Ort, J. T. | Search bioRxiv

bioRxiv2025

Development of avian influenza A(H5) virus datasets for Nextclade enables rapid and accurate clade assignment

The ongoing panzootic of highly pathogenic avian influenza (HPAI) A(H5) viruses is the largest in history, with unprecedented transmission to multiple mammalian species. Avian influenza A viruses of the H5 subtype circulate globally among birds and are classified into distinct clades based on their hemagglutinin (HA) genetic sequences. Thus, the ability to accurately and rapidly assign clades to newly sequenced isolates is key to surveillance and outbreak response. Co-circulation of endemic, low pathogenic avian influenza (LPAI) A(H5) lineages in North American and European wild birds necessitates the ability to rapidly and accurately distinguish between infections arising from these lineages and epizootic HPAI A(H5) viruses. However, currently available clade assignment tools are limited and often require command line expertise, hindering their utility for public health surveillance labs. To address this gap, we have developed datasets to enable A(H5) clade assignments with Nextclade, a drag-and-drop tool originally developed for SARS-CoV-2 genetic clade classification. Using annotated reference datasets for all historical A(H5) clades, clade 2.3.2.1 descendants, and clade 2.3.4.4 descendants provided by the Food and Agriculture Organization/World Health Organization/World Organisation for Animal Health (FAO/WHO/WOAH) H5 Working Group, we identified clade-defining mutations for every established clade to enable tree-based clade assignment. We then created three Nextclade datasets which can be used to assign clades to A(H5) HA sequences and call mutations relative to reference strains through a drag-and-drop interface. Nextclade assignments were benchmarked with 19,834 unique sequences not in the reference set using a pre-released version of LABEL, a well-validated and widely used command line software. Prospective assignment of new sequences with Nextclade and LABEL produced very well-matched assignments (match rates of 97.8% and 99.1% for the 2.3.2.1 and 2.3.4.4 datasets, respectively). The all-clades dataset also performed well (94.8% match rate) and correctly distinguished between all HPAI and LPAI strains. This tool additionally allows for the identification of polybasic cleavage site sequences and potential N-linked glycosylation sites. These datasets therefore provide an alternative, rapid method to accurately assign clades to new A(H5) HA sequences, with the benefit of an easy-to-use browser interface.

bioinformatics↗

bioRxiv2024

Deep mutational scanning of H5 hemagglutinin to inform influenza virus surveillance

H5 influenza is a potential pandemic threat. Previous studies have identified molecular phenotypes of the viral hemagglutinin (HA) protein that contribute to pandemic risk, including cell entry, receptor preference, HA stability, and reduced neutralization by polyclonal sera. Here we use pseudovirus deep mutational scanning to measure how all mutations to a clade 2.3.4.4b H5 HA affect each phenotype. We identify mutations that allow HA to better bind 2-6-linked sialic acids, and show that some viruses already carry mutations that stabilize HA. We also identify recent viral strains with reduced neutralization to sera elicited by candidate vaccine virus. Overall, the systematic nature of deep mutational scanning combined with the safety of pseudoviruses enables comprehensive characterization of mutations to inform surveillance of H5 influenza.

microbiology↗

bioRxiv2023

Wound microbiota-mediated correction of matrix metalloproteinase expression promotes re-epithelialization of diabetic wounds

Chronic wounds are a common and costly complication of diabetes, where multifactorial defects contribute to dysregulated skin repair, inflammation, tissue damage, and infection. We previously showed that aspects of the diabetic foot ulcer microbiota were correlated with poor healing outcomes, but many microbial species recovered remain uninvestigated with respect to wound healing. Here we focused on Alcaligenes faecalis, a Gram-negative bacterium that is frequently recovered from chronic wounds but rarely causes infection. Treatment of diabetic wounds with A. faecalis accelerated healing during early stages. We investigated the underlying mechanisms and found that A. faecalis treatment promotes re-epithelialization of diabetic keratinocytes, a process which is necessary for healing but deficient in chronic wounds. Overexpression of matrix metalloproteinases in diabetes contributes to failed epithelialization, and we found that A. faecalis treatment balances this overexpression to allow proper healing. This work uncovers a mechanism of bacterial-driven wound repair and provides a foundation for the development of microbiota-based wound interventions.

microbiology↗