In 2014, Western Africa experienced an unanticipated explosion of Ebola virus infections. What distinguishes fatal from non-fatal outcomes remains largely unknown, yet is key to optimising personalised treatment strategies. We used transcriptome data for peripheral blood taken from infected and convalescent recovering patients to identify early stage host factors that are associated with acute illness and those that differentiate patient survival from fatality. The data demonstrate that individuals who succumbed to the disease show stronger upregulation of interferon signalling and acute phase responses compared to survivors during the acute phase of infection. Particularly notable is the strong upregulation of albumin and fibrinogen genes, which suggest significant liver pathology. Cell subtype prediction using messenger RNA expression patterns indicated that NK-cell populations increase in patients who survive infection. By selecting genes whose expression properties discriminated between fatal cases and survivors, we identify a small panel of responding genes that act as strong predictors of patient outcome, independent of viral load. Transcriptomic analysis of the host response to pathogen infection using blood samples taken during an outbreak situation can provide multiple levels of information on both disease state and mechanisms of pathogenesis. Host biomarkers were identified that provide high predictive value under conditions where other predictors, such as viral load, are poor prognostic indicators. The data suggested that rapid analysis of the host response to infection in an outbreak situation can provide valuable information to guide an understanding of disease outcome and mechanisms of disease.

Ferrets were experimentally inoculated with SARS-CoV-2 (severe acute respiratory syndrome (SARS)-related coronavirus 2) to assess infection dynamics and host response. During the resulting subclinical infection, viral RNA was monitored between 2 and 21 days post-inoculation (dpi), and reached a peak in the upper respiratory cavity between 4 and 6 dpi. Viral genomic sequence analysis in samples from three animals identified the Y453F nucleotide substitution relative to the inoculum. Viral RNA was also detected in environmental samples, specifically in swabs of ferret fur. Microscopy analysis revealed viral protein and RNA in upper respiratory tract tissues, notably in cells of the respiratory and olfactory mucosae of the nasal turbinates, including olfactory neuronal cells. Antibody responses to the spike and nucleoprotein were detected from 21 dpi, but virus-neutralizing activity was low. A second intranasal inoculation (re-exposure) of two ferrets after a 17-day interval did not produce re-initiation of viral RNA shedding, but did amplify the humoral response in one animal. Therefore, ferrets can be experimentally infected with SARS-CoV-2 to model human asymptomatic infection.

Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO® vaccine against Neisseria meningitidis serogroup B. RV studies have begun to incorporate machine learning (ML) techniques to distinguish bacterial protective antigens (BPAs) from non-BPAs. This research contributes significantly to the RV field by using permutation analysis to demonstrate that a signal for protective antigens can be curated from published data. Furthermore, the effects of the following on an ML approach to RV were also assessed: nested cross-validation, balancing selection of non-BPAs for subcellular localization, increasing the training data, and incorporating greater numbers of protein annotation tools for feature generation. These enhancements yielded a support vector machine (SVM) classifier that could discriminate BPAs (n = 200) from non-BPAs (n = 200) with an area under the curve (AUC) of 0.787. In addition, hierarchical clustering of BPAs revealed that intracellular BPAs clustered separately from extracellular BPAs. However, no immediate benefit was derived when training SVM classifiers on data sets exclusively containing intra- or extracellular BPAs. In conclusion, this work demonstrates that ML classifiers have great utility in RV approaches and will lead to new subunit vaccines in the future.

The global pandemic of coronavirus disease (COVID-19) caused by infection with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has led to an international thrust to study pathogenesis and evaluate interventions. Experimental infection of hamsters and the resulting respiratory disease is one of the preferred animal models since clinical signs of disease and virus shedding are similar to more severe cases of human COVID-19. The main route of challenge has been direct inoculation of the virus via the intranasal route. To resemble the natural infection, we designed a bespoke natural transmission cage system to assess whether recipient animals housed in physically separate adjacent cages could become infected from a challenged donor animal in a central cage, with equal airflow across the two side cages. To optimise viral shedding in the donor animals, a low and moderate challenge dose were compared after direct intranasal challenge, but similar viral shedding responses were observed and no discernible difference in kinetics. The results from our natural transmission set-up demonstrate that most recipient hamsters are infected within the system developed, with variation in the kinetics and levels of disease between individual animals. Common clinical outputs used for the assessment in directly-challenged hamsters, such as weight loss, are less obvious in hamsters who become infected from naturally acquiring the infection. The results demonstrate the utility of a natural transmission model for further work on assessing the differences between virus strains and evaluating interventions using a challenge system which more closely resembles human infection.

The mutation profile of the SARS-CoV-2 Omicron variant poses a concern for naturally acquired and vaccine-induced immunity. We investigated the ability of prior infection with an early SARS-CoV-2, 99.99% identical to Wuhan-Hu-1, to protect against disease caused by the Omicron variant. We established that infection with Omicron in naïve Syrian hamsters resulted in a less severe disease than a comparable dose of prototype SARS-CoV-2 (Australia/VIC01/2020), with fewer clinical signs and less weight loss. We present data to show that these clinical observations were almost absent in convalescent hamsters challenged with the same dose of Omicron 50 days after an initial infection with Australia/VIC01/2020. The data provide evidence for immunity raised against prototype SARS-CoV-2 being protective against Omicron in the Syrian hamster model. Further investigation is required to conclusively determine whether Omicron is less pathogenic in Syrian hamsters and whether this is predictive of pathogenicity in humans.