scholarly journals tRNA-Derived Fragments in Alzheimer’s Disease: Implications for New Disease Biomarkers and Neuropathological Mechanisms

2020 ◽  
pp. 1-14
Author(s):  
Wenzhe Wu ◽  
Inhan Lee ◽  
Heidi Spratt ◽  
Xiang Fang ◽  
Xiaoyong Bao
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurological Diseases ◽  
Disease Stage ◽  
Expression Change ◽  
Disease Biomarkers ◽  
Qrt Pcr ◽  
Rna Fragments ◽  
First Time ◽  
Polyadenylation Factor

Background: Alzheimer’s disease (AD) is the most common type of dementia caused by irreversible neurodegeneration, with the onset mechanisms elusive. tRNA-derived RNA fragments (tRFs), a recently discovered family of small non-coding RNAs (sncRNAs), have been found to associate with many human diseases, including infectious, metabolic, and neurological diseases. However, whether tRFs play a role in human AD development is not known. Objective: This study aimed to explore whether tRFs are involved in human AD. Methods: Thirty-four postmortem human hippocampus samples were used. The expression of Drosha, Dicer, and angiogenin (ANG), three ribonucleases responsible for the biogenesis of sncRNAs, was determined by qRT-PCR and western blot. The tRFs in the hippocampus was detected by qRT-PCR or northern blot. We also used qRT-PCR to quantify NOP2/Sun RNA methyltransferase 2 (NSun2) and polyadenylation factor I subunit 1 (CLP1), two tRNA modification enzymes. Results: tRFs derived from a subset of tRNAs are significantly altered in the hippocampus of AD patients. The expression change of some tRFs showed age- and disease stage-dependent. ANG is significantly enhanced in AD, suggesting its role in inducing tRFs in AD. The expression of NSun2 in AD patients younger than 65 was significantly decreased. According to a previous report supporting NSun2-mediated tRNA methylation modification making tRNA less susceptible to ANG-mediated cleavage, our results suggested that the decrease in NSun2 may make tRNAs less methylated and subsequently enhanced tRF production from ANG-mediated tRNA cleavage. Conclusion: Our studies demonstrated for the first time the involvement of tRFs in human AD.

Alzheimer’s Disease Targeted Nano-Based Drug Delivery Systems

2020 ◽  
Vol 21 (7) ◽  
pp. 628-646
Author(s):  
Gülcem Altinoglu ◽  
Terin Adali
Keyword(s):  
Alzheimer’S Disease ◽  
Drug Delivery ◽  
Alzheimer's Disease ◽  
Neurological Diseases ◽  
Sustained Drug Release ◽  
Physiochemical Properties ◽  
Conventional Drug ◽  
Health Care Burden ◽  
The Central Nervous System ◽  
Effective Drugs

Alzheimer’s disease (AD) is the most common neurodegenerative disease, and is part of a massive and growing health care burden that is destroying the cognitive function of more than 50 million individuals worldwide. Today, therapeutic options are limited to approaches with mild symptomatic benefits. The failure in developing effective drugs is attributed to, but not limited to the highly heterogeneous nature of AD with multiple underlying hypotheses and multifactorial pathology. In addition, targeted drug delivery to the central nervous system (CNS), for the diagnosis and therapy of neurological diseases like AD, is restricted by the challenges posed by blood-brain interfaces surrounding the CNS, limiting the bioavailability of therapeutics. Research done over the last decade has focused on developing new strategies to overcome these limitations and successfully deliver drugs to the CNS. Nanoparticles, that are capable of encapsulating drugs with sustained drug release profiles and adjustable physiochemical properties, can cross the protective barriers surrounding the CNS. Thus, nanotechnology offers new hope for AD treatment as a strong alternative to conventional drug delivery mechanisms. In this review, the potential application of nanoparticle based approaches in Alzheimer’s disease and their implications in therapy is discussed.

Microglia in Alzheimer’s Disease

2020 ◽  
Vol 17 (1) ◽  
pp. 29-43 ◽  
Author(s):  
Patrick Süß ◽  
Johannes C.M. Schlachetzki
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Current Knowledge ◽  
Imaging Techniques ◽  
Amyloid Β ◽  
Disease Stage ◽  
Disease Modifying Therapy ◽  
Transcriptomic Signature

: Alzheimer’s Disease (AD) is the most frequent neurodegenerative disorder. Although proteinaceous aggregates of extracellular Amyloid-β (Aβ) and intracellular hyperphosphorylated microtubule- associated tau have long been identified as characteristic neuropathological hallmarks of AD, a disease- modifying therapy against these targets has not been successful. An emerging concept is that microglia, the innate immune cells of the brain, are major players in AD pathogenesis. Microglia are longlived tissue-resident professional phagocytes that survey and rapidly respond to changes in their microenvironment. Subpopulations of microglia cluster around Aβ plaques and adopt a transcriptomic signature specifically linked to neurodegeneration. A plethora of molecules and pathways associated with microglia function and dysfunction has been identified as important players in mediating neurodegeneration. However, whether microglia exert either beneficial or detrimental effects in AD pathology may depend on the disease stage. : In this review, we summarize the current knowledge about the stage-dependent role of microglia in AD, including recent insights from genetic and gene expression profiling studies as well as novel imaging techniques focusing on microglia in human AD pathology and AD mouse models.

scholarly journals Classic Prescription, Kai-Xin-San, Ameliorates Alzheimer’s Disease as an Effective Multitarget Treatment: From Neurotransmitter to Protein Signaling Pathway

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Sirui Guo ◽  
Jiahong Wang ◽  
Huarong Xu ◽  
Weiwei Rong ◽  
Cheng Gao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Pc12 Cells ◽  
Protein Signaling ◽  
Pi3k Inhibitor Ly294002 ◽  
Single Target ◽  
Biological Methods ◽  
Cognition Impairment ◽  
First Time

Alzheimer’s disease (AD) is a widespread neurodegenerative disease caused by complicated disease-causing factors. Unsatisfactorily, curative effects of approved anti-AD drugs were not good enough due to their actions on single-target, which led to desperate requirements for more effective drug therapies involved in multiple pathomechanisms of AD. The anti-AD effect with multiple action targets of Kai-Xin-San (KXS), a classic prescription initially recorded in Bei Ji Qian Jin Yao Fang and applied in the treatment of dementia for thousands of years, was deciphered with modern biological methods in our study. Aβ25-35 and D-gal-induced AD rats and Aβ25-35-induced PC12 cells were applied to establish AD models. KXS could significantly improve cognition impairment by decreasing neurotransmitter loss and enhancing the expression of PI3K/Akt. For the first time, KXS was confirmed to improve the expression of PI3K/Akt by neurotransmitter 5-HT. Thereinto, PI3K/Akt could further inhibit Tau hyperphosphorylation as well as the apoptosis induced by oxidative stress and neuroinflammation. Moreover, all above-mentioned effects were verified and blocked by PI3K inhibitor, LY294002, in Aβ25-35-induced PC12 cells, suggesting the precise regulative role of KXS in the PI3K/Akt pathway. The utilization and mechanism elaboration of KXS have been proposed and dissected in the combination of animal, molecular, and protein strategies. Our results demonstrated that KXS could ameliorate AD by regulating neurotransmitter and PI3K/Akt signal pathway as an effective multitarget treatment so that the potential value of this classic prescription could be explored from a novel perspective.

scholarly journals Association of CSF Alzheimer's disease biomarkers with postoperative delirium in older adults

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Tamara G. Fong ◽  
Sarinnapha M. Vasunilashorn ◽  
Yun Gou ◽  
Towia A. Libermann ◽  
Simon Dillon ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Older Adults ◽  
Alzheimer's Disease ◽  
Postoperative Delirium ◽  
Disease Biomarkers

scholarly journals Correlation between oligomerizaton of amyloid beta in plasma and other Alzheimer's disease biomarkers

IBRO Reports ◽  
2019 ◽  
Vol 6 ◽  
pp. S91
Author(s):  
Sungmin Kang ◽  
Jeewon Suh ◽  
Jeong Min Pyun ◽  
Young Chul Youn ◽  
Ji Sun Yu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Amyloid Beta ◽  
Disease Biomarkers

scholarly journals Pre‐analytical protocol for measuring Alzheimer's disease biomarkers in fresh CSF

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Oskar Hansson ◽  
Sandra Rutz ◽  
Henrik Zetterberg ◽  
Ekaterina Bauer ◽  
Teresa Hähl ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Biomarkers ◽  
Analytical Protocol

scholarly journals Quantitative Longitudinal Predictions of Alzheimer’s Disease by Multi-Modal Predictive Learning

2021 ◽  
Vol 79 (4) ◽  
pp. 1533-1546
Author(s):  
Mithilesh Prakash ◽  
Mahmoud Abdelaziz ◽  
Linda Zhang ◽  
Bryan A. Strange ◽  
Jussi Tohka ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prediction Models ◽  
Predictive Performance ◽  
Absolute Error ◽  
Disease Stage ◽  
Disease Assessment ◽  
Disease Category ◽  
Modal Data ◽  
Predictive Learning

Background: Quantitatively predicting the progression of Alzheimer’s disease (AD) in an individual on a continuous scale, such as the Alzheimer’s Disease Assessment Scale-cognitive (ADAS-cog) scores, is informative for a personalized approach as opposed to qualitatively classifying the individual into a broad disease category. Objective: To evaluate the hypothesis that the multi-modal data and predictive learning models can be employed for future predicting ADAS-cog scores. Methods: Unimodal and multi-modal regression models were trained on baseline data comprised of demographics, neuroimaging, and cerebrospinal fluid based markers, and genetic factors to predict future ADAS-cog scores for 12, 24, and 36 months. We subjected the prediction models to repeated cross-validation and assessed the resulting mean absolute error (MAE) and cross-validated correlation (ρ) of the model. Results: Prediction models trained on multi-modal data outperformed the models trained on single modal data in predicting future ADAS-cog scores (MAE12, 24 & 36 months= 4.1, 4.5, and 5.0, ρ12, 24 & 36 months= 0.88, 0.82, and 0.75). Including baseline ADAS-cog scores to prediction models improved predictive performance (MAE12, 24 & 36 months= 3.5, 3.7, and 4.6, ρ12, 24 & 36 months= 0.89, 0.87, and 0.80). Conclusion: Future ADAS-cog scores were predicted which could aid clinicians in identifying those at greater risk of decline and apply interventions at an earlier disease stage and inform likely future disease progression in individuals enrolled in AD clinical trials.

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