Development of a Highly Differentiated Human Primary Proximal Tubule MPS Model (aProximate MPS Flow).

The kidney proximal tubule (PT) mediates renal drug elimination in vivo and is a major site of drug-induced toxicity. To reliably assess drug efficacy, it is crucial to construct a model in which PT functions are replicated. Current animal studies have proven poorly predictive of human outcome. To address this, we developed a physiologically relevant micro-physiological system (MPS) model of the human PT, the aProximate MPS Flow platform (Patent No: G001336.GB). In this model, primary human PT cells (hPTCs) are subjected to fluidic media flow and a shear stress of 0.01-0.2 Pa. We observe that these cells replicate the polarity of hPTCs and exhibit a higher expression of all the key transporters of SLC22A6 (OAT1), SLC22A8 (OAT3), SLC22A2 (OCT2), SLC47A1 (MATE1), SLC22A12 (URAT1), SLC2A9 (GLUT9), ABCB1 (MDR1), ABCC2 (MRP2), LRP2 (megalin), CUBN (cubilin), compared with cells grown under static conditions. Immunofluorescence microscopy confirmed an increase in OAT1, OAT3, and cilia protein expression. Increased sensitivity to nephrotoxic protein cisplatin was observed; creatinine and FITC-albumin uptake was significantly increased under fluidic shear stress conditions. Taken together, these data suggest that growing human PT cells under media flow significantly improves the phenotype and function of hPTC monolayers and has benefits to the utility and near-physiology of the model.

Principles of synthetic biology.

In synthetic biology, biological cells and processes are dismantled and reassembled to make novel systems that do useful things. Designs are encoded by deoxyribonucleic acid (DNA); DNA makes biological (bio-)parts; bioparts are combined to make devices; devices are built into biological systems. Computers are used at all stages of the Design-Build-Test-Learn cycle, from mathematical modelling through to the use of robots for the automation of assembly and experimentation. Synthetic biology applies engineering principles of standardisation, modularity, and abstraction, enabling fast prototyping and the ready exchange of designs between synthetic biologists working around the world. Like toy building blocks, compatible modular designs enable bioparts to be combined and optimised easily; biopart specifications are shared in open registries. Synthetic biology is made possible due to major advances in DNA sequencing and synthesis technologies, and through knowledge gleaned in the field of systems biology. Systems biology aims to understand biology across scales, from the molecular and cellular, up to tissues and organisms, and describes cells as complex information-processing systems. By contrast, synthetic biology seeks to design and build its own systems. Applications of synthetic biology are wide-ranging but include impacting healthcare to improve diagnosis and make better treatments for disease; it seeks to improve the environment by finding novel ways to clean up pollution, make industrial processes for chemical synthesis sustainable, and remove the need for damaging farming practices by making better fertilisers. Synthetic biology has the potential to change the way we live and help us to protect the future of our planet.

High content imaging for monitoring signalling dynamics in single cells.

All living cells are sensors of their environment: they sense signals, hormones, cytokines, and growth factors, among others. Binding of these signals to cell surface receptors initiates the transmission of messages along intracellular signalling pathways through protein-protein interactions, enzymatic modifications and conformational changes. Typically, the activation of signalling pathways are monitored in whole populations of cells, giving population average measures, often using experimental methods that destroy and homogenise the cell population. High content imaging is an automated, high-throughput fluorescence microscopy method that enables measurements of signal transduction pathways to be taken from live cells. It can be used to measure signalling dynamics, how the abundance of particular proteins of interest change over time, or to record how particular proteins move and change their localisation in response to a signal from their environment. Using this, and other single cell methods, it is becoming increasingly clear that cells are in fact very variable in their response to a given stimulus and in the quantities of cellular components they express, even in clonal (isogenic) cell lines. This review will discuss how high content imaging has contributed to our growing understanding of cellular heterogeneity. It will discuss how data generated has been combined with information theoretic approaches to quantify the amount of information transferred through noisy signalling pathways. Lastly, the relevance of heterogeneity to our understanding and treatment of disease will be considered, highlighting the importance of single cell measurements.

An information theoretic approach to insulin sensing by human kidney podocytes.

Podocytes are key components of the glomerular filtration barrier (GFB). They are insulin-responsive but can become insulin-resistant, causing features of the leading global cause of kidney failure, diabetic nephropathy. Insulin acts via insulin receptors to control activities fundamental to GFB integrity, but the amount of information transferred is unknown. Here we measure this in human podocytes, using information theory-derived statistics that take into account cell-cell variability. High content imaging was used to measure insulin effects on Akt, FOXO and ERK. Mutual Information (MI) and Channel Capacity (CC) were calculated as measures of information transfer. We find that insulin acts via noisy communication channels with more information flow to Akt than to ERK. Information flow estimates were increased by consideration of joint sensing (ERK and Akt) and response trajectory (live cell imaging of FOXO1-clover translocation). Nevertheless, MI values were always <1Bit as most information was lost through signaling. Constitutive PI3K activity is a predominant feature of the system that restricts the proportion of CC engaged by insulin. Negative feedback from Akt supressed this activity and thereby improved insulin sensing, whereas sensing was robust to manipulation of feedforward signaling by inhibiting PI3K, PTEN or PTP1B. The decisions made by individual podocytes dictate GFB integrity, so we suggest that understanding the information on which the decisions are based will improve understanding of diabetic kidney disease and its treatment.

Exploring Dynamics and Noise in Gonadotropin-Releasing Hormone (GnRH) Signaling.

Gonadotropin-releasing hormone (GnRH) acts via G-protein coupled receptors on pituitary gonadotropes. These are Gq-coupled receptors that mediate acute effects of GnRH on the exocytotic secretion of luteinizing hormone (LH) and follicle-stimulating hormone (FSH), as well as the chronic regulation of their synthesis. FSH and LH control steroidogenesis and gametogenesis in the gonads so GnRH mediates control of reproduction by the central nervous system. GnRH is secreted in short pulses and the effects of GnRH on its target cells are dependent on the dynamics of these pulses. Here we provide a brief overview of the signaling network activated by GnRH with emphasis on the use of high content imaging for their examination. We also describe computational approaches that we have used to simulate GnRH signaling in order to explore dynamics, noise, and information transfer in this system.

Enhanced insulin receptor, but not PI3K, signalling protects podocytes from ER stress.

Disruption of the insulin-PI3K-Akt signalling pathway in kidney podocytes causes endoplasmic reticulum (ER) stress, leading to podocyte apoptosis and proteinuria in diabetic nephropathy. We hypothesised that by improving insulin sensitivity we could protect podocytes from ER stress. Here we use established activating transcription factor 6 (ATF6)- and ER stress element (ERSE)-luciferase assays alongside a novel high throughput imaging-based C/EBP homologous protein (CHOP) assay to examine three models of improved insulin sensitivity. We find that by improving insulin sensitivity at the level of the insulin receptor (IR), either by IR over-expression or by knocking down the negative regulator of IR activity, protein tyrosine-phosphatase 1B (PTP1B), podocytes are protected from ER stress caused by fatty acids or diabetic media containing high glucose, high insulin and inflammatory cytokines TNFα and IL-6. However, contrary to this, knockdown of the negative regulator of PI3K-Akt signalling, phosphatase and tensin homolog deleted from chromosome 10 (PTEN), sensitizes podocytes to ER stress and apoptosis, despite increasing Akt phosphorylation. This indicates that protection from ER stress is conferred through not just the PI3K-Akt pathway, and indeed we find that inhibiting the MEK/ERK signalling pathway rescues PTEN knockdown podocytes from ER stress.

Gonadotropin-releasing hormone signaling: An information theoretic approach.

Gonadotropin-releasing hormone (GnRH) is a peptide hormone that mediates central control of reproduction, acting via G-protein coupled receptors that are primarily Gq coupled and mediate GnRH effects on the synthesis and secretion of luteinizing hormone and follicle-stimulating hormone. A great deal is known about the GnRH receptor signaling network but GnRH is secreted in short pulses and much less is known about how gonadotropes decode this pulsatile signal. Similarly, single cell measures reveal considerable cell-cell heterogeneity in responses to GnRH but the impact of this variability on signaling is largely unknown. Ordinary differential equation-based mathematical models have been used to explore the decoding of pulse dynamics and information theory-derived statistical measures are increasingly used to address the influence of cell-cell variability on the amount of information transferred by signaling pathways. Here, we describe both approaches for GnRH signaling, with emphasis on novel insights gained from the information theoretic approach and on the fundamental question of why GnRH is secreted in pulses.

Information Transfer via Gonadotropin-Releasing Hormone Receptors to ERK and NFAT: Sensing GnRH and Sensing Dynamics.

Information theoretic approaches can be used to quantify information transfer via cell signaling networks. In this study, we do so for gonadotropin-releasing hormone (GnRH) activation of extracellular signal-regulated kinase (ERK) and nuclear factor of activated T cells (NFAT) in large numbers of individual fixed LßT2 and HeLa cells. Information transfer, measured by mutual information between GnRH and ERK or NFAT, was <1 bit (despite 3-bit system inputs). It was increased by sensing both ERK and NFAT, but the increase was <50%. In live cells, information transfer via GnRH receptors to NFAT was also <1 bit and was increased by consideration of response trajectory, but the increase was <10%. GnRH secretion is pulsatile, so we explored information gained by sensing a second pulse, developing a model of GnRH signaling to NFAT with variability introduced by allowing effectors to fluctuate. Simulations revealed that when cell-cell variability reflects rapidly fluctuating effector levels, additional information is gained by sensing two GnRH pulses, but where it is due to slowly fluctuating effectors, responses in one pulse are predictive of those in another, so little information is gained from sensing both. Wet laboratory experiments revealed that the latter scenario holds true for GnRH signaling; within the timescale of our experiments (1 to 2 hours), cell-cell variability in the NFAT pathway remains relatively constant, so trajectories are reproducible from pulse to pulse. Accordingly, joint sensing, sensing of response trajectories, and sensing of repeated pulses can all increase information transfer via GnRH receptors, but in each case the increase is small.

Mathematical modeling of gonadotropin-releasing hormone signaling.

Gonadotropin-releasing hormone (GnRH) acts via G-protein coupled receptors on pituitary gonadotropes to control reproduction. These are Gq-coupled receptors that mediate acute effects of GnRH on the exocytotic secretion of luteinizing hormone (LH) and follicle-stimulating hormone (FSH), as well as the chronic regulation of their synthesis. GnRH is secreted in short pulses and GnRH effects on its target cells are dependent upon the dynamics of these pulses. Here we overview GnRH receptors and their signaling network, placing emphasis on pulsatile signaling, and how mechanistic mathematical models and an information theoretic approach have helped further this field.

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.