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Enregistrement W3088132775 · doi:10.1113/jp280624

Gut amyloid‐β induces cognitive deficits and Alzheimer's disease‐related histopathology in a mouse model

2020· letter· en· W3088132775 sur OpenAlexaff
Ilan Vonderwalde, Emma Finlayson‐Trick

Notice bibliographique

RevueThe Journal of Physiology · 2020
Typeletter
Langueen
DomaineMedicine
ThématiqueAlzheimer's disease research and treatments
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésDementiaHistopathologyGastrointestinal tractCentral nervous systemAmyloid (mycology)BiologyNeuropathologyNeurosciencePathologyGut floraGut–brain axisDiseaseMedicineImmunologyBiochemistry

Résumé

récupéré en direct d'OpenAlex

Amyloid-beta (Aβ) plaques are a pathological hallmark of Alzheimer's disease (AD), the most common cause of age-related dementia. Plaques are formed by oligomers of aggregated Aβ peptides that have been enzymatically cleaved from amyloid precursor protein (APP). Aβ oligomers can cause local toxicity by disrupting synaptic and neuronal activity. Aβ has been extensively characterized within the central nervous system (CNS), but less is known about its role in the enteric nervous system (ENS). Nevertheless, Aβ deposits have been observed within the gastrointestinal tract (GI) of AD patients (Honarpisheh et al. 2020). Consequently, researchers have hypothesized that AD may originate in the gut before manifesting in the brain. The brain–gut–microbiota axis explains the interplay between the brain and the gut in which the brain regulates the GI through the ENS and the gut microbes influence the brain through the release of biologically active substances, such as vitamins, amino acids, and lipids (Askarova et al. 2020). Despite there being over 100 mouse models to study various aspects of AD clinicopathology, few adequately consider this axis and the role of gut Aβ in AD progression (Jankowsky & Zheng 2017). Consequently, new models are required to support this growing field. In a recent issue of The Journal of Physiology, Sun et al. (2020) used a novel dementia mouse model to study the influence of gut Aβ on CNS histopathology and function. The model consisted of injecting Aβ oligomers (labelled or unlabelled) into the gut serosa of male Institute of Cancer Research mice, a strain widely used for disease modelling studies. Fluorescently labelled oligomers were used to assess the spread of Aβ from the stomach and colon in vivo in the month following surgery. Unlabelled oligomers were used to identify Aβ via immunohistochemistry in various tissues including the CNS, vagus nerve, and the GI one year post-surgery. Mice that received unlabelled oligomers were also involved in behavioural analysis and GI motility studies one year post-surgery. Within hours of oligomer administration into the gut serosa, Aβ was detected via fluorescent signals in the smooth muscle, the submucosa, and the cholinergic neurons of the myenteric plexus. In the following month, the Aβ oligomers spread to other regions within the GI and were found near to neuronal networks, as tracked via in vivo imaging and confirmed via immunohistochemistry. At one year post-injection, Aβ was present in the CNS and within the vagus nerve of Aβ-treated animals. Sun et al. (2020) claim that Aβ was widely distributed throughout the whole brains of Aβ-treated mice; however, their representative images (found in Figure 6) show limited staining for Aβ. Since the total number of visible plaques was not counted, it is difficult to quantitatively and objectively compare the two groups. Future experiments could focus on associating cognitive deficits with brain region-specific plaque thresholds, which would establish a reference value for comparison between studies. If the authors were to add multiple time points beyond one year, this would also allow them to investigate whether the total number of plaques increases with time or if the number remains stagnant. The authors suggested two possible hypotheses to describe the transport of Aβ from the gut to the CNS: retrograde Aβ oligomer migration via the vagus nerve or immune-mediated transport of Aβ oligomers via blood monocytes. To study these different hypotheses, future experiments could involve severing the vagus nerve to examine whether transport is blocked or screening blood for Aβ oligomers using techniques such as the detection system proposed by An et al. (2017). Furthermore, future studies should attempt to differentiate whether Aβ oligomers themselves are transported or whether Aβ oligomers are catalysing the propagation of APP cleavage events from the gut to the CNS as these two different routes may require different therapeutic approaches. Regardless, the results from this study suggest that enteric Aβ may indeed lead to Aβ deposition in the CNS, a classic feature of AD. In addition to the histopathological manifestations, AD is behaviourally characterized by progressive memory impairment. When compared with vehicle-treated animals, mice that received enteric Aβ-injections had poorer performance on the novel object recognition task and the spontaneous Y-maze test at one year post-injection. These results indicate that mice receiving Aβ oligomers had poorer long- and short-term spatial memories, respectively. The use of multiple tests provides strong evidence for the observed findings; however, due to the experimental design the results fail to provide insight into the rate and severity of cognitive decline induced by the presence of enteric Aβ. As such, a future study could involve comparing a baseline functional measurement taken prior to surgery with multiple behavioural test time points post-surgery. Interestingly, there were no differences between the two groups in the Morris Water Maze (MWM), which assesses spatial learning and flexibility within long-term memory. In their discussion, the authors suggest that deficits in the MWM may depend on hippocampal neurodegeneration, which was not seen in their study. However, the authors noted plaques in the hippocampus of Aβ-injected mice. This begs the question of whether the presence of plaques alone results in functional deficits or if neurodegeneration is required. According to their data, their results suggest that the presence of plaques in the hippocampus at this time point do not contribute to spatial learning and flexibility deficits. Consequently, it would be interesting to extend the study beyond one year in order to investigate whether differences between the way the two groups perform in the MWM appear, as the plaques may be a starting point for hippocampal degeneration. Finally, Sun et al. (2020) evaluated the effect of Aβ on GI motility as previous research observed that the effects of Aβ on ENS function were minimal (Chalazonitis & Rao, 2018). Specifically, Sun et al. (2020) focused on GI contractility to measure nerve network-mediated motor responsiveness and function. They observed that GI motility was still driven by neuromuscular coupling following surgery and injection, as indicated by tetrodotoxin inhibition in both groups. Furthermore, Sun et al. (2020) found that Aβ had little effect on GI function, as there was only a slight difference in spontaneous contractile response rate and the amplitude of electrical field stimulation-induced contractions in the jejunum, while there was no difference found in any of the other GI segments tested between the group receiving Aβ injections and vehicle controls throughout the GI tract. It is peculiar that the authors chose to include the idea that Aβ perturbs GI function in their manuscript title, when this doesn't significantly appear to be the case. Despite utilizing a model that emphasizes the gut, Sun et al. (2020) do not assess or even suggest a role for gut microbes in AD progression. As previously mentioned, microbes are an important component of the brain–gut–microbiota axis. Changes to gut microbial composition due to a variety of factors including drugs, diet, and age are known to negatively impact cognitive function. Individuals with AD appear to have taxonomically and functionally different microbial gut compositions than those who are healthy (Askarova et al. 2020). In future experiments, it would be important to consider normalizing the gut microbiomes of experimental mice to ensure that caging effects are not impacting cognitive abilities. Furthermore, future experiments could examine how changing the gut microbiome, maybe through diet or antibiotic use, impacts the production and presence of Aβ and its associated effects. In summary, the work by Sun et al. (2020) contributes to the growing body of evidence that AD may arise in the gut before the brain. Using their own mouse model, the researchers observed that enteric Aβ administration produced cognitive impairment and AD-like histopathologies in brain tissue. The results from this study are clinically significant as they suggest that the future of AD management may involve preventing or delaying the transport of Aβ from the gut to the CNS. Furthermore, the future of AD diagnosis may involve early detection in the gut. None. I. V.: Conception and design of the work; drafting the work and revising it critically for important intellectual content; final approval of the version to be published; agreement to be accountable for all aspects of the work E. F.-T.: Conception and design of the work; drafting the work and revising it critically for important intellectual content; final approval of the version to be published; agreement to be accountable for all aspects of the work. None.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,011

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0020,004
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,043
Tête enseignante GPT0,306
Écart entre enseignants0,262 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2020
Routes d'admission1
Résumé présentoui

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