Dataset Information

Elucidation of the inflammatory and neuroprotective molecular network of TNFalpha in neurons related to Alzheimer´s disease

ABSTRACT: Neuroinflammation is a hallmark of various neurological diseases such as Alzheimer’s disease. The aim of the study was to elucidate the dose-dependent in vitro molecular mechanism of TNFα in neurons related to neuroprotection and -degeneration. We performed a quantitative proteomics and phospho-proteomics study in HT22 neuronal cells as well as differentiated human neurons to elucidate alterations in TNFα dose-dependent (0.1 – 100.0 ng/ml) signaling pathways. We report here that 10.0 ng/ml TNFα reduces mitochondrial signaling and inhibits protein translation signaling related to mTOR but leads also at the same time to induction of neuroprotective MAPK-CREB signaling after 24 hours in HT22 cells. Stimulation of TNFα (1.0 ng/ml, 24 hours) in human neurons reveals similar cellular mechanisms on protein translation signaling related to mTOR as seen in HT22 cells at 10.0 ng/ml dose whereas mitochondrial membrane potential and reactive oxygen species level were not changed. SiRNA-mediated knockdown of CREB in human neurons prior to TNFα stimulation led to a reduced number of protein/phospho-protein hits compared to siRNA-mediated knockdown of CREB or TNFα stimulation alone and countermeasures the reduced CREB signaling. In vivo data of TNFα knock-out transgenic mice showed that learning ability does not depend on TNFα during normal aging but that TNFα preserves the learning and memory ability during episodes of systemic inflammation resembling the persistent neuroinflammation status as seen in Alzheimer´s disease. This may be based on modulation of CREB as revealed by our in vitro studies. Our data indicate that several molecular targets and signaling pathways induced by TNFα in neurons resemble those of Alzheimer´s pathology. It may suggest that TNFα functions as a contributing factor to this neurodegenerative disease but has also at the same time neuroprotective properties regulating learning and memory formation under neuroinflammatory status.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human) Mus Musculus (mouse)

SUBMITTER: Stefan J. Kempf

LAB HEAD: Martin R. Larsen

PROVIDER: PXD005675 | Pride | 2017-08-08

REPOSITORIES: Pride

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Publications

TNFα affects CREB-mediated neuroprotective signaling pathways of synaptic plasticity in neurons as revealed by proteomics and phospho-proteomics.

Jensen Pia P Myhre Christa L CL Lassen Pernille S PS Metaxas Athanasios A Khan Asif M AM Lambertsen Kate L KL Babcock Alicia A AA Finsen Bente B Larsen Martin R MR Kempf Stefan J SJ

Oncotarget 20170721

Neuroinflammation is a hallmark of Alzheimer's disease and TNFα as the main inducer of neuroinflammation has neurodegenerative but also pro-regenerative properties, however, the dose-dependent molecular changes on signaling pathway level are not fully understood. We performed quantitative proteomics and phospho-proteomics to target this point.In HT22 cells, we found that TNFα reduced mitochondrial signaling and inhibited mTOR protein translation signaling but also led to induction of neuroprotec ...[more]

PMID: 28753583

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Project description:Brown2004 - NGF and EGF signaling This model is described in the article: The statistical mechanics of complex signaling networks: nerve growth factor signaling. Brown KS, Hill CC, Calero GA, Myers CR, Lee KH, Sethna JP, Cerione RA. Phys Biol 2004 Dec; 1(3-4): 184-195 Abstract: The inherent complexity of cellular signaling networks and their importance to a wide range of cellular functions necessitates the development of modeling methods that can be applied toward making predictions and highlighting the appropriate experiments to test our understanding of how these systems are designed and function. We use methods of statistical mechanics to extract useful predictions for complex cellular signaling networks. A key difficulty with signaling models is that, while significant effort is being made to experimentally measure the rate constants for individual steps in these networks, many of the parameters required to describe their behavior remain unknown or at best represent estimates. To establish the usefulness of our approach, we have applied our methods toward modeling the nerve growth factor (NGF)-induced differentiation of neuronal cells. In particular, we study the actions of NGF and mitogenic epidermal growth factor (EGF) in rat pheochromocytoma (PC12) cells. Through a network of intermediate signaling proteins, each of these growth factors stimulates extracellular regulated kinase (Erk) phosphorylation with distinct dynamical profiles. Using our modeling approach, we are able to predict the influence of specific signaling modules in determining the integrated cellular response to the two growth factors. Our methods also raise some interesting insights into the design and possible evolution of cellular systems, highlighting an inherent property of these systems that we call 'sloppiness.' The figures in the paper show results from computations performed over an ensemble of all parameter sets that fit the available data. This file contains only the best fit parameters. The full ensemble of parameters is available at http://www.lassp.cornell.edu/sethna/GeneDynamics/PC12DataFiles/ (Also, the best-fit parameter set produces a curve for DN Rap1 that is less "peakish" than the ensemble average.) The conversion factors for EGF and NGF concentrations account for their molecular weights and the density of cells in the culture dish. These concentrations are saturating, so the exact values are not critical. Because the Erk data fit to measure only fold changes in activity, there is no absolute scale for the y-axes. Thus the curves from this file have different magnitudes than those published. To reproduce the figures from the paper: 2a) For EGF stimulation, set the initial concentration of EGF to 100 ng/ml * 100020 (molecule/cell)/(ng/ml) = 10002000. For NGF stimulation, set the initial concentration of NGF to 50 ng/ml * 4560 (molecule/cell)/(ng/ml) = 456000 5a) To simulate LY294002 addition, set kPI3KRas and kPI3K to 0. 5b) To simulate a dominant negative Rap1, set kRap1ToBRaf to 0. To simulate a dominant negative Ras, set kRasToRaf1 and kPI3KRas to 0. Almost all the data fit with this model by the authors are from Western blots. Given the uncertainties in antibody effectiveness and other factors, one can't a priori derive a conversion between the arbitrary units for a given set of data and molecules per cell. So the authors used an adjustable "scale factor" that converts between molecules per cell and Western blot units. For the EGF stimulation data in figure 2a) the scale factor conversion is 1.414e-05 (U/mg)/(molecule/cell). For the NGF stimulation data in figure 2a) it is 7.135e-06 (U/mg)/(molecule/cell). This model is hosted on BioModels Database and identified by: BIOMD0000000033. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Dataset Information

Elucidation of the inflammatory and neuroprotective molecular network of TNFalpha in neurons related to Alzheimer´s disease

Publications

TNFα affects CREB-mediated neuroprotective signaling pathways of synaptic plasticity in neurons as revealed by proteomics and phospho-proteomics.

Similar Datasets

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