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.

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.

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.

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.

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.

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.

Mathematical modeling informs the impact of changes in circadian rhythms and meal patterns on insulin secretion.

Disruption of circadian rhythms has been associated with metabolic syndromes, including obesity and diabetes. A variety of metabolic activities are under circadian modulation, as local and global clock gene knockouts result in glucose imbalance and increased risk of metabolic diseases. Insulin release from the pancreatic ß cells exhibits daily variation, and recent studies have found that insulin secretion, not production, is under circadian modulation. As consideration of daily variation in insulin secretion is necessary to accurately describe glucose-stimulated insulin secretion, we describe a mathematical model that incorporates the circadian modulation via insulin granule trafficking. We use this model to understand the effect of oscillatory characteristics on insulin secretion at different times of the day. Furthermore, we integrate the dynamics of clock genes under the influence of competing environmental signals (light/dark cycle and feeding/fasting cycle) and demonstrate how circadian disruption and meal size distribution change the insulin secretion pattern over a 24-h day.

The physiological significance of the circadian dynamics of the HPA axis: Interplay between circadian rhythms, allostasis and stress resilience.

Circadian time-keeping mechanisms preserve homeostasis by synchronizing internal physiology with predictable variations in the environment and temporally organize the activation of physiological signaling mechanisms to promote survival and optimize the allocation of energetic resources. In this paper, we highlight the importance of the robust circadian dynamics of allostatic mediators, with a focus on the hypothalamic-pituitary-adrenal (HPA) axis, for the optimal regulation of host physiology and in enabling organisms to adequately respond and adapt to physiological stressors. We review studies showing how the chronic disruption of circadian rhythms can result in the accumulation of allostatic load, which impacts the appropriate functioning of physiological systems and diminishes the resilience of internal systems to adequately respond to subsequent stressors. A careful consideration of circadian rhythm dynamics leads to a more comprehensive characterization of individual variability in allostatic load and stress resilience. Finally, we suggest that the restoration of circadian rhythms after pathological disruption can enable the re-engagement of allostatic mechanisms and re-establish stress resilience.

Mathematical analysis of circadian disruption and metabolic re-entrainment of hepatic gluconeogenesis: the intertwining entraining roles of light and feeding.

The circadian rhythms influence the metabolic activity from molecular level to tissue, organ, and host level. Disruption of the circadian rhythms manifests to the host's health as metabolic syndromes, including obesity, diabetes, and elevated plasma glucose, eventually leading to cardiovascular diseases. Therefore, it is imperative to understand the mechanism behind the relationship between circadian rhythms and metabolism. To start answering this question, we propose a semimechanistic mathematical model to study the effect of circadian disruption on hepatic gluconeogenesis in humans. Our model takes the light-dark cycle and feeding-fasting cycle as two environmental inputs that entrain the metabolic activity in the liver. The model was validated by comparison with data from mice and rat experimental studies. Formal sensitivity and uncertainty analyses were conducted to elaborate on the driving forces for hepatic gluconeogenesis. Furthermore, simulating the impact of Clock gene knockout suggests that modification to the local pathways tied most closely to the feeding-fasting rhythms may be the most efficient way to restore the disrupted glucose metabolism in liver.

A framework for 2-stage global sensitivity analysis of GastroPlus™ compartmental models.

Parameter sensitivity and uncertainty analysis for physiologically based pharmacokinetic (PBPK) models are becoming an important consideration for regulatory submissions, requiring further evaluation to establish the need for global sensitivity analysis. To demonstrate the benefits of an extensive analysis, global sensitivity was implemented for the GastroPlus™ model, a well-known commercially available platform, using four example drugs: acetaminophen, risperidone, atenolol, and furosemide. The capabilities of GastroPlus were expanded by developing an integrated framework to automate the GastroPlus graphical user interface with AutoIt and for execution of the sensitivity analysis in MATLAB®. Global sensitivity analysis was performed in two stages using the Morris method to screen over 50 parameters for significant factors followed by quantitative assessment of variability using Sobol's sensitivity analysis. The 2-staged approach significantly reduced computational cost for the larger model without sacrificing interpretation of model behavior, showing that the sensitivity results were well aligned with the biopharmaceutical classification system. Both methods detected nonlinearities and parameter interactions that would have otherwise been missed by local approaches. Future work includes further exploration of how the input domain influences the calculated global sensitivity measures as well as extending the framework to consider a whole-body PBPK model.

Pharmacokinetics and Pharmacodynamics of Curcumin in regulating anti-inflammatory and epigenetic gene expression.

Chronic inflammation is a key driver of cancer development. Nitrite levels, which are regulated by inducible nitric oxide synthase (iNOS), play a critical role in inflammation. While the anti-oxidant and anti-inflammatory effects of curcumin, a natural product present in the roots of Curcuma longa have been studied widely, the acute pharmacokinetics (PK) and pharmacodynamics (PD) of curcumin in suppressing pro-inflammatory markers and epigenetic modulators remain unclear. This study evaluated the PK and PD of curcumin-induced suppression of lipopolysaccharide (LPS)-mediated inflammation in rat lymphocytes. LPS was administered intravenously either alone or with curcumin to female Sprague-Dawley rats. Plasma samples were analysed for curcumin concentration and mRNA expression was quantified in lymphocytes. The relative gene expression of several inflammatory and epigenetic modulators was analysed. To investigate the relationship between curcumin concentration and iNOS, TNF-α, and IL-6 gene expression, PK/PD modeling using Jusko's indirect response model (IDR) integrating transit compartments (TC) describing the delayed response was conducted. The concentration-time profile of curcumin exhibited a bi-exponential decline, which was well described by a two-compartmental pharmacokinetic model. Importantly the results demonstrate that LPS induced gene expression of pro-inflammatory markers in lymphocytes, with peak expression at approximately 3 h and curcumin suppressed the gene expression in animals administered with LPS. These effects were well captured using the IDR model and an IDR model with the transit compartments. In summary, the PK/PD modeling approach could potentially provide a robust quantitative framework for evaluating the acute anti-inflammatory and epigenetic effects of curcumin in future clinical trials.

Pharmacokinetics and Pharmacodynamics of the Triterpenoid Ursolic Acid in Regulating the Antioxidant, Anti-inflammatory, and Epigenetic Gene Responses in Rat Leukocytes.

The triterpenoid ursolic acid (UA) has been proposed as a potential cancer chemopreventive agent in many preclinical and clinical studies. In the present work, we aimed to characterize the pharmacokinetics (PK) of UA and to quantitatively assess the antioxidative and anti-inflammatory effects of UA, which are potentially linked to its chemopreventive efficacy. UA was administered intravenously (i.v., 20 mg/kg) or by oral gavage (100 mg/kg) to male Sprague-Dawley rats, and blood samples were collected at a series of designated time points. The plasma concentration of UA was determined using a validated liquid chromatography-mass spectrometry (LC-MS) approach. A biexponential decline in the UA plasma concentration was observed after i.v. dosing and was fitted to a two-compartmental model. The expression levels of phase II drug metabolism (DM)/antioxidant genes and the inflammatory iNos gene in corresponding treatment arms were measured using qPCR as a pharmacodynamic (PD) marker. The expression of phase II DM/antioxidant genes increased and peaked approximately 3 h after 20 mg/kg UA treatment. In a lipopolysaccharide (LPS)-induced acute inflammation model, UA inhibited LPS-stimulated iNos expression and that of the epigenetic markers the DNA methyltransferases (DNMTs) and histone deacetylases (HDACs) in leukocytes. A PK-PD model using Jusko's indirect response model (IDR) with transition compartments (TC) was established to describe the time delay and magnitude of the gene expression elicited by UA. The PK-PD model provided reasonable fitting linking the plasma concentration of UA simultaneously with the PD response based on leukocyte mRNA expression. Overall, our results indicate that UA is effective at inducing various phase II DM/antioxidant genes and inhibiting pro-inflammatory genes in vivo. This PK-PD modeling approach may provide a conceptual framework for the future clinical evaluation of dietary chemopreventive agents in humans.

On the analysis of complex biological supply chains: From Process Systems Engineering to Quantitative Systems Pharmacology.

The use of models in biology has become particularly relevant as it enables investigators to develop a mechanistic framework for understanding the operating principles of living systems as well as in quantitatively predicting their response to both pathological perturbations and pharmacological interventions. This application has resulted in a synergistic convergence of systems biology and pharmacokinetic-pharmacodynamic modeling techniques that has led to the emergence of quantitative systems pharmacology (QSP). In this review, we discuss how the foundational principles of chemical process systems engineering inform the progressive development of more physiologically-based systems biology models.

The role of the hypothalamic-pituitary-adrenal axis in modulating seasonal changes in immunity.

Seasonal changes in environmental conditions are accompanied by significant adjustment of multiple biological processes. In temperate regions, the day fraction, or photoperiod, is a robust environmental cue that synchronizes seasonal variations in neuroendocrine and metabolic function. In this work, we propose a semimechanistic mathematical model that considers the influence of seasonal photoperiod changes as well as cellular and molecular adaptations to investigate the seasonality of immune function. Our model predicts that the circadian rhythms of cortisol, our proinflammatory mediator, and its receptor exhibit seasonal differences in amplitude and phase, oscillating at higher amplitudes in the winter season with peak times occurring later in the day. Furthermore, the reduced photoperiod of winter coupled with seasonal alterations in physiological activity induces a more exacerbated immune response to acute stress, simulated in our studies as the administration of an acute dose of endotoxin. Our findings are therefore in accordance with experimental data that reflect the predominance of a proinflammatory state during the winter months. These changes in circadian rhythm dynamics may play a significant role in the seasonality of disease incidence and regulate the diurnal and seasonal variation of disease symptom severity.

Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine.

Personalized medicine strives to deliver the 'right drug at the right dose' by considering inter-person variability, one of the causes for therapeutic failure in specialized populations of patients. Physiologically-based pharmacokinetic (PBPK) modeling is a key tool in the advancement of personalized medicine to evaluate complex clinical scenarios, making use of physiological information as well as physicochemical data to simulate various physiological states to predict the distribution of pharmacokinetic responses. The increased dependency on PBPK models to address regulatory questions is aligned with the ability of PBPK models to minimize ethical and technical difficulties associated with pharmacokinetic and toxicology experiments for special patient populations. Subpopulation modeling can be achieved through an iterative and integrative approach using an adopt, adapt, develop, assess, amend, and deliver methodology. PBPK modeling has two valuable applications in personalized medicine: (1) determining the importance of certain subpopulations within a distribution of pharmacokinetic responses for a given drug formulation and (2) establishing the formulation design space needed to attain a targeted drug plasma concentration profile. This review article focuses on model development for physiological differences associated with sex (male vs. female), age (pediatric vs. young adults vs. elderly), disease state (healthy vs. unhealthy), and temporal variation (influence of biological rhythms), connecting them to drug product formulation development within the quality by design framework. Although PBPK modeling has come a long way, there is still a lengthy road before it can be fully accepted by pharmacologists, clinicians, and the broader industry.

Human metabolic response to systemic inflammation: assessment of the concordance between experimental endotoxemia and clinical cases of sepsis/SIRS.

INTRODUCTION: Two recent, independent, studies conducted novel metabolomics analyses relevant to human sepsis progression; one was a human model of endotoxin (lipopolysaccharide (LPS)) challenge (experimental endotoxemia) and the other was community acquired pneumonia and sepsis outcome diagnostic study (CAPSOD). The purpose of the present study was to assess the concordance of metabolic responses to LPS and community-acquired sepsis. METHODS: We tested the hypothesis that the patterns of metabolic response elicited by endotoxin would agree with those in clinical sepsis. Alterations in the plasma metabolome of the subjects challenged with LPS were compared with those of sepsis patients who had been stratified into two groups: sepsis patients with confirmed infection and non-infected patients who exhibited systemic inflammatory response syndrome (SIRS) criteria. Common metabolites between endotoxemia and both these groups were individually identified, together with their direction of change and functional classifications. RESULTS: Response to endotoxemia at the metabolome level elicited characteristics that agree well with those observed in sepsis patients despite the high degree of variability in the response of these patients. Moreover, some distinct features of SIRS have been identified. Upon stratification of sepsis patients based on 28-day survival, the direction of change in 21 of 23 metabolites was the same in endotoxemia and sepsis survival groups. CONCLUSIONS: The observed concordance in plasma metabolomes of LPS-treated subjects and sepsis survivors strengthens the relevance of endotoxemia to clinical research as a physiological model of community-acquired sepsis, and gives valuable insights into the metabolic changes that constitute a homeostatic response. Furthermore, recapitulation of metabolic differences between sepsis non-survivors and survivors in LPS-treated subjects can enable further research on the development and assessment of rational clinical therapies to prevent sepsis mortality. Compared with earlier studies which focused exclusively on comparing transcriptional dynamics, the distinct metabolomic responses to systemic inflammation with or without confirmed infection, suggest that the metabolome is much better at differentiating these pathophysiologies. Finally, the metabolic changes in the recovering patients shift towards the LPS-induced response pattern strengthening the notion that the metabolic, as well as transcriptional responses, characteristic to the endotoxemia model represent necessary and "healthy" responses to infectious stimuli.

Pharmacokinetics and pharmacodynamics of 3,3'-diindolylmethane (DIM) in regulating gene expression of phase II drug metabolizing enzymes.

3,3'-Diindolylmethane (DIM) has been investigated as a potential anti-cancer chemopreventive agent in many preclinical and clinical studies. In this study, we sought to characterize the pharmacokinetics of DIM and to build a pharmacokinetic (PK) and pharmacodynamic (PD) model of the DIM-induced gene expression of phase II drug metabolizing enzymes (DME), which potentially links DIM's molecular effects to its in vivo chemopreventive efficacy. DIM (10 mg/kg) was administered intravenously (i.v.) to male Sprague-Dawley rats and blood samples were collected at selected time points for 48 h. The plasma concentration of DIM was determined using a validated HPLC method. The mRNA expression of NQO1, GSTP1 and UGT1A1 in blood lymphocytes was measured using quantitative PCR. An indirect response model was employed to relate the concentration of DIM to the expression of the genes NQO1, GSTP1 and UGT1A1, which were chosen as PD markers for DIM. After i.v. administration, the plasma concentration of DIM declined quickly, and the expression of target genes increased significantly, peaking at 1-2 h and then returning to basal levels after 24 h. The parameters in the PK-PD model were estimated. The PK-PD model aptly described the time delay and magnitude of gene expression induced by DIM. Our results indicate that DIM is effective at inducing various phase II DME, which are capable of detoxify carcinogens. This PK-PD modeling approach provides a framework for evaluating the acute effects of DIM or other similar drugs in clinical trials.

Bioinformatics analysis of transcriptional regulation of circadian genes in rat liver.

BACKGROUND: The circadian clock is a critical regulator of biological functions controlling behavioral, physiological and biochemical processes. Because the liver is the primary regulator of metabolites within the mammalian body and the disruption of circadian rhythms in liver is associated with severe illness, circadian regulators would play a strong role in maintaining liver function. However, the regulatory structure that governs circadian dynamics within the liver at a transcriptional level remains unknown. To explore this aspect, we analyzed hepatic transcriptional dynamics in Sprague-Dawley rats over a period of 24 hours to assess the genome-wide responses. RESULTS: Using an unsupervised consensus clustering method, we identified four major gene expression clusters, corresponding to central carbon and nitrogen metabolism, membrane integrity, immune function, and DNA repair, all of which have dynamics which suggest regulation in a circadian manner. With the assumption that transcription factors (TFs) that are differentially expressed and contain CLOCK:BMAL1 binding sites on their proximal promoters are likely to be clock-controlled TFs, we were able to use promoter analysis to putatively identify additional clock-controlled TFs besides PARF and RORA families. These TFs are both functionally and temporally related to the clusters they regulate. Furthermore, we also identified significant sets of clock TFs that are potentially transcriptional regulators of gene clusters. CONCLUSIONS: All together, we were able to propose a regulatory structure for circadian regulation which represents alternative paths for circadian control of different functions within the liver. Our prediction has been affirmed by functional and temporal analyses which are able to extend for similar studies.

Mathematical modeling of light-mediated HPA axis activity and downstream implications on the entrainment of peripheral clock genes.

In this work we propose a semimechanistic model that describes the photic signal transduction to the hypothalamic-pituitary-adrenal (HPA) axis that ultimately regulates the synchronization of peripheral clock genes (PCGs). Our HPA axis model predicts that photic stimulation induces a type-1 phase response curve to cortisol's profile with increased cortisol sensitivity to light exposure in its rising phase, as well as the shortening of cortisol's period as constant light increases (Aschoff's first rule). Furthermore, our model provides insight into cortisol's phase and amplitude dependence on photoperiods and reveals that cortisol maintains highest amplitude variability when it is entrained by a balanced schedule of light and dark periods. Importantly, by incorporating the links between HPA axis and PCGs we were able to investigate how cortisol secretion impacts the entrainment of a population of peripheral cells and show that disrupted light schedules, leading to blunted cortisol secretion, fail to synchronize a population of PCGs which further signifies the loss of circadian rhythmicity in the periphery of the body.

Time-restricted feeding and the realignment of biological rhythms: translational opportunities and challenges.

It has been argued that circadian dysregulation is not only a critical inducer and promoter of adverse health effects, exacerbating symptom burden, but also hampers recovery. Therefore understanding the health-promoting roles of regulating (i.e., restoring) circadian rhythms, thus suppressing harmful effects of circadian dysregulation, would likely improve treatment. At a critical care setting it has been argued that studies are warranted to determine whether there is any use in restoring circadian rhythms in critically ill patients, what therapeutic goals should be targeted, and how these could be achieved. Particularly interesting are interventional approaches aiming at optimizing the time of feeding in relation to individualized day-night cycles for patients receiving enteral nutrition, in an attempt to re-establish circadian patterns of molecular expression. In this short review we wish to explore the idea of transiently imposing (appropriate, but yet to be determined) circadian rhythmicity via regulation of food intake as a means of exploring rhythm-setting properties of metabolic cues in the context of improving immune response. We highlight some of the key elements associated with his complex question particularly as they relate to: a) stress and rhythmic variability; and b) metabolic entrainment of peripheral tissues as a possible intervention strategy through time-restricted feeding. Finally, we discuss the challenges and opportunities for translating these ideas to the bedside.