An integrative machine-learning meta-analysis of high-throughput omics data identifies age-specific hallmarks of Alzheimer's disease

Exercise-induced modulation of miRNAs and gut microbiome: a holistic approach to neuroprotection in Alzheimer's disease

Alzheimer's disease (AD), a progressive neurodegenerative disorder, is marked by cognitive decline, neuroinflammation, and neuronal loss. MicroRNAs (miRNAs) have emerged as critical regulators of gene expression, influencing key pathways involved in neuroinflammation and neurodegeneration in AD. This review delves into the multifaceted role of exercise in modulating miRNA expression and its interplay with the gut microbiome, proposing a comprehensive framework for neuroprotection in AD. By synthesizing current research, we elucidate how exercise-induced changes in miRNA profiles can mitigate inflammatory responses, promote neurogenesis, and reduce amyloid-beta and tau pathologies. Additionally, we explore the gut-brain axis, highlighting how exercise-driven alterations in gut microbiota composition can further influence miRNA expression, thereby enhancing cognitive function and reducing neuroinflammatory markers. This holistic approach underscores the potential of targeting exercise-regulated miRNAs and gut microbiome interactions as a novel, noninvasive therapeutic strategy to decelerate AD progression and improve quality of life for patients. This approach aims to decelerate disease progression and improve patient outcomes, offering a promising avenue for enhancing the effectiveness of AD management.

Transcriptome analysis of multiple tissues and identification of tissue-specific genes in Lueyang black-bone chicken

Systematically constructing a gene expression atlas of poultry tissues is critically important for advancing poultry research and production. In this study, the gene expression profiles of 9 major tissues of Lueyang black-bone chicken were successfully constructed by transcriptome sequencing technology. Through in-depth analysis of transcriptome data, a total of 10 housekeeping genes (HKGs) and 87 marker genes (MGs) were identified. Furthermore, by applying weighted gene co-expression network analysis (WGCNA), we delineated nine tissue-specific modules and 90 hub genes, offering novel insights into the regulatory networks underlying tissue-specific gene expression. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that HKGs were predominantly involved in maintaining fundamental cellular functions, with significant enrichment in pathways related to oxidative phosphorylation, cell cycle regulation, and DNA replication. MGs were closely associated with tissue-specific physiological functions, providing valuable insights into the molecular mechanisms governing tissue functionality. Notably, through multidimensional validation, EEF1A1 and FTH1 were confirmed to exhibit cross-tissue expression stability, establishing them as ideal reference genes for multi-tissue qPCR experiments in chickens. Additionally, we successfully identified tissue marker genes, including TNNT2, PIT54, SFTPC, and PGM1, which are specific to the heart, liver, lung, and breast muscle, respectively. The results of this study have important scientific value in expanding reference gene selection and elucidating tissue-specific molecular mechanisms, and provide solid theoretical support and technical guidance for poultry breeding improvement and production practice optimization.

Machine learning-based model for predicting all-cause mortality in severe pneumonia

BACKGROUND

Severe pneumonia has a poor prognosis and high mortality. Current severity scores such as Acute Physiology and Chronic Health Evaluation (APACHE-II) and Sequential Organ Failure Assessment (SOFA), have limited ability to help clinicians in classification and management decisions. The goal of this study was to analyse the clinical characteristics of severe pneumonia and develop a machine learning-based mortality-prediction model for patients with severe pneumonia.

METHODS

Consecutive patients with severe pneumonia between 2013 and 2022 admitted to Beijing Chaoyang Hospital affiliated with Capital Medical University were included. In-hospital all-cause mortality was the outcome of this study. We performed a retrospective analysis of the cohort, stratifying patients into survival and non-survival groups, using mainstream machine learning algorithms (light gradient boosting machine, support vector classifier and random forest). We aimed to construct a mortality-prediction model for patients with severe pneumonia based on their accessible clinical and laboratory data. The discriminative ability was evaluated using the area under the receiver operating characteristic curve (AUC). The calibration curve was used to assess the fit goodness of the model, and decision curve analysis was performed to quantify clinical utility. By means of logistic regression, independent risk factors for death in severe pneumonia were figured out to provide an important basis for clinical decision-making.

RESULTS

A total of 875 patients were included in the development and validation cohorts, with the in-hospital mortality rate of 14.6%. The AUC of the model in the internal validation set was 0.8779 (95% CI, 0.738 to 0.974), showing a competitive discrimination ability that outperformed those of traditional clinical scoring systems, that is, APACHE-II, SOFA, CURB-65 (confusion, urea, respiratory rate, blood pressure, age ≥65 years), Pneumonia Severity Index. The calibration curve showed that the in-hospital mortality in severe pneumonia predicted by the model fit reasonably with the actual hospital mortality. In addition, the decision curve showed that the net clinical benefit was positive in both training and validation sets of hospitalised patients with severe pneumonia. Based on ensemble machine learning algorithms and logistic regression technique, the level of ferritin, lactic acid, blood urea nitrogen, creatine kinase, eosinophil and the requirement of vasopressors were identified as top independent predictors of in-hospital mortality with severe pneumonia.

CONCLUSION

A robust clinical model for predicting the risk of in-hospital mortality after severe pneumonia was successfully developed using machine learning techniques. The performance of this model demonstrates the effectiveness of these techniques in creating accurate predictive models, and the use of this model has the potential to greatly assist patients and clinical doctors in making well-informed decisions regarding patient care.

Identification of biomarkers and target drugs for melanoma: a topological and deep learning approach

Introduction

Melanoma, a highly aggressive malignancy characterized by rapid metastasis and elevated mortality rates, predominantly originates in cutaneous tissues. While surgical interventions, immunotherapy, and targeted therapies have advanced, the prognosis for advanced-stage melanoma remains dismal. Globally, melanoma incidence continues to rise, with the United States alone reporting over 100,000 new cases and 7,000 deaths annually. Despite the exponential growth of tumor data facilitated by next-generation sequencing (NGS), current analytical approaches predominantly emphasize single-gene analyses, neglecting critical insights into complex gene interaction networks. This study aims to address this gap by systematically exploring immune gene regulatory dynamics in melanoma progression.

Methods

We developed a bidirectional, weighted, signed, and directed topological immune gene regulatory network to compare transcriptional landscapes between benign melanocytic nevi and cutaneous melanoma. Advanced network analysis tools were employed to identify structural disparities and functional module shifts. Key driver genes were validated through topological centrality metrics. Additionally, deep learning models were implemented to predict drug-target interactions, leveraging molecular features derived from network analyses.

Results

Significant topological divergences emerged between nevi and melanoma networks, with dominant functional modules transitioning from cell cycle regulation in benign lesions to DNA repair and cell migration pathways in malignant tumors. A group of genes, including AURKA, CCNE1, APEX2, and EXOC8, were identified as potential orchestrators of immune microenvironment remodeling during malignant transformation. The deep learning framework successfully predicted 23 clinically actionable drug candidates targeting these molecular drivers.

Discussion

The observed module shift from cell cycle to invasion-related pathways provides mechanistic insights into melanoma progression, suggesting early therapeutic targeting of DNA repair machinery might mitigate metastatic potential. The identified hub genes, particularly AURKA and DDX19B, represent novel candidates for immunomodulatory interventions. Our computational drug prediction strategy bridges molecular network analysis with clinical translation, offering a paradigm for precision oncology in melanoma. Future studies should validate these targets in preclinical models and explore network-based biomarkers for early detection.

Machine Learning and Multi-Omics Integration to Reveal Biomarkers and Microbial Community Assembly Differences in Abnormal Stacking Fermentation of Sauce-Flavor Baijiu

Stacking fermentation is critical in sauce-flavor Baijiu production, but winter production often sees abnormal fermentations, like Waistline and Sub-Temp fermentation, affecting yield and quality. This study used three machine learning models (Logistic Regression, KNN, and Random Forest) combined with multi-omics (metagenomics and flavoromics) to develop a classification model for abnormal fermentation. SHAP analysis identified 13 Sub-Temp Fermentation and 9 Waistline microbial biomarkers, along with 9 Sub-Temp Fermentation and 12 Waistline flavor biomarkers. Komagataeibacter and Gluconacetobacter are key for normal fermentation, while Ligilactobacillus and Lactobacillus are critical in abnormal cases. Excessive acid and ester markers caused unbalanced aromas in abnormal fermentations. Additionally, ecological models reveal the bacterial community assembly in abnormal fermentations was influenced by stochastic factors, while the fungal community assembly was influenced by deterministic factors. RDA analysis shows that moisture significantly drove Sub-Temp fermentation. Differential gene analysis and KEGG pathway enrichment identify metabolic pathways for flavor markers. This study provides a theoretical basis for regulating stacking fermentation and ensuring Baijiu quality.

Proteostasis disruption and senescence in Alzheimer's disease pathways to neurodegeneration

Alzheimer's Disease (AD) is a progressive neurological disease associated with behavioral abnormalities, memory loss, and cognitive impairment that cause major causes of dementia in the elderly. The pathogenetic processes cause complex effects on brain function and AD progression. The proper protein homeostasis, or proteostasis, is critical for cell health. AD causes the buildup of misfolded proteins, particularly tau and amyloid-beta, to break down proteostasis, such aggregates are toxic to neurons and play a critical role in AD pathogenesis. The rise of cellular senescence is accompanied by aging, marked by irreversible cell cycle arrest and the release of pro-inflammatory proteins. Senescent cell build-up in the brains of AD patients exacerbates neuroinflammation and neuronal degeneration. These cells senescence-associated secretory phenotype (SASP) also disturbs the brain environment. When proteostasis failure and cellular senescence coalesce, a cycle is generated that compounds each other. While senescent cells contribute to proteostasis breakdown through inflammatory and degradative processes, misfolded proteins induce cellular stress and senescence. The principal aspects of the neurodegenerative processes in AD are the interaction of cellular senescence and proteostasis failure. This review explores the interconnected roles of proteostasis disruption and cellular senescence in the pathways leading to neurodegeneration in AD.

Unraveling the mechanisms underlying diabetic cataracts: insights from Mendelian randomization analysis

BACKGROUND

Diabetic cataract (DC) is a major cause of blindness, with its pathogenesis involving oxidative stress and ferroptosis, according to recent studies.

METHODS

We performed a Mendelian Randomization (MR) study using GWAS data to select SNPs and assess the causal link between diabetes and cataracts. DC datasets were analyzed for differential gene expression, WGCNA, and protein-protein interactions to identify key oxidative stress and ferroptosis genes. An SVM-RFE algorithm developed a diagnostic model, and ImmuCellAI analyzed immune infiltration patterns.

RESULTS

MR analysis confirmed diabetes as a cataract risk factor and identified core genes related to oxidative stress and ferroptosis in DC. Four key genes (Hspa5/Nfe2l2/Atf3/Stat3) linked to both processes were discovered. Immune infiltration analysis revealed an imbalance associated with these genes.

CONCLUSIONS

A functional interaction between oxidative stress and ferroptosis genes in DC is suggested, with a 4-gene model, indicating their potential as a 'bridge' in DC pathogenesis.

Innovative omics strategies in fermented fruits and vegetables: Unveiling nutritional profiles, microbial diversity, and future prospects

Fermented fruits and vegetables (FFVs) are not only rich in essential nutrients but also contain distinctive flavors, prebiotics, and metabolites. Although omics techniques have gained widespread recognition as an analytical strategy for FFVs, its application still encounters several challenges due to the intricacies of biological systems. This review systematically summarizes the advances, obstacles and prospects of genomics, transcriptomics, proteomics, metabolomics, and multi-omics strategies in FFVs. It is evident that beyond traditional applications, such as the exploration of microbial diversity, protein expression, and metabolic pathways, omics techniques exhibit innovative potential in deciphering stress response mechanisms and uncovering spoilage microorganisms. The adoption of multi-omics strategies is paramount to acquire a multidimensional network fusion, thereby mitigating the limitations of single omics strategies. Although substantial progress has been made, this review underscores the necessity for a comprehensive repository of omics data and the establishment of universal databases to ensure precision in predictions. Furthermore, multidisciplinary integration with other physical or biochemical approaches is imperative, as it enriches our comprehension of this intricate process.

MicroRNAs: pioneering regulators in Alzheimer's disease pathogenesis, diagnosis, and therapy

This article delves into Alzheimer's disease (AD), a prevalent neurodegenerative condition primarily affecting the elderly. It is characterized by progressive memory and cognitive impairments, severely disrupting daily life. Recent research highlights the potential involvement of microRNAs in the pathogenesis of AD. MicroRNAs (MiRNAs), short non-coding RNAs comprising 20-24 nucleotides, significantly influence gene regulation by hindering translation or promoting degradation of target genes. This review explores the role of specific miRNAs in AD progression, focusing on their impact on β-amyloid (Aβ) peptide accumulation, intracellular aggregation of hyperphosphorylated tau proteins, mitochondrial dysfunction, neuroinflammation, oxidative stress, and the expression of the APOE4 gene. Our insights contribute to understanding AD's pathology, offering new avenues for identifying diagnostic markers and developing novel therapeutic targets.

CheekAge: a next-generation buccal epigenetic aging clock associated with lifestyle and health

Epigenetic aging clocks are computational models that predict age using DNA methylation information. Initially, first-generation clocks were developed to make predictions using CpGs that change with age. Over time, next-generation clocks were created using CpGs that relate to both age and health. Since existing next-generation clocks were constructed in blood, we sought to develop a next-generation clock optimized for prediction in cheek swabs, which are non-invasive and easy to collect. To do this, we collected MethylationEPIC data as well as lifestyle and health information from 8045 diverse adults. Using a novel simulated annealing approach that allowed us to incorporate lifestyle and health factors into training as well as a combination of CpG filtering, CpG clustering, and clock ensembling, we constructed CheekAge, an epigenetic aging clock that has a strong correlation with age, displays high test-retest reproducibility across replicates, and significantly associates with a plethora of lifestyle and health factors, such as BMI, smoking status, and alcohol intake. We validated CheekAge in an internal dataset and multiple publicly available datasets, including samples from patients with progeria or meningioma. In addition to exploring the underlying biology of the data and clock, we provide a free online tool that allows users to mine our methylomic data and predict epigenetic age.

Regulatory roles of microRNAs in modulating mitochondrial dynamics, amyloid beta fibrillation, microglial activation, and cholinergic signaling: Implications for alzheimer's disease pathogenesis

Alzheimer's Disease (AD) remains a formidable challenge due to its complex pathology, notably involving mitochondrial dysfunction and dysregulated microRNA (miRNA) signaling. This study delves into the underexplored realm of miRNAs' impact on mitochondrial dynamics and their interplay with amyloid-beta (Aβ) aggregation and tau pathology in AD. Addressing identified gaps, our research utilizes advanced molecular techniques and AD models, alongside patient miRNA profiles, to uncover miRNAs pivotal in mitochondrial regulation. We illuminate novel miRNAs influencing mitochondrial dynamics, Aβ, and tau, offering insights into their mechanistic roles in AD progression. Our findings not only enhance understanding of AD's molecular underpinnings but also spotlight miRNAs as promising therapeutic targets. By elucidating miRNAs' roles in mitochondrial dysfunction and their interactions with hallmark AD pathologies, our work proposes innovative strategies for AD therapy, aiming to mitigate disease progression through targeted miRNA modulation. This contribution marks a significant step toward novel AD treatments, emphasizing the potential of miRNAs in addressing this complex disease.

Postoperative delirium prediction after cardiac surgery using machine learning models

OBJECTIVE

Postoperative delirium (POD) is a common postoperative complication in elderly patients, especially those undergoing cardiac surgery, which seriously affects the short- and long-term prognosis of patients. Early identification of risk factors for the development of POD can help improve the perioperative management of surgical patients. In the present study, five machine learning models were developed to predict patients at high risk of delirium after cardiac surgery and their performance was compared.

METHODS

A total of 367 patients who underwent cardiac surgery were retrospectively included in this study. Using single-factor analysis, 21 risk factors for POD were selected for inclusion in machine learning. The dataset was divided using 10-fold cross-validation for model training and testing. Five machine learning models (random forest (RF), support vector machine (SVM), radial based kernel neural network (RBFNN), K-nearest neighbour (KNN), and Kernel ridge regression (KRR)) were compared using area under the receiver operating characteristic curve (AUC-ROC), accuracy (ACC), sensitivity (SN), specificity (SPE), and Matthews coefficient (MCC).

RESULTS

Among 367 patients, 105 patients developed POD, the incidence of delirium was 28.6 %. Among the five ML models, RF had the best performance in ACC (87.99 %), SN (69.27 %), SPE (95.38 %), MCC (70.00 %) and AUC (0.9202), which was far superior to the other four models.

CONCLUSION

Delirium is common in patients after cardiac surgery. This analysis confirms the importance of the computational ML models in predicting the occurrence of delirium after cardiac surgery, especially the outstanding performance of the RF model, which has practical clinical applications for early identification of patients at risk of developing POD.

Identification the Low Oxidative Stress Subtype of Periodontitis

OBJECTIVE

The aim of this research was to identify the low oxidative stress-related genes expression (L-OS) subtype in patients with periodontitis.

METHODS

Microarray data (MA) were retrieved from the Gene Expression Omnibus database. The different oxidative stress (OS) subtypes of periodontitis were identified by K-means clustering analysis and gene set variation analysis (GSVA). Differentially expressed genes (DEGs) (|Log fold change (FC)| >1, q < 0.05) amongst the OS subtypes and healthy controls (HCs) were identified by Limma R package. The genomic feature of L-OS subtype and corresponding medicines were evaluated and visualised with Drug-Gene Interaction Database and cytoscape-v3.7.2 software (Pearson correlation coefficient > 0.4). Finally, the LASSO-Logistic regression model was adopted to evaluate and predict patients' OS phenotype in routine clinical practice.

RESULTS

The 241 periodontitis samples and 69 HCs were included. Thirty-three DEGs between the L-OS and high oxidative stress-related genes expression (H-OS) subtypes and 96 DEGs, including 8 transcription factors, between L-OS subtype and HCs were identified, respectively. Then, the network of TFs-Genes-Drugs was constructed to determine genomic feature of L-OS subtype. Finally, a 4-gene signature formula and the cutoff value were identified by ML with LASSO model to predict patients' classification.

CONCLUSIONS

For the first time, we identified L-OS subtype of periodontitis and evaluated its genomic feature with MA.

NMNAT2 supports vesicular glycolysis via NAD homeostasis to fuel fast axonal transport

BACKGROUND

Bioenergetic maladaptations and axonopathy are often found in the early stages of neurodegeneration. Nicotinamide adenine dinucleotide (NAD), an essential cofactor for energy metabolism, is mainly synthesized by Nicotinamide mononucleotide adenylyl transferase 2 (NMNAT2) in CNS neurons. NMNAT2 mRNA levels are reduced in the brains of Alzheimer's, Parkinson's, and Huntington's disease. Here we addressed whether NMNAT2 is required for axonal health of cortical glutamatergic neurons, whose long-projecting axons are often vulnerable in neurodegenerative conditions. We also tested if NMNAT2 maintains axonal health by ensuring axonal ATP levels for axonal transport, critical for axonal function.

METHODS

We generated mouse and cultured neuron models to determine the impact of NMNAT2 loss from cortical glutamatergic neurons on axonal transport, energetic metabolism, and morphological integrity. In addition, we determined if exogenous NAD supplementation or inhibiting a NAD hydrolase, sterile alpha and TIR motif-containing protein 1 (SARM1), prevented axonal deficits caused by NMNAT2 loss. This study used a combination of techniques, including genetics, molecular biology, immunohistochemistry, biochemistry, fluorescent time-lapse imaging, live imaging with optical sensors, and anti-sense oligos.

RESULTS

We provide in vivo evidence that NMNAT2 in glutamatergic neurons is required for axonal survival. Using in vivo and in vitro studies, we demonstrate that NMNAT2 maintains the NAD-redox potential to provide "on-board" ATP via glycolysis to vesicular cargos in distal axons. Exogenous NAD+ supplementation to NMNAT2 KO neurons restores glycolysis and resumes fast axonal transport. Finally, we demonstrate both in vitro and in vivo that reducing the activity of SARM1, an NAD degradation enzyme, can reduce axonal transport deficits and suppress axon degeneration in NMNAT2 KO neurons.

CONCLUSION

NMNAT2 ensures axonal health by maintaining NAD redox potential in distal axons to ensure efficient vesicular glycolysis required for fast axonal transport.

Identification of microRNA-mRNA Regulatory Networks with Therapeutic Values in Alzheimer's Disease by Bioinformatics Analysis

Background

Alzheimer's disease (AD) is the most prevalent neurological disorder worldwide, affecting approximately 24 million individuals. Despite more than a century of research on AD, its pathophysiology is still not fully understood.

Objective

Recently, genetic studies of AD have focused on analyzing the general expression profile by employing high-throughput genomic techniques such as microarrays. Current research has leveraged bioinformatics advancements in genetic science to build upon previous efforts.

Methods

Data from the GSE118553 dataset used in this investigation, and the analyses carried out using programs such as Limma and BioBase. Differentially expressed genes (DEGs) and differentially expressed microRNAs (DEmiRs) associated with AD identified in the studied areas of the brain. Target genes of the DEmiRs identified using the MultiMiR package. Gene ontology (GO) completed using the Enrichr website, and the protein-protein interaction (PPI) network for these genes drawn using STRING and Cytoscape software.

Results

The findings introduced DEGs including CTNNB1, PAK2, MAP2K1, PNPLA6, IGF1R, FOXL2, DKK3, LAMA4, PABPN1, and GDPD5, and DEmiRs linked to AD (miR-106A, miR-1826, miR-1253, miR-10B, miR-18B, miR-101-2, miR-761, miR-199A1, miR-379 and miR-668), (miR-720, miR-218-2, miR-25, miR-602, miR-1226, miR-548K, miR-H1, miR-410, miR-548F2, miR-181A2), (miR-1470, miR-651, miR-544, miR-1826, miR-195, miR-610, miR-599, miR-323, miR-587 and miR-340), and (miR-1282, miR-1914, miR-642, miR-1323, miR-373, miR-323, miR-1322, miR-612, miR-606 and miR-758) in cerebellum, frontal cortex, temporal cortex, and entorhinal cortex, respectively.

Conclusions

The majority of the genes and miRNAs identified by our findings may be employed as biomarkers for prediction, diagnosis, or therapy response monitoring.

Frontal Cortex Lipid Alterations During the Onset of Alzheimer's Disease

Background

Although sporadic Alzheimer's disease (AD) is a neurodegenerative disorder of unknown etiology, familial AD is associated with specific gene mutations. A commonality between these forms of AD is that both display multiple pathogenic events including cholinergic and lipid dysregulation.

Objective

We aimed to identify the relevant lipids and the activity of their related receptors in the frontal cortex and correlating them with cognition during the progression of AD.

Methods

MALDI-mass spectrometry imaging (MSI) and functional autoradiography was used to evaluate the distribution of phospholipids/sphingolipids and the activity of cannabinoid 1 (CB1), sphingosine 1-phosphate 1 (S1P1), and muscarinic M2/M4 receptors in the frontal cortex (FC) of people that come to autopsy with premortem clinical diagnosis of AD, mild cognitive impairment (MCI), and no cognitive impairment (NCI).

Results

MALDI-MSI revealed an increase in myelin-related lipids, such as diacylglycerol (DG) 36:1, DG 38:5, and phosphatidic acid (PA) 40:6 in the white matter (WM) in MCI compared to NCI, and a downregulation of WM phosphatidylinositol (PI) 38:4 and PI 38:5 levels in AD compared to NCI. Elevated levels of phosphatidylcholine (PC) 32:1, PC 34:0, and sphingomyelin 38:1 were observed in discrete lipid accumulations in the FC supragranular layers during disease progression. Muscarinic M2/M4 receptor activation in layers V-VI decreased in AD compared to MCI. CB1 receptor activity was upregulated in layers V-VI, while S1P1 was downregulated within WM in AD relative to NCI.

Conclusions

FC WM lipidomic alterations are associated with myelin dyshomeostasis in prodromal AD, suggesting WM lipid maintenance as a potential therapeutic target for dementia.

Alzheimer's disease: The role of proteins in formation, mechanisms, and new therapeutic approaches

Alzheimer's disease (AD) is a progressive neurological disorder that affects the central nervous system (CNS), leading to memory and cognitive decline. In AD, the brain experiences three main structural changes: a significant decrease in the quantity of neurons, the development of neurofibrillary tangles (NFT) composed of hyperphosphorylated tau protein, and the formation of amyloid beta (Aβ) or senile plaques, which are protein deposits found outside cells and surrounded by dystrophic neurites. Genetic studies have identified four genes associated with autosomal dominant or familial early-onset AD (FAD): amyloid precursor protein (APP), presenilin 1 (PS1), presenilin 2 (PS2), and apolipoprotein E (ApoE). The formation of plaques primarily involves the accumulation of Aβ, which can be influenced by mutations in APP, PS1, PS2, or ApoE genes. Mutations in the APP and presenilin (PS) proteins can cause an increased amyloid β peptides production, especially the further form of amyloidogenic known as Aβ42. Apart from genetic factors, environmental factors such as cytokines and neurotoxins may also have a significant impact on the development and progression of AD by influencing the formation of amyloid plaques and intracellular tangles. Exploring the causes and implications of protein aggregation in the brain could lead to innovative therapeutic approaches. Some promising therapy strategies that have reached the clinical stage include using acetylcholinesterase inhibitors, estrogen, nonsteroidal anti-inflammatory drugs (NSAIDs), antioxidants, and antiapoptotic agents. The most hopeful therapeutic strategies involve inhibiting activity of secretase and preventing the β-amyloid oligomers and fibrils formation, which are associated with the β-amyloid fibrils accumulation in AD. Additionally, immunotherapy development holds promise as a progressive therapeutic approach for treatment of AD. Recently, the two primary categories of brain stimulation techniques that have been studied for the treatment of AD are invasive brain stimulation (IBS) and non-invasive brain stimulation (NIBS). In this article, the amyloid proteins that play a significant role in the AD formation, the mechanism of disease formation as well as new drugs utilized to treat of AD will be reviewed.

The Role of microRNAs in Epigenetic Regulation of Signaling Pathways in Neurological Pathologies

In recent times, there has been a significant increase in researchers' interest in the functions of microRNAs and the role of these molecules in the pathogenesis of many multifactorial diseases. This is related to the diagnostic and prognostic potential of microRNA expression levels as well as the prospects of using it in personalized targeted therapy. This review of the literature analyzes existing scientific data on the involvement of microRNAs in the molecular and cellular mechanisms underlying the development of pathologies such as Alzheimer's disease, cerebral ischemia and reperfusion injury, and dysfunction of the blood-brain barrier.

Axonal energy metabolism, and the effects in aging and neurodegenerative diseases

Human studies consistently identify bioenergetic maladaptations in brains upon aging and neurodegenerative disorders of aging (NDAs), such as Alzheimer's disease, Parkinson's disease, Huntington's disease, and Amyotrophic lateral sclerosis. Glucose is the major brain fuel and glucose hypometabolism has been observed in brain regions vulnerable to aging and NDAs. Many neurodegenerative susceptible regions are in the topological central hub of the brain connectome, linked by densely interconnected long-range axons. Axons, key components of the connectome, have high metabolic needs to support neurotransmission and other essential activities. Long-range axons are particularly vulnerable to injury, neurotoxin exposure, protein stress, lysosomal dysfunction, etc. Axonopathy is often an early sign of neurodegeneration. Recent studies ascribe axonal maintenance failures to local bioenergetic dysregulation. With this review, we aim to stimulate research in exploring metabolically oriented neuroprotection strategies to enhance or normalize bioenergetics in NDA models. Here we start by summarizing evidence from human patients and animal models to reveal the correlation between glucose hypometabolism and connectomic disintegration upon aging/NDAs. To encourage mechanistic investigations on how axonal bioenergetic dysregulation occurs during aging/NDAs, we first review the current literature on axonal bioenergetics in distinct axonal subdomains: axon initial segments, myelinated axonal segments, and axonal arbors harboring pre-synaptic boutons. In each subdomain, we focus on the organization, activity-dependent regulation of the bioenergetic system, and external glial support. Second, we review the mechanisms regulating axonal nicotinamide adenine dinucleotide (NAD+) homeostasis, an essential molecule for energy metabolism processes, including NAD+ biosynthetic, recycling, and consuming pathways. Third, we highlight the innate metabolic vulnerability of the brain connectome and discuss its perturbation during aging and NDAs. As axonal bioenergetic deficits are developing into NDAs, especially in asymptomatic phase, they are likely exaggerated further by impaired NAD+ homeostasis, the high energetic cost of neural network hyperactivity, and glial pathology. Future research in interrogating the causal relationship between metabolic vulnerability, axonopathy, amyloid/tau pathology, and cognitive decline will provide fundamental knowledge for developing therapeutic interventions.

NMNAT2 supports vesicular glycolysis via NAD homeostasis to fuel fast axonal transport

Background

Bioenergetic maladaptations and axonopathy are often found in the early stages of neurodegeneration. Nicotinamide adenine dinucleotide (NAD), an essential cofactor for energy metabolism, is mainly synthesized by Nicotinamide mononucleotide adenylyl transferase 2 (NMNAT2) in CNS neurons. NMNAT2 mRNA levels are reduced in the brains of Alzheimer's, Parkinson's, and Huntington's disease. Here we addressed whether NMNAT2 is required for axonal health of cortical glutamatergic neurons, whose long-projecting axons are often vulnerable in neurodegenerative conditions. We also tested if NMNAT2 maintains axonal health by ensuring axonal ATP levels for axonal transport, critical for axonal function.

Methods

We generated mouse and cultured neuron models to determine the impact of NMNAT2 loss from cortical glutamatergic neurons on axonal transport, energetic metabolism, and morphological integrity. In addition, we determined if exogenous NAD supplementation or inhibiting a NAD hydrolase, sterile alpha and TIR motif-containing protein 1 (SARM1), prevented axonal deficits caused by NMNAT2 loss. This study used a combination of genetics, molecular biology, immunohistochemistry, biochemistry, fluorescent time-lapse imaging, live imaging with optical sensors, and anti-sense oligos.

Results

We provide in vivo evidence that NMNAT2 in glutamatergic neurons is required for axonal survival. Using in vivo and in vitro studies, we demonstrate that NMNAT2 maintains the NAD-redox potential to provide "on-board" ATP via glycolysis to vesicular cargos in distal axons. Exogenous NAD+ supplementation to NMNAT2 KO neurons restores glycolysis and resumes fast axonal transport. Finally, we demonstrate both in vitro and in vivo that reducing the activity of SARM1, an NAD degradation enzyme, can reduce axonal transport deficits and suppress axon degeneration in NMNAT2 KO neurons.

Conclusion

NMNAT2 ensures axonal health by maintaining NAD redox potential in distal axons to ensure efficient vesicular glycolysis required for fast axonal transport.

A meta-analysis of RNA-Seq studies to identify novel genes that regulate aging

Aging is a ubiquitous biological process that limits the maximal lifespan of most organisms. Significant efforts by many groups have identified mechanisms that, when triggered by natural or artificial stimuli, are sufficient to either enhance or decrease maximal lifespan. Previous aging studies using the nematode Caenorhabditis elegans (C. elegans) generated a wealth of publicly available transcriptomics datasets linking changes in gene expression to lifespan regulation. However, a comprehensive comparison of these datasets across studies in the context of aging biology is missing. Here, we carry out a systematic meta-analysis of over 1200 bulk RNA sequencing (RNASeq) samples obtained from 74 peer-reviewed publications on aging-related transcriptomic changes in C. elegans. Using both differential expression analyses and machine learning approaches, we mine the pooled data for novel pro-longevity genes. We find that both approaches identify known and propose novel pro-longevity genes. Further, we find that inter-lab experimental variance complicates the application of machine learning algorithms, a limitation that was not solved using bulk RNA-Seq batch correction and normalization techniques. Taken as a whole, our results indicate that machine learning approaches may hold promise for the identification of genes that regulate aging but will require more sophisticated batch correction strategies or standardized input data to reliably identify novel pro-longevity genes.

A set of common buccal CpGs that predict epigenetic age and associate with lifespan-regulating genes

Epigenetic aging clocks are computational models that use DNA methylation sites to predict age. Since cheek swabs are non-invasive and painless, collecting DNA from buccal tissue is highly desirable. Here, we review 11 existing clocks that have been applied to buccal tissue. Two of these were exclusively trained on adults and, while moderately accurate, have not been used to capture health-relevant differences in epigenetic age. Using 130 common CpGs utilized by two or more existing buccal clocks, we generate a proof-of-concept predictor in an adult methylomic dataset. In addition to accurately estimating age (r = 0.95 and mean absolute error = 3.88 years), this clock predicted that Down syndrome subjects were significantly older relative to controls. A literature and database review of CpG-associated genes identified numerous genes (e.g., CLOCK, ELOVL2, and VGF) and molecules (e.g., alpha-linolenic acid, glycine, and spermidine) reported to influence lifespan and/or age-related disease in model organisms.

•

130 CpGs have been used by two or more aging clocks applied to human buccal tissue

•

Common CpG genes are linked to the adaptive immune system and telomere maintenance

•

Common CpGs can be used to build a novel, proof-of-concept epigenetic aging clock

•

Several compounds associated with common CpG genes regulate lifespan in animals