light/dark cycles is crucial for homeostasis maintenance and environmental adaptation. The CTS is organized hierarchically, with the central pacemaker located in the suprachiasmatic nuclei (SCN) generating coherent oscillations that are entrained to light/dark cycles. These oscillations regulate the release of glucocorticoids by the hypothalamus-pituitary-adrenal (HPA) axis, which acts as a systemic entrainer of peripheral clocks throughout the body. The SCN adjusts its network plasticity in response to variations in photoperiod, leading to changes in the rhythmic release of glucocorticoids and ultimately impacting peripheral clocks. However, the effects of photoperiod-induced variations of glucocorticoids on the synchronization of peripheral clocks are not fully understood, and the interaction between jetlag adaption and photoperiod changes is unclear. This study presents a semi-mechanistic mathematical model to investigate how the CTS responds to changes in photoperiod. Specifically, the study focuses on the entrainment properties of a system composed of the SCN, HPA axis, and peripheral clocks. The results show that high-amplitude glucocorticoid rhythms lead to a more coherent phase distribution in the periphery. In addition, our study investigates the effect of photoperiod exposure on jetlag recovery time and phase shift, proposing different interventional strategies for eastward and westward jetlag. The findings suggest that decreasing photic exposure before jetlag during eastward traveling and after jetlag during westward traveling can accelerate jetlag readaptation. The study provides insights into the mechanisms of CTS organization and potential recovery strategies for transitions between time zones and lighting zones.

Light-induced synchronization of the SCN coupled oscillators and implications for entraining the HPA axis.

The suprachiasmatic nucleus (SCN) synchronizes the physiological rhythms to the external light-dark cycle and tunes the dynamics of circadian rhythms to photoperiod fluctuations. Changes in the neuronal network topologies are suggested to cause adaptation of the SCN in different photoperiods, resulting in the broader phase distribution of neuron activities in long photoperiods (LP) compared to short photoperiods (SP). Regulated by the SCN output, the level of glucocorticoids is elevated in short photoperiod, which is associated with peak disease incidence. The underlying coupling mechanisms of the SCN and the interplay between the SCN and the HPA axis have yet to be fully elucidated. In this work, we propose a mathematical model including a multiple-cellular SCN compartment and the HPA axis to investigate the properties of the circadian timing system under photoperiod changes. Our model predicts that the probability-dependent network is more energy-efficient than the distance-dependent network. Coupling the SCN network by intra-subpopulation and inter-subpopulation forces, we identified the negative correlation between robustness and plasticity of the oscillatory network. The HPA rhythms were predicted to be strongly entrained to the SCN rhythms with a pro-inflammatory high-amplitude glucocorticoid profile under SP. The fast temporal topology switch of the SCN network was predicted to enhance synchronization when the synchronization is not complete. These synchronization and circadian dynamics alterations might govern the seasonal variation of disease incidence and its symptom severity.

Towards a comprehensive assessment of QSP models: what would it take?

Quantitative Systems Pharmacology (QSP) has emerged as a powerful ensemble of approaches aiming at developing integrated mathematical and computational models elucidating the complex interactions between pharmacology, physiology, and disease. As the field grows and matures its applications expand beyond the boundaries of research and development and slowly enter the decision making and regulatory arenas. However, widespread acceptance and eventual adoption of a new modeling approach requires assessment criteria and quantifiable metrics that establish credibility and increase confidence in model predictions. QSP aims to provide an integrated understanding of pathology in the context of therapeutic interventions. Because of its ambitious nature and the fact that QSP emerged in an uncoordinated manner as a result of activities distributed across organizations and academic institutions, high entropy characterizes the tools, methods, and computational methodologies and approaches used. The eventual acceptance of QSP model predictions as supporting material for an application to a regulatory agency will require that two key aspects are considered: (1) increase confidence in the QSP framework, which drives standardization and assessment; and (2) careful articulation of the expectations. Both rely heavily on our ability to rigorously and consistently assess QSP models. In this manuscript, we wish to discuss the meaning and purpose of such an assessment in the context of QSP model development and elaborate on the differentiating features of QSP that render such an endeavor challenging. We argue that QSP establishes a conceptual, integrative framework rather than a specific and well-defined computational methodology. QSP elicits the use of a wide variety of modeling and computational methodologies optimized with respect to specific applications and available data modalities, which exceed the data structures employed by chemometrics and PK/PD models. While the range of options fosters creativity and promises to substantially advance our ability to design pharmaceutical interventions rationally and optimally, our expectations of QSP models need to be clearly articulated and agreed on, with assessment emphasizing the scope of QSP studies rather than the methods used. Nevertheless, QSP should not be considered an independent approach, rather one of many in the broader continuum of computational models.

Does seasonality of the microbiota contribute to the seasonality of acute gout flare?

Gout, the most common inflammatory arthritis worldwide, is an auto-inflammatory metabolic disease that leads to monosodium urate crystal deposition. Hyperuricaemia is a significant risk factor for the development of gout; however, hyperuricaemia alone is not sufficient to induce gout.Gout flares have circadian rhythms. Gout flares vary during the day and have strong seasonality, with flares being more common in the spring. The reasons for the predominance of flares in the spring are unclear since serum urate (SU) levels show seasonal variation; however, SU levels are highest in the summer.Immune function varies significantly throughout the year, with enhanced immune responses increasing during the winter. In addition, chronic disruption of circadian rhythms is associated with metabolic syndrome and diseases driven by metabolism. The most telling example relates to Xanthine oxidase (XOD/XDH). The analysis of XOD/XDH established its circadian regulation and demonstrated that inhibition of the activity of XOD is characterised by distinct, crossregulating diurnal/seasonal patterns of activity.The gastrointestinal microbiota of gout patients is highly distinct from healthy individuals. In a small series of gout patients, Bacteroides caccae and Bacteroides xylanisolvens were found to be enriched. Bacteroidales levels were highest during the spring and summer, and loading values were highest in the spring.Our review discusses gout's circadian rhythm and seasonality, possible influences of the microbiome on gout due to our new knowledge that Bacteroidales levels were highest during spring when gout is most common, and potential opportunities for treatment based on our current understanding of this interaction.

Two heads are better than one: current landscape of integrating QSP and machine learning : An ISoP QSP SIG white paper by the working group on the integration of quantitative systems pharmacology and machine learning.

Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer 'omics' data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP + ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices.

Teaching computational systems biology with an eye on quantitative systems pharmacology at the undergraduate level: Why do it, who would take it, and what should we teach?

Computational systems biology (CSB) is a field that emerged primarily as the product of research activities. As such, it grew in several directions in a distributed and uncoordinated manner making the area appealing and fascinating. The idea of not having to follow a specific path but instead creating one fueled innovation. As the field matured, several interdisciplinary graduate programs emerged attempting to educate future generations of computational systems biologists. These educational initiatives coordinated the dissemination of information across student populations that had already decided to specialize in this field. However, we are now entering an era where CSB, having established itself as a valuable research discipline, is attempting the next major step: Entering undergraduate curricula. As interesting as this endeavor may sound, it has several difficulties, mainly because the field is not uniformly defined. In this manuscript, we argue that this diversity is a significant advantage and that several incarnations of an undergraduate-level CSB biology course could, and should, be developed tailored to programmatic needs. In this manuscript, we share our experiences creating a course as part of a Biomedical Engineering program.

Light entrainment of the SCN circadian clock and implications for personalized alterations of corticosterone rhythms in shift work and jet lag.

The suprachiasmatic nucleus (SCN) functions as the central pacemaker aligning physiological and behavioral oscillations to day/night (activity/inactivity) transitions. The light signal entrains the molecular clock of the photo-sensitive ventrolateral (VL) core of the SCN which in turn entrains the dorsomedial (DM) shell via the neurotransmitter vasoactive intestinal polypeptide (VIP). The shell converts the VIP rhythmic signals to circadian oscillations of arginine vasopressin (AVP), which eventually act as a neurotransmitter signal entraining the hypothalamic-pituitary-adrenal (HPA) axis, leading to robust circadian secretion of glucocorticoids. In this work, we discuss a semi-mechanistic mathematical model that reflects the essential hierarchical structure of the photic signal transduction from the SCN to the HPA axis. By incorporating the interactions across the core, the shell, and the HPA axis, we investigate how these coupled systems synchronize leading to robust circadian oscillations. Our model predicts the existence of personalized synchronization strategies that enable the maintenance of homeostatic rhythms while allowing for differential responses to transient and permanent light schedule changes. We simulated different behavioral situations leading to perturbed rhythmicity, performed a detailed computational analysis of the dynamic response of the system under varying light schedules, and determined that (1) significant interindividual diversity and flexibility characterize adaptation to varying light schedules; (2) an individual's tolerances to jet lag and alternating shift work are positively correlated, while the tolerances to jet lag and transient shift work are negatively correlated, which indicates trade-offs in an individual's ability to maintain physiological rhythmicity; (3) weak light sensitivity leads to the reduction of circadian flexibility, implying that light therapy can be a potential approach to address shift work and jet lag related disorders. Finally, we developed a map of the impact of the synchronization within the SCN and between the SCN and the HPA axis as it relates to the emergence of circadian flexibility.

Self-selection of evolutionary strategies: adaptive versus non-adaptive forces.

The evolution of complex genetic networks is shaped over the course of many generations through multiple mechanisms. These mechanisms can be broken into two predominant categories: adaptive forces, such as natural selection, and non-adaptive forces, such as recombination, genetic drift, and random mutation. Adaptive forces are influenced by the environment, where individuals better suited for their ecological niche are more likely to reproduce. This adaptive force results in a selective pressure which creates a bias in the reproduction of individuals with beneficial traits. Non-adaptive forces, in contrast, are not influenced by the environment: Random mutations occur in offspring regardless of whether they improve the fitness of the offspring. Both adaptive and non-adaptive forces play critical roles in the development of a species over time, and both forces are intrinsically linked to one another. We hypothesize that even under a simple sexual reproduction model, selective pressure will result in changes in the mutation rate and genome size. We tested this hypothesis by evolving Boolean networks using a modified genetic algorithm. Our results demonstrate that changes in environmental signals can result in selective pressure which affects mutation rate.

Pathway-level analysis of genome-wide circadian dynamics in diverse tissues in rat and mouse.

A computational framework is developed to enable the characterization of genome-wide, multi-tissue circadian dynamics at the level of "functional groupings of genes" defined in the context of signaling, cellular/genetic processing and metabolic pathways in rat and mouse. Our aim is to identify how individual genes come together to generate orchestrated rhythmic patterns and how these may vary within and across tissues. We focus our analysis on four tissues (adipose, liver, lung, and muscle). A genome-wide pathway-centric analysis enables us to develop a comprehensive picture on how the observed circadian variation at the individual gene level, orchestrates functional responses at the pathway level. Such pathway-based "meta-data" analysis enables the rational integration and comparison across platforms and/or experimental designs evaluating emergent dynamics, as opposed to comparisons of individual elements. One of our key findings is that when considering the dynamics at the pathway level, a complex behavior emerges. Our work proposes that tissues tend to coordinate gene's circadian expression in a way that optimizes tissue-specific pathway activity, depending of each tissue's broader role in homeostasis.

Circadian Effects of Drug Responses.

Circadian rhythms describe physiological systems that repeat themselves with a cycle of approximately 24 h. Our understanding of the cellular and molecular origins of these oscillations has improved dramatically, allowing us to appreciate the significant role these oscillations play in maintaining physiological homeostasis. Circadian rhythms allow living organisms to predict and efficiently respond to a dynamically changing environment, set by repetitive day/night cycles. Since circadian rhythms underlie almost every aspect of human physiology, it is unsurprising that they also influence the response of a living organism to disease, stress, and therapeutics. Therefore, not only do the mechanisms that maintain health and disrupt homeostasis depend on our internal circadian clock, but also the way drugs are perceived and function depends on these physiological rhythms. We present a holistic view of the therapeutic process, discussing components such as disease state, pharmacokinetics, and pharmacodynamics, as well as adverse reactions that are critically affected by circadian rhythms. We outline challenges and opportunities in moving toward personalized medicine approaches that explore and capitalize on circadian rhythms for the benefit of the patient.

Circadian rhythms and the HPA axis: A systems view.

The circadian timing system comprises a network of time-keeping clocks distributed across a living host whose responsibility is to allocate resources and distribute functions temporally to optimize fitness. The molecular structures generating these rhythms have evolved to accommodate the rotation of the earth in an attempt to primarily match the light/dark periods during the 24-hr day. To maintain synchrony of timing across and within tissues, information from the central clock, located in the suprachiasmatic nucleus, is conveyed using systemic signals. Leading among those signals are endocrine hormones, and while the hypothalamic-pituitary-adrenal axis through the release of glucocorticoids is a major pacesetter. Interestingly, the fundamental units at the molecular and physiological scales that generate local and systemic signals share critical structural properties. These properties enable time-keeping systems to generate rhythmic signals and allow them to adopt specific properties as they interact with each other and the external environment. The purpose of this review is to provide a broad overview of these structures, discuss their functional characteristics, and describe some of their fundamental properties as these related to health and disease. This article is categorized under: Immune System Diseases > Computational Models Immune System Diseases > Biomedical Engineering.

Circadian Disruption in Critical Illness.

Circadian rhythms play a vital role in metabolic, hormonal, and immunologic function and are often disrupted in patients in the ICU. Circadian rhythms modulate the molecular machinery that responds to injury and illness which can impact recovery. Potential factors contributing to the alteration in circadian rhythmicity in intensive care unit (ICU) patients include abnormal lighting, noise, altered feeding schedules, extensive patient care interactions and medications. These alterations in circadian rhythms in ICU patients may affect outcomes and therefore, normalization of circadian rhythmicity in critically ill patients may be an important part of ICU care.

Modeling Pathway Dynamics of the Skeletal Muscle Response to Intravenous Methylprednisolone (MPL) Administration in Rats: Dosing and Tissue Effects.

A model-based approach for the assessment of pathway dynamics is explored to characterize metabolic and signaling pathway activity changes characteristic of the dosing-dependent differences in response to methylprednisolone in muscle. To consistently compare dosing-induced changes we extend the principles of pharmacokinetics and pharmacodynamics and introduce a novel representation of pathway-level dynamic models of activity regulation. We hypothesize the emergence of dosing-dependent regulatory interactions is critical to understanding the mechanistic implications of MPL dosing in muscle. Our results indicate that key pathways, including amino acid and lipid metabolism, signal transduction, endocrine regulation, regulation of cellular functions including growth, death, motility, transport, protein degradation, and catabolism are dependent on dosing, exhibiting diverse dynamics depending on whether the drug is administered acutely of continuously. Therefore, the dynamics of drug presentation offer the possibility for the emergence of dosing-dependent models of regulation. Finally, we compared acute and chronic MPL response in muscle with liver. The comparison revealed systematic response differences between the two tissues, notably that muscle appears more prone to adapt to MPL.

Modeling inter-sex and inter-individual variability in response to chronopharmacological administration of synthetic glucocorticoids.

Endogenous glucocorticoids have diverse physiological effects and are important regulators of metabolism, immunity, cardiovascular function, musculoskeletal health and central nervous system activity. Synthetic glucocorticoids have received widespread attention for their potent anti-inflammatory activity and have become an important class of drugs used to augment endogenous glucocorticoid activity for the treatment of a host of chronic inflammatory conditions. Chronic use of synthetic glucocorticoids is associated with a number of adverse effects as a result of the persistent dysregulation of glucocorticoid sensitive pathways. A failure to consider the pronounced circadian rhythmicity of endogenous glucocorticoids can result in either supraphysiological glucocorticoid exposure or severe suppression of endogenous glucocorticoid secretion, and is thought be a causal factor in the incidence of adverse effects during chronic glucocorticoid therapy. Furthermore, given that synthetic glucocorticoids have potent feedback effects on the hypothalamic-pituitary-adrenal (HPA) axis, physiological factors which can give rise to individual variability in HPA axis activity such as sex, age, and disease state might also have substantial implications for therapy. We use a semi-mechanistic mathematical model of the rodent HPA axis to study how putative sex differences and individual variability in HPA axis regulation can influence the effects of long-term synthetic exposure on endogenous glucocorticoid circadian rhythms. Model simulations suggest that for the same drug exposure, simulated females exhibit less endogenous suppression than males considering differences in adrenal sensitivity and negative feedback to the hypothalamus and pituitary. Simulations reveal that homeostatic regulatory variability and chronic stress-induced regulatory adaptations in the HPA axis network can result in substantial differences in the effects of synthetic exposure on the circadian rhythm of endogenous glucocorticoids. In general, our results provide insight into how the dosage and exposure profile of synthetic glucocorticoids could be manipulated in a personalized manner to preserve the circadian dynamics of endogenous glucocorticoids during chronic therapy, thus potentially minimizing the incidence of adverse effects associated with long-term use of glucocorticoids.

At the Interface of Lifestyle, Behavior, and Circadian Rhythms: Metabolic Implications.

Nutrient metabolism is under circadian regulation. Disruption of circadian rhythms by lifestyle and behavioral choices such as work schedules, eating patterns, and social jetlag, seriously impacts metabolic homeostasis. Metabolic dysfunction due to chronic misalignment of an organism's endogenous rhythms is detrimental to health, increasing the risk of obesity, metabolic and cardiovascular disease, diabetes, and cancer. In this paper, we review literature on recent findings on the mechanisms that communicate metabolic signals to circadian clocks and vice versa, and how human behavioral changes imposed by societal and occupational demands affect the physiological networks integrating peripheral clocks and metabolism. Finally, we discuss factors possibly contributing to inter-individual variability in response to circadian changes in the context of metabolic (dys)function.

Allostatic adaptation and personalized physiological trade-offs in the circadian regulation of the HPA axis: A mathematical modeling approach.

The hypothalamic-pituitary-adrenal (HPA) axis orchestrates the physiological response to unpredictable acute stressors. Moreover, the HPA axis exhibits prominent circadian activity and synchronizes peripheral circadian clocks to daily environmental cycles, thereby promoting homeostasis. Persistent disruption of homeostatic glucocorticoid circadian rhythmicity due to chronic stress exposure is correlated with the incidence of various pathological conditions including depression, diabetes and cancer. Allostatic habituation of the HPA axis, such that glucocorticoid levels retain homeostatic levels upon chronic exposure to stress, can therefore confer fitness advantages by preventing the sustained dysregulation of glucocorticoid-responsive signaling pathways. However, such allostatic adaptation results in a physiological cost (allostatic load) that might impair the homeostatic stress-responsive and synchronizing functions of the HPA axis. We use mathematical modeling to characterize specific chronic stress-induced allostatic adaptations in the HPA network. We predict the existence of multiple individualized regulatory strategies enabling the maintenance of homeostatic glucocorticoid rhythms, while allowing for flexible HPA response characteristics. We show that this regulatory variability produces a trade-off between the stress-responsive and time-keeping properties of the HPA axis. Finally, allostatic regulatory adaptations are predicted to cause a time-of-day dependent sensitization of the acute stress response and impair the entrainability of the HPA axis.

Fluorescence Imaging of Actin Turnover Parses Early Stem Cell Lineage Divergence and Senescence.

This study describes a new approach to discern early divergence in stem cell lineage progression via temporal dynamics of the cytoskeletal protein, F-actin. The approach involves real-time labeling of human mesenchymal stem cells (MSCs) and longitudinal tracking of the turnover dynamics of a fluorogenic F-actin specific probe, SiR-actin (SA). Cells cultured in media with distinct lineage factors and labeled with SA showed lineage specific reduction in the actin turnover shortly after adipogenic (few minutes) and chondrogenic (3-4 hours) commitment in contrast to osteogenic and basal cultured conditions. Next, composite staining of SA along with the competing F-actin specific fluorescent conjugate, phalloidin, and high-content image analysis of the complementary labels showed clear phenotypic parsing of the sub-populations as early as 1-hour post-induction across all three lineages. Lastly, the potential of SA-based actin turnover analysis to distinguish cellular aging was explored. In-vitro aged cells were found to have reduced actin turnover within 1-hour of simultaneous analysis in comparison to cells of earlier passage. In summary, SiR-actin fluorescent reporter imaging offers a new platform to sensitively monitor emergent lineage phenotypes during differentiation and aging and resolve some of the earliest evident differences in actin turnover dynamics.