{"messages":[{"status":"ok","cursor":0,"count":30,"total":31442}], "collection":[{"rel_doi":"10.64898\/2026.04.15.718757","rel_title":"Mechanistic insights into the association and activation of the SARS-CoV-2 2'-O-Methyltransferase (NSP16)","rel_date":"2026-04-16","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.04.15.718757","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc0","type":"new results","category":"biophysics"},{"rel_doi":"10.64898\/2026.04.14.718551","rel_title":"Tracing cell communication programs across conditions at single cell resolution with CCC-RISE","rel_date":"2026-04-15","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.04.14.718551","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by_nd","type":"new results","category":"systems biology"},{"rel_doi":"10.64898\/2026.04.10.717770","rel_title":"MIMIQ: Fast mutual information calculation and significance testing for single-cell RNA sequencing analysis","rel_date":"2026-04-13","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.04.10.717770","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by","type":"new results","category":"bioinformatics"},{"rel_doi":"10.64898\/2026.04.11.716570","rel_title":"Serial vaccination expands and refines human CD4+ T cell memory","rel_date":"2026-04-13","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.04.11.716570","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by_nc","type":"new results","category":"immunology"},{"rel_doi":"10.64898\/2026.04.10.717575","rel_title":"Integrated Computational and Experimental Evaluation of selected Flavonoids as a Multi-Target Modulator of Viral Entry and Protease Activity.","rel_date":"2026-04-13","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.04.10.717575","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by_nc","type":"new results","category":"microbiology"},{"rel_doi":"10.64898\/2026.04.10.717462","rel_title":"Loss of host factor-mediated m6Am methylation of the viral RNA cap impairs SARS CoV-2 replication","rel_date":"2026-04-13","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.04.10.717462","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by","type":"new results","category":"molecular biology"},{"rel_doi":"10.64898\/2026.04.09.717495","rel_title":"Phagocytic Clearance of SARS-CoV-2 Nucleocapsid- and RNA-Containing Immune Complexes Drives Inflammatory Cytokine Production and Endothelial Dysfunction","rel_date":"2026-04-10","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.04.09.717495","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_no","type":"new results","category":"immunology"},{"rel_doi":"10.64898\/2026.04.08.717307","rel_title":"Myeloma and therapy reshape the bone marrow niche to durably constrain immune reconstitution and vaccine responsiveness","rel_date":"2026-04-09","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.04.08.717307","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by","type":"new results","category":"cancer biology"},{"rel_doi":"10.64898\/2026.04.09.717442","rel_title":"An imaging flow cytometry method to study platelet-monocyte aggregates using Long COVID as a model","rel_date":"2026-04-09","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.04.09.717442","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by_nc_nd","type":"new results","category":"physiology"},{"rel_doi":"10.64898\/2026.04.08.717316","rel_title":"Role of Nonneutralizing Antibodies and Fc Effector Functions in Inhibiting SARS-CoV-2 Infection","rel_date":"2026-04-09","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.04.08.717316","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by_nc_nd","type":"new results","category":"immunology"},{"rel_doi":"10.64898\/2026.04.07.717019","rel_title":"SKIN AS A POTENTIAL ENTRY POINT FOR SARS-COV-2","rel_date":"2026-04-08","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.04.07.717019","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by","type":"new results","category":"immunology"},{"rel_doi":"10.64898\/2026.04.07.716934","rel_title":"Intranasal Anti-CD3 Antibody Treatment Attenuates Post-COVID Neuroinflammation and Enhances Hippocampal Neurogenesis and Cognitive Function in Mice","rel_date":"2026-04-08","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.04.07.716934","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by_nc_nd","type":"new results","category":"immunology"},{"rel_doi":"10.64898\/2026.04.03.716319","rel_title":"High-speed 3D single-virus tracking reveals actin-aided viral trafficking of SARS-CoV-2 on the plasma membrane","rel_date":"2026-04-06","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.04.03.716319","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by_nc_nd","type":"new results","category":"biophysics"},{"rel_doi":"10.64898\/2026.04.02.716054","rel_title":"Machine Learning-Driven Antigen Selection Reveals Conserved T-Cell Targets for Broad Coronavirus Vaccination","rel_date":"2026-04-03","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.04.02.716054","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_no","type":"new results","category":"immunology"},{"rel_doi":"10.64898\/2026.04.03.716256","rel_title":"The transmembrane domain regulates the kinetics of the SARS-CoV-2 spike conformational transition","rel_date":"2026-04-03","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.04.03.716256","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by_nc","type":"new results","category":"biophysics"},{"rel_doi":"10.64898\/2026.04.02.716254","rel_title":"Frustration Landscapes of Broadly Neutralizing SARS-CoV-2 Spike Antibodies Targeting Conserved Epitopes Reveal Energetic Logic of Escape-Proof and Escape-Prone Mechanisms","rel_date":"2026-04-03","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.04.02.716254","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by","type":"new results","category":"biophysics"},{"rel_doi":"10.64898\/2026.04.01.715984","rel_title":"Conserved stem-loops of the SARS-CoV-2 5'-UTR activate OAS1","rel_date":"2026-04-02","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.04.01.715984","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by_nc","type":"new results","category":"biochemistry"},{"rel_doi":"10.64898\/2026.04.02.716024","rel_title":"TF-IDF k-mer-based Classical and Hybrid Machine Learning Models for SARS-CoV-2 Variant Classification under Imbalanced Genomic Data","rel_date":"2026-04-02","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.04.02.716024","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by","type":"new results","category":"bioinformatics"},{"rel_doi":"10.64898\/2026.03.31.715496","rel_title":"Omicron-Enhanced Immunosuppressive Effects of SARS-CoV-2 ORF3a and ORF9b Accessory Proteins on Monocytic Inflammatory Response","rel_date":"2026-04-01","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.31.715496","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_no","type":"new results","category":"immunology"},{"rel_doi":"10.64898\/2026.03.31.715419","rel_title":"VYD2311 is a promising candidate for passive immunization against COVID-19 in immunocompromised individuals","rel_date":"2026-04-01","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.31.715419","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_no","type":"new results","category":"microbiology"},{"rel_doi":"10.64898\/2026.03.30.715311","rel_title":"Intranasal immunization with live-attenuated RSV-vectored SARS-CoV-2 vaccines elicits antigen-specific systemic and mucosal immunity and protects against viral challenge and natural infection","rel_date":"2026-03-31","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.30.715311","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by_nc_nd","type":"new results","category":"immunology"},{"rel_doi":"10.64898\/2026.03.29.711974","rel_title":"Panmap: Scalable phylogeny-guided alignment, genotyping, and placement on pangenomes","rel_date":"2026-03-30","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.29.711974","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by","type":"new results","category":"bioinformatics"},{"rel_doi":"10.64898\/2026.03.27.26349524","rel_title":"Twelve Distinct Laboratory Methods Used to Measure SARS-CoV-2 in Wastewaters throughout a Three-Year Ontario-Wide, Canada Study: Impact on Public Health Interpretation of Disease Incidence","rel_date":"2026-03-30","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.27.26349524","rel_abs":"Wastewater and environmental monitoring (WEM) was a critical public health surveillance tool for SARS-CoV-2 surveillance during the COVID-19 Pandemic. However, substantial methodological heterogeneity across laboratories continues to challenge the interpretation and thus compromise the actionability of resulting WEM measurements. This study quantifies interlaboratory concordance in SARS-CoV-2 WEM measurements using influent wastewater samples collected between September 2021 and January 2024 at a single wastewater treatment facility within the Ontario Wastewater Surveillance Initiative, analyzed independently by 12 laboratories using their routine methods. In the absence of a known true viral concentration, interlaboratory WEM measurements were evaluated against a facility-specific longitudinal benchmark derived from routine surveillance at the source facility and correlated to clinical surveillance metrics. Concordance was assessed across four WEM measurement units commonly used in practice: SARS-CoV-2 copies\/mL, SARS-CoV-2 copies\/copies of PMMoV, and their standardized counterpart wastewater viral activity level (WVAL) units of WVAL-standardized SARS-CoV-2 copies\/mL and WVAL-standardized SARS-CoV-2 copies\/copies of PMMoV. Measurements in each unit were analyzed using complementary analytical frameworks, including categorical concordance metrics, principal component analysis, and linear mixed-effects modelling. Across the study period, interlaboratory measurements consistently captured benchmark temporal dynamics, including major peaks and periods of low activity, but showed substantial variation in magnitude and public-health interpretation across laboratory methods. Concordance was strongest during epidemiological extremes and deteriorated during transitional periods, increasing the risk of misclassification with potentially implications for public health decision-making. To explore potential laboratory methodological drivers of agreement, associations between the benchmark concordance and the laboratory-specific concentration, extraction, and RT-qPCR analytical steps were assessed using Fishers exact tests, alongside extracted-mass threshold analyses. No single methodological factor showed a statistically significant association with benchmark concordance in this study; however, several parameters, including RNA template volume, total RT-qPCR reaction volume, and extracted mass of analyzed settled solids, may warrant further investigation in future studies.","rel_num_authors":21,"rel_authors":[{"author_name":"Nada Hegazy","author_inst":"University of Ottawa"},{"author_name":"K. Ken Peng","author_inst":"Simon Fraser University; University of Ottawa"},{"author_name":"Johanna de Haan-Ward","author_inst":"University of Ottawa"},{"author_name":"Elizabeth Renouf","author_inst":"University of Ottawa"},{"author_name":"Elizabeth Mercier","author_inst":"University of Ottawa"},{"author_name":"Shen Wan","author_inst":"University of Ottawa"},{"author_name":"X. Joan Hu","author_inst":"Simon Fraser University"},{"author_name":"Charmaine Dean","author_inst":"University of Waterloo"},{"author_name":"Mark Servos","author_inst":"University of Waterloo"},{"author_name":"Elizabeth Edwards","author_inst":"University of Toronto"},{"author_name":"Gustavo Ybazeta","author_inst":"Health Sciences North Research Institute"},{"author_name":"Marc Habash","author_inst":"University of Guelph"},{"author_name":"Lawrence Goodridge","author_inst":"University of Guelph"},{"author_name":"R. Stephen Brown","author_inst":"Queen's University"},{"author_name":"Sarah Jane Payne","author_inst":"Queen's University"},{"author_name":"Andrea Kirkwood","author_inst":"Ontario Tech University"},{"author_name":"Christopher Kyle","author_inst":"Trent University"},{"author_name":"R. Michael McKay","author_inst":"University of Windsor"},{"author_name":"Kimberly Gilbride","author_inst":"Tornoto Metropolitan University"},{"author_name":"Christopher DeGroot","author_inst":"Western University"},{"author_name":"Robert Delatolla","author_inst":"University of Ottawa"}],"version":"1","license":"cc_by_nc_nd","type":"PUBLISHAHEADOFPRINT","category":"epidemiology"},{"rel_doi":"10.64898\/2026.03.27.26349516","rel_title":"Increased Risk of Pulmonary Embolism Following SARS-CoV-2 Activity in Ontario, Canada","rel_date":"2026-03-30","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.27.26349516","rel_abs":"BackgroundSARS-CoV-2 infection is an established prothrombotic trigger, yet the population-level temporal relationship between circulating viral activity and pulmonary embolism (PE) remains poorly characterized. We aimed to evaluate the short-term association between respiratory viral activity and PE hospitalizations, accounting for specific temporal lags.\n\nMethodsWe conducted a population-level time-series analysis of incident PE hospitalizations in Ontario, Canada, from 2011 to 2024. Using distributed lag non-linear models, we assessed the association between standardized weekly activity levels of SARS-CoV-2, influenza A\/B, and respiratory syncytial virus (RSV) and PE risk over a 5-week lag period. Relative risks (RR) per standard deviation (SD) increase in viral activity were estimated via negative binomial regression using cross-basis terms to account for both exposure-response and lag-response non-linearities. Models were adjusted for Fourier seasonal terms and secular trends.\n\nFindingsAmong 70,670 incident PE cases identified between 2011 and 2024, SARS-CoV-2 activity demonstrated a significant temporal association with PE. A cumulative RR increase of 20% per SD in SARS-CoV-2 activity was observed over the five weeks following exposure (RR 1.20; 95% CI 1.05-1.37). The risk followed a distinct delay trajectory: weekly cumulative RRs peaked at week 3 (RR 1.21; 95% CI 1.01-1.45). For the 2020-2024 period, influenza A also showed an association peaking at week 3 without statistical significance (RR 1.17; 95% CI 0.95-1.45).\n\nInterpretationIncreased population-level SARS-CoV-2 activity is associated with a heightened risk of PE, peaking at approximately the third week. This delayed peak suggests a protracted thrombo-inflammatory window, likely driven by sustained endothelial injury. These findings highlight the vascular burden of COVID-19 and suggest that infection prevention measures, including vaccination, may provide significant downstream protection against thromboembolic disease.","rel_num_authors":3,"rel_authors":[{"author_name":"Clara Eunyoung Lee","author_inst":"University of Toronto"},{"author_name":"Natalie J. Wilson","author_inst":"University of Toronto"},{"author_name":"David Fisman","author_inst":"University of Toronto"}],"version":"1","license":"cc_by_nc_nd","type":"PUBLISHAHEADOFPRINT","category":"epidemiology"},{"rel_doi":"10.64898\/2026.03.28.714969","rel_title":"Structure of SARS-CoV-2 spike in complex with its co-receptor the neuronal cell adhesion protein contactin 1","rel_date":"2026-03-29","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.28.714969","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by","type":"new results","category":"biochemistry"},{"rel_doi":"10.64898\/2026.03.25.714316","rel_title":"Characterization of Self-Incompatibility Genes in Brassica rapa var. Toria and Yellow sarson","rel_date":"2026-03-28","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.25.714316","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by_nc_nd","type":"new results","category":"plant biology"},{"rel_doi":"10.64898\/2026.03.27.714475","rel_title":"Impact of viral membrane oxidation on SARS-CoV-2 spike protein transmembrane anchoring stability","rel_date":"2026-03-27","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.27.714475","rel_num_authors":0,"rel_authors":null,"version":"1","license":"cc_by","type":"new results","category":"biochemistry"},{"rel_doi":"10.64898\/2026.03.26.26347671","rel_title":"A protocol for assessment of interventions using a computational phenotype for Long COVID","rel_date":"2026-03-27","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.26.26347671","rel_abs":"BackgroundLong COVID presents with one or multiple symptoms or diagnosable conditions after SARS-CoV-2 infection. To study whether use of the antiviral remdesivir in persons hospitalized with acute COVID-19 is associated with reduced Long COVID, we created a computational phenotype for Long COVID.\n\nMethodsIn electronic health records (EHR) from a multistate healthcare system (US), hospital admissions from 5\/1\/20 - 9\/30\/22 were reviewed. The study group was hospitalized with acute COVID-19 and the control group was hospitalized for other reasons without prior SARS-CoV-2 infection. The populations were balanced with overlap weights based on a high-dimensional propensity score of pre-specified variables and the top 100 comorbidities differing between the groups. Hazard ratios (HR) were calculated for the combined primary outcome: U09.9 (Post-Covid Conditions) or any incident secondary outcome from 90 to 365 days after admission. Secondary outcomes included 27 individual incident diagnoses, corrected for multiplicity with Holm-Bonferroni.\n\nResultsAdmissions included 45,540 with, and 409,186 without COVID-19 during the study period, evaluable for the primary outcome. After weighting, standardized difference was < 0.01 for all measured confounders including demographic and clinical features. In the COVID+ and non-COVID groups 38.0% and 29.3% met the combined primary outcome, respectively. Weighted HR (95%CI) for the primary outcome was 1.37 (1.35, 1.40), p < 0.0001. All secondary outcomes were associated with the COVID+ group, when adjusted for multiplicity. Incident diagnoses with strong associations (HR > 2) included thromboembolism, hair loss, diabetes mellitus, obesity, and hypoxia. Anosmia\/dysgeusia was associated with COVID, but wide confidence intervals reflected few charted diagnoses.\n\nConclusionsManifestations of Long COVID at population scale are detectable as part of routine symptoms and clinical diagnoses in the EHR after admissions for COVID-19, compared with all other hospital admissions. This a prior computational phenotype for Long COVID will be used to assess whether remdesivir use is associated with decreased Long COVID.","rel_num_authors":10,"rel_authors":[{"author_name":"Amitabh Amitabh Gunjan","author_inst":"Providence Global Healthcare Innovation Center, Hyderabad, India"},{"author_name":"Lawrence Huang","author_inst":"Institute for Systems Biology, Seattle, WA, USA"},{"author_name":"Anudeep Appe","author_inst":"Providence Global Healthcare Innovation Center, Hyderabad, India"},{"author_name":"Paul A. McKelvey","author_inst":"Providence Health and Services, Portland, OR, USA"},{"author_name":"Heather A. Algren","author_inst":"Swedish Center for Research and Innovation, Seattle, WA, USA"},{"author_name":"Mark Berry","author_inst":"Gilead Sciences, Inc., Foster City, CA, USA"},{"author_name":"Essy Mozaffari","author_inst":"Gilead Sciences, Inc., Foster City, CA, USA"},{"author_name":"Bill J. Wright","author_inst":"Providence Health and Services, Portland, OR, USA"},{"author_name":"Jennifer J. Hadlock","author_inst":"Institute for Systems Biology, Seattle, WA, USA"},{"author_name":"Jason D. Goldman","author_inst":"Swedish Center for Research and Innovation, Seattle, WA, USA"}],"version":"1","license":"cc_by_nc_nd","type":"PUBLISHAHEADOFPRINT","category":"infectious diseases"},{"rel_doi":"10.64898\/2026.03.24.26349229","rel_title":"A Demographic Look at Cancer Treatment Behaviors during the COVID-19 Pandemic","rel_date":"2026-03-26","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.24.26349229","rel_abs":"Abstract\/SummaryO_ST_ABSBackgroundC_ST_ABSWhile numerous studies have explored the relationship between COVID-19 and cancer, few have specifically examined the significant impact of the pandemic on cancer patients, particularly concerning their treatments and appointments.\n\nObjectivesThis study aims to investigate cancer treatment behaviors during the COVID-19 pandemic.\n\nMethodsThis retrospective quantitative study utilized data from the Centers for Disease Control and Preventions National Health Interview Survey of 2020. The inclusion criteria were as follows: studies conducted within the United States; patients diagnosed with COVID-19 since the pandemic began; patients diagnosed with cancer within the United States; patients undergoing cancer treatment or in remission since the start of the pandemic; patients who experienced a change, delay, or cancellation of treatment due to the COVID-19 pandemic; patients who experienced a change or delay in cancer care due to the COVID-19 pandemic; patients with a weakened immune system due to prescriptions; and patients who took prescription medication within the past 12 months. The variables were analyzed against population characteristics, including age, race, gender, cancer type, and COVID-19 status. Python Jupyter Notebook (packaged by Anaconda Navigator in R Studio, version 6.4.8), Microsoft Excel for data cleaning and assessment, and SPSS were used for statistical analyses.\n\nResultsChi-Square Analysis (p<.05) revealed significant associations between cancer treatment and gender (p=0.009), other cancer treatments and age (p<.001) and education (p<.001), changes in other cancer treatments and gender (p=0.045), race (p<.001), age (p<.001), and education (p=.013), and prescribed medication and gender (p=.009), family income (p<.001), and age (p<.001).\n\nConclusionThe COVID-19 pandemic has significantly impacted cancer care in the U.S., affecting the delivery of treatments. Additional government funding is necessary to help medical facilities develop programs for off-site treatment delivery, to better prepare for future pandemics, and avoid repeating past challenges.","rel_num_authors":1,"rel_authors":[{"author_name":"Jonathan M Acosta Morales","author_inst":"SUNY Downstate"}],"version":"1","license":"cc_by","type":"PUBLISHAHEADOFPRINT","category":"oncology"},{"rel_doi":"10.64898\/2026.03.21.26348591","rel_title":"Disentangling the Shared and Differential Genetic Architecture Between COVID-19 and Other Respiratory Disorders: A Multi-Omics Genome-Wide Analysis","rel_date":"2026-03-26","rel_site":"medRxiv","rel_link":"https:\/\/medrxiv.org\/cgi\/content\/short\/10.64898\/2026.03.21.26348591","rel_abs":"BackgroundA bidirectional relationship has been observed between COVID-19 and respiratory disorders, where respiratory comorbidities increase severity and COVID-19 induces respiratory sequelae. The underlying biological and genetic mechanisms remain unclear. While previous studies have identified overlapping genetic loci, few have systematically disentangled the genetic factors shared between these conditions versus those specific to COVID-19, particularly at a multi-omics level.\n\nMethodsWe developed and applied a unified analytical framework to compare three COVID-19 phenotypes with eight respiratory disorders (including asthma, COPD, IPF, and pneumonia). Utilizing the cofdr method for shared genetic signal analysis and DDx\/mtCOJO for differentiation, we integrated genome-wide association statistics with multi-omics data (transcriptome, splicing, and proteome). This approach allowed for the simultaneous identification of shared genetic signals (concordant or discordant) and disease-specific variants across expression (TWAS), alternative splicing (spTWAS), and protein abundance (PWAS).\n\nResultsWe delineated a comprehensive atlas of 214 differential and numerous shared loci across 24 pairwise comparisons. The shared genetic architecture was characterized by pleiotropic effects in genes such as ATP11A (exhibiting opposing effects in COVID-19 vs. IPF) and GSDMB (shared with COPD). Crucially, differentiation analysis revealed that severe COVID-19 is genetically distinct from other respiratory infections (e.g., pneumonia and influenza) through dysregulated Type I\/III interferon signaling and specific defects in alveolar epithelial and macrophage function, as well as GM-CSF\/surfactant metabolism pathways. These findings provide direct genetic evidence supporting the use of GM-CSF modulators and interferon-lambda for COVID-19 treatment, therapies that have already entered clinical trials. Furthermore, multi-trait conditional analysis prioritized FYCO1 and HCN3 as potential COVID-19-specific risk genes. Splicing analysis underscored the critical role of alternative splicing in both shared and differential architectures, highlighting IFNAR2 isoform regulation as a key discriminator between COVID-19 and other respiratory traits.\n\nConclusionThis study provides the first genome-wide, multi-omics map revealing the shared and differential genetic landscapes of COVID-19 and other respiratory phenotypes. By uncovering specific molecular mechanisms that distinguish COVID-19 pathology, specifically involving surfactant homeostasis and interferon pathways, our findings offer novel insights for targeted drug repurposing and precision risk stratification.","rel_num_authors":4,"rel_authors":[{"author_name":"Xiao Xue","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Yu-Ping LIN","author_inst":"The Chinese University of Hong Kong"},{"author_name":"Yaning FENG","author_inst":"Zhejiang Chinese Medical University;The Chinese University of Hong Kong"},{"author_name":"Hon-Cheong SO","author_inst":"The Chinese University of Hong Kong"}],"version":"1","license":"cc_no","type":"PUBLISHAHEADOFPRINT","category":"genetic and genomic medicine"}]}