MRTF-A-mediated protection against amyloid-β-induced neuronal injury correlates with restoring autophagy via miR-1273g-3p/mTOR axis in Alzheimer models

Unraveling the genetic architecture of blood unfolded p-53 among non-demented elderlies: novel candidate genes for early Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a heritable neurodegenerative disease whose long asymptomatic phase makes the early diagnosis of it pivotal. Blood U-p53 has recently emerged as a superior predictive biomarker for AD in the early stages. We hypothesized that genetic variants associated with blood U-p53 could reveal novel loci and pathways involved in the early stages of AD.

RESULTS

We performed a blood U-p53 Genome-wide association study (GWAS) on 484 healthy and mild cognitively impaired subjects from the ADNI cohort using 612,843 Single nucleotide polymorphisms (SNPs). We performed a pathway analysis and prioritized candidate genes using an AD single-cell gene program. We fine-mapped the intergenic SNPs by leveraging a cell-type-specific enhancer-to-gene linking strategy using a brain single-cell multimodal dataset. We validated the candidate genes in an independent brain single-cell RNA-seq and the ADNI blood transcriptome datasets. The rs279686 between AASS and FEZF1 genes was the most significant SNP (p-value = 4.82 × 10-7). Suggestive pathways were related to the immune and nervous systems. Twenty-three candidate genes were prioritized at 27 suggestive loci. Fine-mapping of 5 intergenic loci yielded nine cell-specific candidate genes. Finally, 15 genes were validated in the independent single-cell RNA-seq dataset, and five were validated in the ADNI blood transcriptome dataset.

CONCLUSIONS

We underlined the importance of performing a GWAS on an early-stage biomarker of AD and leveraging functional omics datasets for pinpointing causal genes in AD. Our study prioritized nine genes (SORCS1, KIF5C, TMEFF2, TMEM63C, HLA-E, ATAT1, TUBB, ARID1B, and RUNX1) strongly implicated in the early stages of AD.

NcRNAs: A synergistically antiapoptosis therapeutic tool in Alzheimer's disease

AIMS

The aim of this review is to systematically summarize and analyze the noncoding RNAs (ncRNAs), especially microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs), in the cell apoptosis among Alzheimer's disease (AD) in recent years to demonstrate their value in the diagnosis and treatment of AD.

METHODS

We systematically summarized in vitro and in vivo studies focusing on the ncRNAs in the regulation of cell apoptosis among AD in PubMed, ScienceDirect, and Google Scholar.

RESULTS

We discover three patterns of ncRNAs (including 'miRNA-mRNA', 'lncRNA-miRNA-mRNA', and 'circRNA-miRNA-mRNA') form the ncRNA-based regulatory networks in regulating cell apoptosis in AD.

CONCLUSIONS

This review provides a future diagnosis and treatment strategy for AD patients based on ncRNAs.

miR-129-5p as a biomarker for pathology and cognitive decline in Alzheimer's disease

BACKGROUND

Alzheimer's dementia (AD) pathogenesis involves complex mechanisms, including microRNA (miRNA) dysregulation. Integrative network and machine learning analysis of miRNA can provide insights into AD pathology and prognostic/diagnostic biomarkers.

METHODS

We performed co-expression network analysis to identify network modules associated with AD, its neuropathology markers, and cognition using brain tissue miRNA profiles from the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) (N = 702) as a discovery dataset. We performed association analysis of hub miRNAs with AD, its neuropathology markers, and cognition. After selecting target genes of the hub miRNAs, we performed association analysis of the hub miRNAs with their target genes and then performed pathway-based enrichment analysis. For replication, we performed a consensus miRNA co-expression network analysis using the ROS/MAP dataset and an independent dataset (N = 16) from the Gene Expression Omnibus (GEO). Furthermore, we performed a machine learning approach to assess the performance of hub miRNAs for AD classification.

RESULTS

Network analysis identified a glucose metabolism pathway-enriched module (M3) as significantly associated with AD and cognition. Five hub miRNAs (miR-129-5p, miR-433, miR-1260, miR-200a, and miR-221) of M3 had significant associations with AD clinical and/or pathologic traits, with miR129-5p by far the strongest across all phenotypes. Gene-set enrichment analysis of target genes associated with their corresponding hub miRNAs identified significantly enriched biological pathways including ErbB, AMPK, MAPK, and mTOR signaling pathways. Consensus network analysis identified two AD-associated consensus network modules and two hub miRNAs (miR-129-5p and miR-221). Machine learning analysis showed that the AD classification performance (area under the curve (AUC) = 0.807) of age, sex, and APOE ε4 carrier status was significantly improved by 6.3% with inclusion of five AD-associated hub miRNAs.

CONCLUSIONS

Integrative network and machine learning analysis identified miRNA signatures, especially miR-129-5p, as associated with AD, its neuropathology markers, and cognition, enhancing our understanding of AD pathogenesis and leading to better performance of AD classification as potential diagnostic/prognostic biomarkers.

Targeting autophagy in Alzheimer's disease: Animal models and mechanisms

Alzheimer's disease (AD) is an age-related progressive neurodegenerative disorder that leads to cognitive impairment and memory loss. Emerging evidence suggests that autophagy plays an important role in the pathogenesis of AD through the regulation of amyloid-beta (Aβ) and tau metabolism, and that autophagy dysfunction exacerbates amyloidosis and tau pathology. Therefore, targeting autophagy may be an effective approach for the treatment of AD. Animal models are considered useful tools for investigating the pathogenic mechanisms and therapeutic strategies of diseases. This review aims to summarize the pathological alterations in autophagy in representative AD animal models and to present recent studies on newly discovered autophagy-stimulating interventions in animal AD models. Finally, the opportunities, difficulties, and future directions of autophagy targeting in AD therapy are discussed.

miR-129-5p as a biomarker for pathology and cognitive decline in Alzheimer's disease

Background

Alzheimer's dementia (AD) pathogenesis involves complex mechanisms, including microRNA (miRNA) dysregulation. Integrative network and machine learning analysis of miRNA can provide insights into AD pathology and prognostic/diagnostic biomarkers.

Methods

We performed co-expression network analysis to identify network modules associated with AD, its neuropathology markers, and cognition using brain tissue miRNA profiles from the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) (N = 702) as a discovery dataset. We performed association analysis of hub miRNAs with AD, its neuropathology markers, and cognition. After selecting target genes of the hub miRNAs, we performed association analysis of the hub miRNAs with their target genes and then performed pathway-based enrichment analysis. For replication, we performed a consensus miRNA co-expression network analysis using the ROS/MAP dataset and an independent dataset (N = 16) from the Gene Expression Omnibus (GEO). Furthermore, we performed a machine learning approach to assess the performance of hub miRNAs for AD classification.

Results

Network analysis identified a glucose metabolism pathway-enriched module (M3) as significantly associated with AD and cognition. Five hub miRNAs (miR-129-5p, miR-433, miR-1260, miR-200a, and miR-221) of M3 had significant associations with AD clinical and/or pathologic traits, with miR129-5p by far the strongest across all phenotypes. Gene-set enrichment analysis of target genes associated with their corresponding hub miRNAs identified significantly enriched biological pathways including ErbB, AMPK, MAPK, and mTOR signaling pathways. Consensus network analysis identified two AD-associated consensus network modules, and two hub miRNAs (miR-129-5p and miR-221). Machine learning analysis showed that the AD classification performance (area under the curve (AUC) = 0.807) of age, sex, and apoE ε4 carrier status was significantly improved by 6.3% with inclusion of five AD-associated hub miRNAs.

Conclusions

Integrative network and machine learning analysis identified miRNA signatures, especially miR-129-5p, as associated with AD, its neuropathology markers, and cognition, enhancing our understanding of AD pathogenesis and leading to better performance of AD classification as potential diagnostic/prognostic biomarkers.

Δ8-THC Protects against Amyloid Beta Toxicity Modulating ER Stress In Vitro: A Transcriptomic Analysis

Alzheimer’s disease (AD) represents the most common form of dementia, characterized by amyloid β (Aβ) plaques and neurofibrillary tangles (NFTs). It is characterized by neuroinflammation, the accumulation of misfolded protein, ER stress and neuronal apoptosis. It is of main importance to find new therapeutic strategies because AD prevalence is increasing worldwide. Cannabinoids are arising as promising neuroprotective phytocompounds. In this study, we evaluated the neuroprotective potential of Δ8-THC pretreatment in an in vitro model of AD through transcriptomic analysis. We found that Δ8-THC pretreatment restored the loss of cell viability in retinoic acid-differentiated neuroblastoma SH-SY5Y cells treated with Aβ1-42. Moreover, the transcriptomic analysis provided evidence that the enriched biological processes of gene ontology were related to ER functions and proteostasis. In particular, Aβ1-42 upregulated genes involved in ER stress and unfolded protein response, leading to apoptosis as demonstrated by the increase in Bax and the decrease in Bcl-2 both at gene and protein expression levels. Moreover, genes involved in protein folding and degradation were also deregulated. On the contrary, Δ8-THC pretreatment reduced ER stress and, as a consequence, neuronal apoptosis. Then, the results demonstrated that Δ8-THC might represent a new neuroprotective agent in AD.

LncRNA PEG11as silencing sponges miR-874-3p to alleviate cerebral ischemia stroke via regulating autophagy in vivo and in vitro

Long non-coding RNAs (lncRNAs) are reportedly involved in the regulation of physiological and pathophysiological processes. However, the potential role of lncRNAs in stroke remains largely undefined. Here, RNA-Seq analysis of lncRNAs found that the lncRNA PEG11as (PEG11as) levels were significantly increased in ischemic brain tissue in a transient middle cerebral artery occlusion/reperfusion (tMCAO/R) mouse model of stroke. To explore the role of PEG11as in stroke, the lentivirus containing PEG11as silencing construct(siRNA-PEG11as) was microinjected intracerebroventricularly into male or transfected to N2a cells and then exposed to tMCAO/R or oxygen-glucose deprivation/reoxygenation (OGD/R). Knockdown of PEG11as expression significantly reduced infarct volume, alleviated neuronal deficits and inhibited neuronal apoptosis in tMCAO/R mice. Mechanistically, as an endogenous microRNA-874-3p (miR-874-3p) sponge, PEG11as silencing inhibited miR-874-3p activity, resulting in downregulation of ATG16L1 expression and subsequent inhibition of neuronal apoptosis by regulating autophagy. Overall, the results of this current study indicate that PEG11as is involved in the pathophysiology of cerebral ischemia, thus providing translational evidence that PEG11as can be envisioned as a novel biomarker or/and therapeutic target for stroke.