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Kroemer, G.

Publications and source records attributed to Kroemer, G..

2 recordsLinked to original sources

Induction of transposable element expression is central to innate sensing

Evidence indicates that transposable elements (TEs) stimulate innate sensing pathways in various pathologies but it is not clear whether they are sensed during normal physiological responses. Here we show that, during activation with an exogenous pathogen associated molecular pattern (PAMP), dendritic cells (DCs) epigenetically remodel heterochromatin at TEs by depleting the methyltransferase Suv39h1 and reducing histone-3 lysine-9 trimethylation (H3K9me3). TLR4 signaling activates TE expression to enhance innate responses through the DNA sensor cGAS. Cytosolic cGAS-bound DNA comprised LINE1 TEs as the predominant endogenous ligands. Concordantly, LINE1 inhibition attenuated the type-I IFN response to LPS and rescued influenza virus infection. We propose that in healthy cells, exogenous PAMPs epigenetically activate self-derived PAMPs (LINE1) that engage cGAS to enhance responses. These data explain why pathogens employ redundant and broad innate immune countermeasures, to suppress activation of host PAMPs and illustrate a hitherto unappreciated role for host genome-derived PAMPs in response to pathogens.

immunology

UPMaBoSS: a novel framework for dynamic cell population modeling.

One of the aims of mathematical modeling is to understand and simulate the effects of biological perturbations and suggest ways to intervene and reestablish proper cell functioning. However, it remains a challenge, especially when considering the dynamics at the level of a cell population, with cells dying, dividing and interacting. Here, we introduce a novel framework for the dynamical modelling of cell populations packaged into a dedicated tool, UPMaBoSS. We rely on the preexisting tool MaBoSS, which enables probabilistic simulations of cellular networks, and add a novel layer to account for cell interactions and population dynamics. We illustrate our methodology by means of a case study dealing with TNF-induced cell death. Interestingly, the simulation of cell population dynamics with UPMaBoSS reveals a mechanism of resistance triggered by TNF treatment. This appoach can be applied to diverse models of cellular networks, for example to study the impact of ligand release or drug treatments on cell fate decisions, such as commitment to proliferation, differentiation, apoptosis, etc. Relatively easy to encode, UPMaBoSS simulations require only moderate computational power and execution time. To ease the reproduction of simulations, we provide several Jupyter notebooks that can be accessed within a new release of the CoLoMoTo Docker image, which contains all required software and the example models.

bioinformatics