Protein-protein interactions underlying the behavioral and psychological symptoms of dementia (BPSD) and Alzheimer’s disease
Authors:
Yimin Mao aff001; Daniel W. Fisher aff003; Shuxing Yang aff001; Rachel M. Keszycki aff003; Hongxin Dong aff003
Authors place of work:
School of Information and Technology, Jiangxi University of Science and Technology, Jiangxi, China
aff001; Applied Science Institute, Jiangxi University of Science and Technology, Jiangxi, China
aff002; Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States of America
aff003
Published in the journal:
PLoS ONE 15(1)
Category:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0226021
Summary
Alzheimer’s Disease (AD) is a devastating neurodegenerative disorder currently affecting 45 million people worldwide, ranking as the 6th highest cause of death. Throughout the development and progression of AD, over 90% of patients display behavioral and psychological symptoms of dementia (BPSD), with some of these symptoms occurring before memory deficits and therefore serving as potential early predictors of AD-related cognitive decline. However, the biochemical links between AD and BPSD are not known. In this study, we explored the molecular interactions between AD and BPSD using protein-protein interaction (PPI) networks built from OMIM (Online Mendelian Inheritance in Man) genes that were related to AD and two distinct BPSD domains, the Affective Domain and the Hyperactivity, Impulsivity, Disinhibition, and Aggression (HIDA) Domain. Our results yielded 8 unique proteins for the Affective Domain (RHOA, GRB2, PIK3R1, HSPA4, HSP90AA1, GSK3beta, PRKCZ, and FYN), 5 unique proteins for the HIDA Domain (LRP1, EGFR, YWHAB, SUMO1, and EGR1), and 6 shared proteins between both BPSD domains (APP, UBC, ELAV1, YWHAZ, YWHAE, and SRC) and AD. These proteins might suggest specific targets and pathways that are involved in the pathogenesis of these BPSD domains in AD.
Keywords:
Protein domains – dementia – Alzheimer's disease – Aggression – Centrality – Pathogenesis – Heat shock response – Protein interaction networks
Introduction
Alzheimer’s Disease (AD) is a progressive neurodegenerative disease and is the most common form of dementia with over 40 million people affected worldwide[1]. Surprisingly, over 90% of AD patients display behavioral and psychological symptoms of dementia (BPSD), including agitation, aggression, irritability, impulsivity, disinhibition, anxiety, depression, apathy, euphoria, and psychosis[2, 3]. BPSD can present at almost any stage of AD, and in some patients, these symptoms can even appear before memory deficits develop[4]. The severity of BPSD increases significantly with disease progression, and BPSD affect the quality of life of both patients and their caregivers[5–7]. Though memory deficits are the best studied aspects of AD, it is BPSD that are often the greatest source of burden for patients and caregivers and are one of the main reasons for institutionalization[8–11]. Along with there being few rigorous studies of BPSD’s biochemical and cellular mechanisms, there are no FDA-approved treatments for BPSD management[12, 13].
Although BPSD present differently in each patient, the presence of certain symptoms makes the co-occurrence of other symptoms more likely. Thus, it has been suggested that certain symptoms cluster into behavioral domains and that these domains may have commonly disturbed molecular pathways at their core, explaining the higher likelihood of certain symptoms presenting together[2]. Among the myriad of BPSD, unbiased clustering approaches generally yield the following 5 domains: Affective Domain; Hyperactivtiy, Impulsivity, Disinhibition, Aggression (HIDA) Domain; Apathy Domain; Psychosis Domain; and Euphoria Domain [2]. However, the distinct molecular mechanisms leading to the common presentation of symptoms in each domain and how AD pathogenesis leads to these molecular alterations is surprisingly unstudied.
Recently, investigation of the interactions between proteins encoded in known disease genes through protein-protein interaction (PPI) networks has become a powerful approach to exploring the etiology and neuropathology of complex diseases, including AD[14, 15]. PPI data can be used at a larger scale to map networks of interactions depending on their functional associations[16]. Research studies based on PPI networks have achieved noteworthy results, revealing disease complexities at the protein and gene levels[14, 15, 17–23]. Among them, some studies[14, 15, 17, 24] have used PPI to discover essential proteins, genes, and associated pathways linked to disease pathogenesis and potential therapies. For example, using such methods, researchers have identified candidate genes[14] and signaling pathways involved in AD pathogenesis in a brain region-specific manner[15]. However, the proteomic links between AD and BPSD have not been investigated.
In this study, using the PPI network analysis approach, we investigated the proteomic links between AD and select BPSD domains, namely the Affective Domain and HIDA Domain. We selected these two BPSD domains as their symptoms are the best studied outside AD pathogenesis. We first chose causative genes related to these symptoms based on prior information from the Online Mendelian Inheritance in Man (OMIM) database and published literature, then we constructed PPI networks related to AD and the two BPSD domains. After, we designed a DBruteForce algorithm to detect shared proteins. Finally, based on the “centrality-lethality” paradigm[25], we designed a “shared protein-degree centrality” principle to identify essential shared proteins between AD and each BPSD domain. Our study reveals intrinsic protein connections between AD and BPSD.
Materials and methods
The analytical framework to identify the shared essential proteins is illustrated schematically in Fig 1. The process consists of three main steps–Construction, Detection, and Identification: 1) Construction involves building the PPI networks from the Interologous Interaction Database (I2D); 2) Detection involves investigating the shared proteins between diseases by designing a DBruteForce algorithm; and 3) Identification involves searching essential shared proteins by designing a “shared protein-degree centrality” principle.
The input terms were identified using the OMIM database and the PPI networks were constructed using information from I2D. The DBruteForce algorithm was used to detect shared proteins and then the “shared protein-degree centrality” principle was applied to identify the essential shared proteins between AD and each BPSD domain. The enrichment analyses were done on the shared proteins that were detected after using the DBruteForce algorithm. Red color indicates the shared proteins between AD with one BPSD domain, and the green color indicates the shared proteins among AD with both domains.
Databases
Two databases: OMIM and I2D were used in this study. OMIM is classical database containing an authoritative compendium of human genotypes and associated phenotypes and contains over 15,000 genes. I2D is an online database of known and predicted mammalian and eukaryotic protein-protein interactions. It has integrated known, experimental, and predicted PPIs for five model organisms and humans. I2D is comprised of more than 687,000 experimental PPIs and about 619,000 predicted interactions. Notably, two points make I2D preferred over other interaction databases: 1) it has been built by mapping high-throughput data between species, with these interactions being considered "predictions"; 2) it remains one of the most comprehensive sources of known and predicted eukaryotic PPIs. The most recent updates for the databases we used were in April 2019.
Identification of input genes relating to AD and BPSD
First, we selected symptom terms that matched our AD and BPSD Domains, including “Alzheimer’s Disease”, “anxiety”, “depression”, “MDD”, “hyperactivity”, “disinhibition”, “impulsivity” and “aggression.” Then, we input these terms into the OMIM database and reviewed the list of hereditary disease genes from the OMIM morbid map (http://www.omim.org). As these search terms are broad, many of the resulting genes identified in OMIM are not clearly associated with these symptoms. For instance, the search term ‘hyperactivity’ can bring up genes involved in both aberrant motor behavior as well as an increased ability of cells to fire an action potential. Thus, two investigators independently determined which potential genes were truly implicated in causing each symptom based on the OMIM report and published literature. Agreement between the investigators was >90%, and a third investigator determined if a gene should be added in the case of a disagreement.
Construction of PPI networks
The “causative proteins” for AD, the Affective Domain, and the HIDA Domain from OMIM were input to the I2D database (http://ophid.utoronto.ca/ophidv2.204/ppi.jsp) with ‘human’ as the chosen target organism, and the resultant PPIs related to AD and BPSD were generated, including predicted and experimental PPIs. To increase the data reliability of protein interactions, all predicted homologous protein interactions were excluded. The interaction network contained the disease-associated proteins (nodes) and their interactions (edges). By exploring the mapping scheme of the UniProt database, corresponding gene names and IDs were retrieved. Because some proteins were given multiple names, the results in tables and figures were presented in the format of gene names and UniProt IDs to avoid ambiguous referencing.
Detection of the shared proteins between AD and BPSD
The connection sets between proteins for AD and the BPSD domains were produced based on the PPI networks constructed for AD or each symptom domain. BruteForce [26] is the simplest string match algorithm for two strings (two Uniprot IDs); however, it us unable to capture intersections between PPI networks. Using this algorithm as an intial framework, we developed Deformation BruteForce algorithm (DBruteForce) to identify the shared proteins between AD and BPSD networks. The DBruteForce algorithm performs intersections of Uniprot IDs for each protein from the AD list with all the proteins in the given BPSD domain list, and these intersection IDs are served as “shared proteins.” Suppose two sets (setA and setB) of nodes interact between AD and a given BPSD domain produced by the PPI networks. PID indicates the Uniprot ID of the protein and is a string. The DBruteForce algorithm used in this study is shown in Fig 2.
Identification of essential shared proteins among AD and BPSD
We identified essential shared proteins between AD and the two BPSD domains based on their node degree centrality. For each node (protein), we applied degree centrality (DC) to assess its role in the network. Given a PPI network, it is represented as an undirected graph G (V, E) with proteins as nodes and interactions as edges. Degree centrality is calculated by:
Functional enrichment analysis
To further study the functions of the proteins in the PPI networks linked to AD and BPSD, an analysis of functional enrichment and enriched pathways was performed with the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases (Version No DAVID 6.8; https://david.ncifcrf.gov/summary.jsp). In the enrichment analyses, functions or pathways were considered enriched if P < .01. To perform these analyses, we annotated, visualized, and integrated discovery by using the DAVID database, which is an online platform providing functional annotation tools to analyze biological meaning behind large gene lists.
Results
Genes and interaction networks
Candidate genes/proteins implicated in the pathogenesis of AD, the symptoms of Affective Domain, and HIDA Domain were obtained from the OMIM database and verified with published literature (The details are provided in S1 Table). The intersects of genes among AD, Affective and HIDA Domains are described in Fig 3. There were 0 intersects of genes between AD and Affective Domain, 1 intersect of genes between AD and HIDA Domain, and 8 intersects of genes between Affective and HIDA Domains. The PPI networks comprising these genes/proteins were constructed utilizing information from I2D. The AD-related proteins produced a network with 14,527 nodes, the proteins related to symptoms in Affective Domain produced a network with 4,825 nodes, and the proteins related to symptoms in the HIDA Domain produced a network with 3,127 nodes. After removing the predicted interactions that were homologous to validated interactions, there were 8,747 interactions for AD, 2,763 interaction for the Affective Domain, and 1,754 interactions for the HIDA Domain as well as 3,415 nodes for AD, 2,111 nodes for the Affective Domain and 1,352 nodes for the HIDA Domain.
A) The intersects of genes between AD and Affective Domain symptoms. B) The intersects of genes between AD and HIDA Domain symptoms. C) The intersects of genes between Affective and HIDA Domain symptoms.
Shared proteins between AD and BPSD
The shared proteins between AD and the BPSD domains were detected using a DBruteForce algorithm (Fig 2). We found 1,099 shared proteins between AD and the Affective Domains (S2 Table), 720 shared proteins between AD and the HIDA Domain (S3 Table), and 401 shared proteins among AD, the Affective Domain, and the HIDA domain (S4 Table, Fig 4).
There were 1099 proteins shared between AD and the Affective Domain, 720 shared between AD and the HIDA Domain, and 401 shared between all three.
Essential shared proteins between AD and BPSD
According to the “shared protein-degree centrality” principle, the proteins with high node degree, defined as node degree greater than the mean, and shared among the domains and AD are counted as essential shared proteins. We identified eight unique essential shared proteins between the Affective Domains and AD, (Fig 5), five unique essential shared proteins between the HIDA Domain and AD (Fig 6), and eight overlapping essential shared proteins between the Affective Domain, HIDA Domain and AD (Fig 7).
There were 14 proteins that were identified as being essential shared proteins between AD and the Affective Domain. Proteins in red are essential shared proteins that are unique to the Affective Domain while those in black were also found to be essential shared proteins between AD, the Affective Domain, and the HIDA Domain.
There were 11 proteins that were identified as being essential shared proteins between AD and the HIDA Domain. Proteins in red are essential shared proteins that are unique to the HIDA Domain while those in black were also found to be essential shared proteins between AD, the Affective Domain, and the HIDA Domain.
6 proteins were identified as being essential shared proteins between AD, the Affective Domain, and the HIDA Domain.
The mean node degree connecting AD and Affective Domain proteins was six and five, respectively. For each protein in Fig 5, the node degree connecting AD and Affective Domain proteins were equal to or greater than the mean node degree, thus, RHOA, APP, UBC, YWHAE, ELAVL1, GRB2, PIK3R1, SRC, HSPA4, YWHAZ, HSP90AA1, GSK3beta, PRKCZ, and FYN were found to be essential shared proteins, in which RHOA and APP are also causative genes related AD, and GSK3beta and PRKCZ are causative genes related to Affective Domain symptoms.
For each protein in Fig 6, the mean of degree centrality for each protein was eight and four between the AD and HIDA PPI networks, respectively. The degree centrality for each protein listed in the Fig 5 is equal to or greater than the mean of degree centrality, thus, APP, LRP1, UBC, YWHAE, EGFR, ELAVL1, YWHAB, YWHAZ, SUMO1, SRC, and EGR1 are essential shared proteins, in which APP and LRP1 are also causative genes related AD.
The mean number linking AD, Affective Domain, and HIDA Domain proteins are eight, eight, and four, respectively. For each protein in Fig 7, the mean number linking AD, Affective Domain, and HIDA Domain proteins are equal to or greater than the mean degree centrality, thus, APP, UBC, YWHAE, ELAVL1, YWHAZ and SRC proteins are essential shared proteins across AD and both BPSD domains.
Functional enrichment analysis
GO term and KEGG pathway enrichment analyses were performed based on the PPI networks of shared proteins between AD and each BPSD Domain. The GO term analysis included three categories: biological processes (BP), molecular functions (MF), and cellular components (CC). Tables 1 and 2 describe five notable GO terms for BP, MF, and CC as well as five notable KEGG pathways that are enriched between AD and the Affective Domain. These notable terms include Vascular Endothelial Growth Factor (VEGF) signaling, chaperone binding, dendritic spine, and NF-kappa-B signaling in BP, MF, CC, and KEGG, respectively. Between AD and the HIDA domain, there were notable enrichments for Ephrin Receptor signaling, SNAP receptor activity, SNARE complex, and Amyotrophic Lateral Sclerosis (ALS) signaling in BP, MF, CC, and KEGG, respectively (Tables 3 and 4). Additional functional enrichment analyses are shown in S4 and S5 Tables.
Discussion
In this study, we used publicly available, bioinformatic databases to investigate the possible interactions between AD and the Affective and HIDA Domains and discovered a specific set of shared essential proteins that may be involved in each BPSD domain. In particular, to derive these shared essential proteins, we selected candidate genes from OMIM associated with AD and commonly co-occurring symptoms within each domain: anxiety and depression for the Affective Domain, and hyperactivity, impulsivity, disinhibition, and aggression for the HIDA Domain. Then, we established PPI networks, where common points of convergence between lists were detected using a DBruteForce algorithm, and essential proteins were identified using the “shared protein-degree centrality” principle. These identified “hubs” present a set of proteins that may be adversely affected during AD pathogenesis and contribute specifically to the symptoms within their associated BPSD domain.
The lists of essential shared proteins between AD, the Affective Domain, and the HIDA Domain are inferences based on known protein interactions and thus represent only potentialities that may be central to BPSD pathogenesis. Moreover, use of degree as the sole metric of protein essentiality may result in biased information, although in general, the higher the number of proteins connected between AD and BPSD, the more likely that these proteins drive biological mechanisms and perform physiological functions that are similarly perturbed in AD and BPSD. However, in some cases, high connectivity does not necessarily imply essentiality, and nodes with low degrees might also be relevant despite their relative underconnectedness. In addition, connectivity between proteins is influenced by how often these proteins are interrogated empirically, thus potentially overrepresenting the essentiality of more commonly studied proteins. Finally, some proteins, such as adapter proteins, ubiquitins, and chaperons that bind nonspecifically to a large number of other proteins are more likely to be represented given their promiscuity and multiple protein-interacting motifs. Therefore, it will be important to verify the current analyses with other algorithms that may better account for these known biases. Additionally, rigorous investigation will be necessary to confirm these potential mechanisms. As complex symptoms like BPSD are likely to result from multiple aberrations among many genes/proteins, commonalities between these proteins in terms of pathway or function may be more informative than the individual proteins investigated in isolation. In addition, it is interesting that there are proteins that appear on both lists, which suggest some common driver of neurodegenerative pathology leading to BPSD. Still, the proteins that are unique to one domain seem more likely to be implicated in the generation of specific symptoms. Regardless, the next few sections will help to contextualize the proteins identified here and point out commonalities that may serve as future hypotheses detailing disruption of convergent pathways that cause these symptoms in AD.
Affective domain
The unique proteins implicated as essential to the Affective Domain broadly fall into two categories functionally, either as chaperones (HSPA4, HSP90AA1) or proteins involved in phosphorylation (FYN, GRB2, GSK3beta, PI3KR1, PRKCZ, RHOA). Though chaperones are likely to be influenced by phosphorylation, and many client proteins for heat shock proteins are kinases[27, 28], both sets of proteins may independently or synergistically affect other pathological mechanisms that ultimately lead to affective symptoms in AD.
One point of convergence among these proteins concerns their regulation of hyperphosphorylated tau (p-tau), an important protein involved in AD pathogenesis. In addition to amyloid-beta (Abeta), p-tau aggregation is a hallmark of AD pathogenesis, with p-tau forming intracellular tangles that, along with Abeta are often used to corroborate post-mortem pathological assessments with ante-mortem clinical diagnosis of AD. In AD, abnormal tau starts developing due to several post-translational modifications that lead to p-tau forming oligomers, paired helical filaments, and straight filaments[29–32]. Though the exact mechanism whereby tau leads to AD pathogenesis is far from clear, tau has been shown to inhibit microtubule formation[33], impair axonal transport[34, 35], and promote neuronal toxicity[36]. One of the earliest steps in AD pathogenesis is the hyperphosphylation of tau at multiple residues, leading to disruption of tau at microtubules and relocation of tau to somatodendritic compartments, influencing synaptic dysfunction[32, 33, 37, 38].
While there is still some debate as to whether the synaptic dysfunction from p-tau stems from pre- or postsynaptic mechanisms[37], it has been well-established that synapse loss is one of the strongest correlates of cognitive dysfunction in AD[39–42]. The loss of synapses is especially intriguing in terms of affective symptoms in AD, as synapse loss in key limbic areas is also correlated with affective symptoms, especially for depression[43–46]. Morphologically, spine loss in the hippocampus and medial prefrontal cortex represents one of the most common pathological sequelae of chronic stress leading to depression[46, 47]. In addition, impairments in proper BDNF expression are similarly noted in both depression and AD[48]. Speculatively, differential post-translational modifications of tau could result in divergent effects on synaptic function in a cell type- and brain area-specific manner, leading to affective symptoms when the balance of certain tau modifications are achieved. With the complex regulation of tau through multiple post-translational modification sites, alternative splicing of 6 isoforms in human brains, and multiple structural confirmations[38, 49, 50], tau does not lack in appropriate complexity for leading to the heterogeneous presentation of symptoms throughout the course of AD.
The longest tau isoform has close to 80 phosphorylation sites, and of these, at least 20 have been implicated in functional alterations of tau[51]. Though many kinases have been associated with tau, one of the most investigated of them is GSK3beta, which phosphorylates tau at multiple residues, resulting in complex functional outcomes on a molecular and cellular basis[52, 53]. GSK3beta’s involvement in AD is further supported by the fact that AD patients have elevated levels of GSK3beta [54, 55] and that familial AD-associated protein PS1 helps localize the kinase to tau[56]. GSK3beta is perhaps the most promiscuous kinase in mammals[57] and so, unsurprisingly, has been associated with the pathogenesis of affective disorders[58, 59]. Notably, many of the treatments for depression have inhibitory effects on GSK3beta, chronic stress increases GSK3beta, and small molecule inhibitors of GSK3beta have been shown to decrease depression-like behavior in pre-clinical models[58]. Highlighting the potential importance of GSK3beta in the Affective Domain of AD, two other kinases that were implicated from our PPI analysis, PI3K and PKC, regulate GSK3beta and have been shown to influence its ability to hyperphosphorylate tau[60, 61]. Thus, GSK3beta represents a possible target for Affective Domain symptoms, and it may be prudent to investigate these symptoms in clinical trials that are testing the effects of GSK3beta inhibitors on cognition in AD. However, if improvement in affective symptoms were realized, further investigation would be needed to determine if inhibition of GSK3beta caused improvements via reduction in p-tau or other mechanisms.
In addition to kinase regulation of p-tau, heat shock proteins have also been implicated in regulating p-tau stabilization and function in AD[62, 63]. Heat shock proteins are induced by cellular stress and are important for protein folding and proteosomal degradation[63]. Of the eight unique proteins identified in our PPI analysis, two are heat shock proteins. HSPA4 is in the HSP110 family and acts as a nucleotide exchange factor for HSP70, aiding in its function[64], and HSP90AA1 is the stress-inducible isoform of HSP90[65]. Both HSP70 and HSP90 have been implicated in the regulation of p-tau, though interestingly, in divergent ways. While HSP70 has been shown to increase p-tau degradation and stabilize tau binding to microtubules[66–68], HSP90 may actually maintain oligomeric p-tau levels, which are quite pathogenic[69, 70]. Thus, the HSP70/HSP90 balance may be a key factor in the regulation of p-tau and resulting affective symptoms. Making matters more interesting, the peptidyl-prolyl isomerase FKBP51 is an interaction partner for HSP90 that seems to facilitate the preservative function of the chaperone on oligomeric p-tau[70]. The point of intrigue comes from the well-described role for FKBP51 in regulation of glucocorticoid receptor localization and function, underlying FKBP51’s prominent role in the manifestation of pathological behavior to chronic, unpredictable, or extreme stress[71]. Though the role for FKBP51 in Affective Domain symptoms remains speculative, the hypothesis that imbalance between HSP70/HSP90 promotes p-tau is intriguing, especially if alterations in subcellular distributions lead to increased p-tau-dependent dysregulation of synaptic activity.
Though many of the proteins identified in this PPI analysis are connected to increased p-tau, a question remains as to how this elevation leads to affective symptoms. As mentioned, one hypothesis is that hyperphosphorylation of tau results in aberrant localization to dendritic compartments, thus leading to postsynaptic dysfunction in key limbic areas affected during AD pathogenesis, such as the medial prefrontal cortex and hippocampus[72]. Interestingly, a few of the proteins identified in our PPI analysis may provide insight into how this postsynaptic dysregulation may be achieved. FYN is a cytosolic tyrosine kinase that was found to be central for oligomeric Abeta-induced synaptic loss and dysfunction[73]. In addition, overexpression of FYN in an AD mouse model accelerated synapse loss and cognitive deficits, with loss of FYN reversing these AD-related sequelae[74, 75]. While the mechanism of synaptic dysfunction was initially unclear, it was discovered that dendritic tau helps localize FYN to the synapse and that loss of functional tau inhibited Abeta-induced synaptic dysfunction[76]. Ultimately, FYN has been shown to regulate the expression of important synaptic plasticity proteins, most notably NMDAR and PSD95, though differential phosphorylation patterns of tau have been shown to promote or ameliorate synaptic dysfunction by this mechanism[76, 77]. Interestingly, in a mouse model of chronic stress, cognitive and affective disorder-like behavior necessitated tau expression to develop. In this same study, glucocorticoids were found to increase p-tau and promote dendritic mislocalization of tau, and FYN was similarly upregulated at the synapse of mice subjected to chronic stress but only if tau expression remained intact, correlating with a change in NMDAR synaptic expression[78]. Though implications for FYN in affective disorders have been less well characterized, there is a report of FYN polymorphisms being associated with trait anxiety[79].
In addition to FYN directly affecting synaptic function via phosphorylation of key postsynaptic density proteins, FYN also regulates RHOA[80, 81], an important regulator of spine morphology and dendritic complexity[82, 83] as well as being another protein identified by our PPI analysis for the Affective Domain. However, while RHOA co-localizes with p-tau[84] and has been shown to influence phosphorylation of tau at certain residues via ROCK[85], it is unclear if a reverse relationship exists whereby p-tau leads to RHOA-dependent alterations of the synapse. A mechanism whereby p-tau would affect RHOA signaling is especially intriguing, as chronic stress has been shown to negatively affect postsynaptic morphology in a RHOA-dependent manner in both medium spiny neurons[83] and pyramidal cells[82], resulting in affective symptoms.
In summary, the eight unique proteins identified by our PPI analysis suggest that regulation of p-tau may be highly involved in the Affective Domain. The increase in p-tau species could lead to greater mislocalization of tau to postsynaptic compartments, which may lead to significant synaptic dysregulation. Plausibly, both FYN and RHOA may be mediators of this synaptic dysfunction through alterations in key synaptic proteins and dendritic structure.
HIDA domain
Unlike the Affective Domain, where proteins can be neatly sorted into two categories, the unique proteins in the HIDA Domain have relatively diverse functions. Specifically, these proteins are Low Density Lipoprotein Receptor-Related Protein 1 (LRP1), a cholesterol receptor that mediates endocytosis and binds to over 40 ligands[86], Epidermal Growth Factor Receptor (EGFR), a receptor tyrosine kinase in the neuregulin/ERBB family[87], 14-3-3beta (YWHAB), a phosphoserine/phosphotyrosine binding protein of the 14-3-3 family that interacts with a wide-range of other proteins to facilitate protein interactions[88], Small Ubiquitin Related Modifier 1 (SUMO1), a ubiquitin-like post-translational modification that affects a broad range of protein functions[89], and Early Growth Receptor 1 (EGR1), a transcription factor that represents an intermediate-early gene marking neuronal activation and synaptic plasticity[90]. All five of these proteins have been implicated in AD pathogenesis: LRP1 is a receptor that interacts with Abeta and APOE, which may influence LRP1’s ability to aid in Abeta clearance[86]; EGFR polymorphisms were found to be inversely proportional to AD risk in a Han Chinese cohort[91]; 14-3-3 can be found around Abeta plaques and facilitates GSKbeta-mediated phosphorylation of tau[92]; SUMO1 expression is altered in cortices of AD patients[93]; and EGR1 inhibition leads to decreased Abeta and p-tau while reversing cognitive deficits in 3xTg AD mice[94]. However, the links between these proteins and HIDA Domain symptoms are sparse. Despite this paucity of direct evidence in HIDA Domain symptoms, some points of convergence in perturbed pathways may suggest how these proteins could influence the development of these symptoms in AD.
To conceptualize how these proteins may lead to HIDA Domain symptoms, understanding the broad mechanisms that underlie these symptoms is helpful. Though at first hyperactivity, impulsivity, disinhibition, and aggression may seem like distinctly different entities, they share a common conceptual framework in a lack of inhibitory control over one’s actions. Unsurprisingly, some common circuitries are perturbed in the development of these symptom, most notably key corticostriatal pathways[95–97].
For instance, aggression is often partitioned into impulsive/reactive aggression and instrumental/proactive aggression, of which the former is most likely to occur in AD[97, 98]. Over many human imaging and animal studies, three important circuits that mediate impulsive aggression seem to emerge: 1) areas that support aggressive impulses, including the amygdala, periaqueductal gray, anteroventral medial hypothalamus, lateral septum, and medial preoptic nucleus, 2) cortical decision-making centers that evaluate the consequences of an action and regulate emotion, such as the orbitofrontal prefrontal cortex (oPFC), ventromedial prefrontal cortex (vmPFC), and anterior cingulate cortex (ACC), and 3) striatal areas mediating response inhibition and encoding the rewarding and reinforcing aspects of aggressive acts[96, 97, 99–101]. In aggression, the cortical decision-making circuitry has an inhibitory influence on the impulses for aggressive behavior, though admittedly this is an overly simplistic description of a complex and nuanced system[97].
When compared to aggression, the circuitry for impulsivity is surprisingly similar: impulsivity can generally be increased with activation of the ventral tegmental area (VTA) to the ventral striatal circuit and is facilitated by reduced activation or lesioning of numerous frontal cortical areas, such as the vmPFC, oPFC, and ACC[95, 99]. In addition, areas like the ventral hippocampus and amygdala can modulate behavior in certain types of impulsivity[95]. For motor response inhibition, which may be associated with agitation and hyperexcitability in AD patients, the ACC, subthalamic nucleus, and certain pre-motor cortical areas, among others, are implicated[95, 102]. Again, while highly simplified, the HIDA Domain seems to be mechanistically united in its dependence on corticostriatal balance.
In addition to corticostriatal involvement, HIDA Domain symptoms have been shown to be influenced by monoaminergic modulation, namely by serotonin, norepinephrine, and dopamine[95, 96, 101]. Monoaminergic regulation of multiple behaviors, including those in the HIDA Domain, is achieved through broad release of these neurotransmitters in almost every area of the brain, especially among neocortical and limbic areas[103, 104]. In general, reduction in signaling of these three molecules leads to a greater burden of HIDA Domain symptoms, especially when affecting cortical and striatal areas[95, 96, 101]. In addition, multiple monoaminergic signaling genes have been implicated in increased risk of impulsivity, aggression, or disinhibition, including Drd1[105], Drd2[106, 107], Drd4[106, 107], 5htr1a[108], 5htr1b[109–111], 5htr2a[112–114], 5htr2b[115], Dat1[116], 5htt[117–119], Maoa[120–122], Comt[116, 123, 124], and Tph2[125]. Thus, reductions in monoaminergic regulation of corticostriatal pathways represents another complementary mechanism whereby HIDA Domain symptoms may appear.
In AD, some of the first areas to show neuropathology, degeneration, and dysfunction include the locus coreuleus (LC), the main center for noradrenergic signaling, and the dorsal raphe nucleus (DRN), a main center that provides serotonergic innervation for limbic and cortical areas[103, 126, 127]. In addition, some degeneration of the VTA, the main output center for the mesolimbic dopamine circuit, exists and has a functional impact on AD symptoms, though to a lesser extent than the LC and DRN[127–129]. Loss of monoaminergic innervation to corticostriatal circuits may therefore represent an early mechanism that leads to HIDA Domain symptoms. In contrast, while the transtentorial cortical region tends to degenerate early in AD, the neocortical regions seem to develop AD-related pathology slightly later in disease progression and radiate from the ventral to dorsal cortices[103, 130]. There is also some evidence that the striatum undergoes significant degeneration with AD[131]. Thus, selective loss of corticostriatal pathways may underlie a relatively late mechanism of HIDA Domain pathogenesis.
Perhaps the most straightforward link between these proteins and the HIDA Domain would be in their ability to influence cell death. LRP1 promotes survival via an Akt-mediated mechanism[132], EGFR protects cells from neurotoxicity through Akt downstream of PS1[133], 14-3-3 is broadly involved in neuronal differentiation and survival[92], inhibition of SUMO1 decreases neuroprotection to ischemia[134], and EGR1 promotes maturation and survival of dentate granule cells[135]. Alterations in these proteins may cause degeneration of cells in monoaminergic or cortical areas that regulate corticostriatal circuitry during AD pathogenesis, thus leading to presentation of HIDA Domain symptoms. While possible, synaptic dysfunction precedes cell death in AD pathogenesis, and it is more likely that HIDA Domain symptoms are present before a large burden of cell death occurs. In addition, it is unclear why these particular proteins would be implicated in being neuroprotective for the HIDA Domain, as few of them have unique ties to cortical or monoaminergic areas, though EGFR and 14-3-3 have been implicated in dopaminergic cell survival[136–139].
A more intriguing link between these proteins involves their ability to regulate axon remodeling and presynaptic function. Though synapse loss is generally agreed to be the best predictor of symptom progression in AD, axonal pathology and dysregulation are some of the earliest events in AD pathogenesis, often being detectable before plaque formation[34, 140, 141]. Specifically, axonal degeneration is typified by axonal swelling, demyelination, axonal atrophy, loss of presynaptic markers, deficits in axonal transport mechanisms, and eventual dying back neuropathy[34, 140, 141]. In addition, presynaptic dysfunction occurs in AD and may further contribute to symptoms in addition to morphological degeneration of axons[142]. Presynaptic dysfunction and axonal degeneration in cortico-cortical, corticostriatal, and monoaminergic axons may thus represent a mechanism whereby HIDA Domain symptoms develop. This may especially be true for monoaminergic axons, which are poorly myelinated and travel far distances, thus making them particularly susceptible to small aberrations in the cellular environment and increasing their dependence on efficient axonal transport[103].
In addition, it has been suggested that the areas of earliest AD pathology are also some of the most plastic structurally, which may be necessitated by the constant remodeling that is required to encode experiences and make decisions in dynamic contexts. Accordingly, it has been suggested that AD occurs as a process of continuous synaptic organization gone awry, leading to dedifferentiation and loss of synapses[143]. In support of AD being a disease of dysregulated structural plasticity, abnormal sprouting at both post- and presynaptic areas is an early hallmark of AD pathogenesis[143].
Thus, it is intriguing that the five unique proteins identified by our PPI analysis are involved in presynaptic function and axon maintenance. For instance, LRP1 is necessary for axonal extension mediated by lipoproteins, and its function can be enhanced by non-pathogenic ApoE3 but not pathogenic ApoE4[144], a process which is dependent on MAPK and intracellular calcium[145]. In addition, LRP1 agonism promotes sensory axon sprouting and regeneration after spinal cord injury[146], and axonal regeneration through the urokinase-plasminogen activator is modulated by LRP1 independent of integrin beta-1[147]. Finally, LRP1 was shown to be necessary for metallothienen-II’s ability to overcome microglial upregulation of TNF-alpha and thus promote axon growth[148]. As LRP1 is a receptor allowing for endocytosis of multiple lipoproteins that can integrate into the plasma membrane, it may be an important regulator of axonal structural plasticity by allowing for the necessary plasma membrane substrates to reach their presynaptic location.
EGFR has also been shown to impact axonal structure. Specifically, EGFR is best known as being necessary for restriction of axonal regeneration in the CNS mediated by inhibitory substrates, such as myelin and chondroitin sulfate proteoglycans[149–151]. Interestingly, EGFR may not just promote inhibition of axonal regeneration, it may also be essential for maintaining the proper balance between dynamic and static filopodia. Specifically, it was shown that EGFR demonstrates an intrinsic asymmetry during axon outgrowth monitored by live cell imaging, and aberrations in EGFR signaling led to reduced outgrowth dynamics and excessive branch formation[152]. Thus, EGFR may be an important protein mediating the “structural plasticity” hypothesis of AD and could exacerbate connectivity breakdown between corticostriatal or monoaminergic pathways.
14-3-3 Proteins have also been highly implicated in axon regeneration and structural plasticity[88, 153]. In particular, knockdown or inhibition of 14-3-3beta can promote axon and neurite outgrowth[154]. Similar to the dynamic role of EGFR, 14-3-3beta has also been shown to be able to promote the switch from attraction to repulsion of axons induced by Sonic Hedgehog signaling[155]. Interestingly, it was shown that 14-3-3 may interact with p-tau to promote tubulin disruption, thus impacting proper axon maintenance or neurite outgrowth and directly linking this axonal regulation with AD pathogenesis[156].
Unlike the previous three proteins, SUMO1 has been more closely linked to presynaptic signaling mechanisms than axonal maintenance. However, it was shown that SUMO1 attaches to the RNA binding protein La to facilitate its retrograde transport, implicating this post-translational modification in axonal transport[157]. Presynaptically, however, SUMO1 has been shown to be necessary for the localization of several key proteins involved in presynaptic vesicle recruitment and release, including synapsin Ia[158], RIM1-alpha[159], and gephryn[160]. In addition, SUMO1 actively influences presynaptic transmission, as evidenced by SUMO1 mediating decreased presynaptic glutamate release in the AD mouse model Tg2576[93]. Thus, SUMO1 may contribute to deficits in axonal transport as well as reduced presynaptic function in AD.
Finally, despite EGR1’s role as a transcription factor, and thus exerting the bulk of its actions in the nucleus, it has also been implicated in influencing axon growth. Specifically, inhibition of EGR1 prevents the ability of NGF to increase neurite outgrowth in a novel pathway whereby EGR1 binding to c-Jun promotes NGF’s downstream effects[161]. In addition, PACAP also depends on EGR1 for its ability to promote neuritogenesis[162]. Lastly, sAPPbeta, the cleavage product of beta-secretase and upstream precursor of Abeta, is able to promote neuritic outgrowth but needs functional EGR1 to promote axon outgrowth, while dendritic outgrowth is independent of intact EGR1[163]. Thus, EGR1 joins the other unique proteins associated with the HIDA Domain in our PPI analysis in its ability to influence the structural plasticity of axons and presynaptic function. Altogether, this neurodegenerative process at presynaptic sites may influence corticostriatal and monoaminergic pathways, thus linking these five proteins identified by our PPI analysis and the HIDA Domain.
Supporting information
S1 Table [xlsx]
Selective genes associated with AD and each BPSD domain.
S2 Table [xlsx]
The shared proteins between AD and the affective domain.
S3 Table [xlsx]
The shared proteins between AD and the HIDA domain.
S4 Table [xlsx]
The enriched GO terms and KEGG pathways between AD and the affective domain.
S5 Table [xlsx]
The enriched GO terms and KEGG pathways between AD and the HIDA domain.
Zdroje
1. Scheltens P, Blennow K, Breteler MMB, de Strooper B, Frisoni GB, Salloway S, et al. Alzheimer's disease. The Lancet. 2016;388(10043):505–17.
2. van der Linde RM, Dening T, Matthews FE, Brayne C. Grouping of behavioural and psychological symptoms of dementia. Int J Geriatr Psychiatry. 2014;29(6):562–8. doi: 10.1002/gps.4037 24677112
3. Kales HC, Lyketsos CG, Miller EM, Ballard C. Management of behavioral and psychological symptoms in people with Alzheimer's disease: an international Delphi consensus. Int Psychogeriatr. 2019;31(1):83–90. doi: 10.1017/S1041610218000534 30068400
4. Gallagher D, Fischer CE, Iaboni A. Neuropsychiatric Symptoms in Mild Cognitive Impairment. Can J Psychiatry. 2017;62(3):161–9. doi: 10.1177/0706743716648296 28212495
5. Paulsen JS, Salmon DP, Thal LJ, Romero R, Weisstein-Jenkins C, Galasko D, et al. Incidence of and risk factors for hallucinations and delusions in patients with probable AD. Neurology. 2000;54(10):1965–71. doi: 10.1212/wnl.54.10.1965 10822438
6. Scarmeas N, Albert M, Brandt J, Blacker D, Hadjigeorgiou G, Papadimitriou A, et al. Motor signs predict poor outcomes in Alzheimer disease. Neurology. 2005;64(10):1696–703. doi: 10.1212/01.WNL.0000162054.15428.E9 15911793
7. Bassiony MM, Steinberg MS, Warren A, Rosenblatt A, Baker AS, Lyketsos CG. Delusions and hallucinations in Alzheimer's disease: prevalence and clinical correlates. Int J Geriatr Psychiatry. 2000;15(2):99–107. doi: 10.1002/(sici)1099-1166(200002)15:2<99::aid-gps82>3.0.co;2-5 10679840
8. Gaugler JE, Yu F, Krichbaum K, Wyman JF. Predictors of nursing home admission for persons with dementia. Med Care. 2009;47(2):191–8. doi: 10.1097/MLR.0b013e31818457ce 19169120
9. Hart DJ, Craig D, Compton SA, Critchlow S, Kerrigan BM, McIlroy SP, et al. A retrospective study of the behavioural and psychological symptoms of mid and late phase Alzheimer's disease. Int J Geriatr Psychiatry. 2003;18(11):1037–42. doi: 10.1002/gps.1013 14618556
10. Tochimoto S, Kitamura M, Hino S, Kitamura T. Predictors of home discharge among patients hospitalized for behavioural and psychological symptoms of dementia. Psychogeriatrics. 2015;15(4):248–54. doi: 10.1111/psyg.12114 25919794
11. Torrisi M, De Cola MC, Marra A, De Luca R, Bramanti P, Calabrò RS. Neuropsychiatric symptoms in dementia may predict caregiver burden: a Sicilian exploratory study. Psychogeriatrics. 2017;17(2):103–7. doi: 10.1111/psyg.12197 27411501
12. Geda YE, Schneider LS, Gitlin LN, Miller DS, Smith GS, Bell J, et al. Neuropsychiatric symptoms in Alzheimer's disease: past progress and anticipation of the future. Alzheimers Dement. 2013;9(5):602–8. doi: 10.1016/j.jalz.2012.12.001 23562430
13. Cerejeira J, Lagarto L, Mukaetova-Ladinska EB. Behavioral and psychological symptoms of dementia. Front Neurol. 2012;3:73. doi: 10.3389/fneur.2012.00073 22586419
14. Krauthammer M, Kaufmann CA, Gilliam TC, Rzhetsky A. Molecular triangulation: bridging linkage and molecular-network information for identifying candidate genes in Alzheimer's disease. PNAS. 2004;101(42):15148–53. doi: 10.1073/pnas.0404315101 15471992
15. Liu Z-P, Wang Y, Zhang X-S, Chen L. Identifying dysfunctional crosstalk of pathways in various regions of Alzheimer's disease brains. BMC Syst Biol. 2010;4 Suppl 2:S11.
16. Safari-Alighiarloo N, Taghizadeh M, Rezaei-Tavirani M, Goliaei B, Peyvandi AA. Protein-protein interaction networks (PPI) and complex diseases. Gastroenterol Hepatol Bed Bench. 2014;7(1):17–31. 25436094
17. Goñi J, Esteban FJ, de Mendizábal NV, Sepulcre J, Ardanza-Trevijano S, Agirrezabal I, et al. A computational analysis of protein-protein interaction networks in neurodegenerative diseases. BMC Syst Biol. 2008;2:52. doi: 10.1186/1752-0509-2-52 18570646
18. Oti M, Snel B, Huynen MA, Brunner HG. Predicting disease genes using protein-protein interactions. J Med Genet. 2006;43(8):691–8. doi: 10.1136/jmg.2006.041376 16611749
19. Kann MG. Protein interactions and disease: computational approaches to uncover the etiology of diseases. Brief Bioinformatics. 2007;8(5):333–46. doi: 10.1093/bib/bbm031 17638813
20. Schuster-Böckler B, Bateman A. Protein interactions in human genetic diseases. Genome Biol. 2008;9(1):R9. doi: 10.1186/gb-2008-9-1-r9 18199329
21. Navlakha S, Kingsford C. The power of protein interaction networks for associating genes with diseases. Bioinformatics. 2010;26(8):1057–63. doi: 10.1093/bioinformatics/btq076 20185403
22. Nguyen T-P, Ho T-B. Detecting disease genes based on semi-supervised learning and protein-protein interaction networks. Artif Intell Med. 2012;54(1):63–71. doi: 10.1016/j.artmed.2011.09.003 22000346
23. Nguyen T-P, Liu W-c, Jordán F. Inferring pleiotropy by network analysis: linked diseases in the human PPI network. BMC Syst Biol. 2011;5:179. doi: 10.1186/1752-0509-5-179 22034985
24. Nguyen R, Morrissey MD, Mahadevan V, Cajanding JD, Woodin MA, Yeomans JS, et al. Parvalbumin and GAD65 interneuron inhibition in the ventral hippocampus induces distinct behavioral deficits relevant to schizophrenia. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience. 2014;34(45):14948–60.
25. Jeong H, Mason SP, Barabási AL, Oltvai ZN. Lethality and centrality in protein networks. Nature. 2001;411(6833):41–2. doi: 10.1038/35075138 11333967
26. Boyer RS, Moore JS. A Fast String Searching Algorithm. Commun ACM. 1977;20(10):762–72.
27. Li J, Buchner J. Structure, function and regulation of the hsp90 machinery. Biomed J. 2013;36(3):106–17. doi: 10.4103/2319-4170.113230 23806880
28. Sima S, Richter K. Regulation of the Hsp90 system. Biochim Biophys Acta Mol Cell Res. 2018;1865(6):889–97. doi: 10.1016/j.bbamcr.2018.03.008 29563055
29. Kidd M. Paired helical filaments in electron microscopy of Alzheimer's disease. Nature. 1963;197:192–3.
30. Grundke-Iqbal I, Iqbal K, Quinlan M, Tung YC, Zaidi MS, Wisniewski HM. Microtubule-associated protein tau. A component of Alzheimer paired helical filaments. J Biol Chem. 1986;261(13):6084–9. 3084478
31. Meraz-Ríos MA, Lira-De León KI, Campos-Peña V, De Anda-Hernández MA, Mena-López R. Tau oligomers and aggregation in Alzheimer's disease. J Neurochem. 2010;112(6):1353–67. doi: 10.1111/j.1471-4159.2009.06511.x 19943854
32. Congdon EE, Sigurdsson EM. Tau-targeting therapies for Alzheimer disease. Nature Reviews Neurology. 2018;14(7):399–415. doi: 10.1038/s41582-018-0013-z 29895964
33. Lindwall G, Cole RD. Phosphorylation affects the ability of tau protein to promote microtubule assembly. J Biol Chem. 1984;259(8):5301–5. 6425287
34. Stokin GB, Lillo C, Falzone TL, Brusch RG, Rockenstein E, Mount SL, et al. Axonopathy and transport deficits early in the pathogenesis of Alzheimer's disease. Science (New York, NY). 2005;307(5713):1282–8.
35. Vossel KA, Zhang K, Brodbeck J, Daub AC, Sharma P, Finkbeiner S, et al. Tau reduction prevents Abeta-induced defects in axonal transport. Science (New York, NY). 2010;330(6001):198.
36. Shafiei SS, Guerrero-Muñoz MJ, Castillo-Carranza DL. Tau Oligomers: Cytotoxicity, Propagation, and Mitochondrial Damage. Front Aging Neurosci. 2017;9:83. doi: 10.3389/fnagi.2017.00083 28420982
37. Jadhav S, Cubinkova V, Zimova I, Brezovakova V, Madari A, Cigankova V, et al. Tau-mediated synaptic damage in Alzheimer's disease. Transl Neurosci. 2015;6(1):214–26. doi: 10.1515/tnsci-2015-0023 28123806
38. Alonso AD, Cohen LS, Corbo C, Morozova V, ElIdrissi A, Phillips G, et al. Hyperphosphorylation of Tau Associates With Changes in Its Function Beyond Microtubule Stability. Frontiers in Cellular Neuroscience. 2018;12:338. doi: 10.3389/fncel.2018.00338 30356756
39. Terry RD, Masliah E, Salmon DP, Butters N, DeTeresa R, Hill R, et al. Physical basis of cognitive alterations in Alzheimer's disease: synapse loss is the major correlate of cognitive impairment. Annals of Neurology. 1991;30(4):572–80. doi: 10.1002/ana.410300410 1789684
40. Arendt T. Synaptic degeneration in Alzheimer's disease. Acta Neuropathol. 2009;118(1):167–79. doi: 10.1007/s00401-009-0536-x 19390859
41. Palop JJ, Mucke L. Amyloid-β Induced Neuronal Dysfunction in Alzheimer’s Disease: From Synapses toward Neural Networks. Nature neuroscience. 2010;13(7):812–8. doi: 10.1038/nn.2583 20581818
42. Canter RG, Penney J, Tsai L-H. The road to restoring neural circuits for the treatment of Alzheimer's disease. Nature. 2016;539(7628):187–96. doi: 10.1038/nature20412 27830780
43. Tata DA, Anderson BJ. The effects of chronic glucocorticoid exposure on dendritic length, synapse numbers and glial volume in animal models: implications for hippocampal volume reductions in depression. Physiology & Behavior. 2010;99(2):186–93.
44. Kang HJ, Voleti B, Hajszan T, Rajkowska G, Stockmeier CA, Licznerski P, et al. Decreased expression of synapse-related genes and loss of synapses in major depressive disorder. Nature Medicine. 2012;18(9):1413–7. doi: 10.1038/nm.2886 22885997
45. Ota KT, Liu R-J, Voleti B, Maldonado-Aviles JG, Duric V, Iwata M, et al. REDD1 is essential for stress-induced synaptic loss and depressive behavior. Nature Medicine. 2014;20(5):531–5. doi: 10.1038/nm.3513 24728411
46. Duman CH, Duman RS. Spine synapse remodeling in the pathophysiology and treatment of depression. Neuroscience Letters. 2015;601:20–9. doi: 10.1016/j.neulet.2015.01.022 25582786
47. McEwen BS. Stress-induced remodeling of hippocampal CA3 pyramidal neurons. Brain Research. 2016;1645:50–4. doi: 10.1016/j.brainres.2015.12.043 26740399
48. Herbert J, Lucassen PJ. Depression as a risk factor for Alzheimer's disease: Genes, steroids, cytokines and neurogenesis—What do we need to know? Frontiers in Neuroendocrinology. 2016;41:153–71. doi: 10.1016/j.yfrne.2015.12.001 26746105
49. Goedert M, Spillantini MG, Jakes R, Rutherford D, Crowther RA. Multiple isoforms of human microtubule-associated protein tau: sequences and localization in neurofibrillary tangles of Alzheimer's disease. Neuron. 1989;3(4):519–26. doi: 10.1016/0896-6273(89)90210-9 2484340
50. Zabik NL, Imhof MM, Martic-Milne S. Structural evaluations of tau protein conformation: methodologies and approaches. Biochem Cell Biol. 2017;95(3):338–49. doi: 10.1139/bcb-2016-0227 28278386
51. Fontaine SN, Sabbagh JJ, Baker J, Martinez-Licha CR, Darling A, Dickey CA. Cellular factors modulating the mechanism of tau protein aggregation. Cellular and molecular life sciences: CMLS. 2015;72(10):1863–79. doi: 10.1007/s00018-015-1839-9 25666877
52. Medina M, Hernández F, Avila J. New Features about Tau Function and Dysfunction. Biomolecules. 2016;6(2).
53. Liu X-J, Wei J, Shang Y-H, Huang H-C, Lao F-X. Modulation of AβPP and GSK3β by Endoplasmic Reticulum Stress and Involvement in Alzheimer's Disease. J Alzheimers Dis. 2017;57(4):1157–70. doi: 10.3233/JAD-161111 28339396
54. Pei JJ, Tanaka T, Tung YC, Braak E, Iqbal K, Grundke-Iqbal I. Distribution, levels, and activity of glycogen synthase kinase-3 in the Alzheimer disease brain. J Neuropathol Exp Neurol. 1997;56(1):70–8. doi: 10.1097/00005072-199701000-00007 8990130
55. Leroy K, Yilmaz Z, Brion JP. Increased level of active GSK-3beta in Alzheimer's disease and accumulation in argyrophilic grains and in neurones at different stages of neurofibrillary degeneration. Neuropathol Appl Neurobiol. 2007;33(1):43–55. doi: 10.1111/j.1365-2990.2006.00795.x 17239007
56. Toglia P, Cheung K-H, Mak D-OD, Ullah G. Impaired mitochondrial function due to familial Alzheimer's disease-causing presenilins mutants via Ca(2+) disruptions. Cell Calcium. 2016;59(5):240–50. doi: 10.1016/j.ceca.2016.02.013 26971122
57. Beurel E, Grieco SF, Jope RS. Glycogen synthase kinase-3 (GSK3): regulation, actions, and diseases. Pharmacol Ther. 2015;148:114–31. doi: 10.1016/j.pharmthera.2014.11.016 25435019
58. Jope RS. Glycogen synthase kinase-3 in the etiology and treatment of mood disorders. Front Mol Neurosci. 2011;4:16. doi: 10.3389/fnmol.2011.00016 21886606
59. Marsden WN. Synaptic plasticity in depression: molecular, cellular and functional correlates. Prog Neuropsychopharmacol Biol Psychiatry. 2013;43:168–84. doi: 10.1016/j.pnpbp.2012.12.012 23268191
60. Talman V, Pascale A, Jäntti M, Amadio M, Tuominen RK. Protein Kinase C Activation as a Potential Therapeutic Strategy in Alzheimer's Disease: Is there a Role for Embryonic Lethal Abnormal Vision-like Proteins? Basic Clin Pharmacol Toxicol. 2016;119(2):149–60. doi: 10.1111/bcpt.12581 27001133
61. Chami B, Steel AJ, De La Monte SM, Sutherland GT. The rise and fall of insulin signaling in Alzheimer's disease. Metab Brain Dis. 2016;31(3):497–515. doi: 10.1007/s11011-016-9806-1 26883429
62. Sulistio YA, Heese K. The Ubiquitin-Proteasome System and Molecular Chaperone Deregulation in Alzheimer's Disease. Mol Neurobiol. 2016;53(2):905–31. doi: 10.1007/s12035-014-9063-4 25561438
63. Lackie RE, Maciejewski A, Ostapchenko VG, Marques-Lopes J, Choy W-Y, Duennwald ML, et al. The Hsp70/Hsp90 Chaperone Machinery in Neurodegenerative Diseases. Front Neurosci. 2017;11:254. doi: 10.3389/fnins.2017.00254 28559789
64. Mohamed BA, Barakat AZ, Zimmermann W-H, Bittner RE, Mühlfeld C, Hünlich M, et al. Targeted disruption of Hspa4 gene leads to cardiac hypertrophy and fibrosis. J Mol Cell Cardiol. 2012;53(4):459–68. doi: 10.1016/j.yjmcc.2012.07.014 22884543
65. Zuehlke AD, Beebe K, Neckers L, Prince T. Regulation and function of the human HSP90AA1 gene. Gene. 2015;570(1):8–16. doi: 10.1016/j.gene.2015.06.018 26071189
66. Petrucelli L, Dickson D, Kehoe K, Taylor J, Snyder H, Grover A, et al. CHIP and Hsp70 regulate tau ubiquitination, degradation and aggregation. Hum Mol Genet. 2004;13(7):703–14. doi: 10.1093/hmg/ddh083 14962978
67. Dou F, Netzer WJ, Tanemura K, Li F, Hartl FU, Takashima A, et al. Chaperones increase association of tau protein with microtubules. PNAS. 2003;100(2):721–6. doi: 10.1073/pnas.242720499 12522269
68. Jinwal UK, Miyata Y, Koren J, Jones JR, Trotter JH, Chang L, et al. Chemical manipulation of hsp70 ATPase activity regulates tau stability. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience. 2009;29(39):12079–88.
69. Dickey CA, Dunmore J, Lu B, Wang J-W, Lee WC, Kamal A, et al. HSP induction mediates selective clearance of tau phosphorylated at proline-directed Ser/Thr sites but not KXGS (MARK) sites. FASEB J. 2006;20(6):753–5. doi: 10.1096/fj.05-5343fje 16464956
70. Blair LJ, Nordhues BA, Hill SE, Scaglione KM, O'Leary JC, Fontaine SN, et al. Accelerated neurodegeneration through chaperone-mediated oligomerization of tau. J Clin Invest. 2013;123(10):4158–69. doi: 10.1172/JCI69003 23999428
71. Matosin N, Halldorsdottir T, Binder EB. Understanding the Molecular Mechanisms Underpinning Gene by Environment Interactions in Psychiatric Disorders: The FKBP5 Model. Biological Psychiatry. 2018;83(10):821–30. doi: 10.1016/j.biopsych.2018.01.021 29573791
72. Thompson SM, Kallarackal AJ, Kvarta MD, Van Dyke AM, LeGates TA, Cai X. An excitatory synapse hypothesis of depression. Trends in Neurosciences. 2015;38(5):279–94. doi: 10.1016/j.tins.2015.03.003 25887240
73. Lambert MP, Barlow AK, Chromy BA, Edwards C, Freed R, Liosatos M, et al. Diffusible, nonfibrillar ligands derived from Abeta1-42 are potent central nervous system neurotoxins. PNAS. 1998;95(11):6448–53. doi: 10.1073/pnas.95.11.6448 9600986
74. Chin J, Palop JJ, Puoliväli J, Massaro C, Bien-Ly N, Gerstein H, et al. Fyn kinase induces synaptic and cognitive impairments in a transgenic mouse model of Alzheimer's disease. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience. 2005;25(42):9694–703.
75. Chin J, Palop JJ, Yu G-Q, Kojima N, Masliah E, Mucke L. Fyn kinase modulates synaptotoxicity, but not aberrant sprouting, in human amyloid precursor protein transgenic mice. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience. 2004;24(19):4692–7.
76. Ittner LM, Ke YD, Delerue F, Bi M, Gladbach A, van Eersel J, et al. Dendritic function of tau mediates amyloid-beta toxicity in Alzheimer's disease mouse models. Cell. 2010;142(3):387–97. doi: 10.1016/j.cell.2010.06.036 20655099
77. Ittner A, Chua SW, Bertz J, Volkerling A, van der Hoven J, Gladbach A, et al. Site-specific phosphorylation of tau inhibits amyloid-β toxicity in Alzheimer's mice. Science (New York, NY). 2016;354(6314):904–8.
78. Lopes S, Vaz-Silva J, Pinto V, Dalla C, Kokras N, Bedenk B, et al. Tau protein is essential for stress-induced brain pathology. PNAS. 2016;113(26):E3755–63. doi: 10.1073/pnas.1600953113 27274066
79. Li J, Zhou H, Ma H, Wei Y, Huang Y, Wu L, et al. Fyn gene polymorphisms contribute to both trait and state anxieties in healthy Chinese-Han individuals. Psychiatr Genet. 2012;22(6):312–3. doi: 10.1097/YPG.0b013e32835862e2 22922807
80. Liang X, Draghi NA, Resh MD. Signaling from integrins to Fyn to Rho family GTPases regulates morphologic differentiation of oligodendrocytes. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience. 2004;24(32):7140–9.
81. Liu H, Nakazawa T, Tezuka T, Yamamoto T. Physical and functional interaction of Fyn tyrosine kinase with a brain-enriched Rho GTPase-activating protein TCGAP. J Biol Chem. 2006;281(33):23611–9. doi: 10.1074/jbc.M511205200 16777849
82. Castañeda P, Muñoz M, García-Rojo G, Ulloa JL, Bravo JA, Márquez R, et al. Association of N-cadherin levels and downstream effectors of Rho GTPases with dendritic spine loss induced by chronic stress in rat hippocampal neurons. J Neurosci Res. 2015;93(10):1476–91. doi: 10.1002/jnr.23602 26010004
83. Fox ME, Chandra R, Menken MS, Larkin EJ, Nam H, Engeln M, et al. Dendritic remodeling of D1 neurons by RhoA/Rho-kinase mediates depression-like behavior. Molecular Psychiatry. 2018.
84. Huesa G, Baltrons MA, Gómez-Ramos P, Morán A, García A, Hidalgo J, et al. Altered distribution of RhoA in Alzheimer's disease and AbetaPP overexpressing mice. J Alzheimers Dis. 2010;19(1):37–56. doi: 10.3233/JAD-2010-1203 20061625
85. Amano M, Kaneko T, Maeda A, Nakayama M, Ito M, Yamauchi T, et al. Identification of Tau and MAP2 as novel substrates of Rho-kinase and myosin phosphatase. J Neurochem. 2003;87(3):780–90. doi: 10.1046/j.1471-4159.2003.02054.x 14535960
86. Shinohara M, Tachibana M, Kanekiyo T, Bu G. Role of LRP1 in the pathogenesis of Alzheimer's disease: evidence from clinical and preclinical studies. J Lipid Res. 2017;58(7):1267–81. doi: 10.1194/jlr.R075796 28381441
87. Mei L, Nave K-A. Neuregulin-ERBB signaling in the nervous system and neuropsychiatric diseases. Neuron. 2014;83(1):27–49. doi: 10.1016/j.neuron.2014.06.007 24991953
88. Cau Y, Valensin D, Mori M, Draghi S, Botta M. Structure, Function, Involvement in Diseases and Targeting of 14-3-3 Proteins: An Update. Curr Med Chem. 2018;25(1):5–21. doi: 10.2174/0929867324666170426095015 28462702
89. Krumova P, Weishaupt JH. Sumoylation in neurodegenerative diseases. Cellular and molecular life sciences: CMLS. 2013;70(12):2123–38. doi: 10.1007/s00018-012-1158-3 23007842
90. Gallo FT, Katche C, Morici JF, Medina JH, Weisstaub NV. Immediate Early Genes, Memory and Psychiatric Disorders: Focus on c-Fos, Egr1 and Arc. Front Behav Neurosci. 2018;12.
91. Chen X, Wang C, Zhou S, Li X, Wu L. The Impact of EGFR Gene Polymorphisms on the Risk of Alzheimer's Disease in a Chinese Han Population: A Case-Controlled Study. Med Sci Monit. 2018;24:5035–40. doi: 10.12659/MSM.907809 30026459
92. Shimada T, Fournier AE, Yamagata K. Neuroprotective function of 14-3-3 proteins in neurodegeneration. Biomed Res Int. 2013;2013:564534. doi: 10.1155/2013/564534 24364034
93. Marcelli S, Ficulle E, Iannuzzi F, Kövari E, Nisticò R, Feligioni M. Targeting SUMO-1ylation Contrasts Synaptic Dysfunction in a Mouse Model of Alzheimer's Disease. Mol Neurobiol. 2017;54(8):6609–23. doi: 10.1007/s12035-016-0176-9 27738871
94. Qin X, Wang Y, Paudel HK. Inhibition of Early Growth Response 1 in the Hippocampus Alleviates Neuropathology and Improves Cognition in an Alzheimer Model with Plaques and Tangles. The American Journal of Pathology. 2017;187(8):1828–47. doi: 10.1016/j.ajpath.2017.04.018 28641077
95. Dalley JW, Robbins TW. Fractionating impulsivity: neuropsychiatric implications. Nat Rev Neurosci. 2017;18(3):158–71. doi: 10.1038/nrn.2017.8 28209979
96. Coccaro EF, Sripada CS, Yanowitch RN, Phan KL. Corticolimbic function in impulsive aggressive behavior. Biological Psychiatry. 2011;69(12):1153–9. doi: 10.1016/j.biopsych.2011.02.032 21531387
97. Blair RJR. The Neurobiology of Impulsive Aggression. J Child Adolesc Psychopharmacol. 2016;26(1):4–9. doi: 10.1089/cap.2015.0088 26465707
98. Victoroff J, Lin FV, Coburn KL, Shillcutt SD, Voon V, Ducharme S. Noncognitive Behavioral Changes Associated With Alzheimer's Disease: Implications of Neuroimaging Findings. J Neuropsychiatry Clin Neurosci. 2018;30(1):14–21. doi: 10.1176/appi.neuropsych.16080155 28876969
99. Hoptman MJ. Impulsivity and aggression in schizophrenia: a neural circuitry perspective with implications for treatment. CNS Spectr. 2015;20(3):280–6. doi: 10.1017/S1092852915000206 25900066
100. Leclerc MP, Regenbogen C, Hamilton RH, Habel U. Some neuroanatomical insights to impulsive aggression in schizophrenia. Schizophr Res. 2018;201:27–34. doi: 10.1016/j.schres.2018.06.016 29908715
101. Waltes R, Chiocchetti AG, Freitag CM. The neurobiological basis of human aggression: A review on genetic and epigenetic mechanisms. Am J Med Genet B Neuropsychiatr Genet. 2016;171(5):650–75. doi: 10.1002/ajmg.b.32388 26494515
102. Whelan R, Conrod PJ, Poline J-B, Lourdusamy A, Banaschewski T, Barker GJ, et al. Adolescent impulsivity phenotypes characterized by distinct brain networks. Nature Neuroscience. 2012;15(6):920–5. doi: 10.1038/nn.3092 22544311
103. Šimić G, Babić Leko M, Wray S, Harrington CR, Delalle I, Jovanov-Milošević N, et al. Monoaminergic neuropathology in Alzheimer’s disease. Progress in Neurobiology. 2017;151:101–38. doi: 10.1016/j.pneurobio.2016.04.001 27084356
104. Trillo L, Das D, Hsieh W, Medina B, Moghadam S, Lin B, et al. Ascending monoaminergic systems alterations in Alzheimer's disease. Translating basic science into clinical care. Neuroscience & Biobehavioral Reviews. 2013;37(8):1363–79.
105. Sweet RA, Nimgaonkar VL, Kamboh MI, Lopez OL, Zhang F, DeKosky ST. Dopamine receptor genetic variation, psychosis, and aggression in Alzheimer disease. Arch Neurol. 1998;55(10):1335–40. doi: 10.1001/archneur.55.10.1335 9779662
106. Colzato LS, van den Wildenberg WPM, Van der Does AJW, Hommel B. Genetic markers of striatal dopamine predict individual differences in dysfunctional, but not functional impulsivity. Neuroscience. 2010;170(3):782–8. doi: 10.1016/j.neuroscience.2010.07.050 20678555
107. Eisenberg DTA, MacKillop J, Modi M, Beauchemin J, Dang D, Lisman SA, et al. Examining impulsivity as an endophenotype using a behavioral approach: a DRD2 TaqI A and DRD4 48-bp VNTR association study. Behavioral and Brain Functions. 2007;3(1):2.
108. Benko A, Lazary J, Molnar E, Gonda X, Tothfalusi L, Pap D, et al. Significant association between the C(−1019)G functional polymorphism of the HTR1A gene and impulsivity. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics. 2010;153B(2):592–9.
109. Hakulinen C, Jokela M, Hintsanen M, Merjonen P, Pulkki-Råback L, Seppälä I, et al. Serotonin receptor 1B genotype and hostility, anger and aggressive behavior through the lifespan: the Young Finns study. J Behav Med. 2013;36(6):583–90. doi: 10.1007/s10865-012-9452-y 22945537
110. Zouk H, McGirr A, Lebel V, Benkelfat C, Rouleau G, Turecki G. The effect of genetic variation of the serotonin 1B receptor gene on impulsive aggressive behavior and suicide. Am J Med Genet B Neuropsychiatr Genet. 2007;144B(8):996–1002. doi: 10.1002/ajmg.b.30521 17510950
111. Assal F, Alarcón M, Solomon EC, Masterman D, Geschwind DH, Cummings JL. Association of the serotonin transporter and receptor gene polymorphisms in neuropsychiatric symptoms in Alzheimer disease. Arch Neurol. 2004;61(8):1249–53. doi: 10.1001/archneur.61.8.1249 15313842
112. Jakubczyk A, Wrzosek M, Łukaszkiewicz J, Sadowska-Mazuryk J, Matsumoto H, Śliwerska E, et al. The CC genotype in HTR2A T102C polymorphism is associated with behavioral impulsivity in alcohol-dependent patients. J Psychiatr Res. 2012;46(1):44–9. doi: 10.1016/j.jpsychires.2011.09.001 21930285
113. Bjork JM, Moeller FG, Dougherty DM, Swann AC, Machado MA, Hanis CL. Serotonin 2a receptor T102C polymorphism and impaired impulse control. American Journal of Medical Genetics. 2002;114(3):336–9. doi: 10.1002/ajmg.10206 11920859
114. Lam LCW, Tang NLS, Ma SL, Zhang W, Chiu HFK. 5-HT2A T102C receptor polymorphism and neuropsychiatric symptoms in Alzheimer's disease. Int J Geriatr Psychiatry. 2004;19(6):523–6. doi: 10.1002/gps.1109 15211529
115. Bevilacqua L, Doly S, Kaprio J, Yuan Q, Tikkanen R, Paunio T, et al. A population-specific HTR2B stop codon predisposes to severe impulsivity. Nature. 2010;468(7327):1061–6. doi: 10.1038/nature09629 21179162
116. Paloyelis Y, Asherson P, Mehta MA, Faraone SV, Kuntsi J. DAT1 and COMT Effects on Delay Discounting and Trait Impulsivity in Male Adolescents with Attention Deficit/Hyperactivity Disorder and Healthy Controls. Neuropsychopharmacology. 2010;35(12):2414–26. doi: 10.1038/npp.2010.124 20736997
117. Sonuga-Barke EJS, Kumsta R, Schlotz W, Lasky-Su J, Marco R, Miranda A, et al. A Functional Variant of the Serotonin Transporter Gene (SLC6A4) Moderates Impulsive Choice in Attention-Deficit/Hyperactivity Disorder Boys and Siblings. Biological Psychiatry. 2011;70(3):230–6. doi: 10.1016/j.biopsych.2011.01.040 21497794
118. Haberstick BC, Smolen A, Hewitt JK. Family-based association test of the 5HTTLPR and aggressive behavior in a general population sample of children. Biological Psychiatry. 2006;59(9):836–43. doi: 10.1016/j.biopsych.2005.10.008 16412987
119. Reif A, Rösler M, Freitag CM, Schneider M, Eujen A, Kissling C, et al. Nature and nurture predispose to violent behavior: serotonergic genes and adverse childhood environment. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology. 2007;32(11):2375–83.
120. Manuck SB, Flory JD, Ferrell RE, Mann JJ, Muldoon MF. A regulatory polymorphism of the monoamine oxidase-A gene may be associated with variability in aggression, impulsivity, and central nervous system serotonergic responsivity. Psychiatry Research. 2000;95(1):9–23. doi: 10.1016/s0165-1781(00)00162-1 10904119
121. Frazzetto G, Di Lorenzo G, Carola V, Proietti L, Sokolowska E, Siracusano A, et al. Early trauma and increased risk for physical aggression during adulthood: the moderating role of MAOA genotype. PLoS ONE. 2007;2(5):e486. doi: 10.1371/journal.pone.0000486 17534436
122. Kuepper Y, Grant P, Wielpuetz C, Hennig J. MAOA-uVNTR genotype predicts interindividual differences in experimental aggressiveness as a function of the degree of provocation. Behavioural Brain Research. 2013;247:73–8. doi: 10.1016/j.bbr.2013.03.002 23499704
123. Soeiro-De-Souza MG, Stanford MS, Bio DS, Machado-Vieira R, Moreno RA. Association of the COMT Met1⁵⁸ allele with trait impulsivity in healthy young adults. Mol Med Rep. 2013;7(4):1067–72. doi: 10.3892/mmr.2013.1336 23440431
124. Rujescu D, Giegling I, Gietl A, Hartmann AM, Möller H-J. A functional single nucleotide polymorphism (V158M) in the COMT gene is associated with aggressive personality traits. Biological Psychiatry. 2003;54(1):34–9. doi: 10.1016/s0006-3223(02)01831-0 12842306
125. Perez-Rodriguez MM, Weinstein S, New AS, Bevilacqua L, Yuan Q, Zhou Z, et al. Tryptophan-hydroxylase 2 haplotype association with borderline personality disorder and aggression in a sample of patients with personality disorders and healthy controls. J Psychiatr Res. 2010;44(15):1075–81. doi: 10.1016/j.jpsychires.2010.03.014 20451217
126. Parvizi J, Van Hoesen GW, Damasio A. The selective vulnerability of brainstem nuclei to Alzheimer's disease. Annals of Neurology. 2001;49(1):53–66. doi: 10.1002/1531-8249(200101)49:1<53::aid-ana30>3.0.co;2-q 11198297
127. Lyness SA, Zarow C, Chui HC. Neuron loss in key cholinergic and aminergic nuclei in Alzheimer disease: a meta-analysis. Neurobiol Aging. 2003;24(1):1–23. doi: 10.1016/s0197-4580(02)00057-x 12493547
128. D'Amelio M, Puglisi-Allegra S, Mercuri N. The role of dopaminergic midbrain in Alzheimer's disease: Translating basic science into clinical practice. Pharmacol Res. 2018;130:414–9. doi: 10.1016/j.phrs.2018.01.016 29391234
129. Nobili A, Latagliata EC, Viscomi MT, Cavallucci V, Cutuli D, Giacovazzo G, et al. Dopamine neuronal loss contributes to memory and reward dysfunction in a model of Alzheimer’s disease. Nat Commun. 2017;8:14727. doi: 10.1038/ncomms14727 28367951
130. Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991;82(4):239–59. doi: 10.1007/bf00308809 1759558
131. de Jong LW, Ferrarini L, van der Grond J, Milles JR, Reiber JHC, Westendorp RGJ, et al. Shape abnormalities of the striatum in Alzheimer's disease. J Alzheimers Dis. 2011;23(1):49–59. doi: 10.3233/JAD-2010-101026 20930298
132. Fuentealba RA, Liu Q, Kanekiyo T, Zhang J, Bu G. Low density lipoprotein receptor-related protein 1 promotes anti-apoptotic signaling in neurons by activating Akt survival pathway. J Biol Chem. 2009;284(49):34045–53. doi: 10.1074/jbc.M109.021030 19815552
133. Bruban J, Voloudakis G, Huang Q, Kajiwara Y, Al Rahim M, Yoon Y, et al. Presenilin 1 is necessary for neuronal, but not glial, EGFR expression and neuroprotection via γ-secretase-independent transcriptional mechanisms. FASEB J. 2015;29(9):3702–12. doi: 10.1096/fj.15-270645 25985800
134. Lee YJ, Castri P, Bembry J, Maric D, Auh S, Hallenbeck JM. SUMOylation participates in induction of ischemic tolerance. J Neurochem. 2009;109(1):257–67. doi: 10.1111/j.1471-4159.2009.05957.x 19200349
135. Veyrac A, Gros A, Bruel-Jungerman E, Rochefort C, Kleine Borgmann FB, Jessberger S, et al. Zif268/egr1 gene controls the selection, maturation and functional integration of adult hippocampal newborn neurons by learning. PNAS. 2013;110(17):7062–7. doi: 10.1073/pnas.1220558110 23569253
136. Iwakura Y, Zheng Y, Sibilia M, Abe Y, Piao Y-S, Yokomaku D, et al. Qualitative and quantitative re-evaluation of epidermal growth factor-ErbB1 action on developing midbrain dopaminergic neurons in vivo and in vitro: target-derived neurotrophic signaling (Part 1). J Neurochem. 2011;118(1):45–56. doi: 10.1111/j.1471-4159.2011.07287.x 21517852
137. Sekiguchi H, Iritani S, Habuchi C, Torii Y, Kuroda K, Kaibuchi K, et al. Impairment of the tyrosine hydroxylase neuronal network in the orbitofrontal cortex of a genetically modified mouse model of schizophrenia. Brain Research. 2011;1392:47–53. doi: 10.1016/j.brainres.2011.03.058 21458426
138. Ding H, Underwood R, Lavalley N, Yacoubian TA. 14-3-3 inhibition promotes dopaminergic neuron loss and 14-3-3θ overexpression promotes recovery in the MPTP mouse model of Parkinson's disease. Neuroscience. 2015;307:73–82. doi: 10.1016/j.neuroscience.2015.08.042 26314634
139. Xu J, Kao S-Y, Lee FJS, Song W, Jin L-W, Yankner BA. Dopamine-dependent neurotoxicity of alpha-synuclein: a mechanism for selective neurodegeneration in Parkinson disease. Nature Medicine. 2002;8(6):600–6. doi: 10.1038/nm0602-600 12042811
140. Kneynsberg A, Combs B, Christensen K, Morfini G, Kanaan NM. Axonal Degeneration in Tauopathies: Disease Relevance and Underlying Mechanisms. Front Neurosci. 2017;11:572. doi: 10.3389/fnins.2017.00572 29089864
141. Honer WG. Pathology of presynaptic proteins in Alzheimer's disease: more than simple loss of terminals. Neurobiol Aging. 2003;24(8):1047–62. doi: 10.1016/j.neurobiolaging.2003.04.005 14643376
142. Bae JR, Kim SH. Synapses in neurodegenerative diseases. BMB Rep. 2017;50(5):237–46. doi: 10.5483/BMBRep.2017.50.5.038 28270301
143. Arendt T. Alzheimer's disease as a disorder of mechanisms underlying structural brain self-organization. Neuroscience. 2001;102(4):723–65. doi: 10.1016/s0306-4522(00)00516-9 11182240
144. Matsuo M, Campenot RB, Vance DE, Ueda K, Vance JE. Involvement of low-density lipoprotein receptor-related protein and ABCG1 in stimulation of axonal extension by apoE-containing lipoproteins. Biochim Biophys Acta. 2011;1811(1):31–8. doi: 10.1016/j.bbalip.2010.10.004 21040802
145. Qiu Z, Hyman BT, Rebeck GW. Apolipoprotein E receptors mediate neurite outgrowth through activation of p44/42 mitogen-activated protein kinase in primary neurons. J Biol Chem. 2004;279(33):34948–56. doi: 10.1074/jbc.M401055200 15169786
146. Yoon C, Van Niekerk EA, Henry K, Ishikawa T, Orita S, Tuszynski MH, et al. Low-density lipoprotein receptor-related protein 1 (LRP1)-dependent cell signaling promotes axonal regeneration. J Biol Chem. 2013;288(37):26557–68. doi: 10.1074/jbc.M113.478552 23867460
147. Merino P, Diaz A, Yepes M. Urokinase-type plasminogen activator (uPA) and its receptor (uPAR) promote neurorepair in the ischemic brain. Receptors Clin Investig. 2017;4(2). 28804736
148. Leung JYK, Bennett WR, King AE, Chung RS. The impact of metallothionein-II on microglial response to tumor necrosis factor-alpha (TNFα) and downstream effects on neuronal regeneration. J Neuroinflammation. 2018;15(1):56. doi: 10.1186/s12974-018-1070-3 29471847
149. Cheng P, Chen K, Yu W, Gao S, Hu S, Sun X, et al. Protein phosphatase 2A (PP2A) activation promotes axonal growth and recovery in the CNS. Journal of the Neurological Sciences. 2015;359(1–2):48–56. doi: 10.1016/j.jns.2015.10.025 26671085
150. Joy MT, Vrbova G, Dhoot GK, Anderson PN. Sulf1 and Sulf2 expression in the nervous system and its role in limiting neurite outgrowth in vitro. Exp Neurol. 2015;263:150–60. doi: 10.1016/j.expneurol.2014.10.011 25448158
151. Leinster VHL, Joy MT, Vuononvirta RE, Bolsover SR, Anderson PN. ErbB1 epidermal growth factor receptor is a valid target for reducing the effects of multiple inhibitors of axonal regeneration. Exp Neurol. 2013;239:82–90. doi: 10.1016/j.expneurol.2012.09.007 23022459
152. Zschätzsch M, Oliva C, Langen M, De Geest N, Ozel MN, Williamson WR, et al. Regulation of branching dynamics by axon-intrinsic asymmetries in Tyrosine Kinase Receptor signaling. Elife. 2014;3:e01699. doi: 10.7554/eLife.01699 24755286
153. Kaplan A, Kent CB, Charron F, Fournier AE. Switching responses: spatial and temporal regulators of axon guidance. Mol Neurobiol. 2014;49(2):1077–86. doi: 10.1007/s12035-013-8582-8 24271658
154. Kaplan A, Morquette B, Kroner A, Leong S, Madwar C, Sanz R, et al. Small-Molecule Stabilization of 14-3-3 Protein-Protein Interactions Stimulates Axon Regeneration. Neuron. 2017;93(5):1082–93.e5. doi: 10.1016/j.neuron.2017.02.018 28279353
155. Yam PT, Kent CB, Morin S, Farmer WT, Alchini R, Lepelletier L, et al. 14-3-3 proteins regulate a cell-intrinsic switch from sonic hedgehog-mediated commissural axon attraction to repulsion after midline crossing. Neuron. 2012;76(4):735–49. doi: 10.1016/j.neuron.2012.09.017 23177959
156. Joo Y, Schumacher B, Landrieu I, Bartel M, Smet-Nocca C, Jang A, et al. Involvement of 14-3-3 in tubulin instability and impaired axon development is mediated by Tau. FASEB J. 2015;29(10):4133–44. doi: 10.1096/fj.14-265009 26103986
157. van Niekerk EA, Willis DE, Chang JH, Reumann K, Heise T, Twiss JL. Sumoylation in axons triggers retrograde transport of the RNA-binding protein La. PNAS. 2007;104(31):12913–8. doi: 10.1073/pnas.0611562104 17646655
158. Tang LTH, Craig TJ, Henley JM. SUMOylation of synapsin Ia maintains synaptic vesicle availability and is reduced in an autism mutation. Nat Commun. 2015;6:7728. doi: 10.1038/ncomms8728 26173895
159. Girach F, Craig Tim J, Rocca Daniel L, Henley Jeremy M. RIM1α SUMOylation Is Required for Fast Synaptic Vesicle Exocytosis. Cell Rep. 2013;5(5):1294–301. doi: 10.1016/j.celrep.2013.10.039 24290762
160. Ghosh H, Auguadri L, Battaglia S, Simone Thirouin Z, Zemoura K, Messner S, et al. Several posttranslational modifications act in concert to regulate gephyrin scaffolding and GABAergic transmission. Nat Commun. 2016;7.
161. Levkovitz Y, Baraban JM. A dominant negative Egr inhibitor blocks nerve growth factor-induced neurite outgrowth by suppressing c-Jun activation: role of an Egr/c-Jun complex. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience. 2002;22(10):3845–54.
162. Ravni A, Vaudry D, Gerdin MJ, Eiden MV, Falluel-Morel A, Gonzalez BJ, et al. A cAMP-dependent, protein kinase A-independent signaling pathway mediating neuritogenesis through Egr1 in PC12 cells. Mol Pharmacol. 2008;73(6):1688–708. doi: 10.1124/mol.107.044792 18362103
163. Chasseigneaux S, Dinc L, Rose C, Chabret C, Coulpier F, Topilko P, et al. Secreted amyloid precursor protein β and secreted amyloid precursor protein α induce axon outgrowth in vitro through Egr1 signaling pathway. PLoS ONE. 2011;6(1):e16301. doi: 10.1371/journal.pone.0016301 21298006
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