PubMed · 1316632
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M Holloway. 1992. The estrogen factor.. https://doi.org/10.1038/scientificamerican0692-26
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Alzheimer's disease (AD) is responsible for 70% of dementia cases worldwide, with tau hyperphosphorylation and amyloid-β plaque accumulation representing its core pathological hallmarks. Genetic predisposition, oxidative stress, and neuroinflammation contribute to disease onset and progression. Non-coding ribonucleic acids (ncRNAs) are a class of RNAs which control gene expression and whose dysregulation in AD patients has been linked to amyloid production, neuroinflammation, and mitochondrial dysfunction, which ranges from impaired energy metabolism to disrupted mitochondrial biogenesis and dynamics. Our descriptive systematic review surveyed the involvement of ncRNAs in mitochondrial dysfunction in AD across experimental and clinical literature. We identified multiple microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) that directly regulate mitophagy, mitochondrial biogenesis, mitochondrial autophagic, and apoptotic pathways, mitochondrial dynamics, and protein import mechanisms in AD models. Among the most important candidates demonstrating clinical dysregulation, miR-140 and lncRNA NEAT1 regulate mitophagy, while miR-9, miR-34a, miR-146a, miR-155, and miR-485 are implicated in mitochondrial biogenesis and miR-204 in mitochondrial autophagy. LncRNA BDNF-AS, miR-148a-3p, miR-21-5p, and miR-103a-3p emerged as regulators of the mitochondrial apoptosis pathway with confirmed clinical dysregulation. Multiple ncRNAs control mitochondrial dynamics, of which miR-195, miR-124, and miR-455-3p have also been studied in AD patients. Additionally, several ncRNAs were found to indirectly regulate mitochondrial fission, autophagy, and apoptosis, although the underlying mechanisms require further characterization. Thus, while ncRNA-centered AD research is in its early stages, current mechanistic and translational evidence supports mitochondrially relevant ncRNAs as promising candidates for biomarker and therapeutic development.
Identifying features that interact to trigger disease, while accounting for heterogeneity across diverse populations, is essential for the development of precision and targeted medicine. Despite the availability of vast and complex health-related datasets, most existing works focus on identifying disease-associated features at the population level or within a few subpopulations, often overlooking individual-level heterogeneity within these groups. To address this limitation, we propose a framework that utilizes localized test statistics to identify disease-associated features tailored to individual profiles. Our method leverages the recently developed knockoffs methodology to control the noise level of the selection set so that the results are replicable. Moreover, it allows for the discovery of hidden heterogeneous effects within the data, as demonstrated in an application to single-cell RNA sequencing data for Alzheimer's disease. By aggregating localized feature selection results, our framework also enables powerful population-level feature selection. Our framework provides a powerful tool for exploratory studies of precision medicine, offering the potential to generate novel hypotheses for confirmatory biological experiments.
BACKGROUND: Mitochondrial dysfunction is a hallmark of Alzheimer's disease (AD), yet specific molecular targets remain to be fully characterized. METHODS: A summary-data-based Mendelian randomization (SMR) framework integrated AD genome-wide association study (GWAS) statistics (39,918 cases) with blood DNA methylation quantitative trait loci (mQTL), gene expression (eQTL), and protein (pQTL) data for 1136 mitochondria-related genes. Associations were assessed using Bayesian colocalization and HEIDI testing. Tissue relevance was evaluated in four brain regions (hippocampus, amygdala, cortex, frontal cortex) using GTEx and external transcriptomic datasets. RESULTS: Screening identified eight candidates supported across blood mQTL and eQTL layers. Stepwise central nervous system (CNS) evaluation singled out biphenyl hydrolase-like (BPHL) as the consistent candidate. Higher genetically predicted BPHL expression was associated with reduced AD risk across the hippocampus (OR=0.920, 95% CI 0.873-0.970), amygdala (OR=0.925, 95%CI 0.880-0.973), cortex (OR=0.943, 95% CI 0.908-0.978), and frontal cortex (OR=0.938, 95%CI 0.901-0.976). These findings aligned with protein-protein interactions connecting BPHL to respiratory complexes and lower BPHL expression in independent AD brains. Functional enrichment converged on oxidative phosphorylation pathways. CONCLUSIONS: By integrating multi-omics data with tissue-specific validation, this study nominates BPHL as a consistent protective candidate in the brain. These findings provide genetic support for mitochondrial molecular perturbations in AD, offering insights for future validation.