Biology subjects

Alon, U.

Publications and source records attributed to Alon, U..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Preventing plasmid multimer formation in commonly used synthetic biology plasmids

Plasmids are an essential tool for basic research and biotechnology applications. To optimize plasmid-based circuits, it is crucial to control plasmid integrity, including the formation of plasmid multimers. Multimers are tandem repeats of entire plasmids formed during replication by failed dimer resolution. Multimers can affect the behavior of synthetic circuits, especially ones that include DNA-editing enzymes. However, occurrence of multimers is not commonly assayed. Here we survey four commonly used plasmid backbones for occurrence of multimers in cloning (JM109) and wild-type (MG1655) strains. We find that multimers occur appreciably only in MG1655, with the fraction of plasmids existing as multimers increasing with both plasmid copy number and culture passaging. In contrast, introduction of multimers into JM109 can produce strains containing only multimers. We present an MG1655{Delta} recA single-locus knockout that avoids multimer production. These results can aid synthetic biologists in improving design and reliability of plasmid-based circuits.

synthetic 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↗

bioRxiv2024

Temporal tissue dynamics from a single snapshot

Physiological and pathological processes such as inflammation or cancer emerge from the interactions between cells over time. However, methods to follow cell populations over time within the native context of a human tissue are lacking, because a biopsy offers only a single snapshot. Here we present one-shot tissue dynamics reconstruction (OSDR), an approach to estimate a dynamical model of cell populations based on a single tissue sample. OSDR uses spatial proteomics to learn how the composition of cellular neighborhoods influences division rate, providing a dynamical model of cell population change over time. We apply OSDR to human breast cancer data, and reconstruct two fixed points of fibroblasts and macrophage interactions. These fixed points correspond to hot and cold fibrosis, in agreement with co-culture experiments that measured these dynamics directly. We then use OSDR to discover a pulse-generating excitable circuit of T and B cells in the tumor microenvironment, suggesting temporal flares of adaptive anti-cancer responses. Finally, we study longitudinal biopsies from a triple negative breast cancer clinical trial, where OSDR predicts the collapse of the tumor cell population in responders versus non-responders based on early treatment biopsies. OSDR can be applied to a wide range of spatial proteomics assays to enable analysis of tissue dynamics based on patient biopsies.

bioinformatics↗

bioRxiv2024

Quantification of beta cell carrying capacity in prediabetes

Prediabetes, a subclinical state of high glucose, carries a risk of transition to diabetes. One cause of prediabetes is insulin resistance, which impairs the ability of insulin to control blood glucose. However, many individuals with high insulin resistance retain normal glucose due to compensation by enhanced insulin secretion by beta cells. Individuals seem to differ in their maximum compensation level, termed beta cell carrying capacity, such that low carrying capacity is associated with a higher risk of prediabetes and diabetes. Carrying capacity has not been quantified using a mathematical model and cannot be estimated directly from measured glucose and insulin levels in patients, unlike insulin resistance and beta cell function which can be estimated using HOMA-IR and HOMA-B formula. Here we present a mathematical model of beta cell compensation and carrying capacity, and develop a new formula called HOMA-C to estimate it from glucose and insulin measurements. HOMA-C estimates the maximal potential beta cell function of an individual, rather than the current beta cell function. We test this approach using longitudinal cohorts of prediabetic people, finding 10-fold variation in carrying capacity. Low carrying capacity is associated with higher risk of transitioning to diabetes. We estimate the timescales of beta cell compensation and insulin resistance using large datasets, showing that, unlike previous mathematical models, the new model can explain the slow rise in glucose over decades. Our mathematical understanding of beta cell carrying capacity may help to assess the risk of prediabetes in each individual.

systems biology↗

bioRxiv2023

Compression of sickspan by interventions that steepen the survival curve

Longevity research aims to extend the period of healthy life, known as the healthspan, and to minimize the duration of disability and morbidity, known as the sickspan. Most longevity interventions in model organisms extend healthspan, but it is not known whether they extend healthspan relative to the lifespan - that is, whether they compress relative sickspan. Here, we present a theory that predicts which interventions effectively compress relative sickspan. The theory is based on the shape of the survival curve - the fraction of organisms surviving as a function of age. Interventions that extend mean lifespan while preserving the shape of the survival curve, known as scaling, are predicted to extend the sickspan proportionally, without compressing it. Such interventions include caloric restriction and many other longevity interventions. Conversely, a small subset of interventions that extend lifespan and steepen the shape of the survival curve, are predicted to compress the relative sickspan. We explain this based on the saturating removal mathematical model of aging, and present evidence from longitudinal health data in mice, Caenorhabditis elegans and Drosophila melanogaster. We discuss longevity interventions in mice that steepen the survival curves, including senolytics, ketogenic diet, and agents that reduce glucose spikes and protect blood vessels, as potential candidates for compressing the sickspan. We apply the theory to combinations of longevity interventions and discuss human healthspan data. This approach offers potential strategies for compressing morbidity and extending healthspan.

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

Pregnancy and postpartum dynamics revealed by an atlas of millions of lab tests

Pregnancy and delivery involve dynamic alterations in many physiological systems. However, the physiological dynamics during pregnancy and after delivery have not been systematically analyzed at high temporal resolution in a large human population. Here we present the dynamics of 76 lab tests based on a cross-sectional analysis of roughly 41 million measurements from over 300,000 pregnancies. We analyzed each test at weekly intervals from 20 weeks preconception to 80 weeks postpartum, providing detailed temporal profiles. About half of the tests take three months to a year to return to baseline during postpartum, highlighting the physiological load of childbirth. The precision of the data revealed effects of preconception supplements, overshoots after delivery and intricate temporal responses to changes in blood volume and renal filtration rate. Pregnancy complications - gestational diabetes, pre-eclampsia and postpartum hemorrhage - showed distinct dynamical changes. These results provide a comprehensive dynamic portrait of the systems physiology of pregnancy.

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↗

bioRxiv2023

Robust differentiation in a synthetic stem-cell circuit

Differentiation is a process fundamental to multicellularity. In its simplest form, differentiation converts self-renewing stem cells into non-proliferative cells with specified function. This process is inherently susceptible to mutant takeover -- mutant stem cells that never differentiate produce excess proliferative daughter cells, driving cancer-like expansion and decreasing the availability of differentiated cells to the organism. It has been proposed that coupling differentiation to an essential trait can select against these mutants by producing a biphasic fitness curve. This would provide mutant stem cells that do not differentiate with a selective disadvantage. However, this theory has yet to be tested experimentally. Here we use "fitness landscape engineering" to design and construct a synthetic biological model of stem cell differentiation in Escherichia coli with biphasic fitness. We find that this circuit is robust to mutations as predicted. Surprisingly, its optimal differentiation rate is robust to a wide range of environmental pressures. This environmental robustness is driven by transit-amplifying cells that differentiate and proliferate irrespective of environment. These results provide new interpretations for natural differentiation mechanisms and suggest strategies for engineering robust, complex multicellular consortia.

synthetic biology↗

bioRxiv2023

p16-dependent upregulation of PD-L1 impairs immunosurveillance of senescent cells

The accumulation of senescent cells promotes aging, but a molecular mechanism that senescent cells use to evade immune clearance and accumulate remains to be elucidated. Here, we report that p16-positive senescent cells upregulate the immune checkpoint protein programmed death-ligand 1 (PD-L1) to accumulate in aging and chronic inflammation. p16-mediated inhibition of CDK4/6 promotes PD-L1 stability in senescent cells via the downregulation of ubiquitin-dependent degradation. p16 expression in infiltrating macrophages induces an immunosuppressive environment that can contribute to an increased burden of senescent cells. Treatment with immunostimulatory anti-PD-L1 antibody enhances the cytotoxic T cell activity and leads to the elimination of p16, PD-L1-positive cells. Our study uncovers a molecular mechanism of p16-dependent regulation of PD-L1 protein stability in senescent cells and reveals the potential of PD-L1 as a target for treating senescence-mediated age-associated diseases.

cell biology↗

bioRxiv2023

Circuit to target approach defines an autocrine myofibroblast loop that drives cardiac fibrosis

Fibrosis is a broad pathology of excessive scarring with substantial medical implications. The fibrotic scar is produced by myofibroblasts that interact with macrophages. Fibrosis is a complex process involving thousands of factors, therefore, to better understand fibrosis and develop new therapeutic approaches, it is necessary to simplify and clarify the underlying concepts. Recently, we described a mathematical model for a macrophage-myofibroblast cell circuit, predicting two types of fibrosis - hot fibrosis with abundant macrophages and myofibroblasts, and cold fibrosis dominated by myofibroblasts alone. To test these concepts and intervention strategies in a medically relevant system, we use a widely studied in-vivo injury model for fibrosis, myocardial infarction (MI). We show that cold fibrosis is the final outcome of MI in both mice and pigs and demonstrate that fibrosis can shift toward healing in regenerative settings. MI begind with an increase of myofibroblasts and macrophages, followed by macrophage decline leading to persistent cold fibrosis (only myofibroblasts). During this process, fibroblasts, unlike macrophages, acquire distinct fate changes. Using mathematical modeling we predict that targeting of the autocrine signal for myofibroblast division could block cold fibrosis. We identify TIMP1 as an autocrine cardiac myofibroblast growth factor in-vitro. Treatment of adult mice after MI with anti-TIMP1 antibodies reduces fibrosis in-vivo. This study shows the utility of the concepts of hot and cold fibrosis and the feasibility of our circuit-to-target approach to reduce fibrosis after acute cardiac injury by inhibiting the myofibroblast autocrine loop.

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↗

bioRxiv2022

Damage dynamics in single E. coli and the role of chance in the timing of cell death

Genetically identical cells in the same stressful condition die at different times. The origin of this stochasticity is unclear; it may arise from different initial conditions that affect the time of demise, or from a stochastic damage accumulation mechanism that erases the initial conditions and instead amplifies noise to generate different lifespans. To address this requires measuring damage dynamics in individual cells over the lifespan, but this has rarely been achieved. Here, we used a microfluidic device to measure membrane damage in 648 carbon-starved E. coli cells at high temporal resolution. We find that initial conditions of damage, size or cell-cycle phase do not explain most of the lifespan variation. Instead, the data points to a stochastic mechanism in which noise is amplified by a rising production of damage that saturates its own removal. Surprisingly, the relative variation in damage drops with age: cells become more similar to each other in terms of relative damage, indicating increasing determinism with age. Thus, chance erases initial conditions and then gives way to increasingly deterministic dynamics that dominate the lifespan distribution.

systems biology↗

bioRxiv2022

Chronic inflammation partially recapitulates the gene expression signature of aging

Understanding the mechanism of rejuvenation is central to aging research. No study has compared the effects of the four major rejuvenation strategies: senolytics, caloric restriction, in vivo partial cellular reprogramming and young/old blood factor exchange, which operate via different modalities. We use mice transcriptional data to compare them to each other and to normal aging. We find a shared gene expression program common to all rejuvenation strategies, in which inflammation declines and metabolism, especially of fatty acids, increases. An inverse signature occurs in normal aging. To test whether inflammation is upstream of the metabolic signature, we studied chronic inflammation in three different organs in young mice. Chronic inflammation was associated with a similar decline in metabolism, suggesting that inflammation is upstream of the metabolic signature. We find that inflammation may also underlie human transcriptional age calculator. We conclude that a core mechanism of rejuvenation acts through reduction of inflammation with downstream effects that enhance metabolism, attenuating the most robust age-related changes. This supports a notion of directly targeting genes associated with these pathways to mitigate age-related deterioration.

bioinformatics↗

bioRxiv2022

Tradeoffs in bacterial physiology determine the efficiency of antibiotic killing

Antibiotics can kill or stop the growth of bacteria, and their effectiveness depends on many factors. It is important to understand the relation between bacterial physiology, the environment and antibiotic action. While many of the mechanistic details of antibiotic action are known, the connection between death rate and bacterial physiology is poorly understood. Death rate in antibiotics has often been shown to rise linearly with growth rate; however, it remains unclear how environmental factors, in concert with whole-cell physiological properties, affect bactericidal activity. To address this, we developed a high-throughput assay to precisely measure antibiotic-mediated bacterial death. We found that death rate is linear in growth rate, but the slope depends on environmental conditions. Specifically, stressors lower the death rate compared to a non-stressed environment with the same growth rate. To understand the role of stress, we developed a mathematical model of bacterial death based on resource allocation that takes into account a newly defined stress-response sector; we identify this sector using RNA-seq. Our model accurately predicts the death rate and minimal inhibitory concentration of antibiotics across a wide range of conditions, including a previously unknown increase in the stress response and protection from death at very low levels of cAMP. The present death-growth model suggests conditions that may improve antibiotic efficacy.

systems biology↗

bioRxiv2021

The dopamine circuit as a reward-taxis navigation system

Research on certain circuits in simple organisms, such as bacterial chemotaxis, has enabled the formulation of mathematical design principles, leading to ever more precise experimental tests, catalyzing quantitative understanding. It would be important to map these principles to the far more complex case of a vertebrate behavioral circuit. Here, we provide such a mapping for the midbrain dopamine system. Dopamine transmission plays a key role in learning, motivation, and movement, but its systems-level function is not fully understood. We develop a minimal mechanistic model of the dopamine circuit based on physiological and behavioral data, and show that it can be mapped mathematically to the bacterial chemotaxis circuit. Just as chemotaxis robustly climbs attractant gradients, the dopamine circuit performs reward-taxis where the attractant is the expected value of reward. The reward-taxis mechanism is based on a circuit feature called fold-change detection, where the circuit outputs the temporal logarithmic derivative of expected reward. The model can explain the general matching law, in which the ratio of responses to concurrent rewards goes as the reward ratio to the power {beta}. It provides an accurate mechanistic value for {beta} as the average gain/baseline ratio of the dopaminergic neurons. Reward-taxis provides testable etiologies for specific dopamine-related disorders.

neuroscience↗

bioRxiv2020

An opponent process for alcohol addiction based on changes in endocrine gland mass

Consuming addictive drugs is often initially pleasurable, but escalating drug intake eventually recruits physiological "anti-reward" systems called opponent processes that cause tolerance and withdrawal symptoms. Opponent processes are fundamental for the addiction process, but their physiological basis is not fully characterized. Here, we propose an opponent processes mechanism centered on the endocrine stress-response, the HPA axis. We focus on alcohol addiction, where the HPA axis is activated and secretes {beta}-endorphin, causing euphoria and analgesia. Using a mathematical model, we show that slow changes in HPA glands act as an opponent process for {beta}-endorphin secretion. The model explains hormone dynamics in alcohol addiction, and experiments on alcohol preference in rodents. The opponent process is based on fold-change detection (FCD) where {beta}-endorphin responses are relative rather than absolute; FCD confers vulnerability to addiction but has adaptive roles for learning. Our model suggests gland-mass changes as potential targets for intervention in addiction.

systems biology↗