Computational modelling of TNFα related pathways regulated by neuroinflammation, oxidative stress and insulin resistance in neurodegeneration
Applied Network Science volume 5, Article number: 72 (2020)
Computational and mathematical modelling towards understanding the structure and dynamics of biological systems has significantly impacted on translational neuroscience to face novel approaches toward neurological disorders such as Alzheimer’s (AD) and Parkinson’s disease (PD). In this study, a computational model of AD and PD have been modelled using biochemical systems theory, and shows how Tumour Necrosis Factor alpha (TNF훼) regulated neuroinflammation, oxidative stress and insulin pathways can dysregulate its downstream signalling cascade that lead to neurodegeneration observed in AD and PD. The experimental data for initial conditions for this model and validation of the model was based on data reported in literature. In simulations, elevations in the aggregations of major proteins involved in the pathology of AD and PD including amyloid beta, alpha synuclein, tau have been modelled. Abnormal aggregation of these proteins and hyperphosphorylation of tau were observed in the model. This aggregation may lead to developing Lewy bodies, fibrils, plaques and tangles inside neurons that trigger apoptosis. An increase in the concentrations of TNF훼 and glutamate during diseased conditions was noted in the model. Accumulation of these proteins may be related to the feedback mechanism of TNF훼 that initiates its own release and the production of excess glutamate. This could lead to the prolonged activation of microglia that result in death of surrounding neurons. With the elevation in reactive oxygen species, oxidative stress also increased. Simulations suggest insulin may be an important factor identifying neurodegeneration in AD and PD, through its action along with the neuroinflammation and oxidative stress. Low insulin level was noticed in the diseased condition due to abnormal protein aggregation that leads to TNFα release. Given the role towards better design of real experiments, accumulation of oligomers of mutated proteins in AD and PD activating microglia and secreting TNFα along with other cytokines map to oxidative stress that led to cell death.
Alzheimer’s disease (AD) is a major neurodegenerative disorder often related to the deposition of amyloid β-peptide (Aβ) plaques in brain tissue followed by formation of neurofibrillary tangles (Murphy and Levine 2010) and is associated to symptoms such as memory loss, alterations in mood and behavior and have been associated with, dementia, disorientation and aphasia (Jahn 2013). Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder that causes death of dopaminergic neurons in substantia nigra pars compacta of the midbrain, which leads to the decline in the synthesis of dopamine (Mhyre et al. 2012). It is also characterised by a large number of motor and non-motor features and by the increase in incidence above the age of 65 (Mahlknecht et al. 2015). The clinical manifestations of PD include resting tremor, muscular rigidity, bradykinesia, depression and postural instability (DeMaagd and Philip 2015). According to the Alzheimer’s Association, National Institutes of Health in the United States of America spends $480 million on Alzheimer’s research compared to $3 billion on HIV/AIDS, $4 billion on heart disease and $6 billion on cancer. PD affects 1–2 per 1000 of the population with its prevalence increasing with age and affecting about 1% of the population above 60 years (Tysnes and Storstein 2017). Therefore, discovery and characterization of accurate biomarkers play a crucial role in disease prediction (Padmanabhan et al. 2017). Both, genetic and environmental factors, have been identified related to the risks of developing AD (Killin et al. 2016). If the condition can be detected earlier, effective treatment can help manage the condition although, often, fewer symptoms manifest in the early stages resulting in late detection of the disease. The biomarkers till date change with the factors and illness, and it may not be expressed all the time. One of the main medications given for PD patients is L-Dopa (Hardebo and Owman 1980). Although, several experimental models to analyse the disease pathology have been developed, there are aspects regarding the pathological cascade of both sporadic and familial conditions (Golde et al. 2013; Ferreira et al. 2015) that need to be addressed. A reason behind this is the limitations in conducting in vitro and in vivo experiments with neurons (Polikov et al. 2007) and the complexity in neuronal circuits. For example, in the case of AD, it is difficult to extract β amyloid aggregates from neural specimens, and previous reports indicate aggregates in saline cause toxicity in vitro (Esparza et al. 2016; Giorgetti et al. 2018). Although there is evidence that amyloid β oligomers contribute chronic neurological manifestations, they are difficult to be detected by conventional staining techniques (Ferreira et al. 2015).
There are some drugs available to manage the symptoms, despite decades of research, no treatment has been reported to completely halt the disease conditions yet (Padmanabhan et al. 2017). Since the disease mechanisms are poorly understood, especially in case of major proteins such as alpha synuclein (αS), amyloid β and tau that form fibrils, plaques and tangles, it is difficult to turn them off and patients are diagnosed late or remain undiagnosed (Bendor et al. 2013; Razzokov et al. 2019). Studies have also suggested the accumulation of Aβ and αS in the brain during the natural process of aging (Li et al. 2004). As these aggregated proteins lead to signal cross-talk within the brain, it may be associated to further signalling cascades which initiates onset of the disease. This includes several abnormal cell damage events such as mitochondrial dysfunction, oxidative stress, hyperphosphorylation of tau, increased neuro-inflammatory responses, decreased neuroplasticity and neurogenesis, neurodegeneration etc. (de JR de Paula et al. 2009). Recent studies have shown that during neuroinflammation, the release of TNFα by activated astrocytes and microglia was linked to AD and PD (Reddy and Seth n.d. ; Olmos and Lladó 2014).
Inflammation in the brain may be promoted by invasion of pathogens, spinal cord injury, aggregation of misfolded proteins, tangles, plaques. The cellular insults activate microglia and initiate the production of proinflammatory cytokines to protect neurons from tissue damage initially, which results in neurodegeneration (Amor et al. 2010). Tumour necrosis factor alpha (TNF훼), a major proinflammatory cytokine, has been known to have an important role in neuroinflammation and glutamate mediated excitotoxicity related to AD and PD (A Frankola et al. 2011; Olmos and Lladó 2014). It has been reported that TNFα along with surface receptors, are present in healthy brain at low levels, and high levels in diseased states (Santello and Volterra 2012). Under normal physiological conditions, the production of excess TNFα is controlled by inhibiting activation of microglia (Wang et al. 2015). During inflammation, astrocytes and microglia become activated through initiation of proinflammatory triggers and cytokines release due to T cells infiltration (Liberman et al. 2019). Aβ and αS have been reported as key proteins that trigger neuroinflammation in AD and PD respectively (Tweedie et al. 2012). Amyloid plaques have been known to cause neurodegeneration due to the toxic effect of Aβ (Pasinetti and Hiller-Sturmhöfel 2008). Hyperphosphorylation of tau forms neurofibrillary tangles that again leads to intracellular lesions in the brain (de JR de Paula et al. 2009). In vitro and in vivo studies have shown that mutations in genes code of amyloid precursor protein, presenilin 1 and 2 trigger plaque formation (Żekanowski et al. 2003). In earlier studies, the involvement and presence of TNFα around Aβ plaques have been reported in the post-mortem brain tissue of both transgenic AD mice and AD conditions (Chang et al. 2017). A study had reported the role of neuroinflammation and TNFα signalling in the early stages of AD and its role in neurodegeneration through accumulation of plaques, neurofibrillary tangles and elevations of misfolded or mutated proteins/genes involved (Montgomery and Bowers 2012). Experiments on both animal and human PD brain tissues suggested that abnormal levels of TNFα released by high microglial activation can lead to dopaminergic cell death (Yiannopoulou and Papageorgiou 2013). Elevation in accumulation of αS aggregates activates microglia in turn increasing the production and release of excess TNFα (Zhang et al. 2018). Both Aβ plaques and αS aggregation can lead to elevated levels of TNFα that eventually results in the progression of AD or PD pathology (Decourt et al. 2016). Cellular and molecular changes implicate increased TNFα levels by microglial activation in both AD and PD, which suggests a commonness in TNFα signalling pathway in the progression of both these diseases (A Frankola et al. 2011). Several studies have indicated changes in oxidative stress due to increased level of reactive oxygen species (ROS) which was induced by TNFα signalling in AD and PD (Fischer and Maier 2015). In both AD and PD conditions, accumulation of abnormal αS and Aβ lead to oxidative stress that trigger the apoptotic pathway (Singh et al. 2019).
Brain’s insulin sensitivity has been studied (Blázquez et al. 2014) and insulin has been known to regulate cellular mechanisms inside the brain (Plum et al. 2005). Insulin has also been documented to regulate various brain functions such as neuroprotection, synaptic plasticity, memory, and reward recognition (Ferrario et al. 2018). The cellular links between insulin resistance and neurodegeneration in PD related pathological mechanisms have been previously discussed (Athauda and Foltynie 2016). Impaired insulin signalling pathway has also been identified as a critical pathological factor contributing to the development of AD (Hölscher 2014). Impaired insulin signalling has been known to be associated to the formation of plaques, tangles, increased oxidative stress, and important factors facilitating neurodegeneration in AD (Rad et al. 2018). Experimental models have shown the critical role of insulin signalling pathway in degradation of Aβ and αS and blocking them led to formation of toxic fibrils and plaques (Sharma and Singh 2016). Insulin signalling and neuroinflammation may be co-related, with an imbalance possibly inducing an elevation in inflammatory cytokines including TNFα as observed during AD and PD (Yang et al. 2018). There is a need to re-analyze the roles and relationships of TNFα signalling, activation of glial cells, oxidative stress, insulin resistance and accumulation of misfolded/mutated protein aggregates that are linked to each other, share common genes and possibly signalling pathways, that may lead to neurodegeneration in both AD and PD pathology.
Modelling disease related signalling pathways in silico helps in understanding the experimentally-relevant relationships between individual proteins, interactions and related perturbations which are crucial for analysing disease mechanisms and for mapping appropriate therapeutic targets (Vidal et al. 2011; Hao et al. 2018). Modelling complex biological pathway networks including their cellular and molecular components, and interactions (Ji et al. 2017) can help connect critical factors statistically relevant as common signaling mechanisms or phentotypic functions to both disorders. Developing computational models can aid reproducing disease pathways and predicting dynamical behaviours essential for approprite protocol design and experimental testing and to map clinical symptoms to molecular processes going through cellular and circuit functions (Conradi et al. 2007; Bartocci and Lió 2016). Using biochemical systems theory (BST), sub-cellular reactions and biochemical pathways were modeled using ordinary differential equations (ODE) for reconstructing signalling dynamics in this study (Savageau et al. 1987). All biochemical reactions involved in disease-related signalling pathways were expressed mathematically using ODE and rate equations were computed using computational tools (Bartocci and Lió 2016).
The objective of this modeling exercise was to map major genes or proteins involved in disease mechanism, the reactions affected by the mutation of these genes and the difference in reactions when compared with healthy controls, action ofpotential drugs. In literature, BST models on oxidative stress and inflammation in insulin resistance were already available for PD condition (Braatz and Coleman 2015). These models explore some of the important pathways involved in PD and the treatment options. Most of the initial conditions for the model parameters were assigned as relative values rather than real data. With the need to model crosstalk and critical networks relevant to neurodegeneration identified by more recent studies, we have incorporated the cross-talk between insulin resistance, oxidative stress and neuroinflammation related to TNFα signalling in normal, AD and PD conditions (Fallahi-Sichani et al. 2011; Sasidharakurup et al. 2020; Su and Wu 2020). The parameteric values relating to biological states and initial conditions for this model were manually extracted from literature on disease models. In a previous study, we had modelled the role of TNFα mediated glutamate excitotoxicity and neuroinflammation (Sasidharakurup et al. 2020) and the variations in TNFα levels during both healthy and diseased conditions were analyzed. Some autocrine loops co-involved in the activation of TNFα were also modeled to study how TNF훼 stimulates its own release. To extend the relationships between AD and PD, this present study focuses on developing a model of TNFα related pathways regulated by neuroinflammation, oxidative stress and insulin resistance during neurodegeneration. In addition, few of the crucial feedback loops involved in TNFα signalling and their emergent properties maintaining the disease condition needed to be incorporated. Aimed towards building a tool for designing experimental interventions and connecting to clinically relevant biomarkers, common cellular components found in both AD and PD conditions, that trigger TNFα signalling such as Aβ, αS, tau phosphorylation, calcium, glutamate etc. were modelled in this paper.
Major pathways involved in TNFα signalling regulated by inflammation, oxidative stress and insulin resistance that leads to neuronal death were modelled in this paper using biochemical systems theory. Pathways were mathematically reconstructed and simulations were done by using CellDesigner (Funahashi et al. 2006). All reactions were modelled as networks with genes, proteins and other cellular components represented as nodes in the network. Reactions were inter-connected using corresponding kinetic formulae. In this reconstruction, all reactions were mathematically represented using ODE, and initial concentration values and rate constant values were assigned.
Enzyme-involved reactions were mathematically expressed using MM kinetics.
Where, Vmax was the maximum rate achieved by the system, at saturating substrate concentration in relation of reaction rate v to [s], where [s] was the concentration of a substrate S. Km was Michealis constant was the rate constant and the rate constant Km (Michaelis constant) was equal to [s] when v is half of Vmax.
Generalized mass action (GMA) kinetics
Each reaction was approximated using the power law equation, represented as a system of ODE (Tucker et al. 2007):
Where i = (1, …, d). Each variable xi represented the concentration of a reactant, and ẋi denoted the time derivative of xi. In the model, the parameters γkp were rate constants, whereas the parameters fijk were kinetic orders.
The concentrations parameters in the MM approximation and in ODEs were estimated from experimental data that have quantified by common molecular biology techniques including ELISA, western blotting, radioenzymatic assay, HPLC, mass spectrometry and the rate parameters were obtained from previous modelling or from in vitro studies on normal and diseased tissues. Table 1, reports the known critical proteins in the model of AD pathway, their concentration values, corresponding experiments, the experimental model and related literature reference. For biologically valid reconstructions, the model included the concentration values of important proteins including TNFα (Mogi et al. 1994), tau (Kester et al. 2009), Aβ (Kester et al. 2009), calcium (Paglia et al. 2016), APP (Davidsson et al. 2001), αS (Hansson et al. 2014), IL-6 (Wu et al. 2015), NOS (Dorheim et al. 1994), GSK-3 (Pei et al. 1997) both in control and AD conditions.
Experiment-derived values (see Table 2) for concentrations of TNFα (Mogi et al. 1994), glutamate (Iwasaki et al. 1992), calcium (Putney and Tomita 2011), TNFR1 (de JR de Paula et al. 2009), αS (Oczkowska et al. 2014), ROS (Tretter and Dam-Vizi 2004), IL-1β (Hu et al. 2015) and tau (Blennow et al. 1995) in control and PD conditions were incorporated into the model.
Using this literature data, the biochemical networks in AD and PD have been modelled from simple reactions to generate complex networks made from proteins and other biomolecules as in experimental observations. For example, in the case of TNFα signalling, the emergent properties of the system (see Fig. 1) were related to three vicious circles (Olmos and Lladó 2014).. As reported earlier, initial concentration values for the model were extracted from available experimental data either from studies conducted on neurons in the post-mortem brain of patients or from experimental animal models. For example, initial concentration values for TNFα in case of normal and diseased conditions were taken from a study by Mogi et al. (1994) where the elevated TNFα concentration level in diseased condition was measured in the striatum and cerebrospinal fluid (CSF). The concentration in CSF was noted as 22.3 + 9.5 pg/ml in control and 96.3 + 9.1 pg/ml in patients. The feedback mechanism in the production of extracellular TNFα involves both microglial and astrocyte TNFα along with their respective TNFR1 receptors (Fig. 1).
The feedback mechanisms involving TNFα release during inflammation can be expressed using a simple feedback loop involved in the TNFα pathway. In the model, the extracellular TNFα (exTNFα) bound to microglial TNFα receptor (mTNFR1) and lead to the production of microglial TNFα (mTNFα) inside the microglia. As in experiments, the excess TNFα was modeled to be released and initiated its own microglial release. exTNFα can also bind to its receptor expressed in astrocyte (aTNFR1) and release TNFα inside astrocyte (aTNFα) which again triggers its own release. The individual interactions involved were represented using mathematical equations employing kinetics law and ODE. Here, k1, k2, k3 .... k6 are the rate constants.
The individual reactions in this loop were expressed as:
Here, extracellular TNFα (exTNFα), microglial TNFα (mTNFα), microglial TNFR1(mTNFR1), astroglial TNFα (aTNFα), astroglial TNFR1(TNFα) were the different reaction species. In a reaction, production of a new species was considered as a positive reaction whereas its degradation/release of the same/new species was a negative reaction. In this BST modelling approach, exTNFα binds to mTNFR1 that lead to the production of mTNFα inside the microglia. Here, the rate of change of mTNFR1 was mathematically represented as in Eq. (3). In the model, when mTNFR1 was considered, the reaction was exTNFα related. It bound to mTNFR1, which was a positive reaction and this led to the production of mTNFα inside the microglia which was modeled as a negative reaction. Similarly, rate changes of other reactions in the model were also converted to mathematical equations according to their rate equations. Here, Eq. (4) was the equation for mTNFα, Eq. (5) for aTNFα, Eq. (6) for aTNFR1 and Eq. (7) for exTNFα respectively. All these biochemical reactions were modelled and related concentration changes over time have been analysed. The predictions from the model were compared with previous existing reports and validated with available experimental evidence.
Pathways involved in AD and PD regulated by TNFα
Emergent properties of vicious loops in TNFα
The pathway of TNFα signalling (see Fig. 2) included some of the feedback loops and reactions involved in TNFα linked neurodegeneration. Here, activated microglia released TNFα which induced its own release and triggers glutamate release (Olmos and Lladó 2014). The excessive glutamate bound to its receptor on microglia which initiated further TNFα release in excess (Clark and Vissel 2016). In a recent study (Wang et al. 2018), on the TNFα in astrocytes to understand the multidrug resistance gene expression, activated astrocytes released TNFα and consequently it stimulated its own release. Also, it initiated astrocytes to produce excess extracellular glutamate (Mahmoud et al. 2019). In neurons, TNFα lead to excess calcium influx and initiated degeneration of the cells. The cross-talk between the dying neuron and microglia maintained activated microglia that released excess TNFα (Kuno et al. 2005; Olmos and Lladó 2014).
Oxidative stress in AD and PD
In our previous study (Sasidharakurup et al. 2017), we had modelled the PD-related mutated genes involved in the ROS pathway. Mutated and misfolded proteins (see Fig. 3) such as Aβ, αS and accumulation of its aggregates along with TNFα that led to the formation of ROS in AD and PD conditions were included in this model (Fischer and Maier 2015). Elevated ROS activates microglia and astrocytes. This was known to lead to cell damage and inflammation and generated a feed-forward loop of neurodegeneration (Fischer and Maier 2015). In AD, ROS activates the p38 pathway, that initiated tau phosphorylation and hyperphosphorylation that created neurofibrillary tangles, eventually leading to cell death (Niranjan 2014). In PD, αS activates the p38 pathway that induced TNFα and inflammatory cytokines in astrocytes which released cytochrome-c that produced mitochondrial oxidative stress (Yu et al. 2017). This uncontrolled process of ROS induced inflammation and TNFα-induced oxidative stress was a common pathway in neurodegeneration of neurons in both AD and PD.
Impaired insulin signalling in AD
The signalling pathway of insulin and co-related cellular reactions involved in AD and PD were also reconstructed (see Fig. 4). Under normal condition, insulin bound to insulin receptor (IR) and initiated insulin receptor substrate − 1(IRS-1) phosphorylation that triggered PI3 kinase activation (Bedse et al. 2015). This allowed signals for other cellular processes in the normal condition such as cell growth and survival. IR activation led inward and outward flow of cellular compounds in normal condition; the metabolic reactions uptook excess components or degraded it and kept the cell functioning normally (Shetty et al. 2012). In the AD brain, accumulated Aβ gets aggregated and formed oligomers that activated microglia and secreted TNFα along with other cytokines (Mandrekar-Colucci and Landreth 2012). Increased levels of TNFα and other secreted inflammatory cytokines together could inhibit the IRS-1 phosphorylation (Rehman and Akash 2016). Decreased PI3k could increase the activity of Glycogen Synthase Kinase-3 (GSK3) that resulted in phosphorylation of tau and formation of neurofibrillary tangles (Ghareeb et al. 2013). Insulin degrading enzyme (IDE) has been known to be involved in insulin and other proteasome degradation including Aβ peptide (Farris et al. 2003). Insulin resistance in the brain reduced IDE which in turn increased GSK3 activity that led to formation of tangles and gradual accumulation of Aβ. Increased GSK3 could also activate pro-apoptotic factors leading to cell death. In AD, soluble Aβ oligomers also blocked IR that disturbed the normal downstream processing of insulin signalling leading to cell death (Arnold et al. 2018). Reduced IR signalling could also affect PI3k signalling pathway and resulted in down-regulation of glucose metabolism, upregulation of tangles and Aβ formation (Gabbouj et al. 2019). This could activate microglia by increasing the level of TNFα and other cytokines, inhibiting IR, and generated oxidative stress potentially leading to cell death.
Neuroinflammation, oxidative stress and insulin signalling under normal condition
The concentration levels of TNFα, insulin, glutamate, TNFR1, calcium, ROS, αS, P38, rate of cell death and microglial activation were reconstructed (see Fig. 5). In control conditions, there was a slight increase in the level of TNFα and its surface receptor TNFR1 in the initial stage but decreased gradually over time. The level of insulin maintained at a constant level. A rapid drop in the level of excess glutamate and decrease in excess calcium influx was observed. The activation of microglial rate was low compared in normal compared to diseased state. The rate of αS remained relatively unvarying when compared to the disease conditions. P38 level was lower and may be hypothesized as not sufficient for phosphorylation of tau protein. Both ROS formation and cell death rate were also significantly lesser in the normal condition.
Autocrine feedback loops led to continuous activation of microglia, release of TNFα and glutamate from both astrocyte and microglia leading dopaminergic cell death
The concentration differences vary across major AD/PD related proteins under normal condition and concentration variations of proteins in TNFα signalling (see Fig. 5) show a difference between states. In disease state, the concentration level of TNFα was high compared to normal conditions. The feedback loops triggering and maintaining microglial activation led to increase in the release of glutamate from both astrocyte and microglia. In disease conditions, simulations predicted elevation in the concentration of TNFα together with its receptor TNFR1.
Low concentration of insulin led to inflammation, tau hyperphosphorylation, oxidative stress that could initiate apoptotic pathway and neurodegeneration
Impaired insulin pathway in AD and PD conditions (see Fig. 6) was observed as a change in the rate of αS aggregation and Aβ plaques being elevated during diseased conditions than in normal. Insulin signalling affected the relative concentration changes in some of the key proteins involved in AD observed as a reduction in the insulin level compared to the control. Elevations in tau hyperphosphorylation could lead to the formation of neurofibrillary tangles observed during AD conditions. Compared to control, the concentration levels of αS aggregation, Lewy bodies and Aβ plaques were also high in diseased state. Since αS aggregates and Aβ plaques could promote tau phosphorylation and Lewy body formation that resulted in cell death, model replicates key players maintaining high levels of inflammation which may consequently promoted impaired insulin signalling in diseased condition and also triggered apoptotic factors that led to cell death. The relative concentration levels of ROS remain modified as tau and Lewy body levels increased suggesting elevations in protein aggregation as mechanisms led to inflammation, oxidative stress and impaired insulin signalling during AD and PD conditions.
Role of oxidative stress in TNFα signalling and microglial activation
The role of oxidative stress in TNFα signalling pathway was modelled and relative concentration differences in cellular factors involved in AD and PD was reconstructed (see Fig. 7). Elevation in the concentration levels of TNFα, inflammatory cytokines, tau protein and Aβ in AD condition when compared to normal condition. Due to this enhancement, an increased activation of microglia was observed. The elevated level of misfolded and phosphorylated tau attributed in the formation of tangles and plaques compared to control. Consequently, this resulted in an increase in NOS and ROS that led to neurodegeneration. A high level of αS aggregation was noted during the PD condition. The level of TNFα, IL-1β and release of cytochrome c was also observed. The production of ROS and microglial activation increased more than in control.
Model validation and evaluation of biological loops
Based on literature, all the connections between reactions in the modelled pathways were established and validated with published data from fluorometric analysis, ELISA, IMR assay, ion exchange chromatography, western blotting. For example, in the biochemical reactions involving TNFα induced ROS production, activation of the p38 pathway by αS induced TNFα that produced mitochondrial oxidative stress as in the experimental studies by (Schäfers et al. 2003) which shows TNFα activated p38 in rats using Western blot assays and immunohistochemistry. Another study by (Sidoti-De Fraisse et al. 1998) had shown evidence on the role of mitochondria and ROS in TNFα induced cell death. The experiment was done in HeLa cells and analyzed using flow cytometry and fluorescence methods. The model describes correlation between GSK3β and tau phosphorylation, which was validated against the experimental studies by (Duka et al. 2009), where it wass shown that GSK3β catalyses the formation of αS oligomers and these oligomers in turn helped to activate GSK3β. This activated form again helps in tau hyperphosphorylation. The inter-relations between these proteins was demonstrated by co-precipitating α-synuclein with tau and GSK3β by GST pull down assay and western blotting (Kawakami et al. 2011). The study had reported the interaction of αS with both tau and GSK-3β. Similarly, all the interactions and correlations between biochemical reactions modelled in this study were validated by comparing with published experimental data.
Concentration variations of some of the reactions and interactions suggested by this model were validated with evidence from both existing experimental and computational models. In this model, simulation suggested high concentration levels in diseased conditions than in control. An experimental study measuring the TNFα levels in the striatum and CSF of both control and PD brains by sandwich enzyme immunoassay (Mogi et al. 1994) had reported that TNFα levels were significantly higher in PD than in control. Elevations in concentration changes of TNFα and its receptor has also been observed in this model according to the feedback mechanism that might be attributed to diseased states as observed in previous computational models (Anderson et al. 2015). Other computational models also have evidence showing increase in the extracellular TNFα and its receptor concentrations could result in negative regulation of other signaling pathways involved in inflammation and apoptosis (Fallahi-Sichani et al. 2011; Su and Wu 2020). In diseased conditions, simulations showed elevations in the concentration of TNFα together with its receptor TNFR1. Experimental studies have also observed that the feedback interaction of TNFα with its receptor TNFR1 induced its own release (Olmos and Lladó 2014). Both experimental and computational studies supported prolonged activation of microglia release proinflammatory cytokines including TNFα and interleukins that disrupted the downstream cellular signaling processes that could cause cell death as suggested in this model (A Frankola et al. 2011; Anderson et al. 2015; Cilfone et al. 2015). TNFα regulated oxidative stress response, and changes in related proteins and other factors also have been modelled and validated with existing evidence from both experimental and computational models (Braatz and Coleman 2015; Fischer and Maier 2015). Simulations suggested a low concentration of insulin led to inflammation and oxidative stress. A recent computational study by (Smith and Shanley 2013) reproduced multiple experimental observations demonstrating regulation of insulin signaling by oxidative stress that led to cell death as predicted by this model.
In this model, we had reconstructed three subnetworks associated with TNFα signalling (inflammation, oxidative stress and insulin signalling), where the emergent properties of these subnetworks could developed the onset and progression of AD and PD. The critical reactions associated with these subnetworks included abnormal aggregations of αS, Aβ and tau, the major cellular components associated with the pathophysiology of both AD and PD. The simulations suggested abnormal aggregation of these proteins along with TNFα could induce oxidative stress, inflammation and insulin resistance in the brain. Simulations showed high concentration levels of αS aggregation and Aβ plaques during diseased conditions. Aggregation and oligomerization under varying concentrations of reactants were modeled. The concentration levels of Aβ plaques, aggregation of αS and tau hyperphosphorylation increased with respect to time in diseased conditions compared to control, as observed in experiments, suggesting the robustness of this model. The concentration change could further lead to inflammation, oxidative stress and insulin resistance that in turn released excess TNFα initiating stress and inflammation creating a feedback loop of neurodegeneration as observed in experimental studies (Mandrekar-Colucci and Landreth 2012; Fischer and Maier 2015).
Although the role of TNFα pathways were independently associated with neuroinflammation, oxidative stress and insulin signalling, the involvement of autocrine loops and interdependency of biochemical reactions and their correlations in disease onset and progression, as modeled in this study, are critical to understand AD, PD and neurodegeneration.
In this study, the contribution of cellular reactions involved in the pathways related to the neurodegeneration processes that leads to Alzheimer’s and Parkinson’s have been modelled to understand the emergent properties. The modelling shows that mutations in some of the proteins such as αS, Aβ and tau share common pathophysiology in both AD and PD. These proteins along with TNFα, ROS and other kinases can induce oxidative stress in neurons that trigger apoptotic pathways. Simulations suggested insulin was a key factor that could trigger and modulated common signalling pathways observed in AD and PD such as neuroinflammation and oxidative stress. It is also associated with the variations in cellular concentrations of αS, Aβ and tau and led to accumulation of toxic cellular oligomers.
Several factors led to microglial activation. and included TNFα, suggesting the role of TNFα-induced neuroinflammation in activation of glial cells that lead to neurodegeneration. The simulations also highlight feedback loops, oxidative stress and insulin pathway in the brain regulated by TNFα. Feedback interaction of TNFα with its receptor TNFR1 induced its own release matching experimental studies (Olmos and Lladó 2014). Increased concentrations in TNFα and its receptor due to the feedback mechanism could be attributed to diseased states. Increases in the level of TNFα in the model led to production of excess glutamate that consequently led to an increase in TNFα concentration level has been observed.
In neurological conditions, simulations suggested a prolonged activation of microglia. Although TNFα and other cytokines come to homeostasis inside cells in time, glial cells can stay active for a longer period. In diseased condition, this prolonged activation of microglia may lead to a release of proinflammatory cytokines including TNFα and interleukin 6 beta (IL-6β) that could disrupt the downstream cellular signalling processes leading to cell death as suggested in experiments (A Frankola et al. 2011). Like relevant clincal markers. Simulations showedelevated levels of mutated AD and PD related protein aggregation in diseased conditions compared to normal conditions. A high level of αS, Aβ and tau protein; key factors in the formation of fibrils, plaques and tangles were related and could induce oxidative stress in the cell. Increase in concentration levels of TNFα, IL-1β and calcium levels have also been observed in diseased conditions. This could be the attributed cause of activating microglia and could lead to production of TNFα and IL-1β, as indicated in simulations.
The insulin signalling pathway in the brain is regulated by cross-talk between several other signalling pathways including TNFα signalling leading to neuroinflammation and oxidative stress. Along with major mutated proteins in AD/PD such as αS, Aβ and tau, dysregulation in these signalling pathways can cause an insulin resistance in the brain. The results also have shown an increased level of ROS in diseased state. The simulations suggest low insulin could lead to high inflammation rate, oxidative stress and cell death when compared to control. It may be predicted that TNFα, ROS and insulin act as reliable biomarkers for both PD and AD.
The control condition may be indicative of the relative concentration changes in TNFα, insulin, glutamate, TNFR1, calcium, ROS, αS, P38, microglial activation and rate of cell death. This may be used as a prediction template for AD/PD relating conditions of neuroinflammation, oxidative stress and insulin resistance. When compared to diseased state, rate of microglial activation, αS, Aβ, P38, ROS formation and cell death rate, tau hyperphosphorylation, oxidative stress and cell death were considerably low.
Given all the three conditions (inflammation, oxidative stress and insulin resistance), diseased state can be identified with high concentration elevations in TNFα, αS, Aβ and tau compared to control. Given the correlations among the feedback loops, PD can be distinguished from normal conditions through high relative concentration differences in TNFα, glutamate, calcium and rate of cell death during neuroinflammation compared to control. In diseased state models associated with oxidative stress, there could be high activation of microglia, increased concentration levels of calcium, ROS, cytochrome c, and proinflammatory caspases was observed compared to control. Insulin involvement in disease state can be identified with high inflammation rate, oxidative stress and cell death compared to control.
The predictions relate experimentally observed concentrations to parameters seen during clinical measurements. The study correlated Aβ toxicity to potential clinical features such as delusions, hallucinations, seizures attributed with tau toxicity. This matches with recent studies; both familial and sporadic PD patients report symptoms related genes indicating abnormal accumulation of αS, Aβ, tau toxicity and other neuroinflammatory cytokines as mentioned in this model and hence the model can be used to predict changes in biomarkers for both prodromal and preclinical diagnosis of the disease (Popescu 2016; He et al. 2018). This model may serve as a design framework for altering experimental interventions. Although cerebro-spinal fluid was the main source of data for several parameters related to initial conditions, given the predictions from the data it may relate to changes in substantia nigra, blood, serum, blood plasma, brain cortex and hippocampus for labelling and further analysis.
This study extends current modeling studies on TNFα mediated glutamate excitotoxicity and neuroinflammation in PD, and a computational model to analyse TNFα signalling pathway was reconstructed to understand how inflammation, oxidative stress and insulin resistance are related to each other in neurodegeneration developing diseases such as AD and PD. The model closely reconstructs known pathways of biomarkers of chronic neuroinflammation, oxidative stress and insulin signalling that are co-involved in AD and PD. Validation of the autocrine loop-related predictions need further laboratory experiments to be carried out and mapping symptoms to concentration changes that may need an extensive analysis of model’s biochemical reactions and their disruptions leading to neurodegeneration.
Tumour Necrosis Factor alpha
Biochemical systems theory
Ordinary differential eqs.
Generalized Mass Action
Microglial TNFα receptor
Astrocyte TNFα receptor
Reactive oxygen species
Nitrogen oxygen species
Insulin receptor substrate − 1
Glycogen Synthase Kinase-3
Insulin degrading enzyme
Interleukin 1 beta
A Frankola K, H Greig N, Luo W, Tweedie D (2011) Targeting TNF-alpha to elucidate and ameliorate Neuroinflammation in neurodegenerative diseases. CNS Neurol Disord - Drug Targets 10:391–403. https://doi.org/10.2174/187152711794653751
Amor S, Puentes F, Baker D, Van Der Valk P (2010) Inflammation in neurodegenerative diseases. Immunology 129:154–169. https://doi.org/10.1111/j.1365-2567.2009.03225.x
Anderson WD, Makadia HK, Greenhalgh AD et al (2015) Computational modeling of cytokine signaling in microglia. Mol BioSyst 11:3332–3346. https://doi.org/10.1039/c5mb00488h
Arnold SE, Arvanitakis Z, Macauley-Rambach SL et al (2018) Brain insulin resistance in type 2 diabetes and Alzheimer disease: concepts and conundrums. Nat Rev Neurol 14:168–181. https://doi.org/10.1038/nrneurol.2017.185
Athauda D, Foltynie T (2016) Insulin resistance and Parkinson’s disease: a new target for disease modification? Prog Neurobiol 145–146:98–120. https://doi.org/10.1016/j.pneurobio.2016.10.001
Bartocci E, Lió P (2016) Computational modeling, formal analysis, and tools for systems biology. PLoS Comput Biol 12:e1004591. https://doi.org/10.1371/journal.pcbi.1004591
Bedse G, Di Domenico F, Serviddio G, Cassano T (2015) Aberrant insulin signaling in Alzheimer’s disease: current knowledge. Front Neurosci 9. https://doi.org/10.3389/fnins.2015.00204
Bendor J, Logan T, Edwards RH (2013) The function of α-Synuclein. https://doi.org/10.1016/j.neuron.2013.09.004
Blázquez E, Velázquez E, Hurtado-Carneiro V, Ruiz-Albusac JM (2014) Insulin in the brain: its pathophysiological implications for states related with central insulin resistance, type 2 diabetes and alzheimer’s disease. Front Endocrinol (Lausanne) 5:1–21. https://doi.org/10.3389/fendo.2014.00161
Blennow K, Wallin A, Ågren H et al (1995) Tau protein in cerebrospinal fluid - a biochemical marker for axonal degeneration in Alzheimer disease? Mol Chem Neuropathol 26:231–245. https://doi.org/10.1007/BF02815140
Braatz EM, Coleman RA (2015) A mathematical model of insulin resistance in Parkinson’s disease. Comput Biol Chem 56:84–97. https://doi.org/10.1016/j.compbiolchem.2015.04.003
Chang R, Yee K-L, Sumbria RK (2017) Tumor necrosis factor α inhibition for Alzheimer’s disease. J Cent Nerv Syst Dis 9:117957351770927. https://doi.org/10.1177/1179573517709278
Cilfone NA, Ford CB, Marino S et al (2015) Computational modeling predicts IL-10 control of lesion sterilization by balancing early host immunity–mediated antimicrobial responses with caseation during mycobacterium tuberculosis infection. J Immunol 194:664–677. https://doi.org/10.4049/jimmunol.1400734
Clark IA, Vissel B (2016) Excess cerebral TNF causing glutamate excitotoxicity rationalizes treatment of neurodegenerative diseases and neurogenic pain by anti-TNF agents. J Neuroinflammation 13. https://doi.org/10.1186/s12974-016-0708-2
Conradi C, Flockerzi D, Raisch J, Stelling J (2007) Subnetwork analysis reveals dynamic features of complex (bio) chemical networks. Proc Natl Acad Sci U S A 104:19175–19180. https://doi.org/10.1073/pnas.0705731104
Davidsson P, Bogdanovic N, Lannfelt L, Blennow K (2001) Reduced expression of amyloid precursor protein, presenilin-1 and rab3a in cortical brain regions in Alzheimer’s disease. Dement Geriatr Cogn Disord 12:243–250. https://doi.org/10.1159/000051266
de JR de Paula V, Guimarães FM, Diniz BS, Forlenza OV (2009) Neurobiological pathways to Alzheimer’s disease: amyloid-beta, TAU protein or both? Dement Neuropsychol 3:188–194. https://doi.org/10.1590/s1980-57642009dn30300003
Decourt B, Lahiri D, Sabbagh M (2016) Targeting tumor necrosis factor alpha for Alzheimer’s disease. Curr Alzheimer Res 13:1–1. https://doi.org/10.2174/1567205013666160930110551
DeMaagd G, Philip A (2015) Parkinson’s disease and its management: part 1: disease entity, risk factors, pathophysiology, clinical presentation, and diagnosis. P T 40:504–532
Dorheim MA, Tracey WR, Pollock JS, Grammas P (1994) Nitric oxide synthase activity is elevated in brain microvessels in alzheimer′s disease. Biochem Biophys Res Commun 205:659–665
Duka T, Duka V, Joyce JN, Sidhu A (2009) α-Synuclein contributes to GSK-3β-catalyzed tau phosphorylation in Parkinson’s disease models. FASEB J 23:2820–2830. https://doi.org/10.1096/fj.08-120410
Esparza TJ, Wildburger NC, Jiang H et al (2016) Soluble amyloid-beta aggregates from human Alzheimer’s disease brains. Sci Rep 6:1–16. https://doi.org/10.1038/srep38187
Fallahi-Sichani M, El-Kebir M, Marino S et al (2011) Multiscale computational modeling reveals a critical role for TNF-α receptor 1 dynamics in tuberculosis granuloma formation. J Immunol 186:3472–3483. https://doi.org/10.4049/jimmunol.1003299
Farris W, Mansourian S, Chang Y et al (2003) Insulin-degrading enzyme regulates the levels of insulin, amyloid β-protein, and the β-amyloid precursor protein intracellular domain in vivo. Proc Natl Acad Sci U S A 100:4162–4167. https://doi.org/10.1073/pnas.0230450100
Ferrario CR, Reagan LP, Dorn WJB (2018) Insulin-mediated synaptic plasticity in the CNS: anatomical, functional and temporal contexts HHS public access. Neuropharmacology 136:182–191. https://doi.org/10.1016/j.neuropharm.2017.12.001
Ferreira ST, Lourenco MV, Oliveira MM, De Felice FG (2015) Soluble amyloid-β oligomers as synaptotoxins leading to cognitive impairment in Alzheimer’s disease. Front Cell Neurosci 9:1–17. https://doi.org/10.3389/fncel.2015.00191
Fischer R, Maier O (2015) Interrelation of oxidative stress and inflammation in neurodegenerative disease: role of TNF. Oxidative Med Cell Longev 2015:1–18
Funahashi A, Matsuoka Y, Jouraku A et al (2006) Celldesigner: a modeling tool for biochemical networks. In: Proceedings of the 38th conference on winter simulation, pp 1707–1712
Gabbouj S, Ryhänen S, Marttinen M et al (2019) Altered insulin signaling in Alzheimer’s disease brain-special emphasis on pi3k-akt pathway. Front Neurosci 13. https://doi.org/10.3389/fnins.2019.00629
Ghareeb DA, Mohamed S, El-Sayed M (2013) The interrelationship between insulin resistane and Alzheimer development. J Biomed Sci Eng 06:754–773. https://doi.org/10.4236/jbise.2013.67093
Giorgetti S, Greco C, Tortora P, Aprile FA (2018) Targeting amyloid aggregation: an overview of strategies and mechanisms. Int J Mol Sci 19. https://doi.org/10.3390/ijms19092677
Golde TE, Borchelt DR, Giasson BI, Lewis J (2013) Thinking laterally about neurodegenerative proteinopathies. J Clin Invest 123:1847–1855. https://doi.org/10.1172/JCI66029
Hansson O, Hall S, Öhrfelt A et al (2014) Levels of cerebrospinal fluid α-synuclein oligomers are increased in Parkinson’s disease with dementia and dementia with Lewy bodies compared to Alzheimer’s disease. Alzheimers Res Ther 6:4–9. https://doi.org/10.1186/alzrt255
Hao T, Wang Q, Zhao L et al (2018) Analyzing of molecular networks for human diseases and drug discovery. Curr Top Med Chem 18:1007–1014. https://doi.org/10.2174/1568026618666180813143408
Hardebo JE, Owman C (1980) Barrier mechanisms for neurotransmitter monoamines and their precursors at the blood-brain interface. Ann Neurol 8:1–11. https://doi.org/10.1002/ana.410080102
He R, Yan X, Guo J et al (2018) Recent advances in biomarkers for Parkinson’s disease. Front Aging Neurosci 10
Hölscher C (2014) Drugs developed for treatment of diabetes show protective effects in Alzheimer’s and Parkinson’s diseases. Sheng Li Xue Bao 66:497–510. https://doi.org/10.13294/j.aps.2014.0059
Hu Y, Yu SY, Zuo LJ et al (2015) Investigation on abnormal iron metabolism and related inflammation in Parkinson disease patients with probable RBD. PLoS One 10:1–13. https://doi.org/10.1371/journal.pone.0138997
Iwasaki Y, Ikeda K, Shiojima T, Kinoshita M (1992) Increased plasma concentrations of aspartate, glutamate and glycine in Parkinson’s disease. Neurosci Lett 145:175–177. https://doi.org/10.1016/0304-3940(92)90015-Y
Jahn H (2013) Memory loss in alzheimer’s disease. Dialogues Clin Neurosci 15:445–454
Ji Z, Yan K, Li W et al (2017) Mathematical and computational modeling in complex biological systems. Biomed Res Int 2017:1–16. https://doi.org/10.1155/2017/5958321
Kawakami F, Suzuki M, Shimada N et al (2011) Stimulatory effect of α -synuclein on the tau-phosphorylation by GSK-3 β. FEBS J 278:4895–4904. https://doi.org/10.1111/j.1742-4658.2011.08389.x
Kester MI, Blankenstein MA, Bouwman FH et al (2009) CSF biomarkers in alzheimer’s disease and controls: associations with apoe genotype are modified by age. J Alzheimers Dis 16:601–607. https://doi.org/10.3233/JAD-2009-0999
Killin LOJ, Starr JM, Shiue IJ, Russ TC (2016) Environmental risk factors for dementia: a systematic review. BMC Geriatr 16:1–28. https://doi.org/10.1186/s12877-016-0342-y
Kuno R, Wang J, Kawanokuchi J et al (2005) Autocrine activation of microglia by tumor necrosis factor-α. J Neuroimmunol 162:89–96. https://doi.org/10.1016/j.jneuroim.2005.01.015
Li W, Lesuisse C, Xu Y et al (2004) Stabilization of α-synuclein protein with aging and familial Parkinson’s disease-linked A53T mutation. J Neurosci 24:7400–7409. https://doi.org/10.1523/JNEUROSCI.1370-04.2004
Liberman AC, Trias E, Da Silva CL et al (2019) Neuroimmune and inflammatory signals in complex disorders of the central nervous system. Neuroimmunomodulation 25:246–270. https://doi.org/10.1159/000494761
Mahlknecht P, Seppi K, Poewe W (2015) The concept of prodromal Parkinson’s disease. J Parkinsons Dis 5:681–697. https://doi.org/10.3233/JPD-150685
Mahmoud S, Gharagozloo M, Simard C, Gris D (2019) Astrocytes maintain glutamate homeostasis in the CNS by controlling the balance between glutamate uptake and release. Cells 8:184. https://doi.org/10.3390/cells8020184
Mandrekar-Colucci S, Landreth GE (2012) Microglia and inflammation in Alzheimers disease. CNS Neurol Disord - Drug Targets 9:156–167. https://doi.org/10.2174/187152710791012071
Mhyre TR, Boyd JT, Hamill RW, Maguire-Zeiss KA (2012) Parkinson’s disease. Subcell Biochem 65:389–455. https://doi.org/10.1007/978-94-007-5416-4_16
Mogi M, Harada M, Riederer P et al (1994) Tumor necrosis factor-α (TNF-α) increases both in the brain and in the cerebrospinal fluid from parkinsonian patients. Neurosci Lett 165:208–210. https://doi.org/10.1016/0304-3940(94)90746-3
Montgomery SL, Bowers WJ (2012) Tumor necrosis factor-alpha and the roles it plays in homeostatic and degenerative processes within the central nervous system. J NeuroImmune Pharmacol 7:42–59. https://doi.org/10.1007/s11481-011-9287-2
Murphy MP, Levine H (2010) Alzheimer’s disease and the amyloid-β peptide. J Alzheimers Dis 19:311–323. https://doi.org/10.3233/JAD-2010-1221
Niranjan R (2014) The role of inflammatory and oxidative stress mechanisms in the pathogenesis of parkinson’s disease: focus on astrocytes. Mol Neurobiol 49:28–38. https://doi.org/10.1007/s12035-013-8483-x
Oczkowska A, Kozubski W, Lianeri M, Dorszewska J (2014) Mutations in PRKN and SNCA Genes Important for the Progress of Parkinson’s Disease. Curr Genomics. 14:502–517. https://doi.org/10.2174/1389202914666131210205839
Olmos G, Lladó J (2014) Tumor necrosis factor alpha: a link between neuroinflammation and excitotoxicity. Mediat Inflamm 2014:861231
Padmanabhan K, Nudelman K, Harenberg S et al (2017) Characterizing gene and protein crosstalks in subjects at risk of developing Alzheimer’s disease: a new computational approach. Processes 5. https://doi.org/10.3390/pr5030047
Paglia G, Miedico O, Cristofano A et al (2016) Distinctive pattern of serum elements during the progression of Alzheimer’s disease. Sci Rep 6:1–12. https://doi.org/10.1038/srep22769
Pasinetti GM, Hiller-Sturmhöfel S (2008) Systems biology in the study of neurological disorders: focus on Alzheimer’s disease. Alcohol Res Heal 31:60–65
Pei JJ, Tanaka T, Tung YC et al (1997) Distribution, levels, and activity of glycogen synthase Kinase-3 in the Alzheimer disease brain. J Neuropathol Exp Neurol 56:70–78. https://doi.org/10.1097/00005072-199701000-00007
Plum L, Schubert M, Brüning JC (2005) The role of insulin receptor signaling in the brain. Trends Endocrinol Metab 16:59–65. https://doi.org/10.1016/j.tem.2005.01.008
Polikov V, Block M, Zhang C, Monty Reichert W, Hong JS (2007) In Vitro models for neuroelectrodes: A paradigm for studying tissue-materials interactions in the brain. In: Indwelling Neural Implants: Strategies for Contending with the in Vivo Environment. pp. 89–115. CRC Press
Popescu C (2016) Mechanisms Implicated in Parkinson Disease from Genetic Perspective. Med Clin Rev 2. https://doi.org/10.21767/2471-299x.1000028
Putney JW, Tomita T (2011) Phospholipase C signaling and calcium influx. Adv Biol Regul 52:152–164. https://doi.org/10.1016/j.advenzreg.2011.09.005.Phospholipase
Rad SK, Arya A, Karimian H et al (2018) Mechanism involved in insulin resistance via accumulation of β-amyloid and neurofibrillary tangles: link between type 2 diabetes and alzheimer’s disease. Drug Des Devel Ther 12:3999–4021. https://doi.org/10.2147/DDDT.S173970
Razzokov J, Yusupov M, Bogaerts A (2019) Oxidation destabilizes toxic amyloid beta peptide aggregation. Sci Rep 9:5476. https://doi.org/10.1038/s41598-019-41931-6
Reddy S, Seth R (2019) Role of Tumor Necrosis Factor in Neurodegeneration. EC Endocrinol Metab Res 4:162–171
Rehman K, Akash MSH (2016) Mechanisms of inflammatory responses and development of insulin resistance: how are they interlinked? J Biomed Sci 23. https://doi.org/10.1186/s12929-016-0303-y
Santello M, Volterra A (2012) TNFα in synaptic function: switching gears. Trends Neurosci 35:638–647. https://doi.org/10.1016/j.tins.2012.06.001
Sasidharakurup H, Melethadathil N, Nair B, Diwakar S (2017) A Systems Model of Parkinson ’ s Disease 21:454–464. https://doi.org/10.1089/omi.2017.0056
Sasidharakurup H, Nair L, Bhaskar K, Diwakar S (2020) Computational Modelling of TNFα pathway in Parkinson’s disease – a systemic perspective. In: Complex Networks and Their Applications VIII, In, pp 762–773
Savageau MA, Voit EO, Irvine DH (1987) Biochemical systems theory and metabolic control theory: 1. Fundamental similarities and differences. Math Biosci 86:127–145. https://doi.org/10.1016/0025-5564(87)90007-1
Schäfers M, Svensson CI, Sommer C, Sorkin LS (2003) Tumor necrosis factor-α induces mechanical allodynia after spinal nerve ligation by activation of p38 MAPK in primary sensory neurons. J Neurosci 23:2517–2521. https://doi.org/10.1523/jneurosci.23-07-02517.2003
Sharma N, Singh AN (2016) Exploring biomarkers for Alzheimer’s disease. J Clin Diagnostic Res 10:KE01–KE06. https://doi.org/10.7860/JCDR/2016/18828.8166
Shetty PK, Galeffi F, Turner DA (2012) Cellular links between neuronal activity and energy homeostasis. Front Pharmacol 3:43. https://doi.org/10.3389/fphar.2012.00043
Sidoti-De Fraisse C, Rincheval V, Risler Y, et al (1998) TNF-a activates at least two apoptotic signaling cascades
Singh A, Kukreti R, Saso L, Kukreti S (2019) Oxidative stress: a key modulator in neurodegenerative diseases. Molecules 24. https://doi.org/10.3390/molecules24081583
Smith GR, Shanley DP (2013) Computational modelling of the regulation of insulin signalling by oxidative stress. BMC Syst Biol 7:1. https://doi.org/10.1186/1752-0509-7-41
Su Z, Wu Y (2020) Computational simulations of TNF receptor oligomerization on plasma membrane. Proteins Struct Funct Bioinforma 88:698–709. https://doi.org/10.1002/prot.25854
Tretter L, Dam-Vizi V (2004) Generation of reactive oxygen species in the reaction catalyzed by alpha-ketoglutarate dehydrogenase. J Neurosci 24:7771–7778. https://doi.org/10.1523/JNEUROSCI.1842-04.2004
Tucker W, Kutalik Z, Moulton V (2007) Estimating parameters for generalized mass action models using constraint propagation. Math Biosci 208:607–620. https://doi.org/10.1016/J.MBS.2006.11.009
Tweedie D, Ferguson RA, Fishman K et al (2012) Tumor necrosis factor-α synthesis inhibitor 3,6′-dithiothalidomide attenuates markers of inflammation, Alzheimer pathology and behavioral deficits in animal models of neuroinflammation and Alzheimer’s disease. J Neuroinflammation 9:575. https://doi.org/10.1186/1742-2094-9-106
Tysnes OB, Storstein A (2017) Epidemiology of Parkinson’s disease. J Neural Transm 124:901–905
Vidal M, Cusick ME, Barabási A-L (2011) Interactome networks and human disease. 144(6):986–998. https://doi.org/10.1016/j.cell.2011.02.016
Wang WY, Tan MS, Yu JT, Tan L (2015) Role of pro-inflammatory cytokines released from microglia in Alzheimer’s disease. Ann Transl Med 3:136. https://doi.org/10.3978/j.issn.2305-5839.2015.03.49
Wang X, Huang S, Jiang Y et al (2018) Reactive astrocytes increase the expression of P-gp and Mrpl via TNF-α and NF-κB signaling. Mol Med Rep 17:1198–1204. https://doi.org/10.3892/mmr.2017.7939
Wu YY, Hsu JL, Wang HC et al (2015) Alterations of the Neuroinflammatory markers IL-6 and TRAIL in Alzheimer’s disease. Dement Geriatr Cogn Dis Extra 5:424–434. https://doi.org/10.1159/000439214
Yang L, Wang H, Liu L, Xie A (2018) The role of insulin/IGF-1/PI3K/Akt/GSK3β signaling in parkinson’s disease dementia. Front Neurosci 12. https://doi.org/10.3389/fnins.2018.00073
Yiannopoulou KG, Papageorgiou SG (2013) Current and future treatments for Alzheimer’s disease. Ther Adv Neurol Disord 6:19–33. https://doi.org/10.1177/1756285612461679
Yu Q, Du F, Douglas JT et al (2017) Mitochondrial dysfunction triggers synaptic deficits via activation of p38 MAP kinase signaling in differentiated Alzheimer’s disease trans-mitochondrial Cybrid cells. J Alzheimers Dis 59:223–239. https://doi.org/10.3233/JAD-170283
Żekanowski C, Styczyńska M, Pepłońska B et al (2003) Mutations in presenilin 1, presenilin 2 and amyloid precursor protein genes in patients with early-onset Alzheimer’s disease in Poland. Exp Neurol 184:991–996. https://doi.org/10.1016/S0014-4886(03)00384-4
Zhang G, Xia Y, Wan F et al (2018) New perspectives on roles of alpha-Synuclein in Parkinson’s disease. Front Aging Neurosci 10:1–20. https://doi.org/10.3389/fnagi.2018.00370
This work derives direction and ideas from the Chancellor of Amrita Vishwa Vidyapeetham, Sri Mata Amritanandamayi Devi. The authors acknowledge undergraduate student interns, Kanishka Bhaskar, Lakshmi Nair, Meera Nair, Namitha Nalarajan, Arathy Koranath and Shibin Mubarak for their contributions in this article.
This study was partially supported by the Department of Science and Technology Grant DST/CSRI/2017/31, Government of India.
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Sasidharakurup, H., Diwakar, S. Computational modelling of TNFα related pathways regulated by neuroinflammation, oxidative stress and insulin resistance in neurodegeneration. Appl Netw Sci 5, 72 (2020). https://doi.org/10.1007/s41109-020-00307-w
- Tumor necrosis factor alpha
- Parkinson’s disease
- Alzheimer’s disease
- Systems theory
- Mathematical modelling
- Oxidative stress