Biology subjects

Preiser, W.

Publications and source records attributed to Preiser, W..

3 recordsLinked to original sources

bioRxiv2025

Identifying genomic surveillance gaps in Africa for the global public health response to West Nile Virus

BackgroundWest Nile Virus (WNV) is a zoonotic flavivirus of significant One Health relevance and is classified as a priority pathogen with a high-risk of causing public health emergencies of global concern. WNV is endemic to Africa; however, the availability of genomic sequences from the continent remains limited. MethodsWe review the extent of polymerase chain reaction testing and genomic sequencing of WNV conducted across Africa. Using phylogeographic methods, we map the spatiotemporal spread of the virus across the continent and globally. FindingsOur study shows that WNV has been detected in 39 African countries (including Comoros, Seychelles, and Mauritius), the Canary Islands, and Reunion Island. Publications including molecular data originate from 24 countries; however, genomic sequences are publicly available for only 16 countries. We identify regions with detected viral circulation but lacking molecular surveillance. Further, we list such regions that overlap with Key Biodiversity Areas (sites harbouring significant bird diversity) as they may host high viral circulation, and high human population density that may be susceptible to spillover. InterpretationWe recognise significant knowledge gaps on the true disease burden, molecular epidemiology, and distribution of WNV in Africa. Addressing these gaps requires an integrated One Health surveillance approach which is challenging to establish. We propose three key surveillance needs as potential starting points to improve our understanding of the virus in Africa to strengthen the global public health response to this disease. FundingRockefeller Foundation, the National Institute of Health USA, Institute of Human Virology Nigeria, Global Health EDCTP3 Joint Undertaking, the Health Emergency Preparedness and Response Umbrella Program, managed by the World Bank Group, the Medical Research Foundation, and the Wellcome Trust.

molecular biology↗

bioRxiv2022

Distinct phenotype of SARS-CoV-2 Omicron BA.1 in human primary cells but no increased host range in cell lines of putative mammalian reservoir species

SARS-CoV-2s genetic plasticity has led to several variants of concern (VOCs). Here we studied replicative capacity for seven SARS-CoV-2 isolates (B.1, Alpha, Beta, Gamma, Delta, Zeta, and Omicron BA.1) in primary reconstituted airway epithelia (HAE) and lung-derived cell lines. Furthermore, to investigate the host range of Delta and Omicron compared to ancestral SARS-CoV-2, we assessed replication in 17 cell lines from 11 non-primate mammalian species, including bats, rodents, insectivores and carnivores. Only Omicrons phenotype differed in vitro, with rapid but short replication and efficient production of infectious virus in nasal HAEs, in contrast to other VOCs, but not in lung cell lines. No increased infection efficiency for other species was observed, but Delta and Omicron infection efficiency was increased in A549 cells. Notably replication in A549 and Calu3 cells was lower than in nasal HAE. Our results suggest better adaptation of VOCs towards humans, without an extended host range.

microbiology↗

bioRxiv2022

Insights to HIV-1 coreceptor usage by estimating HLA adaptation with Bayesian generalized linear mixed models

The mechanisms triggering the human immunodeficiency virus type I (HIV-1) to switch the coreceptor usage from CCR5 to CXCR4 during the course of infection are not entirely understood. While low CD4+ T cell counts are associated with CXCR4 usage, a predominance of CXCR4 usage with still high CD4+ T cell counts remains puzzling. Here, we explore the hypothesis that viral adaptation to the human leukocyte antigen (HLA) complex, especially to the HLA class II alleles, contributes to the coreceptor switch. To this end, we sequence the viral gag and env protein with corresponding HLA class I and II alleles of a new cohort of 312 treatment-naive, subtype C, chronically-infected HIV-1 patients from South Africa. To estimate HLA adaptation, we develop a novel computational approach using Bayesian generalized linear mixed models (GLMMs). Our model allows to consider the entire HLA repertoire without restricting the model to pre-learned HLA-polymorphisms as well as to correct for phylogenetic relatedness of the viruses within the model itself to account for founder effects. Using our model, we observe that CXCR4-using variants are more adapted than CCR5-using variants (p-value =1.34e-2). Additionally, adapted CCR5-using variants have a significantly lower predicted false positive rate (FPR) by the geno2pheno[coreceptor] tool compared to the non-adapted CCR5-using variants (p-value =2.21e-2), where a low FPR is associated with CXCR4 usage. Consequently, estimating HLA adaptation can be an asset in predicting not only coreceptor usage, but also an approaching coreceptor switch in CCR5-using variants. We propose the usage of Bayesian GLMMs for modeling virus-host adaptation in general. Author summaryViral control is currently our only counter mechanism against HIV-1 with no practicable cure nor a vaccine at hand. In treatment-naive patients, HLA adaptation and coreceptor usage of HIV-1 play a major role in their capability to control the virus. The interplay between both factors, however, has remained unexplored so far. Assessing the degree of viral HLA adaptation is challenging due to the exceptional genetic diversity of both the HLA complex and HIV-1. Therefore, current approaches constrain the adaptation prediction to a set of p-value selected HLA-polymorphism candidates. The selection of these candidates, however, requires extensive external large-scale population-based experiments that are not always available for the population of interest, especially not for newly emerging viruses. In this work, we present a novel computational approach using Bayesian generalized linear mixed models (GLMMs) that enables not only to predict the adaptation to the complete HLA profile of a patient, but also to handle phylogenetic-dependencies of the variants within the model directly. Using this light-weight approach for modeling (any) virus-host adaptation, we show that HLA adaptation is associated with coreceptor usage.

bioinformatics↗