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.

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.

Conformational Dynamics of the Receptor-Binding Domain of the SARS-CoV-2 Spike Protein.

Variants of SARS-CoV-2 keep emerging and causing new waves of COVID-19 around the world. Effective new approaches in drug development are based on the binding of agents, such as neutralizing monoclonal antibodies to a receptor-binding domain (RBD) of SARS-CoV-2 spike protein. However, mutations in RBD may lower the affinity of previously developed antibodies. Therefore, rapid analysis of new variants and selection of a binding partner with high affinity is of great therapeutic importance. Here, we explore a computational approach based on molecular dynamics simulations and conformational clusterization techniques for the wild-type and omicron variants of RBD. Biochemical experiments support the hypothesis of the presence of several conformational states within the RBD assembly. The development of such an approach will facilitate the selection of neutralization drugs with higher affinity based on the primary structure of the target antigen.

Structural and Computational Study of the GroEL-Prion Protein Complex.

The molecular chaperone GroEL is designed to promote protein folding and prevent aggregation. However, the interaction between GroEL and the prion protein, PrPC, could lead to pathogenic transformation of the latter to the aggregation-prone PrPSc form. Here, the molecular basis of the interactions in the GroEL-PrP complex is studied with cryo-EM and molecular dynamics approaches. The obtained cryo-EM structure shows PrP to be bound to several subunits of GroEL at the level of their apical domains. According to MD simulations, the disordered N-domain of PrP forms much more intermolecular contacts with GroEL. Upon binding to the GroEL, the N-domain of PrP begins to form short helices, while the C-domain of PrP exhibits a tendency to unfold its α2-helix. In the absence of the nucleotides in the system, these processes are manifested at the hundred nanoseconds to microsecond timescale.

Novel cryo-EM structure of an ADP-bound GroEL-GroES complex.

The GroEL-GroES chaperonin complex is a bacterial protein folding system, functioning in an ATP-dependent manner. Upon ATP binding and hydrolysis, it undergoes multiple stages linked to substrate protein binding, folding and release. Structural methods helped to reveal several conformational states and provide more information about the chaperonin functional cycle. Here, using cryo-EM we resolved two nucleotide-bound structures of the bullet-shaped GroEL-GroES1 complex at 3.4 Å resolution. The main difference between them is the relative orientation of their apical domains. Both structures contain nucleotides in cis and trans GroEL rings; in contrast to previously reported bullet-shaped complexes where nucleotides were only present in the cis ring. Our results suggest that the bound nucleotides correspond to ADP, and that such a state appears at low ATP:ADP ratios.