Cognitive Impairment in Nonagenarians: Potential Metabolic Mechanisms Revealed by the Synergy of In Silico Gene Expression Modeling and Pathway Enrichment Analysis.

Previous studies examining the molecular and genetic basis of cognitive impairment, particularly in cohorts of long-living adults, have mainly focused on associations at the genome or transcriptome level. Dozens of significant dementia-associated genes have been identified, including APOE, APOC1, and TOMM40. However, most of these studies did not consider the intergenic interactions and functional gene modules involved in cognitive function, nor did they assess the metabolic changes in individual brain regions. By combining functional analysis with a transcriptome-wide association study, we aimed to address this gap and examine metabolic pathways in different areas of the brain of older adults. The findings from our previous genome-wide association study in 1155 older adults, 179 of whom had cognitive impairment, were used as input for the PrediXcan gene prediction algorithm. Based on the predicted changes in gene expression levels, we conducted a transcriptome-wide association study and functional analysis using the KEGG and HALLMARK databases. For a subsample of long-living adults, we used logistic regression to examine the associations between blood biochemical markers and cognitive impairment. The functional analysis revealed a significant association between cognitive impairment and the expression of NADH oxidoreductase in the cerebral cortex. Significant associations were also detected between cognitive impairment and signaling pathways involved in peroxisome function, apoptosis, and the degradation of lysine and glycan in other brain regions. Our approach combined the strengths of a transcriptome-wide association study with the advantages of functional analysis. It demonstrated that apoptosis and oxidative stress play important roles in cognitive impairment.

Prevalence of the cancer-associated germline variants in Russian adults and long-living individuals: using the ACMG recommendations and computational interpreters for pathogenicity assessment.

Background: Population studies are essential for gathering critical disease prevalence data. Automated pathogenicity assessment tools enhance the capacity to interpret and annotate large amounts of genetic data. In this study, we assessed the prevalence of cancer-associated germline variants in Russia using a semiautomated variant interpretation algorithm. Methods: We examined 74,996 Russian adults (Group 1) and 2,872 long-living individuals aged ≥ 90 years (Group 2) for variants in 28 ACMG-recommended cancer-associated genes in three steps: InterVar annotation; ClinVar interpretation; and a manual review of the prioritized variants based on the available data. Using the data on the place of birth and the region of residence, we determined the geographical distribution of the detected variants and tracked the migration dynamics of their carriers. Results: We report 175 novel del-VUSs. We detected 232 pathogenic variants, 46 likely pathogenic variants, and 216 del-VUSs in Group 1 and 19 pathogenic variants, 2 likely pathogenic variants, and 16 del-VUSs in Group 2. For each detected variant, we provide a description of its functional significance and geographical distribution. Conclusion: The present study offers extensive genetic data on the Russian population, critical for future genetic research and improved primary cancer prevention and genetic screening strategies. The proposed hybrid assessment algorithm streamlines variant prioritization and pathogenicity assessment and offers a reliable and verifiable way of identifying variants of uncertain significance that need to be manually reviewed.

Machine Learning-Based Decision-Making in Geriatrics: Aging Phenotype Calculator and Survival Prognosis.

Aging is a natural process with varying effects. As we grow older, our bodies become more susceptible to aging-associated diseases. These diseases, individually or collectively, lead to the formation of distinct aging phenotypes. Identifying these aging phenotypes and understanding the complex interplay between coexistent diseases would facilitate more personalized patient management, a better prognosis, and a prolonged lifespan. Many studies distinguish between successful aging and frailty. However, this simple distinction fails to reflect the diversity of underlying causes. In this study, we sought to establish the underlying causes of frailty and determine the patterns in which these causes converge to form aging phenotypes. We conducted a comprehensive geriatric examination, cognitive assessment, and survival analysis of 2,688 long-living adults (median age = 92 years). The obtained data were clustered and used as input data for the Aging Phenotype Calculator, a multiclass classification model validated on an independent dataset of 96 older adults. The accuracy of the model was assessed using the receiver operating characteristic curve and the area under the curve. Additionally, we analyzed socioeconomic factors that could contribute to specific aging patterns. We identified five aging phenotypes: non-frailty, multimorbid frailty, metabolic frailty, cognitive frailty, and functional frailty. For each phenotype, we determined the underlying diseases and conditions and assessed the survival rate. Additionally, we provided management recommendations for each of the five phenotypes based on their distinct features and associated challenges. The identified aging phenotypes may facilitate better-informed decision-making. The Aging Phenotype Calculator (ROC AUC = 92%) may greatly assist geriatricians in patient management.

Characterisation of Neurospheres-Derived Cells from Human Olfactory Epithelium.

A major problem in psychiatric research is a deficit of relevant cell material of neuronal origin, especially in large quantities from living individuals. One of the promising options is cells from the olfactory neuroepithelium, which contains neuronal progenitors that ensure the regeneration of olfactory receptors. These cells are easy to obtain with nasal biopsies and it is possible to grow and cultivate them in vitro. In this work, we used RNAseq expression profiling and immunofluorescence microscopy to characterise neurospheres-derived cells (NDC), that simply and reliably grow from neurospheres (NS) obtained from nasal biopsies. We utilized differential expression analysis to explore the molecular changes that occur during transition from NS to NDC. We found that processes associated with neuronal and vascular cells are downregulated in NDC. A comparison with public transcriptomes revealed a depletion of neuronal and glial components in NDC. We also discovered that NDC have several metabolic features specific to neuronal progenitors treated with the fungicide maneb. Thus, while NDC retain some neuronal/glial identity, additional protocol alterations are needed to use NDC for mass sample collection in psychiatric research.

Glucose deprivation stimulates Cu(2+) toxicity in cultured cerebellar granule neurons and Cu(2+)-dependent zinc release.

Copper chloride (0.01mM, 2h) did not have significant influence on the survival of cerebellar granule neurons (CGNs) incubated in balanced salt solution. However, CuCl2 caused severe neuronal damage by glucose deprivation (GD). The glutamate NMDA-receptors blocker MK-801 partially and antioxidant N-acetyl-l-cysteine (NAC) or Zn(2+) chelator, N,N,N',N'-tetrakis(2-pyridylmethyl)ethylenediamine (TPEN) almost entirely protected CGNs from this toxic effect. Measurements of intracellular calcium ions using Fluo-4 AM, or zinc ions with FluoZin-3 AM demonstrated that 1 h-exposure to GD induced intensive increase of Fluo-4 but not FluoZin-3 fluorescence in neurons. The supplementation of solution with CuCl2 caused an increase of FluoZin-3, Fluo-4 and CellROX Green (reactive oxygen species probe) fluorescence by GD. The stimulation of Fluo-4 but not FluoZin-3 fluorescence by copper could be prevented partially by MK-801 and as well as CellROX Green fluorescence by NAC at GD. This data imply that during GD copper ions induce intense displacement zinc ions from intracellular stores, in addition free radical production, glutamate release and Ca(2+) overload of CGNs, that causes death of neurons as a result.