Reprogramming viral immune evasion for a rational design of next-generation vaccines for RNA viruses.

Type I interferons (IFNs-α/ß) are antiviral cytokines that constitute the innate immunity of hosts to fight against viral infections. Recent studies, however, have revealed the pleiotropic functions of IFNs, in addition to their antiviral activities, for the priming of activation and maturation of adaptive immunity. In turn, many viruses have developed various strategies to counteract the IFN response and to evade the host immune system for their benefits. The inefficient innate immunity and delayed adaptive response fail to clear of invading viruses and negatively affect the efficacy of vaccines. A better understanding of evasion strategies will provide opportunities to revert the viral IFN antagonism. Furthermore, IFN antagonism-deficient viruses can be generated by reverse genetics technology. Such viruses can potentially serve as next-generation vaccines that can induce effective and broad-spectrum responses for both innate and adaptive immunities for various pathogens. This review describes the recent advances in developing IFN antagonism-deficient viruses, their immune evasion and attenuated phenotypes in natural host animal species, and future potential as veterinary vaccines.

The impact of signal variability on epidemic growth rate estimation from wastewater surveillance data (preprint)

Background: Testing samples of waste water for markers of infectious disease became a widespread method of surveillance during the COVID-19 pandemic. While these data generally correlate well with other indicators of national prevalence, samples that cover localised regions tend to be highly variable over short time scales. Methods: We introduce a procedure for estimating the real-time growth rate of pathogen prevalence using time series data from wastewater sampling. The number of copies of a target gene found in a sample is modelled as time-dependent random variable whose distribution is estimated using maximum likelihood. The output depends on a hyperparameter that controls the sensitivity to variability in the underlying data. We apply this procedure to data reporting the number of copies of the N1 gene of SARS-CoV-2 collected at water treatment works across Scotland between February 2021 and February 2023. Results: The real-time growth rate of the SARS-CoV-2 prevalence is estimated at 121 wastewater sampling sites covering a diverse range of locations and population sizes. We find that the sensitivity of the fitting procedure to natural variability determines its reliability in detecting the early stages of an epidemic wave. Applying the procedure to hospital admissions data, we find that changes in the growth rate are detected an average of 2 days earlier in wastewater than in hospital admissions data. Conclusion: We provide a robust method to generate reliable estimates of epidemic growth from highly variable data. Applying this method to samples collected at wastewater treatment works provides highly responsive situational awareness to inform public health.

Time intervals between COVID-19 cases, and more severe outcomes (preprint)

A critical factor in infectious disease control is the risk of an outbreak overwhelming local healthcare capacity. The overall demand on healthcare services will depend on disease severity, but the precise timing and size of peak demand also depends on the time interval (or clinical time delay) between initial infection, and development of severe disease. A broader distribution of intervals may draw that demand out over a longer period, but have a lower peak demand. These interval distributions are therefore important in modelling trajectories of e.g. hospital admissions, given a trajectory of incidence. Conversely, as testing rates decline, an incidence trajectory may need to be inferred through the delayed, but relatively unbiased signal of hospital admissions. Healthcare demand has been extensively modelled during the COVID-19 pandemic, where localised waves of infection have imposed severe stresses on healthcare services. While the initial acute threat posed by this disease has since subsided from immunity buildup from vaccination and prior infection, prevalence remains high and waning immunity may lead to substantial pressures for years to come. In this work, then, we present a set of interval distributions, for COVID-19 cases and subsequent severe outcomes; hospital admission, ICU admission, and death. These may be used to model more realistic scenarios of hospital admissions and occupancy, given a trajectory of infections or cases. We present a method for obtaining empirical distributions using COVID-19 outcomes data from Scotland between September 2020 and January 2022 (N = 31724 hospital admissions, N = 3514 ICU admissions, N = 8306 mortalities). We present separate distributions for individual age, sex, and deprivation of residing community. We show that, while the risk of severe disease following COVID-19 infection is substantially higher for the elderly or those residing in areas of high deprivation, the length of stay shows no strong dependence, suggesting that severe outcomes are equally severe across risk groups. As Scotland and other countries move into a phase where testing is no longer abundant, these intervals may be of use for retrospective modelling of patterns of infection, given data on severe outcomes.

Spatial analysis of COVID-19 booster vaccine uptake in Scotland, and projection of future distributions (preprint)

Vaccine hesitancy is one of the critical challenges for the implementation of a successful vaccination strategy. Rates of vaccine hesitancy and refusal vary substantially across different socioeconomic groups, and can result in those considered most vulnerable to disease having the lowest levels of uptake. Widespread coverage of COVID-19 vaccination is of particular importance as prevalence remains high, in effort to reduce overall burden from serious disease. Scotlands COVID-19 vaccination programme has progressed to booster vaccinations, however uptake is falling across successive doses, and there is concern that some vulnerable individuals will not have sustained protection. To this end we analyse uptake in Scotlands first (starting September 2021) booster dose round, as a benchmark for future rounds. We fit a machine learning model to explain variation in uptake across Scotland at fine population scales. The model is able to estimate a neighbourhoods booster uptake with high precision using its population structure and relative deprivation alone, without any knowledge of geographic location. This is indicative of a strong relationship between increasing local deprivation and falling uptake, and specifically in those failing to return for a booster, despite getting a first dose. Geographically, this manifests as clusters of lower uptake, coinciding with communities with higher deprivation. With an upcoming booster rollout in Autumn 2022, we use first booster uptake as a baseline, to generate a set of plausible distributions for future uptake, if nationwide uptake were to fall. We make the core assumption that as uptake falls, trends with respect to deprivation will persist. Projected uptake declines more rapidly in clusters of more deprived neighbourhoods. If these projected distributions were to manifest, gaps in immunity would emerge in more deprived communities, which have historically had the highest rates of COVID-19 hospitalisation and mortality.

Analysis of the Role of N-Linked Glycosylation in Cell Surface Expression, Function, and Binding Properties of SARS-CoV-2 Receptor ACE2.

Human angiotensin I-converting enzyme 2 (hACE2) is a type I transmembrane glycoprotein that serves as the major cell entry receptor for SARS-CoV and SARS-CoV-2. The viral spike (S) protein is required for the attachment to ACE2 and subsequent virus-host cell membrane fusion. Previous work has demonstrated the presence of N-linked glycans in ACE2. N-glycosylation is implicated in many biological activities, including protein folding, protein activity, and cell surface expression of biomolecules. However, the contribution of N-glycosylation to ACE2 function is poorly understood. Here, we examined the role of N-glycosylation in the activity and localization of two species with different susceptibility to SARS-CoV-2 infection, porcine ACE2 (pACE2) and hACE2. The elimination of N-glycosylation by tunicamycin (TM) treatment, or mutagenesis, showed that N-glycosylation is critical for the proper cell surface expression of ACE2 but not for its carboxiprotease activity. Furthermore, nonglycosylable ACE2 was localized predominantly in the endoplasmic reticulum (ER) and not at the cell surface. Our data also revealed that binding of SARS-CoV or SARS-CoV-2 S protein to porcine or human ACE2 was not affected by deglycosylation of ACE2 or S proteins, suggesting that N-glycosylation does not play a role in the interaction between SARS coronaviruses and the ACE2 receptor. Impairment of hACE2 N-glycosylation decreased cell-to-cell fusion mediated by SARS-CoV S protein but not that mediated by SARS-CoV-2 S protein. Finally, we found that hACE2 N-glycosylation is required for an efficient viral entry of SARS-CoV/SARS-CoV-2 S pseudotyped viruses, which may be the result of low cell surface expression of the deglycosylated ACE2 receptor. IMPORTANCE Understanding the role of glycosylation in the virus-receptor interaction is important for developing approaches that disrupt infection. In this study, we showed that deglycosylation of both ACE2 and S had a minimal effect on the spike-ACE2 interaction. In addition, we found that the removal of N-glycans of ACE2 impaired its ability to support an efficient transduction of SARS-CoV and SARS-CoV-2 S pseudotyped viruses. Our data suggest that the role of deglycosylation of ACE2 on reducing infection is likely due to a reduced expression of the viral receptor on the cell surface. These findings offer insight into the glycan structure and function of ACE2 and potentially suggest that future antiviral therapies against coronaviruses and other coronavirus-related illnesses involving inhibition of ACE2 recruitment to the cell membrane could be developed.

COVID-19 mass testing: harnessing the power of wastewater epidemiology (preprint)

BackgroundCOVID-19 patients shed SARS-CoV-2 RNA in their faeces. We hypothesised that detection of SARS-CoV-2 RNA in wastewater treatment plant (WWTP) influent could be a valuable tool to assist in public health decision making. We aimed to rapidly develop and validate a scalable methodology for the detection of SARS-CoV-2 RNA in wastewater that could be implemented at a national level and to determine the relationship between the wastewater signal and COVID-19 cases in the community. MethodsWe developed a filtration-based methodology for the concentration of SARS-CoV-2 from WWTP influent and subsequent detection and quantification by RT-qPCR. This methodology was used to monitor 28 WWTPs across Scotland, serving 50% of the population. For each WWTP catchment area, we collected data describing COVID-19 cases and deaths. We quantified spatial and temporal relationships between SARS-CoV-2 RNA in wastewater and COVID-19 cases. FindingsDaily WWTP SARS-CoV-2 influent viral RNA load, calculated using daily influent flow rates, had the strongest correlation ({rho}>0.9) with COVID-19 cases within a catchment. As the incidence of COVID-19 cases within a community increased, a linear relationship emerged between cases and influent viral RNA load. There were significant differences between WWTPs in their capacity to predict case numbers based on influent viral RNA load, with the limit of detection ranging from twenty-five cases for larger plants to a single case in smaller plants. InterpretationThe levels of SARS-CoV-2 RNA in WWTP influent provide a cost-effective and unbiased measure of COVID-19 incidence within a community, indicating that national scale wastewater-based epidemiology can play a role in COVID-19 surveillance. In Scotland, wastewater testing has been expanded to cover 75% of the population, with sub-catchment sampling being used to focus surge testing. SARS-CoV-2 variant detection, assessment of vaccination on community transmission and surveillance for other infectious diseases represent promising future applications. FundingThis study was funded by project grants from the Scottish Government via the Centre of Expertise for Waters (CD2019/06) and The Natural Environment Research Councils COVID-19 Rapid Response grants (NE/V010441/1). The Roslin Institute receives strategic funding from the Biotechnology and Biological Sciences Research Council (BB/P013740/1, BBS/E/D/20002173). Sample collection and supplementary analysis was funded and undertaken by Scottish Water and the majority of the sample analysis was funded and undertaken by the Scottish Environment Protection Agency.

Disentangling the roles of human mobility and deprivation on the transmission dynamics of COVID-19 using a spatially explicit simulation model. (preprint)

Restrictions on mobility are a key component of infectious disease controls, preventing the spread of infections to as yet unexposed areas, or to regions which have previously eliminated outbreaks. However, even under the most severe restrictions, some travel must inevitably continue, at the very minimum to retain essential services. For COVID-19, most countries imposed severe restrictions on travel at least as soon as it was clear that containment of local outbreaks would not be possible. Such restrictions are known to have had a substantial impact on the economy and other aspects of human health, and so quantifying the impact of such restrictions is an essential part of evaluating the necessity for future implementation of similar measures. In this analysis, we built a simulation model using National statistical data to record patterns of movements to work, and implement levels of mobility recorded in real time via mobile phone apps. This model was fitted to the pattern of deaths due to COVID-19 using approximate Bayesian inference. Our model is able to recapitulate mortality considering the number of deaths and datazones (DZs, which are areas containing approximately 500-1000 residents) with deaths, as measured across 32 individual council areas (CAs) in Scotland. Our model recreates a trajectory consistent with the observed data until 1st of July. According to the model, most transmission was occurring locally (i.e. in the model, 80% of transmission events occurred within spatially defined communities of approximately 100 individuals). We show that the net effect of the various restrictions put into place in March can be captured by a reduction in transmission down to 12% of its pre-lockdown rate effective 28th March. By comparing different approaches to reducing transmission, we show that, while the timing of COVID-19 restrictions influences the role of the transmission rate on the number of COVID-related deaths, early reduction in long distance movements does not reduce death rates significantly. As this movement of individuals from more infected areas to less infected areas has a minimal impact on transmission, this suggests that the fraction of population already immune in infected communities was not a significant factor in these early stages of the national epidemic even when local clustering of infection is taken into account. The best fit model also shows a considerable influence of the health index of deprivation (part of the index of multiple deprivations or iMD) on mortality. The most likely value has the CA with the highest level of health-related deprivation to have on average, a 2.45 times greater mortality rate due to COVID-19 compared to the CA with the lowest, showing the impact of health-related deprivation even in the early stages of the COVID-19 national epidemic.

COVID-19 control across urban-rural gradients (preprint)

Controlling the regional re-emergence of SARS-CoV-2 after its initial spread in ever-changing personal contact networks and disease landscapes is a challenging task. In a landscape context, contact opportunities within and between populations are changing rapidly as lockdown measures are relaxed and a number of social activities re-activated. Using an individual-based metapopulation model, we explored the efficacy of different control strategies across an urban-rural gradient in Wales, UK. Our model shows that isolation of symptomatic cases, or regional lockdowns in response to local outbreaks, have limited efficacy unless the overall transmission rate is kept persistently low. Additional isolation of non-symptomatic infected individuals, who may be detected by effective test and trace strategies, is pivotal to reduce the overall epidemic size over a wider range of transmission scenarios. We define an urban-rural gradient in epidemic size as a correlation between regional epidemic size and connectivity within the region, with more highly connected urban populations experiencing relatively larger outbreaks. For interventions focused on regional lockdowns, the strength of such gradients in epidemic size increased with higher travel frequencies, indicating a reduced efficacy of the control measure in the urban regions under these conditions. When both non-symptomatic and symptomatic individuals are isolated or regional lockdown strategies are enforced, we further found the strongest urban-rural epidemic gradients at high transmission rates. This effect was reversed for strategies targeted at symptomatics only. Our results emphasise the importance of test-and-tracing strategies and maintaining low transmission rates for efficiently controlling COVID19 spread, both at landscape scale and in urban areas.