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Relationships between the amyloid precursor protein and its various proteolytic fragments and neuronal systems


Alzheimer's disease (AD) is a progressive neurodegenerative disease and in its familial form is associated with mutations in the amyloid precursor protein (APP) and the presenilins (PSs). Much data regarding the interactions of APP, its proteolytic fragments and PS have been generated, expanding our understanding of the roles of these proteins in mechanisms underlying cognitive function and revealing many complex relationships with wide ranging cellular systems. In this review, we examine the multiple interactions of APP and its proteolytic fragments with other neuronal systems in terms of feedback loops and use these relationships to build a map. We highlight the complexity involved in the APP proteolytic system and discuss alternative perspectives on the roles of APP and its proteolytic fragments in dynamic processes associated with disease progression in AD. We highlight areas where data are missing and suggest potential confounding factors. We suggest that a systems biology approach enhances representations of the data and may be more useful in modelling both normal cognition and disease processes.


Alzheimer's disease (AD) is a clinico-pathological diagnosis in which progressive neurodegenerative disease is associated with the dementia syndrome. Neuropathologically, AD is generally characterised as including deposits of amyloid beta protein (Aβ) in senile and neuritic plaques [1] and neurofibrillary tangles of paired helical filaments of hyperphosphorylated tau [2, 3]. Currently, AD is classified as familial AD, which has an early age of onset and is associated with mutations in either the amyloid precursor protein (APP) or the presenilins (PSs), and sporadic AD, which has a later age of onset and a less clear aetiology [4, 5]. Down's syndrome, with an extra copy of APP on chromosome 21, also leads to a form of AD with increasing age. The genetic data associated with familial AD [6] strongly suggest that PS, APP and its proteolytic fragment Aβ are involved in AD disease progression but exactly how they contribute to both normal cognition and dementia is the focus of much debate.

Many non-genetic factors also appear to affect disease initiation and progression, including diet, exercise and education, and many models have been proposed to account for their roles in AD. Population studies (for example, the Medical Research Council Cognitive Function and Aging Study [7]) demonstrate that vascular and AD-associated pathologies are common in people with and without any clinical manifestation of dementia. Coupled with data suggesting that the relationship between amyloid pathology and dementia attenuates with age [8], this suggests that sporadic AD especially has a complex disease aetiology [9].

Many models of disease progression have been proposed to account for the accumulating data on AD. These models form the basis of experimental design and affect how findings are interpreted. Data relevant to disease progression in AD are accumulating from many diverse fields, including neuropsychology, brain imaging, molecular biology and genetics. Integrating these data is becoming harder as expertise is required in so many areas.

The pre-eminent model of the last few decades has been the amyloid cascade hypothesis [1012], based on genetic data and the role of PS in the proteolysis of APP to release the 39 to 43 residue Aβ. This model proposes that overproduction of Aβ, or an increase in Aβ(1-42) relative to Aβ(1-40), is causal in the disease process. In this model, Aβ, perhaps in a specific aggregation state, is proposed as neurotoxic. An alternative interpretation of data relating to APP, PS and Aβ is the presenilin hypothesis [13], where loss or altered function of PS is proposed as the causal factor in disease progression. Other models suggest different initiating factors, such as cholesterol [14], mitochondria and apoptosis [15], insulin signalling and energy metabolism [16, 17], cholinergic insufficiency [18], Ca2+ deregulation [19, 20], signalling decoherence [21] or a combination of multiple factors (for example, [22, 23]). No single model adequately explains how APP, PS, cholesterol and other factors interact with synaptic plasticity, learning and memory in normal and disease states. This suggests that models currently in use in AD research require re-evaluation.

Recent approaches to integrating the data relevant to AD have involved the use of text mining, bioinformatic databases and network analysis for gene co-expression and protein-protein interactions [2433]. Studies using automated interrogation of genomic and proteomic databases, such as UNIPROTKB [34], NCBI Entrez Gene [35], and IntAct [36], and text mining in literature databases such as Pubmed [37] have confirmed the involvement of many previously highlighted neurological processes, extended the connectivity between known molecular targets and revealed new targets for investigation (summarised in Table 1).

Table 1 Overview of protein-protein interaction network studies

Understanding how APP contributes to synaptic plasticity is important in understanding normal cognition and disease progression in AD. Here, we approach the process of integrating the available data relating to the relationships between APP and its proteolytic fragments and neuronal processes based on a systems biology approach and build a map of relationships. We are not aiming to define protein interactions nor find new targets for investigation, but to highlight relationships in the existing literature that illustrate the complexity involved and how this relates to our current understanding of the role of APP in AD. We highlight areas where data are missing and predict relationships that may confound current disease models.

Building the map

Relationships involving APP and each of its proteolytic fragments were identified from literature searches using relevant key words. Each interaction identified was manually entered into a non-hierarchical network using the mapping software Compendium [38]. This software allowed each molecule or neuronal system to be represented as a node within the map. Each node in the map could contain further networks of nodes, leading to a multi-dimensional organisation of information. These further nodes represented information relevant to the molecule of the parent node and could include notes describing possible relevance, questions raised and links to relevant entries on various bioinformatic databases. The map is not meant to be read as a protein-protein interaction network (PPI), but rather was designed to summarise and collate information relevant to the APP system in a more general way. Physiological outcomes, such as changes in signalling, were included, even though the precise molecular interactions are not clear. Any additional details, such as new biological relationships or new molecular interactions, can be added as they arise. Relationships between molecules are represented by connectors, with annotation if required. Figure 1 shows a limited map built in this way from references cited in the text, with additional references listed in Table 2.

Figure 1

A limited map of amyloid precursor protein relationships showing molecules/neuronal systems as nodes and interactions between them as arrows. Evidence for each relationship is summarised in Table 2. Numbers are referred to in the text. Aβ, amyloid beta protein; Abl, tyrosine-protein kinase ABL1; AChR, acetylcholine receptor; ADAM, a disintegrin and metalloproteinase domain-containing protein; AICD, APP intracellular domain; Akt, RAC-alpha serine/threonine-protein kinase; AMPAR, AMPA receptor; ApoE, apolipoprotein E; APP, amyloid precursor protein; BACE, beta-site amyloid precursor protein cleaving enzyme; CD74, HLA class II histocompatibility antigen gamma chain; ECM, extracellular matrix; IDE, insulin degrading enzyme; JIP1, C-Jun-amino-terminal kinase-interacting protein 1; JNK, C-Jun amino-terminal kinase; Lyn, tyrosine-protein kinase Lyn; PKA, protein kinase A; PKC, protein kinase C; LRP, low-density lipoprotein receptor; LTP, long-term potentiation; mAChR, muscarinic acetylcholine receptor; mGluR, metabotropic glutamate receptor; nAChR, nicotinic acetylcholine receptor; NMDAR, NMDA receptor; P2Y2, P2Y purinoceptor 2; PI3K, phosphoinositide 3-kinase; sAPP, secreted amyloid precursor protein; Src, proto-oncogene tyrosine-protein kinase Src; TIMP, tissue inhibitor of metalloproteinases; TNF, tumour necrosis factor.

Table 2 Additional references supporting the network diagram in Figure 1

Unlike more automated network studies such as that by Perreau and colleagues [25] where nodes were included in the network only if there was direct evidence of molecular interaction, or various studies [3033] where co-expression criteria were applied, no specific criteria were imposed and any relationship found was included. Known differences in peptide behaviour (for example, [39, 40]) necessitated separate nodes for Aβ(1-40) and Aβ(1-42). This was extended systematically to include all the peptide fragments, including P3(17-40), P3(17-42), secreted APP (sAPP)α and sAPPβ (the extracellular amino-terminal fragments following α- and β-cleavage, respectively), C31, and so on to take account of any potential behavioural differences. This is in contrast to various network constructions where the various proteolytic fragments are collapsed into an APP parental node, possibly reflecting the UNIPROTKB identifier (P05067) being the same for each peptide. Additionally, different aggregation states of each of the peptides (monomers, dimers, oligomers or fibrils) were considered separately. These additional details have been omitted from Figure 1 for clarity. Since there was little or no evidence for many of the Aβ-type peptides [41], these were collapsed into a separate node (not shown).

The relationships in Figure 1 were analysed manually by identifying feedback loops. Direct feedback loops were defined as relationships between APP, any of its proteolytic fragments and any other molecule or functional change that was represented at more than one point in the map. Possible indirect feedback loops between any neural system, such as neurotransmitter signalling, Ca2+ homeostasis or the inflammation cascade, were also investigated by looking for molecules or functional change in these systems that interact with APP or any of its proteolytic fragments at more than one point in the map.

Following a brief summary of APP cleavage, two examples of the relationships between APP pathways and neuronal processes, looking at the extracellular matrix (ECM) and endocytosis, serve to illustrate the implications of the complexity involved.

The amyloid precursor protein


APP is a type I transmembrane protein [42] with a large extracellular amino-terminal domain, a transmembrane domain and an intracellular carboxy-terminal domain that resembles a cell surface receptor [43]. It is expressed in several isoforms, with APP695 the main isoform expressed by neurons. APP has a high turnover, with a half life ranging from approximately 1 hour [4446] to approximately 4 hours [4749]. In addition to its functions at the cell surface, full length mature APP is also processed via competing pathways that release proteolytic fragments [5, 43, 50] (recently reviewed in [51]). In effect there are four possible routes: APP may remain functionally active at the cell surface; it may be internalised and recycled or degraded; it may be cleaved via the α-pathway, releasing the sAPPα fragment, leaving the membrane bound C83 carboxy-terminal fragment that is further processed to the P3 fragment; or it may be cleaved via the β-pathway releasing sAPPβ, leaving the membrane bound C99 carboxy-terminal fragment that is further processed to the Aβ fragment.

As can be seen in Figure 1, α- and β-cleavage pathways (numbered 1 and 2, respectively) converge on a further intramembrane γ-cleavage (numbered 3) by the PS-containing γ-secretase complex to release the variable length Aβ and P3 peptides and the variable length APP intracellular domain (AICD). Additional cleavages, including caspase cleavage of the carboxyl terminus at residue D664 of APP695, producing the alternative length carboxy-terminal fragment C31 [51], and cleavage by BACE2 (beta-site amyloid precursor protein cleaving enzyme 2) within the Aβ sequence [52], have been omitted for clarity.

Unprocessed APP is degraded or recycled via the endosomal or lysosomal pathways and may be recycled back to the membrane and processed within approximately 30 minutes [46], with perhaps a third to a half being processed via the cleavage pathways as measured by secreted sAPPα/β [46]. Regulatory relationships promoting expression or cleavage have been collapsed into summary boxes in Figure 1 for simplicity.

Complex feedback pathways involving APP and its proteolytic fragments; the extracellular matrix and endocytosis

The extracellular matrix

APP interacts with itself and its own proteolytic fragments at many points in the network with various functional consequences. At the cell surface, the large amino-terminal domain of full length APP interacts with many components of the ECM, including heparins and heparin sulphate proteoglycans [42], laminin, collagen [53, 54] and β-1-integrin [43, 55, 56], and contributes to early neuronal development, axon and neurite growth, cell adhesion and synaptic plasticity [43, 57]. Many of these interactions are now represented in MatrixDB [58], an ECM interaction database.

The amino-terminal domain of APP binds heparin in a pH- and zinc-dependant manner and this binding promotes the formation of APP homodimers [42, 5961]. Depending on various factors, including cell compartment and pH, this dimerisation may influence APP proteolysis via α- or β-cleavages due to conformational changes [59], though this requires further investigation.

The large amino-terminal fragment released by α-cleavage, sAPPα, is a soluble monomer. It retains two heparin binding sites and has been shown to bind heparin as a dimer [60]. The ability of sAPPα to disrupt APP dimerisation at the cell surface is thought to contribute to its neuroprotective actions [6264] and may partly explain why sAPPα is approximately 100 times more neuroprotective against excitotoxicity, glucose deprivation and the addition of Aβ in hippocampal cultures than sAPPβ, which lacks the second carboxy-terminal heparin binding site [63]. Dementia status has been associated with both reduced sAPPα levels in cerebrospinal fluid [65] and an increased half life of sAPPα [49].

Differing by only 16 carboxy-terminal residues, sAPPα and sAPPβ share a high degree of sequence homology and have the potential to compete with each other for binding sites. Since both sAPPβ and sAPPα are constantly present in the ECM in dynamically changing ratios, we predict that sAPPβ could act as an antagonist of sAPPα/APP (shown in Figure 2 as competitive inhibition (line 1)), with the potential to modulate various interactions between APP and sAPPα and the ECM. We predict, therefore, that any functional outcome from the interactions of the APP pathway will depend on the synergy between APP and its amino-terminal fragments.

Figure 2

Feedback between amyloid precursor protein and its proteolytic fragments. Inhibition is shown in red, enhancement in green; numbers are referred to in the text. Functionally different aggregation states for the Aβ-type peptides are collapsed into P3 and Aβ nodes for clarity. Aβ, amyloid beta protein; AICD, APP intracellular domain; APP, amyloid precursor protein; PKC, protein kinase C; sAPP, secreted amyloid precursor protein.

Aβ interacts with heparins in the ECM and at high levels may prevent the catabolism of proteoglycans and promote amyloid formation [66]. Reciprocally, heparins modulate many of the interactions involving Aβ. Heparins enhance both nucleation and elongation processes in the aggregation of Aβ [67] and modulate interactions of Aβ; for example, heparins can limit the neurotoxic and pro-inflammatory activity of Aβ in a dose-dependent manner [68]. Additionally, heparins contribute to the uptake of Aβ by a pathway shared with apolipoprotein E [69] and also contribute to the regulation of the β-secretase BACE in a dose-dependent manner, with low concentrations promoting and high concentrations inhibiting the activation of BACE1 [70].

In terms of direct feedback, Aβ may be part of a negative feedback loop involved in the regulation of APP expression and the promotion of α-cleavage. Fibrillar forms of various proteins, including Aβ, interact with amino-terminal APP and increase APP expression [71] (Figure 2, line 2) and Aβ(1-40) may up-regulate expression of α-secretase via integrin receptors and matrix metallo-proteinase-9 activity [72] (Figure 2, line 3). Additionally, Aβ has many indirect negative feedback relationships with α-cleavage - for example, Aβ inhibits protein kinase C [73] and insulin signalling [74], both involved in up-regulation of α-cleavage [75] (Figure 2, line 5) - and additionally may negatively feedback on APP expression and its own production via a pathway involving Fe65 and glycogen synthase kinase 3 [76]. Reciprocally, changes to gene expression by sAPPα via the activation of NF-κβ may attenuate the interactions of Aβ [77] and this can be understood as reciprocal indirect feedback from the α-pathway on the β-pathway (Figure 2, line 4).

The data reviewed above suggest that Aβ may contribute to both direct and indirect feedback loops in the ECM at multiple points, with consequences for the interactions of APP, sAPPα, Aβ and sAPPβ that may ultimately affect cell adhesion, neurite outgrowth and synaptic plasticity. These effects will depend on the specific sequence length, concentration and aggregation state of the Aβ-type peptides, which may dynamically change in response to the neuronal environment.


Increases in endocytosis, often associated with synaptic activity, lead to both an increase in Aβ [78] and a reduction in α-cleavage and lower secreted sAPPα levels [79]. This may reflect the regulatory role of compartmentation in APP proteolysis, where α-cleavage occurs at the cell surface and Aβ production is associated with factors that promote endocytosis. No studies could be found investigating the role of endocytosis on P3 production, so this cannot be discussed specifically. Endocytosis allows a degree of dissociation between the α- and β-cleavages and γ-cleavage, resulting in levels of sAPPα and sAPPβ that may be independent of Aβ(1-40/42) and P3 levels, respectively. This means that in terms of function, APP processing via the β-pathway has the potential to either: modulate actions of sAPPα via competition with sAPPβ; or modulate the actions of sAPPα and initiate actions mediated by Aβ.

P3 corresponds to Aβ(17-40/42) and we predict that this shared sequence could allow P3 to modulate the interactions of Aβ (shown in Figure 2 as competitive inhibition), especially where those interactions involve the carboxy-terminal amino acids. While no studies can be found that address this particular question directly, various amino-terminal truncated forms of Aβ have been associated with diffuse plaques [80, 81] and the Aβ(17-40) fragment, corresponding to P3, has been studied in terms of aggregation [82]. It is clear that amino-truncated Aβ peptides, including P3, have potential for aggregation and can interact with Aβ, but how this affects the physiological behaviour of neuronal systems is not clear. In terms of functional flow through the APP pathway, processing via the α-pathway to release sAPPα and P3 could either: initiate actions of sAPPα; or initiate actions of sAPPα and modulate actions mediated by Aβ.

In terms of modelling these interactions in functional contexts, PPI networks may require a more detailed approach, where each sequence length and aggregation state is represented as an individual node.

The effects of concentration, sequence length aggregation state and affinity

Aβ is produced in a range of sequence lengths [41] and can form monomers, dimers, oligomers and fibrils. The amount of Aβ produced is regulated and can dynamically change in response to many factors, including increased cholesterol [83], increased synaptic activity [84], heparins via activation of BACE1 [70], reduced acetylcholine (Ach) signalling via muscarinic receptors [70], sustained increased cytosolic Ca2+ [85], and hypoxia [86]. Changes in concentration are a well recognised mechanism of regulation in cellular processes; for example, Aβ(1-40) and Aβ(1-42) both promote angiogenesis at nanomolar concentrations but inhibit it at higher micromolar concentrations [87].

Different sequence lengths show different behaviours; for example, Aβ(1-42) is more prone to aggregation than Aβ(1-40) due to a more rigid carboxyl terminus [88]. Aβ monomers appear to have structured and unstructured regions, with differences in aqueous conformations between Aβ(1-40) and Aβ(1-42) [89]. While both Aβ(1-40) and Aβ(1-42) reduced currents via α7 and α4β2 ACh receptors (AChRs), only Aβ(1-40) increased glutamatergic signalling via AMPA receptors [40]. Another study found that the effects of Aβ(1-40) and Aβ(1-42) on α7 AChRs, as measured by ACh release and Ca2+ influx, were different [39]. Additionally, Aβ(1-40) may protect neurons from the effects of Aβ(1-42) by disrupting the aggregation of Aβ(1-42) [90] and this raises the possibility that P3-type peptides may act in similar ways. The functional effects of sequence length are only just beginning to be elucidated and should be expanded to include all the Aβ-type peptides, including P3. In addition to differences in peptide sequence and concentration, aggregation state also modulates function. Aβ oligomers appear to be spherical [91, 92] and Aβ fibrils appear as fine fibres [92]. Aβ oligomers, but not monomers or fibrils, enhance lipid release [93] and at low concentrations enhance neuronal differentiation [92] whereas Aβ fibrils, but not monomers or oligomers, may interact with APP and increase its expression [71]. While both soluble and aggregated forms of Aβ(1-40) inhibit long-term potentiation, the fibrillar forms may additionally affect neurotransmitter release [94].

Within a specific cellular compartment, whether one interaction is more likely to occur than another depends on the relative affinities of the reactants involved. While dissociation constants have been measured for some reactions in rodent models (for example, K(i) values of Aβ(1-42) for α7 nicotinic AChRs from rat and guinea pig are 4.1 and 5.0 pM, respectively, and the K(i) of Aβ(1-42) with α4β2 AChRs is approximately 5,000-fold lower at 30 and 23 nM, respectively [95]), we are missing dissociation constants for most interactions in humans.

Taken together, the data discussed above suggest that the interactions of APP and its proteolytic fragments with synaptic systems are complex and involve subtle changes in the relative ratios between APP and all its fragments. Given the complexity of the APP system, multiple neuronal systems have the potential to regulate the expression and proteolysis of APP and could also initiate imbalance within this system, leading to multiple possible disease pathways.

This complexity has the potential to confound purely empirical studies into the behaviour of the peptides in model systems. Over-expressing Aβ peptides in, for example, mouse models without knowing their concentrations, sequences and aggregation states and the background concentrations, sequences and aggregation states of the other relevant peptides in the experimental system that is being studied means that we do not know if the experimental results obtained are due to the properties of the Aβ peptides themselves or reflect a disrupted balance in a complex system over time, or perhaps both. While this distinction may seem subtle, given the self-organising and self-referencing properties of the human brain, an approach based on the principles in systems biology may better represent the roles of the APP pathway in functional contexts.


It is not possible to include the full extent of the known interaction of APP and its proteolytic fragments here and further interactions may yet be discovered. Data relating to the roles of APP and its proteolytic fragments in synaptic plasticity and AD have accumulated from many diverse fields and these need to be placed into context. One way of doing this is to investigate the relationships between APP, its proteolytic fragments and wider neuronal systems by mapping networks of interactions.

It is interesting that the networks generated in the various studies reviewed in Table 1 do not always overlap and different studies highlight different pathways or biological processes, for example, Fe2+ [28], apoptosis [96], or cardiovascular disease/diabetes [31]. Each study has different starting points, inclusion/exclusion criteria and network construction methods, so this lack of agreement is no surprise. It is difficult to assess the degree to which the various starting points, criteria and network construction methods bias results towards an outcome.

The study by Soler-Lopez and colleagues [33] may not model the interactions of full length APP in the membrane adequately, as many of the ECM proteins that might be expected to interact are excluded due to difficulties involved in expressing them in the experimental microarray used. This may shift the focus of their network more towards intracellular interactions. Given the importance of the various interactions of APP with components of the ECM, any study excluding such proteins and proteoglycans could be seriously confounded.

While the study by Perreau and colleagues [25] maps PPIs to specific domains of full length APP, they do not distinguish between the different proteolytic peptides. Additionally, transient interactions and biologically essential post-translational modifications, such as glycosylation and phosphorylation, cannot yet be fully represented in PPI networks [27], severely limiting the modelling of regulation and control in these models.

Studies comparing mouse and human gene expression networks [32] or aging and AD pathways [30] have revealed interesting results. Perhaps most striking is the study revealing a human-specific network for PS function in oligodendrocytes and myelination, with the potential to confound the current approach to modelling AD in rodents [32]. Miller and colleagues [30, 32] also show important contributions from both neuronal and glial pathways, perhaps reflecting the contributions of glia to disease pathways found in humans [97, 98]. The differences found in networks between glia and neurons could be extended to differentiate between specific neuronal types and future network models may usefully investigate why cholinergic neurons in AD appear more susceptible to degeneration than others.

We do not yet have totally inclusive, gene co-expression or PPI models of APP and related pathways to the level of detail required and this places limitations on their interpretation. Given the limitations, however, network studies do show the usefulness of the systems biology approach in integrating huge volumes of data and a simple search of patents in Google with the terms 'protein interaction network Alzheimer disease' retrieves more than 700 applications based on this approach. Network studies highlight the idea that multiple processes are involved in AD disease pathways, reveal new targets for further investigation and have generated new hypotheses. Network models have the potential to avoid biases inherent in other approaches, such as the emphasis on Aβ in animal models based on the more reductionist amyloid cascade hypothesis, and allow multiple disease pathways to be represented in a single model, perhaps better relating to the processes involved in human disease.

We have suggested that APP and its proteolytic fragments appear to dynamically modulate each other by multiple mechanisms with evidence for both direct and indirect feedback loops between Aβ, sAPPα, sAPPβ and full length APP. Additionally, the fragments derived from γ-cleavage, including the variable length fragments, P3 and Aβ, have the potential to interact with each other and modulate multiple neuronal processes in subtle and dynamic ways. Unfortunately, evidence for the role(s) of P3 is almost entirely missing from the literature and that for sAPPβ is limited. Without this information, we cannot have a full picture of the contributions of the APP proteolytic system to normal neuronal processes and how these change during disease progression.

Given the number and range of reciprocal feedback relationships that APP and its proteolytic fragments are involved in, the effects of perturbing the APP proteolytic system may be better understood as dynamic, synergistic actions involving multiple players. A wide range of neuronal processes have the potential to interact with and regulate APP processing, allowing APP to monitor the current state of the cell. The multiple proteolytic pathways allow APP to assimilate signals from wide-ranging cellular systems and pass these signals on via the ratios of APP fragments. The constant turnover of APP contributes to the ability of neurons to sense the current state of the cell and use this information to set in motion future responses. The potential for subtly different signals both from the α- and β-pathways via alternative peptides and the complex feedback relationships between them described above fits well with the fine control required for integrating neuronal systems.

The predicted synergy between APP and all its proteolytic fragments implies that the ratios of full length APP and all the various fragments, not just Aβ(1-40) and Aβ(1-42), are more important than absolute levels in coordinating neuronal responses. The complexity of the relationships described here has the potential to confound purely empirical investigations into the activities of any one proteolytic fragment studied in isolation. To avoid these predicted confounding effects, experimental approaches should be extended to account for the different concentrations, affinities and aggregation states of each peptide length, and experimental systems should be followed over time for each biological process in a systematic manner. With over 40 Aβ-type peptides [41], this presents a challenge.

Generating useful network models will require a more detailed and comprehensive approach to experimental design, involving a range of experimental controls to account for all the confounding factors. These data will be essential for generating network models that are truly relevant to our understanding of normal and abnormal processes in the context of the human population.



amyloid beta protein




acetylcholine receptor


Alzheimer's disease


amyloid precursor protein


beta-site amyloid precursor protein cleaving enzyme


extracellular matrix




nuclear factor


protein-protein interaction




secreted amyloid precursor protein.


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This work was supported by funding from the Cambridge and Peterborough CLAHRC.

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Correspondence to Sally Hunter.

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The authors declare that they have no competing interests.

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SH wrote the paper in discussion and with contributions from CB.

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Hunter, S., Brayne, C. Relationships between the amyloid precursor protein and its various proteolytic fragments and neuronal systems. Alz Res Therapy 4, 10 (2012).

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  • Amyloid Precursor Protein
  • Amyloid Precursor Protein Processing
  • Proteolytic Fragment
  • Amyloid Cascade Hypothesis
  • Amyloid Precursor Protein Expression