Lack of Causal Relationships Between Chronic Hepatitis C Virus Infection and Alzheimer’s Disease

From Viral Infections to Alzheimer's Disease: Unveiling the Mechanistic Links Through Systems Bioinformatics

BACKGROUND

Emerging evidence suggests that viral infections may contribute to Alzheimer's disease (AD) onset and/or progression. However, the extent of their involvement and the mechanisms through which specific viruses increase AD susceptibility risk remain elusive.

METHODS

We used an integrative systems bioinformatics approach to identify viral-mediated pathogenic mechanisms, by which Herpes Simplex Virus 1 (HSV-1), Human Cytomegalovirus (HCMV), Epstein-Barr virus (EBV), Kaposi Sarcoma-associated Herpesvirus (KSHV), Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), Influenza A Virus (IAV) and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) could facilitate AD pathogenesis via virus-host protein-protein interactions (PPIs). We also explored potential synergistic pathogenic effects resulting from herpesvirus reactivation (HSV-1, HCMV, and EBV) during acute SARS-CoV-2 infection, potentially increasing AD susceptibility.

RESULTS

Herpesviridae members (HSV-1, EBV, KSHV, HCMV) impact AD-related processes like amyloid-β (Aβ) formation, neuronal death, and autophagy. Hepatitis viruses (HBV, HCV) influence processes crucial for cellular homeostasis and dysfunction, they also affect microglia activation via virus-host PPIs. Reactivation of HCMV during SARS-CoV-2 infection could potentially foster a lethal interplay of neurodegeneration, via synergistic pathogenic effects on AD-related processes like response to unfolded protein, regulation of autophagy, response to oxidative stress, and Aβ formation.

CONCLUSIONS

These findings underscore the complex link between viral infections and AD development. Viruses impact AD-related processes through shared and distinct mechanisms, potentially influencing variations in AD susceptibility.

Association Between COVID-19 and Neurological Diseases: Evidence from Large-Scale Mendelian Randomization Analysis and Single-Cell RNA Sequencing Analysis

Observational studies have suggested that SARS-CoV-2 infection increases the risk of neurological diseases, but it remains unclear whether the association is causal. The present study aims to evaluate the causal relationships between SARS-CoV-2 infections and neurological diseases and analyzes the potential routes of SARS-CoV-2 entry at the cellular level. We performed Mendelian randomization (MR) analysis with CAUSE method to investigate causal relationship of SARS-CoV-2 infections with neurological diseases. Then, we conducted single-cell RNA sequencing (scRNA-seq) analysis to obtain evidence of potential neuroinvasion routes by measuring SARS-CoV-2 receptor expression in specific cell subtypes. Fast gene set enrichment analysis (fGSEA) was further performed to assess the pathogenesis of related diseases. The results showed that the COVID-19 is causally associated with manic (delta_elpd, - 0.1300, Z-score: - 2.4; P = 0.0082) and epilepsy (delta_elpd: - 2.20, Z-score: - 1.80; P = 0.038). However, no significant effects were observed for COVID-19 on other traits. Moreover, there are 23 cell subtypes identified through the scRNA-seq transcriptomics data of epilepsy, and SARS-CoV-2 receptor TTYH2 was found to be specifically expressed in oligodendrocyte and astrocyte cell subtypes. Furthermore, fGSEA analysis showed that the cell subtypes with receptor-specific expression was related to methylation of lysine 27 on histone H3 (H3K27ME3), neuronal system, aging brain, neurogenesis, and neuron projection. In summary, this study shows causal links between SARS-CoV-2 infections and neurological disorders such as epilepsy and manic, supported by MR and scRNA-seq analysis. These results should be considered in further studies and public health measures on COVID-19 and neurological diseases.

m5C RNA methylation: a potential mechanism for infectious Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disorder caused by a variety of factors, including age, genetic susceptibility, cardiovascular disease, traumatic brain injury, and environmental factors. The pathogenesis of AD is largely associated with the overproduction and accumulation of amyloid-β peptides and the hyperphosphorylation of tau protein in the brain. Recent studies have identified the presence of diverse pathogens, including viruses, bacteria, and parasites, in the tissues of AD patients, underscoring the critical role of central nervous system infections in inducing pathological changes associated with AD. Nevertheless, it remains unestablished about the specific mechanism by which infections lead to the occurrence of AD. As an important post-transcriptional RNA modification, RNA 5-methylcytosine (m5C) methylation regulates a wide range of biological processes, including RNA splicing, nuclear export, stability, and translation, therefore affecting cellular function. Moreover, it has been recently demonstrated that multiple pathogenic microbial infections are associated with the m5C methylation of the host. However, the role of m5C methylation in infectious AD is still uncertain. Therefore, this review discusses the mechanisms of pathogen-induced AD and summarizes research on the molecular mechanisms of m5C methylation in infectious AD, thereby providing new insight into exploring the mechanism underlying infectious AD.

Prediction of conversion from mild cognitive impairment to Alzheimer's disease and simultaneous feature selection and grouping using Medicaid claim data

BACKGROUND

Due to the heterogeneity among patients with Mild Cognitive Impairment (MCI), it is critical to predict their risk of converting to Alzheimer's disease (AD) early using routinely collected real-world data such as the electronic health record data or administrative claim data.

METHODS

The study used MarketScan Multi-State Medicaid data to construct a cohort of MCI patients. Logistic regression with tree-guided lasso regularization (TGL) was proposed to select important features and predict the risk of converting to AD. A subsampling-based technique was used to extract robust groups of predictive features. Predictive models including logistic regression, generalized random forest, and artificial neural network were trained using the extracted features.

RESULTS

The proposed TGL workflow selected feature groups that were robust, highly interpretable, and consistent with existing literature. The predictive models using TGL selected features demonstrated higher prediction accuracy than the models using all features or features selected using other methods.

CONCLUSIONS

The identified feature groups provide insights into the progression from MCI to AD and can potentially improve risk prediction in clinical practice and trial recruitment.

New Insights into the Molecular Interplay between Human Herpesviruses and Alzheimer’s Disease—A Narrative Review

Human herpesviruses (HHVs) have been implicated as possible risk factors in Alzheimer’s disease (AD) pathogenesis. Persistent lifelong HHVs infections may directly or indirectly contribute to the generation of AD hallmarks: amyloid beta (Aβ) plaques, neurofibrillary tangles composed of hyperphosphorylated tau proteins, and synaptic loss. The present review focuses on summarizing current knowledge on the molecular mechanistic links between HHVs and AD that include processes involved in Aβ accumulation, tau protein hyperphosphorylation, autophagy, oxidative stress, and neuroinflammation. A PubMed search was performed to collect all the available research data regarding the above mentioned mechanistic links between HHVs and AD pathology. The vast majority of research articles referred to the different pathways exploited by Herpes Simplex Virus 1 that could lead to AD pathology, while a few studies highlighted the emerging role of HHV 6, cytomegalovirus, and Epstein–Barr Virus. The elucidation of such potential links may guide the development of novel diagnostics and therapeutics to counter this devastating neurological disorder that until now remains incurable.

Assessing the Association Between Lead Pollution and Risk of Alzheimer’s Disease by Integrating Multigenomics

Alzheimer’s disease (AD) is a life-threatening neurodegenerative disease of the elderly. In recent observations, exposure to heavy metals environmental may increase the risk of AD. However, there are few studies on the causal relationship between heavy metal exposure and AD. In this study, we integrated two large-scale summaries of AD genome-wide association study (GWAS) datasets and a blood lead level GWAS dataset and performed the two-sample Mendelian randomization analysis to assess the causality of blood lead level and AD risk. The results showed that there is a significantly positive causality between blood lead level and AD risk both in the inverse-variance weighted (IVW) model and the weighted median estimator (WME) model. An independent additional verification also reached a consistent conclusion. These findings further confirm the conclusions of previous studies and improve the understanding of the relationship between AD pathogenesis and the toxicity of lead in environmental pollution.