COVID-19 Phenotypes and Comorbidity: A Data-Driven, Pattern Recognition Approach Using National Representative Data from the United States.

The aim of our study was to determine COVID-19 syndromic phenotypes in a data-driven manner using the survey results based on survey results from Carnegie Mellon University's Delphi Group. Monthly survey results (>1 million responders per month; 320,326 responders with a certain COVID-19 test status and disease duration <30 days were included in this study) were used sequentially in identifying and validating COVID-19 syndromic phenotypes. Logistic Regression-weighted multiple correspondence analysis (LRW-MCA) was used as a preprocessing procedure, in order to weigh and transform symptoms recorded by the survey to eigenspace coordinates, capturing a total variance of >75%. These scores, along with symptom duration, were subsequently used by the Two Step Clustering algorithm to produce symptom clusters. Post-hoc logistic regression models adjusting for age, gender, and comorbidities and confirmatory linear principal components analyses were used to further explore the data. Model creation, based on August's 66,165 included responders, was subsequently validated in data from March-December 2020. Five validated COVID-19 syndromes were identified in August: 1. Afebrile (0%), Non-Coughing (0%), Oligosymptomatic (ANCOS); 2. Febrile (100%) Multisymptomatic (FMS); 3. Afebrile (0%) Coughing (100%) Oligosymptomatic (ACOS); 4. Oligosymptomatic with additional self-described symptoms (100%; OSDS); 5. Olfaction/Gustatory Impairment Predominant (100%; OGIP). Our findings indicate that the COVID-19 spectrum may be undetectable when applying current disease definitions focusing on respiratory symptoms alone.

Outside-in induction of the IFITM3 trafficking system by infections, including SARS-CoV-2, in the pathobiology of Alzheimer's disease.

BACKGROUND: IFITM3 is a viral restriction protein that enables sequestration of viral particles and subsequent trafficking to lysosomes. Recently, IFITM3 upregulation was found to induce gamma - secretase activity and the production of amyloid beta. The purpose of this study was to determine whether dysregulation of IFITM3-dependent pathways was present in neurons and peripheral immune cells donated by AD patients. As a secondary aim, we sought to determine whether these perturbations could be induced by viruses, including SARS-CoV-2. METHODS: Gene set enrichment analyses (GSEA) previously performed on publicly available transcriptomic data from tissues donated by AD patients were screened for enriched pathways containing IFITM3. Subsequently, signature containing IFITM3, derived from entorhinal cortex (EC) neurons containing neurofibrillary tangles (NFT) was screened for overlap with curated, publicly available, viral infection-induced gene signatures (including SARS-CoV-2). RESULTS: GSEA determined that IFITM3 gene networks are significantly enriched both in CNS sites (entorhinal and hippocampal cortices) and in peripheral blood mononuclear cells (PBMCs) donated by AD patients. Overlap screening revealed that IFITM3 signatures are induced by several viruses, including SARS-CoV, MERS-CoV, SARS-CoV-2 and HIV-1 (adjusted p-value <0.001; Enrichr Database). DISCUSSION: A data-driven analysis of AD tissues revealed IFITM3 gene signatures both in the CNS and in peripheral immune cells. GSEA revealed that an IFITM3 derived gene signature extracted from EC/NFT neurons overlapped with those extracted from publicly available viral infection datasets, including SARS-CoV-2. Our results are in line with currently emerging evidence on IFITM3's role in AD, and SARS-CoV-2's potential contribution in the setting of an expanded antimicrobial protection hypothesis.