Modeling physiologic variability in human endotoxemia

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.

Detecting Pathogen Exposure During the Non-symptomatic Incubation Period Using Physiological Data: Proof of Concept in Non-human Primates

Background and Objectives: Early warning of bacterial and viral infection, prior to the development of overt clinical symptoms, allows not only for improved patient care and outcomes but also enables faster implementation of public health measures (patient isolation and contact tracing). Our primary objectives in this effort are 3-fold. First, we seek to determine the upper limits of early warning detection through physiological measurements. Second, we investigate whether the detected physiological response is specific to the pathogen. Third, we explore the feasibility of extending early warning detection with wearable devices.

Research Methods: For the first objective, we developed a supervised random forest algorithm to detect pathogen exposure in the asymptomatic period prior to overt symptoms (fever). We used high-resolution physiological telemetry data (aortic blood pressure, intrathoracic pressure, electrocardiograms, and core temperature) from non-human primate animal models exposed to two viral pathogens: Ebola and Marburg (N = 20). Second, to determine reusability across different pathogens, we evaluated our algorithm against three independent physiological datasets from non-human primate models (N = 13) exposed to three different pathogens: Lassa and Nipah viruses and Y. pestis. For the third objective, we evaluated performance degradation when the algorithm was restricted to features derived from electrocardiogram (ECG) waveforms to emulate data from a non-invasive wearable device.

Results: First, our cross-validated random forest classifier provides a mean early warning of 51 ± 12 h, with an area under the receiver-operating characteristic curve (AUC) of 0.93 ± 0.01. Second, our algorithm achieved comparable performance when applied to datasets from different pathogen exposures – a mean early warning of 51 ± 14 h and AUC of 0.95 ± 0.01. Last, with a degraded feature set derived solely from ECG, we observed minimal degradation – a mean early warning of 46 ± 14 h and AUC of 0.91 ± 0.001.

Conclusion: Under controlled experimental conditions, physiological measurements can provide over 2 days of early warning with high AUC. Deviations in physiological signals following exposure to a pathogen are due to the underlying host’s immunological response and are not specific to the pathogen. Pre-symptomatic detection is strong even when features are limited to ECG-derivatives, suggesting that this approach may translate to non-invasive wearable devices.

Mathematical modeling of mammalian circadian clocks affecting drug and disease responses

To align with daily environmental changes, most physiological processes in mammals exhibit a time-of-day rhythmicity. This circadian control of physiology is intrinsically driven by a cell-autonomous clock gene network present in almost all cells of the body that drives rhythmic expression of genes that regulate numerous molecular and cellular processes. Accordingly, many aspects of pharmacology and toxicology also oscillate in a time-of-day manner giving rise to diverse effects on pharmacokinetics and pharmacodynamics. Genome-wide studies and mathematical modeling are available tools that have significantly improved our understanding of these nonlinear aspects of physiology and therapeutics. In this manuscript current literature and our prior work on the model-based approaches that have been used to explore circadian genomic systems of mammals are reviewed. Such basic understanding and having an integrative approach may provide new strategies for chronotherapeutic drug treatments and yield new insights for the restoration of the circadian system when altered by diseases.

Asymmetry in Signal Oscillations Contributes to Efficiency of Periodic Systems

Oscillations are an important feature of cellular signaling that result from complex combinations of positive- and negative-feedback loops. The encoding and decoding mechanisms of oscillations based on amplitude and frequency have been extensively discussed in the literature in the context of intercellular and intracellular signaling. However, the fundamental questions of whether and how oscillatory signals offer any competitive advantages-and, if so, what-have not been fully answered. We investigated established oscillatory mechanisms and designed a study to analyze the oscillatory characteristics of signaling molecules and system output in an effort to answer these questions. Two classic oscillators, Goodwin and PER, were selected as the model systems, and corresponding no-feedback models were created for each oscillator to discover the advantage of oscillating signals. Through simulating the original oscillators and the matching no-feedback models, we show that oscillating systems have the capability to achieve better resource-to-output efficiency, and we identify oscillatory characteristics that lead to improved efficiency.

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.

Physiologic variability at the verge of systemic inflammation: multiscale entropy of heart rate variability is affected by very low doses of endotoxin

INTRODUCTION

Human injury or infection induces systemic inflammation with characteristic neuroendocrine responses. Fluctuations in autonomic function during inflammation are reflected by beat-to-beat variation in heart rate, termed heart rate variability (HRV). In the present study, we determine threshold doses of endotoxin needed to induce observable changes in markers of systemic inflammation, investigate whether metrics of HRV exhibit a differing threshold dose from other inflammatory markers, and investigate the size of data sets required for meaningful use of multiscale entropy (MSE) analysis of HRV.

METHODS

Healthy human volunteers (n = 25) were randomized to receive placebo (normal saline) or endotoxin/lipopolysaccharide (LPS): 0.1, 0.25, 0.5, 1.0, or 2.0 ng/kg administered intravenously. Vital signs were recorded every 30 min for 6 h and then at 9, 12, and 24 h after LPS. Blood samples were drawn at specific time points for cytokine measurements. Heart rate variability analysis was performed using electrocardiogram epochs of 5 min. Multiscale entropy for HRV was calculated for all dose groups to scale factor 40.

RESULTS

The lowest significant threshold dose was noted in core temperature at 0.25 ng/kg. Endogenous tumor necrosis factor α and interleukin 6 were significantly responsive at the next dosage level (0.5 ng/kg) along with elevations in circulating leukocytes and heart rate. Responses were exaggerated at higher doses (1 and 2 ng/kg). Time domain and frequency domain HRV metrics similarly suggested a threshold dose, differing from placebo at 1.0 and 2.0 ng/kg, below which no clear pattern in response was evident. By applying repeated-measures analysis of variance across scale factors, a significant decrease in MSE was seen at 1.0 and 2.0 ng/kg by 2 h after exposure to LPS. Although not statistically significant below 1.0 ng/kg, MSE unexpectedly decreased across all groups in an orderly dose-response pattern not seen in the other outcomes.

CONCLUSIONS

By using repeated-measures analysis of variance across scale factors, MSE can detect autonomic change after LPS challenge in a group of 25 subjects using electrocardiogram epochs of only 5 min and entropy analysis to scale factor of only 40, potentially facilitating MSE's wider use as a research tool or bedside monitor. Traditional markers of inflammation generally exhibit threshold dose behavior. In contrast, MSE's apparent continuous dose-response pattern, although not statistically verifiable in this study, suggests a potential subclinical harbinger of infectious or other insult. The possible derangement of autonomic complexity prior to or independent of the cytokine surge cannot be ruled out. Future investigation should focus on confirmation of overt inflammation following observed decreases in MSE in a clinical setting.

Integrated transcriptional and metabolic profiling in human endotoxemia

In this meta-study, we aimed to integrate biological insights gained from two levels of -omics analyses on the response to systemic inflammation induced by lipopolysaccharide in humans. We characterized the interplay between plasma metabolite compositions and transcriptional response of leukocytes through integration of transcriptomics with plasma metabonomics. We hypothesized that the drastic changes in the immediate environment of the leukocytes might have an adaptive effect on shaping their transcriptional response in conjunction with the initial inflammatory stimuli. Indeed, we observed that leukocytes, most notably, tune the activity of lipid- and protein-associated processes at the transcriptional level in accordance with the fluctuations in metabolite compositions of surrounding plasma. A closer look into the transcriptional control of only metabolic pathways uncovered alterations in bioenergetics and defenses against oxidative stress closely associated with mitochondrial dysfunction and shifts in energy production observed during inflammatory processes.

Day-night variation in heart rate variability changes induced by endotoxaemia in healthy volunteers

BACKGROUND

Morbidity and mortality in response to sepsis may be dependent on clock time for the initiation of sepsis. Endotoxaemia, an experimental model for systemic inflammation, induces alterations in sympatico-vagal balance in the autonomic nervous system (ANS). The activity of sympathetic and parasympathetic activity can be estimated by measuring heart rate variability (HRV). Based on the intimate link between ANS and the inflammatory response, we hypothesized, that HRV changes seen during endotoxaemia would be different based on time of the day the endotoxaemia is initiated. We investigated day/night variation in endotoxaemia-induced changes in HRV.

METHODS

A randomized, crossover study with 12 healthy men (age 18-31) was conducted. Endotoxaemia were induced by lipopolysaccharide (LPS) endotoxin 0.3 ng/kg b.w. in two visits (day visit and night visit). At the day visit, endotoxaemia were induced at 12:00 h, and at the night visit it was induced at 24:00 h. Holter recordings were started 1 h before administration of LPS, and continued for 10 h. Time-domain and frequency-domain parameters of HRV were analysed.

RESULTS

A total of nine persons finished the study with valid recordings. Endotoxaemia at both night and day resulted in a significant depression in HRV parameters high-frequency power (HF), low-frequency power (LF), standard deviation of normal-to-normal (NN) intervals, root mean square of successive differences and proportion of NN50 divided by total number of NNs (P<0.001). The ratio LF/HF and mean heart rate significantly increased by endotoxaemia (P<0.001). At night-time endotoxaemia, a more pronounced depression of LF, HF and SDNN (P<0.01) and a more pronounced increase in the ratio of LF/HF and mean heart rate (P<0.01) occurred compared with day-time endotoxaemia.

CONCLUSION

Endotoxaemia induced changes in HRV exhibit a day-night difference. This difference may have clinical consequences in patients with sepsis.

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.

Electronic supplementary material

The online version of this article (doi:10.1186/s13054-015-0783-2) contains supplementary material, which is available to authorized users.

Circadian characteristics of permissive and suppressive effects of cortisol and their role in homeostasis and the acute inflammatory response

In this work we explore a semi-mechanistic model that considers cortisol's permissive and suppressive effects through the regulation of cytokine receptors and cytokines respectively. Our model reveals the proactive role of cortisol during the resting period and its reactive character during the body's activity phase. Administration of an acute LPS dose during the night, when cortisol's permissive effects are higher than suppressive, leads to increased cytokine levels compared to LPS administration at morning when cortisol's suppressive effects are higher. Interestingly, our model presents a hysteretic behavior where the relative predominance of permissive or suppressive effects results not only from cortisol levels but also from the previous states of the model. Therefore, for the same cortisol levels, administration of an inflammatory stimulus at cortisol's ascending phase, that follows a time period where cytokine receptor expression is elevated ultimately sensitizing the body for the impending stimulus, leads to higher cytokine expression compared to administration of the same stimulus at cortisol's descending phase.

On heart rate variability and autonomic activity in homeostasis and in systemic inflammation

Analysis of heart rate variability (HRV) is a promising diagnostic technique due to the noninvasive nature of the measurements involved and established correlations with disease severity, particularly in inflammation-linked disorders. However, the complexities underlying the interpretation of HRV complicate understanding the mechanisms that cause variability. Despite this, such interpretations are often found in literature. In this paper we explored mathematical modeling of the relationship between the autonomic nervous system and the heart, incorporating basic mechanisms such as perturbing mean values of oscillating autonomic activities and saturating signal transduction pathways to explore their impacts on HRV. We focused our analysis on human endotoxemia, a well-established, controlled experimental model of systemic inflammation that provokes changes in HRV representative of acute stress. By contrasting modeling results with published experimental data and analyses, we found that even a simple model linking the autonomic nervous system and the heart confound the interpretation of HRV changes in human endotoxemia. Multiple plausible alternative hypotheses, encoded in a model-based framework, equally reconciled experimental results. In total, our work illustrates how conventional assumptions about the relationships between autonomic activity and frequency-domain HRV metrics break down, even in a simple model. This underscores the need for further experimental work towards unraveling the underlying mechanisms of autonomic dysfunction and HRV changes in systemic inflammation. Understanding the extent of information encoded in HRV signals is critical in appropriately analyzing prior and future studies.