Biology subjects

Milo, T.

Publications and source records attributed to Milo, T..

9 recordsLinked to original sources

bioRxiv2026

Adrenal remodeling encodes stress axis memory to shape fentanyl seeking

The hypothalamic-pituitary-adrenal (HPA) axis is implicated in substance use disorders, yet the features of this circuit that contribute to addiction vulnerability remain unclear. Here, we show that fentanyl engages with different components of the HPA axis, and this engagement varies across timescales. Within minutes, fentanyl activated corticotropin-releasing hormone neurons in the paraventricular nucleus of the hypothalamus (PVNCRH) and transiently increased adrenocorticotropic hormone (ACTH) levels. Hours to days later, fentanyl suppressed PVN activity, reduced adrenal size, and impaired corticosterone release. Conversely, ACTH-induced adrenal hypertrophy, increased corticosterone, suppressed fentanyl-evoked PVNCRH activity, and elevated fentanyl-seeking behavior. These findings identify adrenal remodeling as a physiological record of stress history that shapes neuroendocrine and behavioral responses to fentanyl, mechanistically linking stress and addiction while revealing treatment targets for opioid dependence.

neuroscience↗

bioRxiv2024

Unifying regulatory motifs in endocrine circuits

In this study we identify unifying design principles in human endocrine systems. We find that 43 hormone systems, controlling diverse physiological functions, fall into 5 classes of circuits with shared structure - thus only a small number of the possible circuits actually occur. Each class uses a different regulatory logic to perform specific dynamical functions, such as homeostasis, acute input-output response or adjustable set points. The circuits employ interactions on two timescales: hormone secretion on the scale of minutes-hours and growth and shrinkage of endocrine gland mass on the scale of months, which impacts the amount of hormone the glands secrete. This two-timescale principle recurs in several classes of circuits, including the most complex class, which has an intermediate gland, the pituitary. We analyze the pituitary circuit in detail and find tradeoffs between endocrine amplification, buffering of hypersecreting tumors, and rapid response times. These unifying principles of regulation build a foundation for systems endocrinology.

systems biology↗

bioRxiv2024

Hormone circuit analysis explains why most HPA drugs fail for mood disorders and predicts the few that work

Elevated cortisol causes morbidity in chronic stress and mood disorders, including metabolic and cardiovascular diseases. There is therefore interest in developing drugs that lower cortisol by targeting its endocrine pathway, the hypothalamic-pituitary-adrenal (HPA) axis. Several promising HPA-modulating drugs have, however, failed to lower long-term cortisol in mood disorders such as major depressive disorder despite their effectiveness in situations where high cortisol is caused by a tumor (Cushings syndrome). Why these drugs failed is not well understood. Here we use a mathematical model of the HPA axis to show that the pituitary and the adrenal glands compensate for the effect of drugs by adjusting their functional mass, a feedback compensation that is absent in Cushing tumors. To find potential drug targets, we carried out a systematic in silico analysis of points of intervention in the HPA axis. We find that only two interventions that target corticotropin-releasing hormone (CRH) can lower long-term cortisol. Other drug targets either fail to lower cortisol due to gland-mass compensation or lower cortisol but harm other aspects of the HPA axis. Thus, we identify potential drug targets, including CRH-neutralizing antibodies and CRH-synthesis inhibitors, for lowering long-term cortisol in mood disorders and in those suffering from chronic stress. More generally, this study indicates that understanding the slow compensatory mechanisms in endocrine axes can be crucial in order to prioritize drug targets.

systems biology↗

bioRxiv2023

Longitudinal hair cortisol in bipolar disorder and a mechanism based on HPA dynamics

Bipolar disorder (BD) is a dynamic disease in which mania, depression and mixed states vary on a timescale of months to years. BD patients characteristically exhibit elevated levels of the hormone cortisol. Persistently elevated cortisol can also cause mood episodes in a substantial fraction of the general population. Although BD is a dynamic disease that is related to cortisol, longitudinal cortisol dynamics in BD have rarely been studied. Here we use hair to measure past cortisol where each cm of hair corresponds to a month of growth. Cortisol was measured in 12 cm hair samples from people with BD (n=26) and controls (n=59), corresponding to one year of cortisol data. We found that hair cortisol exhibited a frequency spectrum with enhanced year-scale fluctuations whose amplitude was about 4-fold higher on average in BD compared to controls. Cortisol in the proximal 2 cm hair segment correlated with mood scales that report on mood in the past two months. In line with the notion that cortisol correlates with mood, we find that the mean frequency spectrum of depression (n=266) and mania (n=273) scores from a large longitudinal study of BD is similar to the hair cortisol spectrum from the present cohort. Taken together, these results suggest a mechanism for BD as the intersection of two neuropsychological traits: cortisol-induced mood episodes (CIM) and high emotional reactivity (ER). High ER causes fluctuations in which cortisol is elevated for months, as shown by a mathematical model of the hypothalamic-pituitary-adrenal (HPA) axis that regulates cortisol. In individuals with CIM, the magnitude of these persistent cortisol fluctuations can be high enough to trigger mood episodes. Thus, this study combines longitudinal cortisol measurements and mathematical modeling to provide a potential mechanistic link between the timescales of cortisol and moods in BD.

systems biology↗

bioRxiv2023

Unifying design principles of endocrine gland mass and its regulatory circuits

Hormones are regulatory molecules that impact physiological functions. Much is known about individual hormones, but general rules that connect the regulatory logic of different hormone systems are limited. In this study, we analyzed a range of human hormone systems using a mathematical approach to integrate knowledge on endocrine cells, target tissues and regulation, to uncover unifying principles and regulatory circuits. We find that the number of cells in an endocrine gland is proportional to the number of cells in its target tissues, as one single endocrine cell serves approximately 2000 target cells. We identified five classes of regulatory circuits, each has specific regulatory functions such as homeostasis or allostasis. The most complex class includes an intermediate gland, the pituitary, which can otherwise be considered redundant and exposes to fragilities. We suggest a tradeoff: with the price of fragilities comes advantages -amplification, buffering of hypersecreting tumors, and faster response times. By elucidating these unifying principles and circuits, this study deepens our understanding of the control of endocrine processes and builds the foundation for systems endocrinology.

physiology↗

bioRxiv2023

Excitable dynamics of flares and relapses in autoimmune diseases

Many autoimmune diseases show flares in which symptoms erupt and then decline. A prominent example is multiple sclerosis (MS) in its relapsing-remitting phase. Mathematical models attempting to capture the flares in multiple sclerosis have often been oscillatory in nature, assuming a regular pattern of symptom flare-ups and remissions. However, this fails to account for the non-periodic nature of flares, which can appear at seemingly random intervals. Here we propose that flares resemble excitable dynamics triggered by stochastic events and show that a minimal mathematical model of autoimmune cells and inhibitory regulatory cells can provide such excitability. In our model, autoimmune response releases antigens that cause autoimmune cells to expand in a positive feedback loop, while regulatory cells inhibit the autoimmune cells in a negative feedback loop. The model can quantitatively explain the decline of MS relapses during pregnancy and their postpartum surge based on lymphocyte dynamics, as well as the decline in MS relapses with age. The model also points to potential therapeutic targets and predicts that even small modulation of regulatory T cell production, removal or activity can have a large effect on relapse rate. Excitable dynamics may underlie flares and relapses found across autoimmune diseases, thus providing an understanding that may help improve treatment strategies.

systems biology↗

bioRxiv2022

A cell circuit approach to dissect fibroblast-macrophage interactions in the tumor microenvironment

The tumor microenvironment (TME) is composed of various nonmalignant cell types that interact with each other and with cancer cells, impacting all aspects of cancer biology. The TME is complex and heterogeneous, and thus simplifying systems and concepts are needed. Here we provide a tractable experimental system and powerful mathematical circuit concepts to identify the main molecular interactions that govern the composition of the TME. We focus on two major components of the TME - cancer associated fibroblasts (CAFs) and tumor associated macrophages (TAMs), define their interactions and verify our predictions in mouse and human breast cancer. We measure the population dynamics starting from many initial conditions of co-cultures of macrophages and organ-derived fibroblasts from mammary, lung, and fat, and explore the effects of cancer-conditioned medium on the circuits. We define the circuits and their inferred parameters from the data using a mathematical approach, and quantitatively compare the cell circuits in each condition. We find that while the homeostatic steady-states are similar between the organs, the cancer-conditioned medium profoundly changes the circuit. Fibroblasts in all contexts depend on autocrine secretion of growth factors whereas macrophages are more dependent on external cues, including paracrine growth factors secreted from fibroblasts and cancer cells. Transcriptional profiling reveals the molecular underpinnings of the cell circuit interactions and the primacy of the fibroblast autocrine loop. The same fibroblast growth factors are shared by the co-cultures and mouse and human breast cancer. The cell circuit approach thus provides a quantitative account of cell interactions in the cancer microenvironment.

cancer biology↗

bioRxiv2020

Human hormone seasonality

Hormones control the major biological functions of stress response, growth, metabolism and reproduction. In animals these hormones show pronounced seasonality, with different set-points for different seasons. In humans, the seasonality of these hormones remains unclear, due to a lack of datasets large enough to discern common patterns and cover all hormones. Here, we analyze an Israeli health record on 46 million person-years, including millions of hormone blood tests. We find clear seasonal patterns: the effector hormones peak in winter-spring, whereas most of their upstream regulating pituitary hormones peak only months later, in summer. This delay of months is unexpected because known delays in the hormone circuits last hours. We explain the precise delays and amplitudes by proposing and testing a mechanism for the circannual clock: the gland masses grow with a timescale of months due to trophic effects of the hormones, generating a feedback circuit with a natural frequency of about a year that can entrain to the seasons. Thus, humans may show coordinated seasonal set-points with a winter-spring peak in the growth, stress, metabolism and reproduction axes.

systems biology↗

bioRxiv2020

Hormonal dysregulation after prolonged HPA axis activation can be explained by changes of adrenal and corticotroph masses

Stress activates a complex network of hormones known as the Hypothalamic-Pituitary-Adrenal (HPA) axis. The HPA axis is dysregulated in chronic stress and psychiatric disorders, but the origin of this dysregulation is unclear and cannot be explained by current HPA models. To address this, we developed a new mathematical model for the HPA axis that incorporates changes in the total functional mass of the HPA hormone-secreting glands. The mass changes are caused by the HPA hormones which act as growth factors for the glands in the axis. We find that the HPA axis shows the property of dynamical compensation, where gland masses adjust over weeks to buffer variation in physiological parameters. These mass changes explain the experimental findings on dysregulation of cortisol and ACTH dynamics in alcoholism, anorexia and postpartum. Dysregulation occurs for a wide range of parameters, and is exacerbated by impaired glucocorticoid receptor (GR) feedback, providing an explanation for the implication of GR in mood disorders. These findings suggest that gland-mass dynamics may play an important role in the pathophysiology of stress-related disorders. Author SummaryThe HPA axis is a neuroendocrine axis that is activated in response to stressors. The classical description of this axis includes three hormones that act in a cascade, with the final hormone cortisol inhibiting the two upstream hormones, ACTH and CRH. This classical picture has timescales of hours due to hormone half-lives, and cannot explain phenomena on the scale of weeks to months associated with this axis, such as the dysregulation observed in depression, alcohol addiction, postpartum, and other conditions. Here, we use a minimal-model approach to add to the classical model two known interactions in which CRH and ACTH not only regulate downstream hormones, but also act as growth factors for the cells that secrete these hormones. This creates a physiological circuit that can maintain total cell mass and buffer parameter changes. It has a fragility in which after prolonged stress, the total cell functional masses grow and take weeks to return to baseline. This is sufficient to explain the specific dynamics of hormone dysregulation found in several contexts. It also quantifies the effect of the cortisol (glucocorticoid) receptor on resilience to prolonged stress. Our findings suggest that interactions between hormones and cell functional mass may play an important role in HPA axis regulation on the timescale of weeks to months. Synopsis O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY The HPA axis helps the body adapt to stress, but becomes dysregulated after prolonged activation, with clinical consequences. The origin of this dysregulation is unclear. We provide a mechanism for dysregulation based on the effect of the HPA hormones as growth factors for their downstream glands. O_LIA mathematical model that includes gland functional mass dynamics, introduces a new slow timescale of weeks to the HPA axis; previous models had only fast timescales of hours. C_LIO_LIThe gland masses grow during prolonged activation, providing dynamical compensation, and recover with overshoots over weeks after withdrawal of activation. C_LIO_LIThese overshoots are sufficient to explain the observed HPA dysregulation in pathological conditions, and clarify the role of glucocorticoid receptors in resilience to prolonged stress. C_LI

physiology↗