The relationship of early- and late-onset Alzheimer's disease genes with COVID-19.

Individuals with Alzheimer's disease and other neurodegenerative diseases have been exposed to excess risk by the COVID-19 pandemic. COVID-19's main manifestations include high body temperature, dry cough, and exhaustion. Nevertheless, some affected individuals may have an atypical presentation at diagnosis but suffer neurological signs and symptoms as the first disease manifestation. These findings collectively show the neurotropic nature of SARS-CoV-2 virus and its ability to involve the central nervous system. In addition, Alzheimer's disease and COVID-19 has a number of common risk factors and comorbid conditions including age, sex, hypertension, diabetes, and the expression of APOE ε4. Until now, a plethora of studies have examined the COVID-19 disease but only a few studies has yet examined the relationship of COVID-19 and Alzheimer's disease as risk factors of each other. This review emphasizes the recently published evidence on the role of the genes of early- or late-onset Alzheimer's disease in the susceptibility of individuals currently suffering or recovered from COVID-19 to Alzheimer's disease or in the susceptibility of individuals at risk of or with Alzheimer's disease to COVID-19 or increased COVID-19 severity and mortality. Furthermore, the present review also draws attention to other uninvestigated early- and late-onset Alzheimer's disease genes to elucidate the relationship between this multifactorial disease and COVID-19.

ActiveDriverDB: Interpreting Genetic Variation in Human and Cancer Genomes Using Post-translational Modification Sites and Signaling Networks (2021 Update).

Deciphering the functional impact of genetic variation is required to understand phenotypic diversity and the molecular mechanisms of inherited disease and cancer. While millions of genetic variants are now mapped in genome sequencing projects, distinguishing functional variants remains a major challenge. Protein-coding variation can be interpreted using post-translational modification (PTM) sites that are core components of cellular signaling networks controlling molecular processes and pathways. ActiveDriverDB is an interactive proteo-genomics database that uses more than 260,000 experimentally detected PTM sites to predict the functional impact of genetic variation in disease, cancer and the human population. Using machine learning tools, we prioritize proteins and pathways with enriched PTM-specific amino acid substitutions that potentially rewire signaling networks via induced or disrupted short linear motifs of kinase binding. We then map these effects to site-specific protein interaction networks and drug targets. In the 2021 update, we increased the PTM datasets by nearly 50%, included glycosylation, sumoylation and succinylation as new types of PTMs, and updated the workflows to interpret inherited disease mutations. We added a recent phosphoproteomics dataset reflecting the cellular response to SARS-CoV-2 to predict the impact of human genetic variation on COVID-19 infection and disease course. Overall, we estimate that 16-21% of known amino acid substitutions affect PTM sites among pathogenic disease mutations, somatic mutations in cancer genomes and germline variants in the human population. These data underline the potential of interpreting genetic variation through the lens of PTMs and signaling networks. The open-source database is freely available at www.ActiveDriverDB.org.