Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Mamchur A

PROVIDER: S-EPMC10970146 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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

Mamchur Aleksandra A Zelenova Elena E Dzhumaniiazova Irina I Erema Veronika V Kashtanova Daria D Ivanov Mikhail M Bruttan Maria M Gusakova Mariia M Terekhov Mikhail M Yudin Vladimir V Rumyantseva Antonina A Matkava Lorena L Strazhesko Irina I Isaev Ruslan R Kruglikova Anna A Maytesyan Lilit L Tarasova Irina I Beloshevskaya Olga O Mkhitaryan Elen E Kraevoy Sergey S Tkacheva Olga O Yudin Sergey S

International journal of molecular sciences 20240315 6

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 individua ...[more]

PMID: 38542318

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Project description:IntroductionAdvances in mobile computing platforms and the rapid development of wearable devices have made possible the continuous monitoring of patients with mild cognitive impairment (MCI) and their daily activities. Such rich data can reveal more subtle changes in patients' behavioral and physiological characteristics, providing new ways to detect MCI anytime, anywhere. Therefore, we aimed to investigate the feasibility and validity of digital cognitive tests and physiological sensors applied to MCI assessment.MethodsWe collected photoplethysmography (PPG), electrodermal activity (EDA) and electroencephalogram (EEG) signals from 120 participants (61 MCI patients, 59 healthy controls) during rest and cognitive testing. The features extracted from these physiological signals involved the time domain, frequency domain, time-frequency domain and statistics. Time and score features during the cognitive test are automatically recorded by the system. In addition, selected features of all modalities were classified by tenfold cross-validation using five different classifiers.ResultsThe experimental results showed that the weighted soft voting strategy combining five classifiers achieved the highest classification accuracy (88.9%), precision (89.9%), recall (88.2%), and F1 score (89.0%). Compared to healthy controls, the MCI group typically took longer to recall, draw, and drag. Moreover, during cognitive testing, MCI patients showed lower heart rate variability, higher electrodermal activity values, and stronger brain activity in the alpha and beta bands.DiscussionIt was found that patients' classification performance improved when combining features from multiple modalities compared to using only tablet parameters or physiological features, indicating that our scheme could reveal MCI-related discriminative information. Furthermore, the best classification results on the digital span test across all tasks suggest that MCI patients may have deficits in attention and short-term memory that came to the fore earlier. Finally, integrating tablet cognitive tests and wearable sensors would provide a new direction for creating an easy-to-use and at-home self-check MCI screening tool.

Project description:Breast cancer is one of the most common types of cancer in females. While drug combinations have shown potential in breast cancer treatments, identifying new effective drug pairs is challenging due to the vast number of possible combinations among available compounds. Efforts have been made to accelerate the process with in silico predictions. Here, we developed a Boolean model of signaling pathways in breast cancer. The model was tailored to represent five breast cancer cell lines by integrating information about cell-line specific mutations, gene expression, and drug treatments. The models reproduced cell-line specific protein activities and drug-response behaviors in agreement with experimental data. Next, we proposed a calculation of protein synergy scores (PSSs), determining the effect of drug combinations on individual proteins' activities. The PSSs of selected proteins were used to investigate the synergistic effects of 150 drug combinations across five cancer cell lines. The comparison of the highest single agent (HSA) synergy scores between experiments and model predictions from the MDA-MB-231 cell line achieved the highest Pearson's correlation coefficient of 0.58 with a great balance among the classification metrics (AUC = 0.74, sensitivity = 0.63, and specificity = 0.64). Finally, we clustered drug pairs into groups based on the selected PSSs to gain further insights into the mechanisms underlying the observed synergistic effects of drug pairs. Clustering analysis allowed us to identify distinct patterns in the protein activities that correspond to five different modes of synergy: 1) synergistic activation of FADD and BID (extrinsic apoptosis pathway), 2) synergistic inhibition of BCL2 (intrinsic apoptosis pathway), 3) synergistic inhibition of MTORC1, 4) synergistic inhibition of ESR1, and 5) synergistic inhibition of CYCLIN D. Our approach offers a mechanistic understanding of the efficacy of drug combinations and provides direction for selecting potential drug pairs worthy of further laboratory investigation.

Project description:BackgroundIt was reported that metabolic syndrome increases the risk for cognitive impairment and circadian rhythm may influence cognition behavior. Identifying the potential risk factors is essential to screen individuals with neuronal dysfunction, neuronal loss, and cognitive decline and prevent cognitive impairment and dementia development.MethodsWe clarified participants by the presence of metabolic syndrome (MetS) and circadian syndrome (CircS) and employed three multivariable Generalized Estimating Equation (GEE) models to control the potential confounding factors and estimate the β values for cognitive function using as referents those had neither MetS nor CircS at baseline. The cognitive function consists of episodic memory and executive function was estimated via the modified Telephone Interview for Cognitive Status (TICS) every two years until 2015.ResultsThe mean age of the participants was 58.80 (8.93) years and 49.92% (male). The prevalence of MetS and CircS was 42.98% and 36.43%, respectively. 1,075 (11.00%) and 435 (4.45%) participants had either MetS or CircS alone and 3,124 (31.98%) had both CircS and MetS. Participants with both MetS and CircS compared with normal had a significantly decreased cognitive function score during the 4-years cohort (β = -0.32, 95% CI: -0.63, -0.01) with the complete model, as well as among participants who suffered from CircS alone (β = -0.82, 95% CI: -1.47, -0.16), while not among participants with MetS alone (β = 0.13, 95% CI: -0.27, 0.53). Specifically, compared with the normal population a significantly lower score was discovered in the episodic memory (β = -0.51, 95% CI: -0.95, -0.07), while slightly lower in executive function (β = -0.33, 95% CI: -0.68, -0.01) among individuals with CircS alone.ConclusionsIndividuals with CircS alone or both MetS and CircS have a high risk of cognitive impairment. The association was even stronger in participants with CircS alone than those with both MetS and CircS, suggesting CircS probably have a stronger association with cognitive functioning than MetS and could be a better predictor for cognitive impairment.

Dataset Information

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

Publications

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

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