Scheidereit, C. | Search bioRxiv

bioRxiv2019

Human endogenous retrovirus HERV-K(HML-2) RNA causes neurodegeneration through Toll-like receptors

Although human endogenous retroviruses (HERVs) represent a substantial proportion of the human genome and some HERVs have been suggested to be involved in neurological disorders, little is known about their biological function and pathophysiological relevance. HERV-K(HML-2) comprises evolutionarily young proviruses transcribed in the brain. We report that RNA derived from an HERV-K(HML-2) env gene region binds to the human RNA-sensing Toll-like receptor (TLR) 8, activates human TLR8, as well as murine Tlr7, and causes neurodegeneration through TLR8 and Tlr7 in neurons and microglia. HERV-K(HML-2) RNA introduced extracellularly into the cerebrospinal fluid (CSF) of either C57BL/6 wild-type mice or APPPS1 mice, a mouse model for Alzheimers disease (AD), resulted in neurodegeneration. Tlr7-deficient mice were protected against neurodegenerative effects, but were re-sensitized towards HERV-K(HML-2) RNA when neurons ectopically expressed murine Tlr7 or human TLR8. Accordingly, transcriptome datasets of human brain samples from AD patients revealed a specific correlation of upregulated HERV-K(HML-2) and TLR8 RNA expression. HERV-K(HML-2) RNA was detectable more frequently in CSF from AD individuals compared to controls. Our data establish HERV-K(HML-2) RNA as an endogenous ligand for human TLR8 and murine Tlr7 and imply a functional contribution of specific human endogenous retroviral transcripts to neurodegenerative processes such as AD.

neuroscience↗

bioRxiv2019

A quantitative modular modeling approach reveals the consequences of different A20 feedback implementations for the NF-kB signaling dynamics

Signaling pathways involve complex molecular interactions and are controlled by non-linear regulatory mechanisms. If details of regulatory mechanisms are not fully elucidated, they can be implemented by different, equally reasonable mathematical representations in computational models. The study presented here focusses on NF-{kappa}B signaling, which is regulated by negative feedbacks via I{kappa}B and A20. A20 inhibits NF-{kappa}B activation indirectly through interference with proteins that transduce the signal from the TNF receptor complex to activate the I{kappa}B kinase (IKK) complex. We focus on the question how different implementations of the A20 feedback impact the dynamics of NF-{kappa}B. To this end, we develop a modular modeling approach that allows combining previously published A20 modules with a common pathway core module. The resulting models are based on a comprehensive experimental data set and therefore show quantitatively comparable NF-{kappa}B dynamics. Based on defined measures for the initial and long-term behavior we analyze the effects of a wide range of changes in the A20 feedback strength, the I{kappa}B feedback strength and the TNF stimulation strength on NF-{kappa}B dynamics. This shows similarities between the models but also model-specific differences. In particular, the A20 feedback strength and the TNF stimulation strength affect initial and long-term NF-{kappa}B concentrations differently in the analyzed models. We validated our model predictions experimentally by varying TNF concentrations applied to HeLa cells. These time course data indicate that only one of the A20 feedback models appropriately describes the impact of A20 on the NF-{kappa}B dynamics.\n\nAuthor summaryModels are abstractions of reality and simplify a complex biological process to its essential components and regulations while preserving its particular spatial-temporal characteristics. Modelling of biological processes is based on assumptions, in part to implement the necessary simplifications but also to cope with missing knowledge and experimental information. In consequence, biological processes have been implemented by different, equally reasonable mathematical representations in computational models. We here focus on the NF-{kappa}B signaling pathway and develop a modular modeling approach to investigate how different implementations of a negative feedback regulation impact the dynamical behavior of a computational model. Our analysis shows similarities of the models with different implementations but also reveals implementation-specific differences. The identified differences are used to design and perform informative experiments that elucidate unknown details of the regulatory feedback mechanism.

systems biology↗