Transport Of Tau Research Articles

Expression of Concern: Molecular motors implicated in the axonal transport of tau and α-synuclein.

Expression of Concern: Molecular motors implicated in the axonal transport of tau and α-synuclein.

Why slow axonal transport is bidirectional – can axonal transport of tau protein rely only on motor-driven anterograde transport?

Slow axonal transport (SAT) moves multiple proteins from the soma, where they are synthesized, to the axon terminal. Due to the great lengths of axons, SAT almost exclusively relies on active transport, which is driven by molecular motors. The puzzling feature of slow axonal transport is its bidirectionality. Although the net direction of SAT is anterograde, from the soma to the terminal, experiments show that it also contains a retrograde component. One of the proteins transported by SAT is the microtubule-associated protein tau. To better understand why the retrograde component in tau transport is needed, we used the perturbation technique to analyze how the full tau SAT model can be simplified for the specific case when retrograde motor-driven transport and diffusion-driven transport of tau are negligible and tau is driven only by anterograde (kinesin) motors. The solution of the simplified equations shows that without retrograde transport the tau concentration along the axon length stays almost uniform (decreases very slightly), which is inconsistent with the experimenal tau concentration at the outlet boundary (at the axon tip). Thus kinesin-driven transport alone is not enough to explain the empirically observed distribution of tau, and the retrograde motor-driven component in SAT is needed.

The Expression of Cellular Prion Protein, PrPC, Favors pTau Propagation and Blocks NMDAR Signaling in Primary Cortical Neurons.

The N-methyl-D-aspartate receptor (NMDAR) is a target in current treatments for Alzheimer's disease (AD). The human prion protein (PrPC) has an important role in the pathophysiology of AD. We hypothesized that PrPC modulates NMDA signaling, thus being a process associated with Alzheimer's disease. NMDAR signaling was characterized in the absence or presence of PrPC in cAMP level determination, mitogen-activated protein kinase (MAPK) pathway and label-free assays in homologous and heterologous systems. Bioluminescence resonance energy transfer was used to detect the formation of NMDAR-PrPC complexes. AXIS™ Axon Isolation Devices were used to determine axonal transport of Tau and pTau proteins in cortical primary neurons in the absence or presence of PrPC. Finally, proximity ligation assays were used to quantify NMDA-PrPC complex formation in neuronal primary cultures isolated from APPSw/Ind transgenic mice, an Alzheimer's disease model expressing the Indiana and Swedish mutated version of the human amyloid precursor protein (APP). We discovered a direct interaction between the PrPC and the NMDAR and we found a negative modulation of NMDAR-mediated signaling due to the NMDAR-PrPC interaction. In mice primary neurons, we identified NMDA-PrPC complexes where PrPC was capable of blocking NMDAR-mediated effects. In addition, we observed how the presence of PrPC results in increased neurotoxicity and neuronal death. Similarly, in microglial primary cultures, we observed that PrPC caused a blockade of the NMDA receptor link to the MAPK signaling cascade. Interestingly, a significant increase in NMDA-PrPC macromolecular complexes was observed in cortical neurons isolated from the APPSw,Ind transgenic model of AD. PrPC can interact with the NMDAR, and the interaction results in the alteration of the receptor functionality. NMDAR-PrPC complexes are overexpressed in neurons of APPSw/Ind mouse brain. In addition, PrPC exacerbates axonal transport of Tau and pTau proteins.

A Connectivity between LRP-1 and Alzheimer’s Disease: Modulating the Transport and Clearance of Tau and Aβ Clearance from the Brain to the Liver

A paucity of knowledge exists in our understanding of the axonal clearance of both beta amyloid (Aβ) and Tau proteins to the liver. Peripheral clearance by the liver can remove 40–50% of Aβ burden in the brain through the glial-lymphatic (glymphatic) system, transport across the blood brain barrier and mediated by low-density lipoprotein receptor-related peptide 1 (LRP-1). The lack of LDL receptors (LDLR) enhances amyloid deposition in the brain while LDLR overexpression increases the rate of Aβ entering the hepatic system. It’s important to consider the effects of organ failure on the development of AD. There are four main factors which contribute to clearance from the body. Blood flow, transport across the blood brain barrier, uptake into the liver, and elimination from the body through bile excretion all play an important role in the clearance of bioaccumulated toxic Aβ and Tau AD inducing proteins.

Supporting anti‐tau antibody trials using computer simulation of spatial tau propagation

AbstractBackgroundAlthough tau pathology is a major driver of AD pathology, the mere complexity of tau biology significantly hampers the identification and validation of the most impactful target. Recent clinical trials using antibodies against various forms of tau have come up short in improving cognitive readout.MethodA Quantitative Systems Pharmacology computer platform of tau biology has been developed including tau species diversity, neuronal activity‐dependent tau secretion, tau binding to membrane surface receptors, subsequent internalization at the synaptic cleft and along the axonal membrane following a diffusion gradient, slow motor‐mediated axonal transport, intracellular degradation and templating of monomeric tau by seed‐competent tau to form higher‐order aggregates. Polysynaptic progression is implemented by modeling secretion from the first synapse and uptake in the second axon.ResultThe platform was calibrated to reproduce preclinical experiments from in‐vitro cellular assays and after injection of brain extract in Tg models. The model is humanized using published data on seed‐competent tau from postmortem AD brain at different Braak stages and spatio‐temporal progression over neuronal projections. By exploring the effect of target mediated drug disposal of antibodies, we show that depending upon the disease state only between 15 and 30% of misfolded tau protein can be captured by antibodies currently in clinical trials. Introducing PSP specific uptake parameters from preclinical experiments, the simulations suggest an even lower efficacy due to the higher affinity of PSP‐tau for membrane surface receptors. We further identify conditions under which specificity (i.e. monomeric vs misfolded tau) of antibodies outweigths sensitivity (affinity). Finally, the simulations suggest that reduced oligomerization and increased degradation have a greater impact on the accumulation of seed‐competent tau in distal regions, likely because they act during the long intracellular residence of misfolded tau protein.ConclusionAdvanced Quantitative Systems Pharmacology computer modeling combining drug exposure with mechanistic modeling allows to ‘humanize’ preclinical experiments and aims to support tauopathy R&D projects.

Axonal transport dynamics explain directional bias in tau deposition.

Emerging evidence indicates that defects in axonal transport, an early feature of AD pathology1 , could play a role in how directional biases develop on regional level2 . Specifically, kinesin-1, a motor protein responsible for the anterograde-directed transport of axonal cargoes including tau, is dysregulated by hyperphosphorylated tau species3,4 . Here we demonstrate that a mathematical model of tau-modified axonal transport can reproduce directional biases in tauopathy spread observed in mouse models of disease. We implemented a mathematical model incorporating the effects of aggregation and fragmentation of pathological tau complexes of two types, soluble and insoluble, on axonal transport and diffusion. We simulated the spatiotemporal profiles of each tau species in a multicompartment, two-neuron system using biologically plausible parameters and time scales. Notably, anterograde axonal transport velocity is modeled as being locally enhanced by the presence of soluble pathological tau3 and hindered by insoluble tau4 . We find that changing the balance of tau transport feedback parameters can elicit anterograde and retrograde biases in the distributions of soluble and insoluble tau between somatodendritic compartments in our two-neuron system (Figures 1 & 2). Changing the aggregation and fragmentation parameters perturbs this balance, suggesting complex interplay between these distinct molecular processes (Figures 3 & 4). The model also recreates spread biases comparable to those derived from network spread using AD-like and non-AD-like mouse tauopathy models2 (Figure 5). Our model of tau axonal transport feedback provides a parsimonious, biologically plausible explanation for apparent directionality observed in mouse model tauopathy spread, linking microscopic differences in tau conformational states and variance in clinical presentation.

Emergence of directional bias in tau deposition from axonal transport dynamics.

Defects in axonal transport may partly underpin the differences between the observed pathophysiology of Alzheimer's disease (AD) and that of other non-amyloidogenic tauopathies. Particularly, pathological tau variants may have molecular properties that dysregulate motor proteins responsible for the anterograde-directed transport of tau in a disease-specific fashion. Here we develop the first computational model of tau-modified axonal transport that produces directional biases in the spread of tau pathology. We simulated the spatiotemporal profiles of soluble and insoluble tau species in a multicompartment, two-neuron system using biologically plausible parameters and time scales. Changes in the balance of tau transport feedback parameters can elicit anterograde and retrograde biases in the distributions of soluble and insoluble tau between compartments in the system. Aggregation and fragmentation parameters can also perturb this balance, suggesting a complex interplay between these distinct molecular processes. Critically, we show that the model faithfully recreates the characteristic network spread biases in both AD-like and non-AD-like mouse tauopathy models. Tau transport feedback may therefore help link microscopic differences in tau conformational states and the resulting variety in clinical presentations.

Predicting brain atrophy from tau pathology: a summary of clinical findings and their translation into personalized models

For more than 25 years, the amyloid hypothesis–the paradigm that amyloid is the primary cause of Alzheimer’s disease–has dominated the Alzheimer’s community. Now, increasing evidence suggests that tissue atrophy and cognitive decline in Alzheimer’s disease are more closely linked to the amount and location of misfolded tau protein than to amyloid plaques. However, the precise correlation between tau pathology and tissue atrophy remains unknown. Here we integrate multiphysics modeling and Bayesian inference to create personalized tau-atrophy models using longitudinal clinical images from the Alzheimer’s Disease Neuroimaging Initiative. For each subject, we infer three personalized parameters, the tau misfolding rate, the tau transport coefficient, and the tau-induced atrophy rate from four consecutive annual tau positron emission tomography scans and structural magnetic resonance images. Strikingly, the tau-induced atrophy coefficient of 0.13/year (95% CI: 0.097-0.189) was fairly consistent across all subjects suggesting a strong correlation between tau pathology and tissue atrophy. Our personalized whole brain atrophy rates of 0.68-1.68%/year (95% CI: 0.5-2.0) are elevated compared to healthy subjects and agree well with the atrophy rates of 1-3%/year reported for Alzheimer’s patients in the literature. Once comprehensively calibrated with a larger set of longitudinal images, our model has the potential to serve as a diagnostic and predictive tool to estimate future atrophy progression from clinical tau images on a personalized basis. Statement of SignificanceDeveloping predictive, patient-specific models of Alzheimer’s disease pathology and progression is of paramount importance for effective patient care and potential treatment. Tissue atrophy, the reduction of brain volume, is an important biomarker for Alzheimer’s disease. Similarly, the pathology associated with tau proteins is thought to play a central role in Alzheimer’s disease progression, local atrophy, and a patient’s cognitive decline. The main question is: how do combine the mechanisms of tau propagation and atrophy in a single model that can make the best use of existing data? Here, we first review the dynamics of atrophy in for Alzheimer’s disease and describe a mathematical model that couples tau propagation and atrophy. We then investigate how to fit the model parameters using the longitudinal structural neuroimaging data of four subjects, from the ADNI database, and a Bayesian Markov Chain Monte Carlo inference method. Our approach shows that network neurodegeneration models may hold promise for the predictive, patient-specific modeling of AD using AV-1451 tau PET and T1 structural MRI data.

Modeling tau transport in the axon initial segment

By assuming that tau protein can be in seven kinetic states, we developed a model of tau protein transport in the axon and in the axon initial segment (AIS). Two separate sets of kinetic constants were determined, one in the axon and the other in the AIS. This was done by fitting the model predictions in the axon with experimental results and by fitting the model predictions in the AIS with the assumed linear increase of the total tau concentration in the AIS. The calibrated model was used to make predictions about tau transport in the axon and in the AIS. To the best of our knowledge, this is the first paper that presents a mathematical model of tau transport in the AIS. Our modeling results suggest that binding of free tau to microtubules creates a negative gradient of free tau in the AIS. This leads to diffusion-driven tau transport from the soma into the AIS. The model further suggests that slow axonal transport and diffusion-driven transport of tau work together in the AIS, moving tau anterogradely. Our numerical results predict an interplay between these two mechanisms: as the distance from the soma increases, the diffusion-driven transport decreases, while motor-driven transport becomes larger. Thus, the machinery in the AIS works as a pump, moving tau into the axon.

Extracellular Vesicles: A Possible Link between HIV and Alzheimer's Disease-Like Pathology in HIV Subjects?

The longevity of people with HIV/AIDS has been prolonged with the use of antiretroviral therapy (ART). The age-related complications, especially cognitive deficits, rise as HIV patients live longer. Deposition of beta-amyloid (Aβ), a hallmark of Alzheimer’s disease (AD), has been observed in subjects with HIV-associated neurocognitive disorders (HAND). Various mechanisms such as neuroinflammation induced by HIV proteins (e.g., Tat, gp120, Nef), excitotoxicity, oxidative stress, and the use of ART contribute to the deposition of Aβ, leading to dementia. However, progressive dementia in older subjects with HIV might be due to HAND, AD, or both. Recently, extracellular vesicles (EVs)/exosomes, have gained recognition for their importance in understanding the pathology of both HAND and AD. EVs can serve as a possible link between HIV and AD, due to their ability to package and transport the toxic proteins implicated in both AD and HIV (Aβ/tau and gp120/tat, respectively). Given that Aß is also elevated in neuron-derived exosomes isolated from the plasma of HIV patients, it is reasonable to suggest that neuron-to-neuron exosomal transport of Aβ and tau also contributes to AD-like pathology in HIV-infected subjects. Therefore, exploring exosomal contents is likely to help distinguish HAND from AD. However, future prospective clinical studies need to be conducted to compare the exosomal contents in the plasma of HIV subjects with and without HAND as well as those with and without AD. This would help to find new markers and develop new treatment strategies to treat AD in HIV-positive subjects. This review presents comprehensive literatures on the mechanisms contributing to Aβ deposition in HIV-infected cells, the role of EVs in the propagation of Aβ in AD, the possible role of EVs in HIV-induced AD-like pathology, and finally, possible therapeutic targets or molecules to treat HIV subjects with AD.

A Numerical Study of Sensitivity Coefficients for a Model of Amyloid Precursor Protein and Tubulin-Associated Unit Protein Transport and Agglomeration in Neurons at the Onset of Alzheimer's Disease.

Modeling of intracellular processes occurring during the development of Alzheimer's disease (AD) can be instrumental in understanding the disease and can potentially contribute to finding treatments for the disease. The model of intracellular processes in AD, which we previously developed, contains a large number of parameters. To distinguish between more important and less important parameters, we performed a local sensitivity analysis of this model around the values of parameters that give the best fit with published experimental results. We show that the influence of model parameters on the total concentrations of amyloid precursor protein (APP) and tubulin-associated unit (tau) protein in the axon is reciprocal to the influence of the same parameters on the average velocities of the same proteins during their transport in the axon. The results of our analysis also suggest that in the beginning of AD the aggregation of amyloid-β and misfolded tau protein have little effect on transport of APP and tau in the axon, which suggests that early damage in AD may be reversible.

Investigating sensitivity coefficients characterizing the response of a model of tau protein transport in an axon to model parameters

Evaluating the sensitivity of biological models to various model parameters is a critical step towards advancing our understanding of biological systems. In this paper, we investigated sensitivity coefficients for a model simulating transport of tau protein along the axon. This is an important problem due to the relevance of tau transport and agglomeration to Alzheimer’s disease and other tauopathies, such as some forms of parkinsonism. The sensitivity coefficients that we obtained characterize how strongly three observables (the tau concentration, average tau velocity, and the percentage of tau bound to microtubules) depend on model parameters. The fact that the observables strongly depend on a parameter characterizing tau transition from the retrograde to the anterograde kinetic states suggests the importance of motor-driven transport of tau. The observables are sensitive to kinetic constants characterizing tau concentration in the free (cytosolic) state only at small distances from the soma. Cytosolic tau can only be transported by diffusion, suggesting that diffusion-driven transport of tau only plays a role in the proximal axon. Our analysis also shows the location in the axon in which an observable has the greatest sensitivity to a certain parameter. For most parameters, this location is in the proximal axon. This could be useful for designing an experiment aimed at determining the value of this parameter. We also analyzed sensitivity of the average tau velocity, the total tau concentration, and the percentage of microtubule-bound tau to cytosolic diffusivity of tau and diffusivity of bound tau along the MT lattice. The model predicts that at small distances from the soma the effect of these two diffusion processes is comparable.

Simulating the effect of formation of amyloid plaques on aggregation of tau protein.

In this paper, we develop a mathematical model that enables the investigation of the production and intracellular transport of amyloid precursor protein (APP) and tau protein in a neuron. We also investigate the aggregation of APP fragments into amyloid-β (Aβ) as well as tau aggregation into tau oligomers and neurofibrillary tangles. Using the developed model, we investigate how Aβ aggregation can influence tau transport and aggregation in both the soma and the axon. We couple the Aβ and tau agglomeration processes by assuming that the value of the kinetic constant that describes the autocatalytic growth (self-replication) reaction step of tau aggregation is proportional to the Aβ concentration. The model predicts that APP and tau are distributed differently in the axon. While APP has a uniform distribution along the axon, tau's concentration first decreases and then increases towards the synapse. Aβ is uniformly produced along the axon while misfolded tau protein is mostly produced in the proximal axon. The number of Aβ and tau polymers originating from the axon is much smaller than the number of Aβ and tau polymers originating from the soma. The rate of production of misfolded tau polymers depends on how strongly their production is facilitated by Aβ.

How the formation of amyloid plaques and neurofibrillary tangles may be related: a mathematical modelling study.

We develop a mathematical model that enables us to investigate possible mechanisms by which two primary markers of Alzheimer's disease (AD), extracellular amyloid plaques and intracellular tangles, may be related. Our model investigates the possibility that the decay of anterograde axonal transport of amyloid precursor protein (APP), caused by toxic tau aggregates, leads to decreased APP transport towards the synapse and APP accumulation in the soma. The developed model thus couples three processes: (i) slow axonal transport of tau, (ii) tau misfolding and agglomeration, which we simulated by using the Finke-Watzky model and (iii) fast axonal transport of APP. Because the timescale for tau agglomeration is much larger than that for tau transport, we suggest using the quasi-steady-state approximation for formulating and solving the governing equations for these three processes. Our results suggest that misfolded tau most likely accumulates in the beginning of the axon. The analysis of APP transport suggests that APP will also likely accumulate in the beginning of the axon, causing an increased APP concentration in this region, which could be interpreted as a 'traffic jam'. The APP flux towards the synapse is significantly reduced by tau misfolding, but not due to the APP traffic jam, which can be viewed as a symptom, but rather due to the reduced affinity of kinesin-1 motors to APP-transporting vesicles.

INTRODUCING A NEW SYSTEMS PATHOLOGY PARADIGM OF ALZHEIMER'S DISEASE

Systems biology approaches to Alzheimer's disease (AD) are comparatively rare [1] and reductionist concepts still prevail. We challenge the idea that either tau or Aβ are the causual agents in the pathology of AD, and instead consider the role systemic factors play in its neurodegenerative spread through the brain. In a new systems pathology paradigm, we depart from a neuron-centric view of AD and instead expand it to consider the role of interactions between an ecosystem of cell types that play out in the complex spatial architecture in brain tissue. A mathematical model for the dynamics of AD is developed that draws together the latest results from molecular biology, pathology and imaging. The modelling approach incorporates data that explicitly describes the three-dimensional geometry of intracellular space. It also includes both the relative abundance and distribution of glial and neuronal cells, and their specific anatomical details such as the dendritic network. The mathematical equation set describes both intra- and inter-cellular processes including signaling, cell movement, and transport of both tau and Aβ in the intra- and inter-cellular space. The model shows how characteristics of the spatio-temporal development of the disease can be understood in terms of the interactions between key elements, including tau and Aβ, as part of an integrated system, as opposed to considering these elements independently. More importantly, the resulting modelling framework constitutes a new systems pathology platform for AD that allows in silico experimentation to develop and test hypotheses to ultimately help integrate both accepted and developing theories from across the AD research community. We demonstrate the feasibility of tissue-level modelling of AD and suggest the potential of whole-brain modelling of AD. Our results suggest a shift in perspective from reductionist hypotheses about causal agents to systems-level thinking, may provide new insights into the processes that regulate the dynamics of the disease. [1] Rollo JL et.al. Unraveling the mechanistic complexity of Alzheimer's disease through systems biology. Alzheimer's & Dementia: The Journal of the Alzheimer's Association, Vol.12, Issue 6, 708–718.

Simulating tubulin-associated unit transport in an axon: using bootstrapping for estimating confidence intervals of best-fit parameter values obtained from indirect experimental data.

In this paper, we first develop a model of axonal transport of tubulin-associated unit (tau) protein. We determine the minimum number of parameters necessary to reproduce published experimental results, reducing the number of parameters from 18 in the full model to eight in the simplified model. We then address the following questions: Is it possible to estimate parameter values for this model using the very limited amount of published experimental data? Furthermore, is it possible to estimate confidence intervals for the determined parameters? The idea that is explored in this paper is based on using bootstrapping. Model parameters were estimated by minimizing the objective function that simulates the discrepancy between the model predictions and experimental data. Residuals were then identified by calculating the differences between the experimental data and model predictions. New, surrogate 'experimental' data were generated by randomly resampling residuals. By finding sets of best-fit parameters for a large number of surrogate data the histograms for the model parameters were produced. These histograms were then used to estimate confidence intervals for the model parameters, by using the percentile bootstrap. Once the model was calibrated, we applied it to analysing some features of tau transport that are not accessible to current experimental techniques.

Studying tau protein propagation and pathology in the mouse brain using adeno-associated viruses.

The progressive spread of pathological brain lesions containing aggregated tau protein is a hallmark of Alzheimer's disease and other neurodegenerative diseases. In AD, this process follows a distinct pattern along neuronal connections from the entorhinal cortex to hippocampal areas and further on through the limbic system. In other tauopathies, the spread of tau appears less hierarchical throughout the brain, and also nonpathological tau is reported to cross-synaptic connections in the brain. To be able to study the process of cell-to-cell transport of tau and the associated neurotoxicity in the brain in vivo, adeno-associated virus-mediated expression of tau can be used to express different forms of tau in distinct brain areas in rodent models. As an example, we describe how the expression of FTD-mutant human tauP301L in the entorhinal cortex of wild-type mice can be used to study the propagation of tau to connected neurons and to determine pathological consequences such as tau hyperphosphorylation, misfolding, and gliosis. The approach described can easily be translated to study other aggregating and/or propagating proteins in the brain such as synuclein, Abeta, or SOD1.

Tracking Tau in Neurons: How to Transfect and Track Exogenous Tau into Primary Neurons.

Primary neurons have proved to be an essential tool for investigating neuronal polarity in general and polarized Tau distribution in particular. However, mature primary neurons are notoriously difficult to transfect with nonviral vectors and are very sensitive both to cytoskeletal manipulation and to imaging. Common nonviral transfections require the use of a monolayer of supportive glia or high density cultures, both of which complicate imaging. Here, we provide a simple nonviral transfection method enabling transfection of Tau to achieve expression levels comparable to endogenous Tau. This allows to investigate specific effects on, e.g., distribution and transport of Tau, without grossly affecting other cytoskeleton-based parameters such as microtubule density or microtubule-based transport.

What mechanisms of tau protein transport could be responsible for the inverted tau concentration gradient in degenerating axons?

In tauopathies, such as Alzheimer's disease (AD), microtubule (MT)-associated protein tau detaches from MTs and aggregates, eventually forming insoluble neurofibrillary tangles. In a healthy axon, the tau concentration increases toward the axon terminal, but in a degenerating axon, the tau concentration gradient is inverted and the highest tau concentration is in the soma. In this article, we developed a mathematical model of tau transport in axons. We calibrated and tested the model by using published distributions of tau concentration and tau average velocity in a healthy axon. According to published research, the inverted tau concentration gradient may be one of the reasons leading to AD. We therefore used the model to investigate what modifications in tau transport can lead to the inverted tau concentration gradient. We investigated whether tau detachment from MTs due to tau hyperphosphorylation can cause the inverted tau concentration gradient. We found that the assumption that most tau molecules are detached from MTs does not consistently predict the inverted tau concentration gradient; the predicted tau distribution becomes more uniform if the axon length is increased. We then hypothesized that in degenerating axons some tau remains bound to MTs and participates in the component 'a' of slow axonal transport but that the rate of tau reversals from anterograde to retrograde motion increases. We demonstrated that this hypothesis results in a tau distribution where the tau concentration has its maximum value at the axon hillock and its minimum value at the axon terminal, in agreement with what is observed in AD. Our results thus suggest that defects in active transport of tau may be a contributing factor to the onset of neural degeneration.

Can numerical modeling help understand the fate of tau protein in the axon terminal?

In this paper, we used mathematical modeling to investigate the fate of tau protein in the axon terminal. We developed a comprehensive model of tau transport that accounts for transport of cytosolic tau by diffusion, diffusion transport of microtubule (MT)-bound tau along the MT lattice, active motor-driven transport of MT-bound tau via slow axonal transport mechanism, and degradation of tau in the axon due to tau's finite half-life. We investigated the effect of different assumptions concerning the fate of tau in the terminal on steady-state transport of tau in the axon. In particular, we studied two possible scenarios: (i) tau is destroyed in the terminal and (ii) there is no tau destruction in the terminal, and to avoid tau accumulation we postulated zero flux of tau at the terminal. We found that the tau concentration and percentage of MT-bound tau are not very sensitive to the assumption concerning the fate of tau in the terminal, but the tau's flux and average velocity of tau transport are very sensitive to this assumption. This suggests that measuring the velocity of tau transport and comparing it with the results of mathematical modeling for different assumptions concerning tau's fate in the terminal can provide information concerning what happens to tau in the terminal.