Biology subjects

Eaton, B. P.

Publications and source records attributed to Eaton, B. P..

2 recordsLinked to original sources

bioRxiv2025

Monkeypox Virus Clade IIb Isolate Exhibits Reduced Virulence Relative to Clade IIa Isolates in Multiple Murine Models

Monkeypox virus (MPXV) is the causative agent of mpox disease in humans. The virus is comprised of two clades, Central African clade I and West African clade II with case fatality rates of [~]11 and [~]4%, respectively. Since the discovery of mpox disease in 1970, the virus has been restricted to Africa. However, in 2022, a previously unrecognized subclade IIb caused the largest global outbreak of mpox disease with a case fatality rate of [~]0.2%. The difference in virulence of MPXV subclades in human infection warrants further investigation, however, one critical limitation is the lack of susceptible small animal models. In this study, we investigated the susceptibility of four murine models, including CAST-EiJ and three immunocompromised models (C57BL/6 Ifnar-/-, C57BL/6 Ifngr-/-, and C57BL/6 Ifnar-/-/Ifngr-/-) to MPXV clade IIa (WR 7-61 and US-2003) and IIb (MA-2022) isolates. All four mouse models were susceptible to clade IIa infection, leading to severe disease marked by decreased body temperature, weight loss, and lethality. In contrast, clade IIb infection produced minimal to mild disease at similar doses in all four murine models. The clade IIb isolate produced severe disease (40% lethality) at only the highest dose (8.0 log10 PFU) in the most susceptible immunocompromised mouse model, C57BL/6 Ifnar-/-/Ifngr-/-. This is the first demonstration of lethal disease with clade IIb in a murine model. In addition, these data demonstrate that clade IIa is [~]100- to 100,000-fold more virulent than clade IIb and provide three additional murine models for investigating MPXV infection and pathogenesis. IMPORTANCEMpox is an emerging human disease caused by four distinct MPXV subclades (Ia, Ib, IIa, and IIb). Despite genetic similarities, the case fatality rate varies considerably between the subclades: Ia ([~]11%), Ib and IIa ([~]4%), and IIb ([~]0.2%). Since 2022, multiple mpox outbreaks have occurred due to previously unrecognized subclades, leading to the declaration of two public health emergencies by the World Health Organization. This unprecedented global spread, coupled with the variation in severity of human disease, underscores the importance of research into the pathogenesis of emerging MPXV subclades. However, a critical limitation is the lack of suitable small animal models. This study identifies three additional murine models susceptible to MPXV clade II infection and demonstrates significant virulence differences between clade IIa and IIb. These models will enable rapid characterization of previously unrecognized subclades and will facilitate countermeasure development.

microbiology↗

bioRxiv2025

High-throughput virus quantification using cytopathic effect area analysis by deep learning

Traditional infectivity-based virion quantification methods, such as 50% tissue culture infectious dose (TCID50) and plaque assays, are typically performed in 6-, 12-, 24-, or 48-well cell culture plates and require manual work and analysis based on legacy protocols. Adaptation of these methods to high-throughput formats (96-, 384- or 1,536-well plates) is challenging due to both assay automation constraints and the lack of well surface area available for reliable analysis. Here, we present a scalable alternative to traditional methods that uses whole-well image thresholding to quantify infectious virions by measuring virus-induced cytopathic effect (CPE) via cell lysis and detachment. The CPE area assay is best positioned as a practical high-throughput preliminary screening tool, effectively quantifying samples within the assay range and flagging samples above or below the concentration thresholds. To improve analysis efficiency and reduce user bias in CPE area selection, we evaluated an nnU-Net model for automated image segmentation against images segmented by manually defined brightness thresholds. The model achieved perfect correlation with manual thresholding (R2=1.00), showing minimal differences in the identified CPE area and thus validating nnU-Net as a reliable alternative to manual analysis. This approach provides two complementary pipelines: manual thresholding, which tolerates adjustments of hyperparameters, and fully automated segmentation via nnU-Net, which streamlines analysis and enhances throughput. This flexible CPE area assay enables accurate and automated quantification in high-throughput screening formats, thereby greatly accelerating a routine laboratory task while decreasing subjectivity and bias. Author summaryMeasuring the amount of virus in a biosample is a critical part of viral research and testing of new treatments. Traditional methods are slow, labor-intensive, and not easily scaled up to handle large numbers of samples. In this study, we developed a new approach that makes the process faster, more efficient, scalable, and less reliant on manual steps. We used microscopy imaging to capture how cells respond to virus infections and analyzed these images automatically to identify and measure areas where the virus had damaged cells. The analysis pipeline allows researchers to fine-tune settings or run the process using machine learning. This method is flexible and adaptable to a variety of viruses and experiments. By increasing the number of samples that can be tested at once while reducing the time and effort needed for analysis, our approach has the potential to accelerate research in virology, drug development, and public health.

microbiology↗