HormoneBayes: A novel Bayesian framework for the analysis of pulsatile hormone dynamics.

The hypothalamus is the central regulator of reproductive hormone secretion. Pulsatile secretion of gonadotropin releasing hormone (GnRH) is fundamental to physiological stimulation of the pituitary gland to release luteinizing hormone (LH) and follicle stimulating hormone (FSH). Furthermore, GnRH pulsatility is altered in common reproductive disorders such as polycystic ovary syndrome (PCOS) and hypothalamic amenorrhea (HA). LH is measured routinely in clinical practice using an automated chemiluminescent immunoassay method and is the gold standard surrogate marker of GnRH. LH can be measured at frequent intervals (e.g., 10 minutely) to assess GnRH/LH pulsatility. However, this is rarely done in clinical practice because it is resource intensive, and there is no open-access, graphical interface software for computational analysis of the LH data available to clinicians. Here we present hormoneBayes, a novel open-access Bayesian framework that can be easily applied to reliably analyze serial LH measurements to assess LH pulsatility. The framework utilizes parsimonious models to simulate hypothalamic signals that drive LH dynamics, together with state-of-the-art (sequential) Monte-Carlo methods to infer key parameters and latent hypothalamic dynamics. We show that this method provides estimates for key pulse parameters including inter-pulse interval, secretion and clearance rates and identifies LH pulses in line with the widely used deconvolution method. We show that these parameters can distinguish LH pulsatility in different clinical contexts including in reproductive health and disease in men and women (e.g., healthy men, healthy women before and after menopause, women with HA or PCOS). A further advantage of hormoneBayes is that our mathematical approach provides a quantified estimation of uncertainty. Our framework will complement methods enabling real-time in-vivo hormone monitoring and therefore has the potential to assist translation of personalized, data-driven, clinical care of patients presenting with conditions of reproductive hormone dysfunction.

Steady-state running rate sets the speed and accuracy of accumulation of swimming bacteria.

We study the chemotaxis of a population of genetically identical swimming bacteria undergoing run and tumble dynamics driven by stochastic switching between clockwise and counterclockwise rotation of the flagellar rotary system, where the steady-state rate of the switching changes in different environments. Understanding chemotaxis quantitatively requires that one links the measured steady-state switching rates of the rotary system, as well as the directional changes of individual swimming bacteria in a gradient of chemoattractant/repellant, to the efficiency of a population of bacteria in moving up/down the gradient. Here we achieve this by using a probabilistic model, parametrized with our experimental data, and show that the response of a population to the gradient is complex. We find the changes to the steady-state switching rate in the absence of gradients affect the average speed of the swimming bacterial population response as well as the width of the distribution. Both must be taken into account when optimizing the overall response of the population in complex environments.

The Origin of GnRH Pulse Generation: An Integrative Mathematical-Experimental Approach.

Fertility critically depends on the gonadotropin-releasing hormone (GnRH) pulse generator, a neural construct comprised of hypothalamic neurons coexpressing kisspeptin, neurokoinin-B and dynorphin. Here, using mathematical modeling and in vivo optogenetics we reveal for the first time how this neural construct initiates and sustains the appropriate ultradian frequency essential for reproduction. Prompted by mathematical modeling, we show experimentally using female estrous mice that robust pulsatile release of luteinizing hormone, a proxy for GnRH, emerges abruptly as we increase the basal activity of the neuronal network using continuous low-frequency optogenetic stimulation. Further increase in basal activity markedly increases pulse frequency and eventually leads to pulse termination. Additional model predictions that pulsatile dynamics emerge from nonlinear positive and negative feedback interactions mediated through neurokinin-B and dynorphin signaling respectively are confirmed neuropharmacologically. Our results shed light on the long-elusive GnRH pulse generator offering new horizons for reproductive health and wellbeing.SIGNIFICANCE STATEMENT The gonadotropin-releasing hormone (GnRH) pulse generator controls the pulsatile secretion of the gonadotropic hormones LH and FSH and is critical for fertility. The hypothalamic arcuate kisspeptin neurons are thought to represent the GnRH pulse generator, since their oscillatory activity is coincident with LH pulses in the blood; a proxy for GnRH pulses. However, the mechanisms underlying GnRH pulse generation remain elusive. We developed a mathematical model of the kisspeptin neuronal network and confirmed its predictions experimentally, showing how LH secretion is frequency-modulated as we increase the basal activity of the arcuate kisspeptin neurons in vivo using continuous optogenetic stimulation. Our model provides a quantitative framework for understanding the reproductive neuroendocrine system and opens new horizons for fertility regulation.

Information Transfer in Gonadotropin-releasing Hormone (GnRH) Signaling: EXTRACELLULAR SIGNAL-REGULATED KINASE (ERK)-MEDIATED FEEDBACK LOOPS CONTROL HORMONE SENSING.

Cell signaling pathways are noisy communication channels, and statistical measures derived from information theory can be used to quantify the information they transfer. Here we use single cell signaling measures to calculate mutual information as a measure of information transfer via gonadotropin-releasing hormone (GnRH) receptors (GnRHR) to extracellular signal-regulated kinase (ERK) or nuclear factor of activated T-cells (NFAT). This revealed mutual information values <1 bit, implying that individual GnRH-responsive cells cannot unambiguously differentiate even two equally probable input concentrations. Addressing possible mechanisms for mitigation of information loss, we focused on the ERK pathway and developed a stochastic activation model incorporating negative feedback and constitutive activity. Model simulations revealed interplay between fast (min) and slow (min-h) negative feedback loops with maximal information transfer at intermediate feedback levels. Consistent with this, experiments revealed that reducing negative feedback (by expressing catalytically inactive ERK2) and increasing negative feedback (by Egr1-driven expression of dual-specificity phosphatase 5 (DUSP5)) both reduced information transfer from GnRHR to ERK. It was also reduced by blocking protein synthesis (to prevent GnRH from increasing DUSP expression) but did not differ for different GnRHRs that do or do not undergo rapid homologous desensitization. Thus, the first statistical measures of information transfer via these receptors reveals that individual cells are unreliable sensors of GnRH concentration and that this reliability is maximal at intermediate levels of ERK-mediated negative feedback but is not influenced by receptor desensitization.

Stochastic Simulation of Biomolecular Networks in Dynamic Environments.

Simulation of biomolecular networks is now indispensable for studying biological systems, from small reaction networks to large ensembles of cells. Here we present a novel approach for stochastic simulation of networks embedded in the dynamic environment of the cell and its surroundings. We thus sample trajectories of the stochastic process described by the chemical master equation with time-varying propensities. A comparative analysis shows that existing approaches can either fail dramatically, or else can impose impractical computational burdens due to numerical integration of reaction propensities, especially when cell ensembles are studied. Here we introduce the Extrande method which, given a simulated time course of dynamic network inputs, provides a conditionally exact and several orders-of-magnitude faster simulation solution. The new approach makes it feasible to demonstrate-using decision-making by a large population of quorum sensing bacteria-that robustness to fluctuations from upstream signaling places strong constraints on the design of networks determining cell fate. Our approach has the potential to significantly advance both understanding of molecular systems biology and design of synthetic circuits.

Information transfer by leaky, heterogeneous, protein kinase signaling systems.

Cells must sense extracellular signals and transfer the information contained about their environment reliably to make appropriate decisions. To perform these tasks, cells use signal transduction networks that are subject to various sources of noise. Here, we study the effects on information transfer of two particular types of noise: basal (leaky) network activity and cell-to-cell variability in the componentry of the network. Basal activity is the propensity for activation of the network output in the absence of the signal of interest. We show, using theoretical models of protein kinase signaling, that the combined effect of the two types of noise makes information transfer by such networks highly vulnerable to the loss of negative feedback. In an experimental study of ERK signaling by single cells with heterogeneous ERK expression levels, we verify our theoretical prediction: In the presence of basal network activity, negative feedback substantially increases information transfer to the nucleus by both preventing a near-flat average response curve and reducing sensitivity to variation in substrate expression levels. The interplay between basal network activity, heterogeneity in network componentry, and feedback is thus critical for the effectiveness of protein kinase signaling. Basal activity is widespread in signaling systems under physiological conditions, has phenotypic consequences, and is often raised in disease. Our results reveal an important role for negative feedback mechanisms in protecting the information transfer function of saturable, heterogeneous cell signaling systems from basal activity.

Pulsatile hormonal signaling to extracellular signal-regulated kinase: exploring system sensitivity to gonadotropin-releasing hormone pulse frequency and width.

Gonadotropin-releasing hormone (GnRH) is secreted in brief pulses that stimulate synthesis and secretion of pituitary gonadotropin hormones and thereby mediate control of reproduction. It acts via G-protein-coupled receptors to stimulate effectors, including ERK. Information could be encoded in GnRH pulse frequency, width, amplitude, or other features of pulse shape, but the relative importance of these features is unknown. Here we examine this using automated fluorescence microscopy and mathematical modeling, focusing on ERK signaling. The simplest scenario is one in which the system is linear, and response dynamics are relatively fast (compared with the signal dynamics). In this case integrated system output (ERK activation or ERK-driven transcription) will be roughly proportional to integrated input, but we find that this is not the case. Notably, we find that relatively slow response kinetics lead to ERK activity beyond the GnRH pulse, and this reduces sensitivity to pulse width. More generally, we show that the slowing of response kinetics through the signaling cascade creates a system that is robust to pulse width. We, therefore, show how various levels of response kinetics synergize to dictate system sensitivity to different features of pulsatile hormone input. We reveal the mathematical and biochemical basis of a dynamic GnRH signaling system that is robust to changes in pulse amplitude and width but is sensitive to changes in receptor occupancy and frequency, precisely the features that are tightly regulated and exploited to exert physiological control in vivo.

The fidelity of dynamic signaling by noisy biomolecular networks.

Cells live in changing, dynamic environments. To understand cellular decision-making, we must therefore understand how fluctuating inputs are processed by noisy biomolecular networks. Here we present a general methodology for analyzing the fidelity with which different statistics of a fluctuating input are represented, or encoded, in the output of a signaling system over time. We identify two orthogonal sources of error that corrupt perfect representation of the signal: dynamical error, which occurs when the network responds on average to other features of the input trajectory as well as to the signal of interest, and mechanistic error, which occurs because biochemical reactions comprising the signaling mechanism are stochastic. Trade-offs between these two errors can determine the system's fidelity. By developing mathematical approaches to derive dynamics conditional on input trajectories we can show, for example, that increased biochemical noise (mechanistic error) can improve fidelity and that both negative and positive feedback degrade fidelity, for standard models of genetic autoregulation. For a group of cells, the fidelity of the collective output exceeds that of an individual cell and negative feedback then typically becomes beneficial. We can also predict the dynamic signal for which a given system has highest fidelity and, conversely, how to modify the network design to maximize fidelity for a given dynamic signal. Our approach is general, has applications to both systems and synthetic biology, and will help underpin studies of cellular behavior in natural, dynamic environments.

The magnitude and colour of noise in genetic negative feedback systems.

The comparative ability of transcriptional and small RNA-mediated negative feedback to control fluctuations or 'noise' in gene expression remains unexplored. Both autoregulatory mechanisms usually suppress the average (mean) of the protein level and its variability across cells. The variance of the number of proteins per molecule of mean expression is also typically reduced compared with the unregulated system, but is almost never below the value of one. This relative variance often substantially exceeds a recently obtained, theoretical lower limit for biochemical feedback systems. Adding the transcriptional or small RNA-mediated control has different effects. Transcriptional autorepression robustly reduces both the relative variance and persistence (lifetime) of fluctuations. Both benefits combine to reduce noise in downstream gene expression. Autorepression via small RNA can achieve more extreme noise reduction and typically has less effect on the mean expression level. However, it is often more costly to implement and is more sensitive to rate parameters. Theoretical lower limits on the relative variance are known to decrease slowly as a measure of the cost per molecule of mean expression increases. However, the proportional increase in cost to achieve substantial noise suppression can be different away from the optimal frontier-for transcriptional autorepression, it is frequently negligible.

NK3R signalling in the posterodorsal medial amygdala is involved in stress-induced suppression of pulsatile LH secretion in female mice.

Psychosocial stress negatively impacts reproductive function by inhibiting pulsatile luteinizing hormone (LH) secretion. The posterodorsal medial amygdala (MePD) is responsible in part for processing stress and modulating the reproductive axis. Activation of the neurokinin 3 receptor (NK3R) suppresses the gonadotropin-releasing hormone (GnRH) pulse generator, under hypoestrogenic conditions, and NK3R activity in the amygdala has been documented to play a role in stress and anxiety. We investigate whether NK3R activation in the MePD is involved in mediating the inhibitory effect of psychosocial stress on LH pulsatility in ovariectomised female mice. First, we administered senktide, an NK3R agonist, into the MePD and monitored the effect on pulsatile LH secretion. We then delivered SB222200, a selective NK3R antagonist, intra-MePD in the presence of predator odour, 2,4,5-trimethylthiazole (TMT) and examined the effect on LH pulses. Senktide administration into the MePD dose-dependently suppresses pulsatile LH secretion. Moreover, NK3R signalling in the MePD mediates TMT-induced suppression of the GnRH pulse generator, which we verified using a mathematical model. The model verifies our experimental findings: (i) predator odour exposure inhibits LH pulses, (ii) activation of NK3R in the MePD inhibits LH pulses and (iii) NK3R antagonism in the MePD blocks stressor-induced inhibition of LH pulse frequency in the absence of ovarian steroids. These results demonstrate for the first time that NK3R neurons in the MePD mediate psychosocial stress-induced suppression of the GnRH pulse generator.

Quantifying the variability in the assessment of reproductive hormone levels.

OBJECTIVE: To quantify how representative a single measure of reproductive hormone level is of the daily hormonal profile using data from detailed hormonal sampling in the saline placebo-treated arm conducted over several hours. DESIGN: Retrospective analysis of data from previous interventional research studies evaluating reproductive hormones. SETTING: Clinical Research Facility at a tertiary reproductive endocrinology centre at Imperial College Hospital NHS Foundation Trust. PATIENTS: Overall, 266 individuals, including healthy men and women (n = 142) and those with reproductive disorders and states (n = 124 [11 with functional hypothalamic amenorrhoea, 6 with polycystic ovary syndrome, 62 women and 32 men with hypoactive sexual desire disorder, and 13 postmenopausal women]), were included in the analysis. INTERVENTIONS: Data from 266 individuals who had undergone detailed hormonal sampling in the saline placebo-treated arms of previous research studies was used to quantify the variability in reproductive hormones because of pulsatile secretion, diurnal variation, and feeding using coefficient of variation (CV) and entropy. MAIN OUTCOME MEASURES: The ability of a single measure of reproductive hormone level to quantify the variability in reproductive hormone levels because of pulsatile secretion, diurnal variation, and nutrient intake. RESULTS: The initial morning value of reproductive hormone levels was typically higher than the mean value throughout the day (percentage decrease from initial morning measure to daily mean: luteinizing hormone level 18.4%, follicle-stimulating hormone level 9.7%, testosterone level 9.2%, and estradiol level 2.1%). Luteinizing hormone level was the most variable (CV 28%), followed by sex-steroid hormone levels (testosterone level 12% and estradiol level 13%), whereas follicle-stimulating hormone level was the least variable reproductive hormone (CV 8%). In healthy men, testosterone levels fell between 9:00 am and 5:00 pm by 14.9% (95% confidence interval 4.2, 25.5%), although morning levels correlated with (and could be predicted from) late afternoon levels in the same individual (r2 = 0.53, P<.0001). Testosterone levels were reduced more after a mixed meal (by 34.3%) than during ad libitum feeding (9.5%), after an oral glucose load (6.0%), or an intravenous glucose load (7.4%). CONCLUSION: Quantification of the variability of a single measure of reproductive hormone levels informs the reliability of reproductive hormone assessment.

The prospect of artificial intelligence to personalize assisted reproductive technology.

Infertility affects 1-in-6 couples, with repeated intensive cycles of assisted reproductive technology (ART) required by many to achieve a desired live birth. In ART, typically, clinicians and laboratory staff consider patient characteristics, previous treatment responses, and ongoing monitoring to determine treatment decisions. However, the reproducibility, weighting, and interpretation of these characteristics are contentious, and highly operator-dependent, resulting in considerable reliance on clinical experience. Artificial intelligence (AI) is ideally suited to handle, process, and analyze large, dynamic, temporal datasets with multiple intermediary outcomes that are generated during an ART cycle. Here, we review how AI has demonstrated potential for optimization and personalization of key steps in a reproducible manner, including: drug selection and dosing, cycle monitoring, induction of oocyte maturation, and selection of the most competent gametes and embryos, to improve the overall efficacy and safety of ART.

Estradiol elicits distinct firing patterns in arcuate nucleus kisspeptin neurons of females through altering ion channel conductances.

Hypothalamic kisspeptin (Kiss1) neurons are vital for pubertal development and reproduction. Arcuate nucleus Kiss1 (Kiss1ARH) neurons are responsible for the pulsatile release of Gonadotropin-releasing Hormone (GnRH). In females, the behavior of Kiss1ARH neurons, expressing Kiss1, Neurokinin B (NKB), and Dynorphin (Dyn), varies throughout the ovarian cycle. Studies indicate that 17ß-estradiol (E2) reduces peptide expression but increases Vglut2 mRNA and glutamate neurotransmission in these neurons, suggesting a shift from peptidergic to glutamatergic signaling. To investigate this shift, we combined transcriptomics, electrophysiology, and mathematical modeling. Our results demonstrate that E2 treatment upregulates the mRNA expression of voltage-activated calcium channels, elevating the whole-cell calcium current and that contribute to high-frequency burst firing. Additionally, E2 treatment decreased the mRNA levels of Canonical Transient Receptor Potential (TPRC) 5 and G protein-coupled K+ (GIRK) channels. When TRPC5 channels in Kiss1ARH neurons were deleted using CRISPR, the slow excitatory postsynaptic potential (sEPSP) was eliminated. Our data enabled us to formulate a biophysically realistic mathematical model of the Kiss1ARH neuron, suggesting that E2 modifies ionic conductances in Kiss1ARH neurons, enabling the transition from high frequency synchronous firing through NKB-driven activation of TRPC5 channels to a short bursting mode facilitating glutamate release. In a low E2 milieu, synchronous firing of Kiss1ARH neurons drives pulsatile release of GnRH, while the transition to burst firing with high, preovulatory levels of E2 would facilitate the GnRH surge through its glutamatergic synaptic connection to preoptic Kiss1 neurons.

Synthesis of vancomycin fluorescent probes that retain antimicrobial activity, identify Gram-positive bacteria, and detect Gram-negative outer membrane damage.

Antimicrobial resistance is an urgent threat to human health, and new antibacterial drugs are desperately needed, as are research tools to aid in their discovery and development. Vancomycin is a glycopeptide antibiotic that is widely used for the treatment of Gram-positive infections, such as life-threatening systemic diseases caused by methicillin-resistant Staphylococcus aureus (MRSA). Here we demonstrate that modification of vancomycin by introduction of an azide substituent provides a versatile intermediate that can undergo copper-catalysed azide-alkyne cycloaddition (CuAAC) reaction with various alkynes to readily prepare vancomycin fluorescent probes. We describe the facile synthesis of three probes that retain similar antibacterial profiles to the parent vancomycin antibiotic. We demonstrate the versatility of these probes for the detection and visualisation of Gram-positive bacteria by a range of methods, including plate reader quantification, flow cytometry analysis, high-resolution microscopy imaging, and single cell microfluidics analysis. In parallel, we demonstrate their utility in measuring outer-membrane permeabilisation of Gram-negative bacteria. The probes are useful tools that may facilitate detection of infections and development of new antibiotics.

Proofreading of misincorporated nucleotides in DNA transcription.

The accuracy of DNA transcription is crucial for the proper functioning of the cell. Although RNA polymerases demonstrate selectivity for correct nucleotides, additional active mechanisms of transcriptional error correction are required to achieve observed levels of fidelity. Recent experimental findings have shed light on a particular mechanism of transcriptional error correction involving: (i) diffusive translocation of the RNA polymerase along the DNA (backtracking) and (ii) irreversible RNA cleavage. This mechanism achieves preferential cleavage of misincorporated nucleotides by biasing the local rates of translocation. Here, we study how misincorporated nucleotides affect backtracking dynamics and how this effect determines the level of transcriptional fidelity. We consider backtracking as a diffusive process in a periodic, one-dimensional energy landscape, which at a coarse-grained level gives rise to a hopping process between neighboring local minima. We propose a model for how misincorporated nucleotides deform this energy landscape and hence affect the hopping rates. In particular, we show that this model can be used to derive both the theoretical limit on the fidelity (i.e. the minimum fraction of misincorporated nucleotides) and the actual fidelity relative to this optimum, achieved for specific combinations of the cleavage and polymerization rates. Finally, we study how external factors influencing backtracking dynamics affect transcriptional fidelity. We show that biologically relevant loads, similar to those exerted by nucleosomes or other transcriptional barriers, increase error correction.

Proofreading of misincorporated nucleotides in DNA transcription.

The accuracy of DNA transcription is crucial for the proper functioning of the cell. Although RNA polymerases demonstrate selectivity for correct nucleotides, additional active mechanisms of transcriptional error correction are required to achieve observed levels of fidelity. Recent experimental findings have shed light on a particular mechanism of transcriptional error correction involving: (i) diffusive translocation of the RNA polymerase along the DNA (backtracking) and (ii) irreversible RNA cleavage. This mechanism achieves preferential cleavage of misincorporated nucleotides by biasing the local rates of translocation. Here, we study how misincorporated nucleotides affect backtracking dynamics and how this effect determines the level of transcriptional fidelity. We consider backtracking as a diffusive process in a periodic, one-dimensional energy landscape, which at a coarse-grained level gives rise to a hopping process between neighbouring local minima. We propose a model for how misincorporated nucleotides deform this energy landscape and hence affect the hopping rates. In particular, we show that this model can be used to derive both the theoretical limit on the fidelity (i.e. the minimum fraction of misincorporated nucleotides) and the actual fidelity relative to this optimum, achieved for specific combinations of the cleavage and polymerization rates. Finally, we study how external factors influencing backtracking dynamics affect transcriptional fidelity. We show that biologically relevant loads, similar to those exerted by nucleosomes or other transcriptional barriers, increase error correction.

Modelling KNDy neurons and gonadotropin-releasing hormone pulse generation.

The pulsatile release of gonadotropin-releasing hormone (GnRH) and its frequency are crucial for healthy reproductive function. To understand what drives GnRH pulses, a combination of experimental and mathematical modelling approaches has been used. Early work focussed on the possibility that GnRH pulse generation is an intrinsic feature of GnRH neurons, with autocrine feedback generating pulsatility. However, there is now ample evidence suggesting that a network of upstream neurons secreting kisspeptin, neurokinin-B and dynorphin are the source of this GnRH pulse generator. The interplay of slow positive and negative feedback via neurokinin-B and dynorphin, respectively, allows the network to act as a relaxation oscillator, driving pulsatile secretion of kisspeptin, and consequently, of GnRH and LH. Here, we review the mathematical modelling approaches exploring both scenarios and suggest that with pulsatile GnRH secretion driven by the KNDy pulse generator, autocrine feedback still has the potential to modulate GnRH output.

Quantitative approaches in clinical reproductive endocrinology.

Understanding the human hypothalamic-pituitary-gonadal (HPG) axis presents a major challenge for medical science. Dysregulation of the HPG axis is linked to infertility and a thorough understanding of its dynamic behaviour is necessary to both aid diagnosis and to identify the most appropriate hormonal interventions. Here, we review how quantitative models are being used in the context of clinical reproductive endocrinology to: 1. analyse the secretory patterns of reproductive hormones; 2. evaluate the effect of drugs in fertility treatment; 3. aid in the personalization of assisted reproductive technology (ART). In this review, we demonstrate that quantitative models are indispensable tools enabling us to describe the complex dynamic behaviour of the reproductive axis, refine the treatment of fertility disorders, and predict clinical intervention outcomes.

Mathematical models in GnRH research.

Mathematical modelling is an indispensable tool in modern biosciences, enabling quantitative analysis and integration of biological data, transparent formulation of our understanding of complex biological systems, and efficient experimental design based on model predictions. This review article provides an overview of the impact that mathematical models had on GnRH research. Indeed, over the last 20 years mathematical modelling has been used to describe and explore the physiology of the GnRH neuron, the mechanisms underlying GnRH pulsatile secretion, and GnRH signalling to the pituitary. Importantly, these models have contributed to GnRH research via novel hypotheses and predictions regarding the bursting behaviour of the GnRH neuron, the role of kisspeptin neurons in the emergence of pulsatile GnRH dynamics, and the decoding of GnRH signals by biochemical signalling networks. We envisage that with the advent of novel experimental technologies, mathematical modelling will have an even greater role to play in our endeavour to understand the complex spatiotemporal dynamics underlying the reproductive neuroendocrine system.

Nutrient and salt depletion synergistically boosts glucose metabolism in individual Escherichia coli cells.

The interaction between a cell and its environment shapes fundamental intracellular processes such as cellular metabolism. In most cases growth rate is treated as a proximal metric for understanding the cellular metabolic status. However, changes in growth rate might not reflect metabolic variations in individuals responding to environmental fluctuations. Here we use single-cell microfluidics-microscopy combined with transcriptomics, proteomics and mathematical modelling to quantify the accumulation of glucose within Escherichia coli cells. In contrast to the current consensus, we reveal that environmental conditions which are comparatively unfavourable for growth, where both nutrients and salinity are depleted, increase glucose accumulation rates in individual bacteria and population subsets. We find that these changes in metabolic function are underpinned by variations at the translational and posttranslational level but not at the transcriptional level and are not dictated by changes in cell size. The metabolic response-characteristics identified greatly advance our fundamental understanding of the interactions between bacteria and their environment and have important ramifications when investigating cellular processes where salinity plays an important role.