Strategies to gain novel Alzheimer’s disease diagnostics and therapeutics using modulators of ABCA transporters

Adenosine-triphosphate-(ATP)-binding cassette (ABC) transport proteins are ubiquitously present membrane-bound efflux pumps that distribute endo- and xenobiotics across intra- and intercellular barriers. Discovered over 40 years ago, ABC transporters have been identified as key players in various human diseases, such as multidrug-resistant cancer and atherosclerosis, but also neurodegenerative diseases, such as Alzheimer’s disease (AD). Most prominent and well-studied are ABCB1, ABCC1, and ABCG2, not only due to their contribution to the multidrug resistance (MDR) phenotype in cancer, but also due to their contribution to AD. However, our understanding of other ABC transporters is limited, and most of the 49 human ABC transporters have been largely neglected as potential targets for novel small-molecule drugs. This is especially true for the ABCA subfamily, which contains several members known to play a role in AD initiation and progression. This review provides up-to-date information on the proposed functional background and pathological role of ABCA transporters in AD. We also provide an overview of small-molecules shown to interact with ABCA transporters as well as potential in silico, in vitro, and in vivo methodologies to gain novel templates for the development of innovative ABC transporter-targeting diagnostics and therapeutics

Isotope‐labeled amyloid‐β does not transmit to the brain in a prion‐like manner after peripheral administration

Findings of early cerebral amyloid-β deposition in mice after peripheral injection of amyloid-β-containing brain extracts, and in humans following cadaveric human growth hormone treatment raised concerns that amyloid-β aggregates and possibly Alzheimer’s disease may be transmissible between individuals. Yet, proof that Aβ actually reaches the brain from the peripheral injection site is lacking. Here, we use a proteomic approach combining stable isotope labeling of mammals and targeted mass spectrometry. Specifically, we generate 13C-isotope-labeled brain extracts from mice expressing human amyloid-β and track 13C-lysine-labeled amyloid-β after intraperitoneal administration into young amyloid precursor protein-transgenic mice. We detect injected amyloid-β in the liver and lymphoid tissues for up to 100 days. In contrast, injected 13C-lysine-labeled amyloid-β is not detectable in the brain whereas the mice incorporate 13C-lysine from the donor brain extracts into endogenous amyloid-β. Using a highly sensitive and specific proteomic approach, we demonstrate that amyloid-β does not reach the brain from the periphery. Our study argues against potential transmissibility of Alzheimer’s disease while opening new avenues to uncover mechanisms of pathophysiological protein deposition.Peer reviewe

Machine Learning-Supported Analyses Improve Quantitative Histological Assessments of Amyloid-β Deposits and Activated Microglia

Background: Detailed pathology analysis and morphological quantification is tedious and prone to errors. Automatic image analysis can help to increase objectivity and reduce time. Here, we present the evaluation of the DeePathology STUDIO™ for automatic analysis of histological whole-slide images using machine learning/artificial intelligence. Objective: To evaluate and validate the use of DeePathology STUDIO for the analysis of histological slides at high resolution. Methods: We compared the DeePathology STUDIO and our current standard method using macros in AxioVision for the analysis of amyloid-β (Aβ) plaques and microglia in APP-transgenic mice at different ages. We analyzed density variables and total time invested with each approach. In addition, we correlated Aβ concentration in brain tissue measured by ELISA with the results of Aβ staining analysis. Results: DeePathology STUDIO showed a significant decrease of the time for establishing new analyses and the total analysis time by up to 90%. On the other hand, both approaches showed similar quantitative results in plaque and activated microglia density in the different experimental groups. DeePathology STUDIO showed higher sensitivity and accuracy for small-sized plaques. In addition, DeePathology STUDIO allowed the classification of plaques in diffuse- and dense-packed, which was not possible with our traditional analysis. Conclusion: DeePathology STUDIO substantially reduced the effort needed for a new analysis showing comparable quantitative results to the traditional approach. In addition, it allowed including different objects (categories) or cell types in a single analysis, which is not possible with conventional methods

Development of deep learning models for microglia analyses in brain tissue using DeePathology™ STUDIO

Background Interest in artificial intelligence-driven analysis of medical images has seen a steep increase in recent years. Thus, our paper aims to promote and facilitate the use of this state-of-the-art technology to fellow researchers and clinicians. New method We present custom deep learning models generated in DeePathology™ STUDIO without the need for background knowledge in deep learning and computer science underlined by practical suggestions. Results We describe the general workflow in this commercially available software and present three real-world examples how to detect microglia on IBA1-stained mouse brain sections including their differences, validation results and analysis of a sample slide. Comparison with existing methods Deep-learning assisted analysis of histological images is faster than classical analysis methods, and offers a wide variety of detection possibilities that are not available using methods based on staining intensity. Conclusions Reduced researcher bias, increased speed and extended possibilities make deep-learning assisted analysis of histological images superior to traditional analysis methods for histological images

Fingolimod as a Treatment in Neurologic Disorders Beyond Multiple Sclerosis

Fingolimod is an approved treatment for relapsing–remitting multiple sclerosis (MS), and its properties in different pathways have raised interest in therapy research for other neurodegenerative diseases. Fingolimod is an agonist of sphingosine-1-phosphate (S1P) receptors. Its main pharmacologic effect is immunomodulation by lymphocyte homing, thereby reducing the numbers of T and B cells in circulation. Because of the ubiquitous expression of S1P receptors, other effects have also been described. Here, we review preclinical experiments evaluating the effects of treatment with fingolimod in neurodegenerative diseases other than MS, such as Alzheimer’s disease or epilepsy. Fingolimod has shown neuroprotective effects in different animal models of neurodegenerative diseases, summarized here, correlating with increased brain-derived neurotrophic factor and improved disease phenotype (cognition and/or motor abilities). As expected, treatment also induced reductions in different neuroinflammatory markers because of not only inhibition of lymphocytes but also direct effects on astrocytes and microglia. Furthermore, fingolimod treatment exhibited additional effects for specific neurodegenerative disorders, such as reduction of amyloid-β production, and antiepileptogenic properties. The neuroprotective effects exerted by fingolimod in these preclinical studies are reviewed and support the translation of fingolimod into clinical trials as treatment in neurodegenerative diseases beyond neuroinflammatory conditions (MS)

Aging, NRF2, and TAU: A Perfect Match for Neurodegeneration?

Although the trigger for the neurodegenerative disease process is unknown, the relevance of aging stands out as a major risk for the development of neurodegeneration. In this review, we highlighted the relationship between the different cellular mechanisms that occur as a consequence of aging and transcription factor nuclear factor erythroid-2-related factor 2 (NRF2) and the connection with the TAU protein. We focused on the relevance of NRF2 in the main processes involved in neurodegeneration and associated with aging, such as genomic instability, protein degradation systems (proteasomes/autophagy), cellular senescence, and stem cell exhaustion, as well as inflammation. We also analyzed the effect of aging on TAU protein levels and its aggregation and spread process. Finally, we investigated the interconnection between NRF2 and TAU and the relevance of alterations in the NRF2 signaling pathway in both primary and secondary tauopathies. All these points highlight NRF2 as a possible therapeutic target for tauopathies

Dimethyl fumarate does not mitigate cognitive decline and β-amyloidosis in female APPPS1 mice

Introduction Alzheimer’s disease (AD) is the leading cause of dementia and a major global health issue. Currently, only limited treatment options are available to patients. One possibility to expand the treatment repertoire is repurposing of existing drugs such as dimethyl fumarate (DMF). DMF is approved for treatment of multiple sclerosis and previous animal studies have suggested that DMF may also have a beneficial effect for the treatment of AD. Methods We used an APPPS1 transgenic model of senile β-amyloidosis and treated female mice orally with DMF in two treatment paradigms (pre and post onset). We quantified learning and memory parameters, β-amyloidosis, and neuroinflammation to determine the potential of DMF as AD therapeutics. Results Treatment with DMF had no influence on water maze performance, β-amyloid accumulation, plaque formation, microglia activation, and recruitment of immune cells to the brain. Compared to vehicle-treated animals, oral DMF treatment could not halt or retard disease progression in the mice. Discussion Our results do not favour the use of DMF as treatment for AD. While our results stand in contrast to previous findings in other models, they emphasize the importance of animal model selection and suggest further studies to elucidate the mechanisms leading to conflicting results

Time- and Sex-Dependent Effects of Fingolimod Treatment in a Mouse Model of Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common cause of dementia. Fingolimod has previously shown beneficial effects in different animal models of AD. However, it has shown contradictory effects when it has been applied at early disease stages. Our objective was to evaluate fingolimod in two different treatment paradigms. To address this aim, we treated male and female APP-transgenic mice for 50 days, starting either before plaque deposition at 50 days of age (early) or at 125 days of age (late). To evaluate the effects, we investigated the neuroinflammatory and glial markers, the Aβ load, and the concentration of the brain-derived neurotrophic factor (BDNF). We found a reduced Aβ load only in male animals in the late treatment paradigm. These animals also showed reduced microglia activation and reduced IL-1β. No other treatment group showed any difference in comparison to the controls. On the other hand, we detected a linear correlation between BDNF and the brain Aβ concentrations. The fingolimod treatment has shown beneficial effects in AD models, but the outcome depends on the neuroinflammatory state at the start of the treatment. Thus, according to our data, a fingolimod treatment would be effective after the onset of the first AD symptoms, mainly affecting the neuroinflammatory reaction to the ongoing Aβ deposition

A New Tool for the Analysis of the Effect of Intracerebrally Injected Anti-Amyloid-β Compounds

Background: A wide range of techniques has been developed over the past decades to characterize amyloid-β (Aβ) pathology in mice. Until now, no method has been established to quantify spatial changes in Aβ plaque deposition due to targeted delivery of substances using ALZET® pumps. Objective: Development of a methodology to quantify the local distribution of Aβ plaques after intracerebral infusion of compounds. Methods: We have developed a toolbox to quantify Aβ plaques in relation to intracerebral injection channels using Zeiss AxioVision® and Microsoft Excel® software. For the proof of concept, intracerebral stereotactic surgery was performed in 50-day-old APP-transgenic mice injected with PBS. At the age of 100 days, brains were collected for immunhistological analysis. Results: The toolbox can be used to analyze and evaluate Aβ plaques (number, size, and coverage) in specific brain areas based on their location relative to the point of the injection or the injection channel. The tool provides classification of Aβ plaques in pre-defined distance groups using two different approaches. Conclusion: This new analytic toolbox facilitates the analysis of long-term continuous intracerebral experimental compound infusions using ALZET® pumps. This method generates reliable data for Aβ deposition characterization in relation to the distribution of experimental compounds

Detection and Prediction of Mild Cognitive Impairment in Alzheimer's Disease Mice

Background: The recent failure of clinical trials to treat Alzheimer’s disease (AD) indicates that the current approach of modifying disease is either wrong or is too late to be efficient. Mild cognitive impairment (MCI) denotes the phase between the preclinical phase and clinical overt dementia. AD mouse models that overexpress human amyloid-β (Aβ) are used to study disease pathogenesis and to conduct drug development/testing. However, there is no direct correlation between the Aβ deposition, the age of onset, and the severity of cognitive dysfunction. Objective: To detect and predict MCI when Aβ plaques start to appear in the hippocampus of an AD mouse. Methods: We trained wild-type and AD mice in a Morris water maze (WM) task with different inter-trial intervals (ITI) at 3 months of age and assessed their WM performance. Additionally, we used a classification algorithm to predict the genotype (APPtg versus wild-type) of an individual mouse from their respective WM data. Results: MCI can be empirically detected using a short-ITI protocol. We show that the ITI modulates the spatial learning of AD mice without affecting the formation of spatial memory. Finally, a simple classification algorithm such as logistic regression on WM data can give an accurate prediction of the cognitive dysfunction of a specific mouse. Conclusion: MCI can be detected as well as predicted simultaneously with the onset of Aβ deposition in the hippocampus in AD mouse model. The mild cognitive impairment prediction can be used for assessing the efficacy of a treatment