Computational modelling of the regulation of Insulin signalling by oxidative stress

BACKGROUND: Existing models of insulin signalling focus on short term dynamics, rather than the longer term dynamics necessary to understand many physiologically relevant behaviours. We have developed a model of insulin signalling in rodent adipocytes that includes both transcriptional feedback through the Forkhead box type O (FOXO) transcription factor, and interaction with oxidative stress, in addition to the core pathway. In the model Reactive Oxygen Species are both generated endogenously and can be applied externally. They regulate signalling though inhibition of phosphatases and induction of the activity of Stress Activated Protein Kinases, which themselves modulate feedbacks to insulin signalling and FOXO. RESULTS: Insulin and oxidative stress combined produce a lower degree of activation of insulin signalling than insulin alone. Fasting (nutrient withdrawal) and weak oxidative stress upregulate antioxidant defences while stronger oxidative stress leads to a short term activation of insulin signalling but if prolonged can have other effects including degradation of the insulin receptor substrate (IRS1) and FOXO. At high insulin the protective effect of moderate oxidative stress may disappear. CONCLUSION: Our model is consistent with a wide range of experimental data, some of which is difficult to explain. Oxidative stress can have effects that are both up- and down-regulatory on insulin signalling. Our model therefore shows the complexity of the interaction between the two pathways and highlights the need for such integrated computational models to give insight into the dysregulation of insulin signalling along with more data at the individual level. A complete SBML model file can be downloaded from BIOMODELS (https://www.ebi.ac.uk/biomodels-main) with unique identifier MODEL1212210000. Other files and scripts are available as additional files with this journal article and can be downloaded from https://github.com/graham1034/Smith2012_insulin_signalling

Cell Senescence-Independent Changes of Human Skin Fibroblasts with Age

Skin ageing is defined, in part, by collagen depletion and fragmentation that leads to a loss of mechanical tension. This is currently believed to reflect, in part, the accumulation of senescent cells. We compared the expression of genes and proteins for components of the extracellular matrix (ECM) as well as their regulators and found that in vitro senescent cells produced more matrix metalloproteinases (MMPs) than proliferating cells from adult and neonatal donors. This was consistent with previous reports of senescent cells contributing to increased matrix degradation with age; however, cells from adult donors proved significantly less capable of producing new collagen than neonatal or senescent cells, and they showed significantly lower myofibroblast activation as determined by the marker α-SMA. Functionally, adult cells also showed slower migration than neonatal cells. We concluded that the increased collagen degradation of aged fibroblasts might reflect senescence, the reduced collagen production likely reflects senescence-independent processes

TimiRGeN: R/Bioconductor package for time series microRNA-mRNA integration and analysis

Motivation: The analysis of longitudinal datasets and construction of gene regulatory networks (GRNs) provide a valuable means to disentangle the complexity of microRNA (miRNA)-mRNA interactions. However, there are no computational tools that can integrate, conduct functional analysis and generate detailed networks from longitudinal miRNA-mRNA datasets. Results: We present TimiRGeN, an R package that uses time point-based differential expression results to identify miRNA-mRNA interactions influencing signaling pathways of interest. miRNA-mRNA interactions can be visualized in R or exported to PathVisio or Cytoscape. The output can be used for hypothesis generation and directing in vitro or further in silico work such as GRN construction

Predominant Asymmetrical Stem Cell Fate Outcome Limits the Rate of Niche Succession in Human Colonic Crypts.

Stem cell (SC) dynamics within the human colorectal crypt SC niche remain poorly understood, with previous studies proposing divergent hypotheses on the predominant mode of SC self-renewal and the rate of SC replacement. Here we use age-related mitochondrial oxidative phosphorylation (OXPHOS) defects to trace clonal lineages within human colorectal crypts across the adult life-course. By resolving the frequency and size distribution of OXPHOS-deficient clones, quantitative analysis shows that, in common with mouse, long-term maintenance of the colonic epithelial crypt relies on stochastic SC loss and replacement mediated by competition for limited niche access. We find that the colonic crypt is maintained by ~5 effective SCs. However, with a SC loss/replacement rate estimated to be slower than once per year, our results indicate that the vast majority of individual SC divisions result in asymmetric fate outcome. These findings provide a quantitative platform to detect and study deviations from human colorectal crypt SC niche homeostasis during the process of colorectal carcinogenesis.Wellcome Trus

A Stochastic Step Model of Replicative Senescence Explains ROS Production Rate in Ageing Cell Populations

Increases in cellular Reactive Oxygen Species (ROS) concentration with age have been observed repeatedly in mammalian tissues. Concomitant increases in the proportion of replicatively senescent cells in ageing mammalian tissues have also been observed. Populations of mitotic human fibroblasts cultured in vitro, undergoing transition from proliferation competence to replicative senescence are useful models of ageing human tissues. Similar exponential increases in ROS with age have been observed in this model system. Tracking individual cells in dividing populations is difficult, and so the vast majority of observations have been cross-sectional, at the population level, rather than longitudinal observations of individual cells

Increasing extracellular H2O2 produces a bi-phasic response in intracellular H2O2, with peroxiredoxin hyperoxidation only triggered once the cellular H2O2-buffering capacity is overwhelmed

Reactive oxygen species, such as H2O2, can damage cells but also promote fundamental processes, including growth, differentiation and migration. The mechanisms allowing cells to differentially respond to toxic or signaling H2O2 levels are poorly defined. Here we reveal that increasing external H2O2 produces a bi-phasic response in intracellular H2O2. Peroxiredoxins (Prx) are abundant peroxidases which protect against genome instability, ageing and cancer. We have developed a dynamic model simulating in vivo changes in Prx oxidation. Remarkably, we show that the thioredoxin peroxidase activity of Prx does not provide any significant protection against external rises in H2O2. Instead, our model and experimental data are consistent with low levels of extracellular H2O2 being efficiently buffered by other thioredoxin-dependent activities, including H2O2-reactive cysteines in the thiol-proteome. We show that when extracellular H2O2 levels overwhelm this buffering capacity, the consequent rise in intracellular H2O2 triggers hyperoxidation of Prx to thioredoxin-resistant, peroxidase-inactive form/s. Accordingly, Prx hyperoxidation signals that H2O2 defenses are breached, diverting thioredoxin to repair damage

Method for modeling, optimizing, parameterizing, testing and/or validating a dynamic network or network perturbation

The present invention relates to a method (2) for predicting the response of a structure (4), of a group of structures (16) and/or of a network profile (18), as a result to a perturbation, in particular by an agent (6) or a combination of agents (8) through modelling, optimizing, paramaterizing, testing and/or validating a dynamic network or network perturbations.<br/

Method for modeling, optimizing, parameterizing, testing and/or validating a dynamic network or network perturbation

The present invention relates to a method (2) for predicting the response of a structure (4), of a group of structures (16) and/or of a network profile (18), as a result to a perturbation, in particular by an agent (6) or a combination of agents (8) through modelling, optimizing, paramaterizing, testing and/or validating a dynamic network or network perturbations