Investigating sialic acid usage in species D human adenoviruses

Human adenoviruses are phylogenetically divided across seven species, A-G, causing transient mild illnesses, except in immunocompromised individuals. Their double stranded DNA genome is amenable to genetic manipulations, enabling development of highly engineered virotherapies. Species D adenoviruses have naturally low seroprevalence rates, an important trait in avoiding neutralising anti-vector immunity. We previously demonstrated that HAdV-D26, the platform of the Janssen SARS-CoV2 vaccine, uses sialic acid as a primary cell entry receptor. Here, we structurally and biologically investigated sialic acid usage across species D. We solved multiple structures of species D adenovirus fiber knob proteins alone and in complex with sialic acid, identifying a conserved binding pocket common with known sialic acid binders HAdV-D26 and 37. Using fiber-knob pseudotyped viruses, we demonstrate significantly reduced transduction in cells treated with neuraminidase to remove sialic acid residues in HAdV-D26 and 53, with HAdV-D15, 24 and 29 also demonstrating non-significant reductions. IC50 data also showed highlighted binding to CAR, although at a significantly lower affinity compared to the CAR-binding HAdV-C5. Improved understanding of the usage of sialic acid as a receptor will enable better exploitation of the species D adenoviruses as therapeutic vectors. Our findings raise the possibility of a conserved sialic acid binding pocket within species D adenoviruses resulting in varying affinity levels. Further evaluation of specific glycan binding patterns used by these viruses, as observed between HAdV-D37 and GD1a glycan, will better inform the design of appropriate antivirals to contain adenovirus outbreaks as well as the engineering of targeted vectors for translational applications.

Establishing a National Emergency Medical Team (EMT) in the Federated States of Micronesia (FSM)

Background/Introduction:The Federated States of Micronesia (FSM) is a Pacific Island nation of just over 100,000 people, with 607 islands spread over an ocean territory of over 2.5 million square kilometers. The nation is regularly struck by disasters and outbreaks - most recently Typhoons Wutip and Maysak, which affected populations on hundreds of islands. To strengthen rapid response to future emergencies, the FSM Government, with support from WHO, Japan, and USAID, is now developing a national Emergency Medical Team.1Objectives:To describe the development of FSM’s national EMT.Method/Description:FSM’s EMT development began with a workshop facilitated by WHO in November 2019, with participants from the National Department of Health & Social Affairs (DHSA) and representatives from all four of FSM’s State Health Departments. A national EMT focal point was jointly hired by WHO and FSM’s DHSA, a Technical Working Group was established, and SOPs developed. WHO initiated procurement of a tailored Pacific EMT cache for FSM’s EMT, along with other Pacific EMTs. FSM’s EMT development has been slowed by COVID-19, but there are plans to train team members from the country’s four States to be ready for self-sufficient national deployments by early 2023.Results/Outcomes:With support from partners, FSM is developing a self-sufficient national EMT capable of rapid response across hundreds of islands spread over a vast ocean territory.Conclusion:FSM is strengthening readiness for future emergencies by developing a national EMT capable of rapid response to the most remote and austere post-disaster conditions, including throughout its remote outer islands.

A Hybrid Computational Framework for Intelligent Inter- continent SARS-CoV-2 Sub-strains Characterization and Prediction (preprint)

Whereas accelerated attention beclouded early stages of the coronavirus spread, knowledge of actual pathogenicity and origin of possible sub-strains remained unclear. By harvesting the Global initiative on Sharing All Influenza Data (GISAID) database (https://www.gisaid.org/), between December 2019 and January 15, 2021, a total of 8864 human SARS-CoV-2 complete genome sequences processed by gender, across 6 continents (88 countries) of the world, Antarctica exempt, were analyzed. We hypothesized that data speaks for itself and can discern true and explainable patterns of the disease. Identical genome diversity and pattern correlates analysis performed using a hybrid of biotechnology and machine learning methods corroborate the emergence of inter- and intra- SARS-CoV-2 sub-strains. Interestingly, some viral sub-strain patterns progressively transformed into new sub-strain clusters indicating varying amino acid and strong nucleotide association derived from same lineage. A novel cognitive approach to knowledge mining from enriched genome datasets and output classification targets, helped intelligent prediction of emerging or new viral sub-strains. Classification results outsmarted state-of-the-art methods and sustained an increase in sub-strains within the various continents with nucleotide mutations dynamically varying between individuals in close association with the virus adaptability to its host/environment. They also offer explanations for the growing concerns and next wave(s) of the virus. Defuzzifying confusable pattern clusters for comparative performance with the proposed cognitive solution is a possible future research direction of this paper.