Biology subjects

Alon, U.

Publications and source records attributed to Alon, U..

52 records · Page 3Linked to original sources

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

Cancer-associated fibroblast compositions change with breast cancer progression linking S100A4 and PDPN ratios with clinical outcome

Tumors are supported by cancer-associated fibroblasts (CAFs). CAFs are heterogeneous and carry out distinct cancer-associated functions. Understanding the full repertoire of CAFs and their dynamic changes could improve the precision of cancer treatment. CAFs are usually analyzed at a single time-point using specific markers, and it is therefore unclear whether CAFs display plasticity as tumors evolve. Here, we analyze thousands of CAFs using index and transcriptional single-cell sorting, at several time-points along breast tumor progression in mice, uncovering distinct subpopulations. Strikingly, the transcriptional programs of these subpopulations change over time and in metastases, transitioning from an immune-regulatory program to wound healing and antigen-presentation programs, indicating that CAFs and their functions are dynamic. Two main CAF subpopulations are also found in human breast tumors, where their ratio is associated with disease outcome across subtypes, and is particularly correlated with BRCA mutations in triple-negative breast cancer. These findings indicate that the repertoire of CAFs changes over time in breast cancer progression, with direct clinical implications.

cancer 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↗

bioRxiv2019

Endocrine autoimmune disease as a fragility of immune-surveillance against hypersecreting mutants

Many endocrine organs show prevalent autoimmune diseases (AID) such as type-1-diabetes and Hashimotos-thyroiditis. The fundamental origins of these diseases is unclear. Here we address AID from the viewpoint of feedback control. Endocrine tissues maintain their mass by feedback-loops that balance cell proliferation and removal according to input signals related to the hormone function. Such feedback is unstable to mutant cells that mis-sense the signal, and therefore hyper-proliferate and hyper-secrete the hormone. We hypothesize that in order to prevent these mutants from expanding, each organ has a dedicated autoimmune surveillance of hyper-secreting mutants (ASHM), in which hyper-secreting cells are preferentially eliminated, at the cost of a fragility to AID. ASHM correctly predicts the identity of the self-antigens and the presence of T-cells against these self-antigens in healthy individuals. It offers a predictive theory for which tissues get frequent AID, and which do not and instead show frequent mutant-expansion disease (e.g. hyperparathyroidism).

immunology↗

bioRxiv2019

Principles of Cell Circuits for Tissue Repair and Fibrosis

Tissue-repair is a protective response after injury, but repetitive or prolonged injury can lead to fibrosis, a pathological state of excessive scarring. To pinpoint the dynamic mechanisms underlying fibrosis, it is important to understand the principles of the cell circuits that carry out tissue-repair. In this study, we establish a cell-circuit framework for the myofibroblast-macrophage circuit in wound-healing, including the accumulation of scar-forming extracellular matrix. We find that fibrosis results from multistability between three outcomes, which we term hot fibrosis characterized by many macrophages, cold fibrosis lacking macrophages, and normal wound-healing. The cell-circuit framework clarifies several unexplained phenomena including the paradoxical effect of macrophage depletion, the limited time-window in which removing inflammation leads to healing, the effects of cellular senescence, and why scar maturation takes months. We define key parameters that control the transition from healing to fibrosis, which may serve as potential targets for therapeutic reduction of fibrosis.

systems biology↗

bioRxiv2019

Multidimensional extracellular-matrix analysis reveals a silent pre-clinical signature in inflammatory disease

Identification of early processes leading to complex tissue pathologies, such as inflammatory bowel diseases, poses a major scientific and clinical challenge that is imperative for improved diagnosis and treatment. Most studies of inflammation onset focus on cellular processes and signaling molecules, while overlooking the environment in which they take place, the continuously remodeled extracellular matrix. In this study, we used colitis models for investigating extracellular-matrix dynamics during disease onset, while treating the matrix as a complete and defined entity. Through the analysis of matrix structure, stiffness and composition, we unexpectedly revealed that even prior to the first clinical symptoms, the colon displays its own unique extracellular-matrix signature and found specific markers of clinical potential, which were also validated in human subjects. We also show that the emergence of this pre-symptomatic matrix is mediated by sub-clinical infiltration of neutrophils and monocytes bearing remodeling enzymes. Remarkably, whether the inflammation is chronic or acute, its matrix signature converges at pre-symptomatic states. We suggest that the existence of a pre-symptomatic extracellular-matrix is general and relevant to a wide range of diseases.

systems biology↗

bioRxiv2018

Senescent cells and the dynamics of aging

Genetically-identical organisms age at different rates, but the reason for this is unclear. The rate of aging is due to the accumulation of damage, such as senescent cells (SnC) which are a major driver of aging in mammals, but the accumulation dynamics of SnC are unknown. Here, we combine theory and experiments in mice and find that SnC are rapidly produced and removed with a half-life of days in young mice. In old mice SnC half-life grows to weeks, because SnC saturate their own removal process. This saturation leads to persistent fluctuations known as critical-slowing-down: fluctuations are amplified to long-lasting differences in SnC between individuals at old ages. Rapid turnover with saturating removal is a general framework that may apply to any aging-related form of damage, and can quantitatively explain the demographic laws of mortality in worms, flies, mice and humans.

systems biology↗

bioRxiv2018

Identity domains in complex behavior: Toward a biology of personality

Personality traits offer considerable insight into the biological basis of individual differences. However, existing approaches toward understanding personality across species rely on subjective criteria and limited sets of behavioral readouts, resulting in noisy and often inconsistent outcomes. Here, we introduce a mathematical framework for studying individual differences along dimensions with maximum consistency and discriminative power. We validate this framework in mice, using data from a system for high-throughput longitudinal monitoring of group-housed mice that yields a variety of readouts from all across an individuals behavioral repertoire. We describe a set of stable traits that capture variability in behavior and gene expression in the brain, allowing for better informed mechanistic investigations into the biology of individual differences.

animal behavior and cognition↗

bioRxiv2018

Universal cancer tasks, evolutionary tradeoffs, and the functions of driver mutations

Recent advances have led to an appreciation of the vast molecular diversity of cancer. Detailed data has enabled powerful methods to sort tumors into groups with benefits for prognosis and treatment. We are still missing, however, a general theoretical framework to understand the diversity of tumor gene-expression and mutations. To address this, we present a framework based on multi-task evolution theory, using the fact that tumors evolve in the body, and that tumors are faced with multiple tasks that contribute to their fitness. In accordance with the theory, we find that tradeoff between tasks constrains tumor gene-expression to a continuum bounded by a polyhedron. The vertices of the polyhedron are gene-expression profiles each specializing in one task, allowing the tasks to be identified. We find five universal cancer tasks across tissue-types: cell-division, biomass & energy, lipogenesis, immune-interaction and invasion & tissue remodeling. Tumors whose gene-expression lies close to a vertex are task specialists. We find evidence that such specialists are more sensitive to drugs that interfere with this task. We find that driver mutations, but not passenger mutations, tune gene-expression towards specialization in specific tasks. This approach can integrate additional types of molecular data into a theoretically-based framework for understanding tumor diversity.

cancer biology↗

bioRxiv2018

Central dogma rates and the trade-off between precision and economy

Steady-state protein abundance is set by four rates: transcription, translation, mRNA decay and protein decay. A given protein abundance can be obtained from infinitely many combinations of these rates. This raises the question of whether the natural rates for each gene result from historical accidents, or are there rules that give certain combinations a selective advantage? We address this question using high-throughput measurements in rapidly growing cells from diverse organisms to find that about half of the rate combinations do not exist: genes that combine high transcription with low translation are strongly depleted. This depletion is due to a trade-off between precision and economy: high transcription decreases stochastic fluctuations but increases transcription costs. Our theory quantitatively explains which rate combinations are missing, and predicts the curvature of the fitness function for each gene. It may guide the design of gene circuits with desired expression levels and noise.

systems biology↗

bioRxiv2018

Tradeoffs and cultural diversity

Culture is humanitys prime adaptation. Which cultural traits contribute to adaptive value at the level of cultural groups, and how they do so, is, however, unclear and debated. Here, we apply an approach from systems biology, known as Pareto task inference (ParTI), to bring a fresh perspective to these questions. ParTI considers systems that need to perform multiple tasks. No system can be optimal at all tasks at once, leading to a fundamental tradeoff. These tradeoffs result in specific patterns in multivariate trait data: data falls inside polygons in trait space, whose vertices are trait combinations that are adaptive for each of the tasks. ParTI can infer the number of adaptive tasks and their nature without need for a-priori assumptions, based on the vertices of these polygons. Here, we applied ParTI to two datasets of cultural traits, on Austronesian cultures and modern hunter-gatherers, adjusting for phylogeny and spatial diffusion effects. We find evidence that these independent datasets show a tradeoff between the same three tasks. We identify the tasks as resource defense, resource competition, and mobility/exchange. Specific combinations of cultural traits are adaptive for each of these tasks. This approach can be widely applied to any large multivariate dataset to study trade-offs in cultural evolution.

evolutionary biology↗

bioRxiv2018

A bacterial growth law out of steady-state

Bacterial growth depends on numerous reactions, and yet follows surprisingly simple laws that inspired biologists for decades. Growth laws until now primarily dealt with steady-state exponential growth in constant conditions. However, bacteria in nature often face fluctuating environments, with nutritional upshifts and downshifts. We therefore ask whether there are growth laws that apply to changing environments. We derive a law for strong upshifts using an optimal resource-allocation model that was previously calibrated at steady-state growth: the post-shift growth rate equals the geometrical mean of the pre-shift growth rate and the growth rate on saturating carbon. We test this using chemostat and robotic batch culture experiments, as well as previous data from several species, and find good agreement with the model predictions. The increase in growth rate after an upshift indicates that ribosomes have spare capacity. We demonstrate theoretically that spare ribosomal capacity has the cost of slow steady-state growth, but is beneficial in fluctuating environments because it prevents large overshoots in intracellular metabolites after an upshift and allows rapid response to change. We also provide predictions for downshifts for future experimental tests. Spare capacity appears in diverse biological systems, and the present study quantifies the optimal degree of spare capacity, which rises the slower the growth rate, and suggests that it can be precisely regulated.

systems biology↗

bioRxiv2018

Evolutionary tradeoffs and the structure of allelic polymorphisms

Populations of organisms show prevalent genetic differences called polymorphisms. Understanding the effects of polymorphisms is of central importance in biology and medicine. Here, we ask which polymorphisms occur at high frequency when organisms evolve under tradeoffs between multiple tasks. Multiple tasks present a problem, because it is not possible to be optimal at all tasks simultaneously and hence compromises are necessary. Recent work indicates that tradeoffs lead to a simple geometry of phenotypes in the space of traits: phenotypes fall on the Pareto front, which is shaped as a polytope: a line, triangle, tetrahedron etc. The vertices of these polytopes are the optimal phenotypes for a single task. Up to now, work on this Pareto approach has not considered its genetic underpinnings. Here, we address this by asking how the polymorphism structure of a population is affected by evolution under tradeoffs. We simulate a multi-task selection scenario, in which the population evolves to the Pareto front: the line segment between two archetypes or the triangle between three archetypes. We find that polymorphisms that become prevalent in the population have pleiotropic phenotypic effects that align with the Pareto front. Similarly, epistatic effects between prevalent polymorphisms are parallel to the front. Alignment with the front occurs also for asexual mating. Alignment is reduced when drift or linkage is strong, and is replaced by a more complex structure in which many perpendicular allele effects cancel out. Aligned polymorphism structure allows mating to produce offspring that stand a good chance of being optimal multi-taskers in at least one of the locales available to the species.

systems biology↗

bioRxiv2017

A Note On Dynamical Compensation And Its Relation To Parameter Identifiability

We recently identified a motif for dynamical compensation (DC) - a property where a system maintains the dynamics and steady-state of a regulated variable robust in the face of fluctuations in key parameters. Such parameters are therefore unidentifiable from measurements of the regulated variable at steady-state. On the other hand, since the models showing dynamical compensation are typically non-redundant, their parameters are identifiable from experimental data. We clarify this apparent discrepancy by requiring that the parameters of DC circuits be identifiable both away from steady-state and when measuring other system variables. We use this observation to provide a definition for DC in terms of parameter identifiability and discuss its relevance for the examples provided in Karin et al.

systems biology↗

bioRxiv2016

Neuronal components of evaluating the human 1 origin of abstract shapes

Communication through visual symbols is a key aspect of human culture. However, to what extent can people distinguish between human-origin and artificial symbols, and the neuronal mechanisms underlying this process are not clear. Using fMRI we contrasted brain activity during presentation of human-created abstract shapes and random-algorithm created shapes, both sharing similar low level features.\n\nWe found that participants correctly identified most shapes as human or random. The lateral occipital complex (LOC) was the main brain region showing preference to human-made shapes, independently of task. Furthermore, LOC activity was parametrically correlated to beauty and familiarity scores of the shapes (rated following the scan). Finally, a model classifier based only on LOC activity showed human level accuracy at discriminating between human-made and randomly-made shapes.\n\nOur results highlight the sensitivity of the human brain to social and cultural cues, and point to high-order object areas as central nodes underlying this capacity.

neuroscience↗

bioRxiv2016

Dynamic proteomics of HSV1 infection reveals molecular events that govern non-stochastic infection outcomes

Viral infection is usually studied at the level of cell populations, averaging over hundreds of thousands of individual cells. Moreover, measurements are typically done by analyzing a few time points along the infection process. While informative, such measurements are limited in addressing how cell variability affects infection outcome. Here we employ dynamic proteomics to study virus-host interactions, using the human pathogen Herpes Simplex virus 1 as a model. We tracked >50,000 individual cells as they respond to HSV1 infection, allowing us to model infection kinetics and link infection outcome (productive or not) with the cell state at the time of initial infection. We find that single cells differ in their preexisting susceptibility to HSV1, and that this is partially mediated by their cell-cycle position. We also identify specific changes in protein levels and localization in infected cells, attesting to the power of the dynamic proteomics approach for studying virus-host interactions.

systems biology↗