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.
Synchronization of biological functions to environmental signals enables organisms to anticipate and appropriately respond to daily external fluctuations and is critical to the maintenance of homeostasis. Misalignment of circadian rhythms with environmental cues is associated with adverse health outcomes. Cortisol, the downstream effector of hypothalamic-pituitary-adrenal (HPA) activity, facilitates synchronization of peripheral biological processes to the environment. Cortisol levels exhibit substantial seasonal rhythmicity, with peak levels occurring during the short-photoperiod winter months and reduced levels occurring in the long-photoperiod summer season. Seasonal changes in cortisol secretion could therefore alter its entraining capabilities, resulting in a season-dependent modification in the alignment of biological activities with the environment. We develop a mathematical model to investigate the influence of photoperiod-induced seasonal differences in the circadian rhythmicity of the HPA axis on the synchronization of the peripheral circadian clock and cell cycle in a heterogeneous cell population. Model simulations predict that the high-amplitude cortisol rhythms in winter result in the greatest entrainment of peripheral oscillators. Furthermore, simulations predict a circadian gating of the cell cycle with respect to the expression of peripheral clock genes. Seasonal differences in cortisol rhythmicity are also predicted to influence mitotic synchrony, with a high-amplitude winter rhythm resulting in the greatest synchrony and a shift in timing of the cell cycle phases, relative to summer. Our results highlight the primary interactions among the HPA axis, the peripheral circadian clock, and the cell cycle and thereby provide an improved understanding of the implications of circadian misalignment on the synchronization of peripheral regulatory processes.
In this short review, we discuss evidence supporting the modulation of peripheral circadian systems as a therapeutic strategy for rheumatoid arthritis (RA). We first review the role of proinflammatory cytokines and oxidative stress, two of the primary mediators of chronic inflammation in RA, and their regulation by circadian clock machinery. We further highlight the role of environmental and metabolic signals in regulating the central and peripheral circadian clocks, with an emphasis on seasonal variations in photoperiod and rhythmic metabolic input, respectively. Finally, we hypothesize that the entrainment and realignment of peripheral clock rhythms have the ability to modulate these mediators, improving clinical outcomes in RA patients. Our discussion emphasizes the use of light therapy and time-restricted feeding for entraining peripheral clocks either via the entrainment of the central circadian clock in suprachiasmatic nuclei (SCN) or directly by uncoupling the peripheral circadian clocks from SCN. In doing so, we highlight the use of nonpharmacologic interventions as a potential strategy for improving clinical outcomes in chronic inflammatory conditions such as RA.
Cortisol dynamics are governed by the integration of influences from the suprachiasmatic nucleus (SCN), the hypothalamic-pituitary-adrenal (HPA) axis, and metabolic enzymes, such as the 11β-hydroxysteroid dehydrogenase (HSD) family, which are highly expressed in hepatic and renal tissue. The coordinated regulation of cortisol dynamics is essential for the maintenance of a healthy state, and aberrant cortisol circadian rhythms are associated with various pathophysiological conditions. The duration of the light-dark cycle, or photoperiod, which regulates SCN activity, varies seasonally, and the shorter photoperiod winter season is associated with elevated cortisol levels, peak inflammatory disease incidence, and symptom exacerbation. Elevated expression and activity of 11β-HSD1 protein, assumed to also occur during the winter, have been allied with numerous inflammatory conditions. A comprehensive understanding of the communication between the underlying regulatory mechanisms of cortisol as well as how changes in their activity could lead to the development of disease is yet to be elucidated. In this work, we propose the use of a semimechanistic mathematical model to explore the impact of the hepato-hypothalamic-pituitary-adrenal-renal axis in modulating neuroendocrine-immune system dynamics. Our model predicts the predominance of a winter proinflammatory state and that genetic variations could alter 11β-HSD enzyme functionality, rendering certain subpopulations more susceptible to disease as a consequence of HPA axis dysregulation.
In relapsed and refractory multiple myeloma (RRMM), there are few treatment options once patients progress from the established standard of care. Several bispecific T-cell engagers (TCE) are in clinical development for multiple myeloma (MM), designed to promote T-cell activation and tumor killing by binding a T-cell receptor and a myeloma target. In this study we employ both computational and experimental tools to investigate how a novel trispecific TCE improves activation, proliferation, and cytolytic activity of T-cells against MM cells. In addition to binding CD3 on T-cells and CD38 on tumor cells, the trispecific binds CD28, which serves as both co-stimulation for T-cell activation and an additional tumor target. We have established a robust rule-based quantitative systems pharmacology (QSP) model trained against T-cell activation, cytotoxicity, and cytokine data, and used it to gain insight into the complex dose response of this drug. We predict that CD3-CD28-CD38 killing capacity increases rapidly in low dose levels, and with higher doses, killing plateaus rather than following the bell-shaped curve typical of bispecific TCEs. We further predict that dose–response curves are driven by the ability of tumor cells to form synapses with activated T-cells. When competition between cells limits tumor engagement with active T-cells, response to therapy may be diminished. We finally suggest a metric related to drug efficacy in our analysis—“effective” receptor occupancy, or the proportion of receptors engaged in synapses. Overall, this study predicts that the CD28 arm on the trispecific antibody improves efficacy, and identifies metrics to inform potency of novel TCEs.
We combine optical scatter imaging with principal component analysis (PCA) to classify apoptosis-competent Bax/Bak-expressing, and apoptosis resistant Bax/Bak-null immortalized baby mouse kidney cells. We apply PCA to 100 stacks each containing 236 dark-field cell images filtered with an optically implemented Gabor filter with period between 0.3 and 2.9 μm. Each stack yields an "eigencell" image corresponding to the first principal component obtained at one of the 100 Gabor filter periods used. At each filter period, each cell image is multiplied by (projected onto) the eigencell image. A Feature Matrix consisting of 236 × 100 scalar values is thus constructed with significantly reduced dimension compared to the initial dataset. Utilizing this Feature Matrix, we implement a supervised linear discriminant analysis and classify successfully the Bax/Bak-expressing and Bax/Bak-null cells with 94.7% accuracy and an area under the curve (AUC) of 0.993. Applying a feature selection algorithm further reveals that the Gabor filter period ranges most significant for the classification correspond to both large (likely nuclear) features as well as small sized features (likely organelles present in the cytoplasm). Our results suggest that cells with a genetic defect in their apoptosis pathway can be differentiated from their normal counterparts by label-free multi-parametric optical scatter data.
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