Cognition in Alzheimer's disease: theoretic models and clinical implications

Integration of bulk RNA sequencing and single-cell analysis reveals a global landscape of DNA damage response in the immune environment of Alzheimer's disease

Background

We developed a novel system for quantifying DNA damage response (DDR) to help diagnose and predict the risk of Alzheimer's disease (AD).

Methods

We thoroughly estimated the DDR patterns in AD patients Using 179 DDR regulators. Single-cell techniques were conducted to validate the DDR levels and intercellular communications in cognitively impaired patients. The consensus clustering algorithm was utilized to group 167 AD patients into diverse subgroups after a WGCNA approach was employed to discover DDR-related lncRNAs. The distinctions between the categories in terms of clinical characteristics, DDR levels, biological behaviors, and immunological characteristics were evaluated. For the purpose of choosing distinctive lncRNAs associated with DDR, four machine learning algorithms, including LASSO, SVM-RFE, RF, and XGBoost, were utilized. A risk model was established based on the characteristic lncRNAs.

Results

The progression of AD was highly correlated with DDR levels. Single-cell studies confirmed that DDR activity was lower in cognitively impaired patients and was mainly enriched in T cells and B cells. DDR-related lncRNAs were discovered based on gene expression, and two different heterogeneous subtypes (C1 and C2) were identified. DDR C1 belonged to the non-immune phenotype, while DDR C2 was regarded as the immune phenotype. Based on various machine learning techniques, four distinctive lncRNAs associated with DDR, including FBXO30-DT, TBX2-AS1, ADAMTS9-AS2, and MEG3 were discovered. The 4-lncRNA based riskScore demonstrated acceptable efficacy in the diagnosis of AD and offered significant clinical advantages to AD patients. The riskScore ultimately divided AD patients into low- and high-risk categories. In comparison to the low-risk group, high-risk patients showed lower DDR activity, accompanied by higher levels of immune infiltration and immunological score. The prospective medications for the treatment of AD patients with low and high risk also included arachidonyltrifluoromethane and TTNPB, respectively.

Conclusions

In conclusion, immunological microenvironment and disease progression in AD patients were significantly predicted by DDR-associated genes and lncRNAs. A theoretical underpinning for the individualized treatment of AD patients was provided by the suggested genetic subtypes and risk model based on DDR.

Neuropsychological patterns in magnetic resonance imaging-defined subgroups of patients with degenerative dementia

We hypothesized that specific neuropsychological deficits were associated with specific patterns of atrophy. A magnetic resonance imaging volumetric study and a neuropsychological protocol were obtained for patients with several frontotemporal lobar dementia phenotypes including a social/dysexecutive (SOC/EXEC, n = 17), progressive nonfluent aphasia (n = 9), semantic dementia (n = 7), corticobasal syndrome (n = 9), and Alzheimer's disease (n = 21). Blinded to testing results, patients were partitioned according to pattern of predominant cortical atrophy; our partitioning algorithm had been derived using seriation, a hierarchical classification technique. Neuropsychological test scores were regressed versus these atrophy patterns as fixed effects using the covariate total atrophy as marker for disease severity. The results showed the model accounted for substantial variance. Furthermore, the "large-scale networks" associated with each neuropsychological test conformed well to the known literature. For example, bilateral prefrontal cortical atrophy was exclusively associated with SOC/EXEC dysfunction. The neuropsychological principle of "double dissociation" was supported not just by such active associations but also by the "silence" of locations not previously implicated by the literature. We conclude that classifying patients with degenerative dementia by specific pattern of cortical atrophy has the potential to predict individual patterns of cognitive deficits.