Rodney Stuart Daniels

Background: Surveillance of influenza infections and virus characterisation are essential to guide prevention strategies. In the Central African Republic (CAR), data on influenza viruses are patchy and poorly documented. Objective: To study the clinical, seasonal, genetic and phenotypic characteristics of influenza viruses circulating in the CAR population. Materials and Methods: From January 2015 to December 2018, the presence of influenza A and B viruses in patients presenting with influenza‐like illness (ILI) symptoms or severe acute respiratory infections (SARI) was investigated by RT‐qPCR. Influenza genetic diversity was evaluated by phylogenetic analyses, and antigenic properties were investigated by haemagglutination inhibition assays, whereas reduced susceptibility to neuraminidase inhibitors was assessed through the presence of known genetic markers and neuraminidase assay. The relationship between patients' clinical characteristics and infection status was investigated using statistical analyses. Results: Over the surveillance period influenza viruses were detected in 9.7% of samples ( n = 6134), with the highest intensity of circulation occurring in 2016 (15.8%), attributed mainly to A(H3N2). Periods of increased influenza transmission (June to October) generally coincided with rainy seasons; however, variations in terms of monthly distribution of cases between years were evident. Hospitalisation rates (SARI) were most frequent in infants (0–11 months, 37.9%) and young children (1–4 years, 24.8%), whereas influenza prevalences were highest in the 15–49 (12.0%) and ≥ 50 (15.2%) years old categories. A new A(H1N1)pdm09 6B.1 hemagglutinin subgroup characterised by amino acid substitutions S84N, S162N and I216T was detected in 2015, with associated antigenic drift, and subsequently, two of these viruses showed highly reduced inhibition by oseltamivir. Conclusion: This study showcases the value of local influenza sentinel networks to specifically inform vaccination strategies and highlights the need for improved strain characterisation in tropical regions.

Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here, we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997—2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection ynamics, presumably via heterosubtypic cross-immunity.

Seasonal influenza vaccines have aimed to generate neutralizing anti-HA antibodies for protection since licensure. More recently, anti-NA antibodies have been established as an additional correlate of protection.

The COVID-19 pandemic is the first to have emerged when Next Generation Sequencing was readily available and it has played the major role in following evolution of the causative agent, Severe Acute Respiratory Syndrome Coronavirus 2. Response to the pandemic was greatly facilitated though use of existing influenza surveillance networks: World Health Organization (WHO) Global Influenza Surveillance and Response System (GISRS), focussing largely on human influenza, and the OFFLU network of expertise on avian influenza established by the Food and Agricultural Organization of the United Nations (FAO) and the World Organization for Animal Health (WOAH). Data collection/deposition platforms associated with these networks, notably WHO’s FluNet and the Global Initiative on Sharing All Influenza Data (GISAID) were/are being used intensely. Measures introduced to combat COVID-19 resulted in greatly decreased circulation of human seasonal influenza viruses for approximately 2 years, but circulation continued in the animal sector with an upsurge in the spread of highly pathogenic avian influenza subtype H5N1 with large numbers of wild bird deaths, culling of many poultry flocks and sporadic spill over into mammalian species, including humans, thereby increasing pandemic risk potential. While there are proposals/implementations to extend use of GISRS and GISAID to other infectious disease agents (e.g. Respiratory Syncytial Virus and Monkeypox), there is need to ensure that influenza surveillance is maintained and improved in both human and animal sectors in a sustainable manner to be truly prepared (early detection) for the next influenza pandemic.

Immune imprinting is a driver known to shape the anti-hemagglutinin (HA) antibody landscape of individuals born within the same birth cohort. With the HA and neuraminidase (NA) proteins evolving at different rates under immune selection pressures, anti-HA and anti-NA antibody responses since childhood influenza infections have not been evaluated in parallel at the individual level. This is partly due to the limited knowledge of changes in NA antigenicity, as seasonal influenza vaccines have focused on generating neutralising anti-HA antibodies against HA antigenic variants. Here we systematically characterised the NA antigenic variants of seasonal A(H1N1) viruses from 1977 to 1991 and completed the antigenic profile of N1 NAs from 1977 to 2015. We identified that NA proteins of A/USSR/90/77, A/Singapore/06/86, and A/Texas/36/91 were antigenically distinct and mapped N386K as a key determinant of the NA antigenic change from A/USSR/90/77 to A/Singapore/06/86. With comprehensive panels of HA and NA antigenic variants of A(H1N1) and A(H1N1)pdm09 viruses, we determined hemagglutinin inhibition (HI) and neuraminidase inhibition (NI) antibodies from 130 subjects born between 1950-2015. Age-dependent imprinting was observed for both anti-HA and anti-NA antibodies, with the peak HI and NI titers predominantly detected from subjects at 4-12 years old during the year of initial virus isolation, except the age-independent anti-HA antibody response against A(H1N1)pdm09 viruses. More participants possessed antibodies that reacted to multiple antigenically distinct NA proteins than those with antibodies that reacted to multiple antigenically distinct HA proteins. Our results support the need to include NA proteins in seasonal influenza vaccine preparations. IMPORTANCE Seasonal influenza vaccines have aimed to generate neutralizing anti-HA antibodies for protection since licensure. More recently, anti-NA antibodies have been established as an additional correlate of protection. While HA and NA antigenic changes occurred discordantly, the anti-HA and anti-NA antibody profiles have rarely been analysed in parallel at the individual level, due to the limited knowledge on NA antigenic changes. By characterizing NA antigenic changes of A(H1N1) viruses, we determined the anti-HA and anti-NA antibody landscape against antigenically distinct A(H1N1) and A(H1N1)pdm09 viruses using sera of 130 subjects born between 1950-2015. We observed age-dependent imprinting of both anti-HA and anti-NA antibodies against strains circulated during the first decade of life. 67.7% (88/130) and 90% (117/130) of participants developed cross-reactive antibodies to multiple HA and NA antigens at titers ≥1:40. With slower NA antigenic changes and cross-reactive anti-NA antibody responses, including NA protein in influenza vaccine preparation may enhance vaccine efficacy. (150 words)

Several variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have emerged during the current coronavirus disease 2019 (COVID-19) pandemic. Although antibody cross-reactivity with the spike glycoproteins (S) of diverse coronaviruses, including endemic common cold coronaviruses (HCoVs), has been documented, it remains unclear whether such antibody responses, typically targeting the conserved S2 subunit, contribute to protection when induced by infection or through vaccination. Using a mouse model, we found that prior HCoV-OC43 S–targeted immunity primes neutralizing antibody responses to otherwise subimmunogenic SARS-CoV-2 S exposure and promotes S2-targeting antibody responses. Moreover, vaccination with SARS-CoV-2 S2 elicited antibodies in mice that neutralized diverse animal and human alphacoronaviruses and betacoronaviruses in vitro and provided a degree of protection against SARS-CoV-2 challenge in vivo. Last, in mice with a history of SARS-CoV-2 Wuhan–based S vaccination, further S2 vaccination induced broader neutralizing antibody response than booster Wuhan S vaccination, suggesting that it may prevent repertoire focusing caused by repeated homologous vaccination. These data establish the protective value of an S2-targeting vaccine and support the notion that S2 vaccination may better prepare the immune system to respond to the changing nature of the S1 subunit in SARS-CoV-2 variants of concern, as well as to future coronavirus zoonoses.

Surface antigens of pathogens are commonly targeted by vaccine-elicited antibodies but antigenic variability, notably in RNA viruses such as influenza, HIV and SARS-CoV-2, pose challenges for control by vaccination. For example, influenza A(H3N2) entered the human population in 1968 causing a pandemic and has since been monitored, along with other seasonal influenza viruses, for the emergence of antigenic drift variants through intensive global surveillance and laboratory characterisation. Statistical models of the relationship between genetic differences among viruses and their antigenic similarity provide useful information to inform vaccine development though accurate identification of causative mutations is complicated by highly correlated genetic signals that arise due to the evolutionary process. Here, using a sparse hierarchical Bayesian analogue of an experimentally validated model for integrating genetic and antigenic data, we identify the genetic changes in influenza A(H3N2) virus that underpin antigenic drift. We show that incorporating protein structural data into variable selection helps resolve ambiguities arising due to correlated signals, with the proportion of variables representing haemagglutinin positions decisively included, or excluded, increased from 59.8% to 72.4%. The accuracy of variable selection judged by proximity to experimentally determined antigenic sites was improved simultaneously. Structure-guided variable selection thus improves confidence in the identification of genetic explanations of antigenic variation and we also show that prioritising the identification of causative mutations is not detrimental to the predictive capability of the analysis. Indeed, incorporating structural information into variable selection resulted in a model that could more accurately predict antigenic assay titres for phenotypically-uncharactrised virus from genetic sequence. Combined, these analyses have the potential to inform choices of reference viruses, the targeting of laboratory assays, and predictions of the evolutionary success of different genotypes, and can therefore be used to inform vaccine selection processes.

Influenza C virus (ICV) infection of humans is common, with the great majority of people being infected during childhood, though reinfection can occur throughout life. While infection normally results in “cold-like” symptoms, severe disease cases have been reported in recent years.

Neuraminidase (NA) inhibitors (NAI), oseltamivir and zanamivir, are the main antiviral medications for influenza and monitoring of susceptibility to these antivirals is routinely done by determining 50 % inhibitory concentrations (IC₅₀) with MUNANA substrate. During 2010–2019, levels of A(H3N2) viruses presenting reduced NAI inhibition (RI) were low (~0.75 %) but varied year-on-year. The highest proportions of viruses showing RI were observed during the 2013–2014, 2016–2017 and 2017–2018 Northern Hemisphere seasons. The majority of RI viruses were found to contain positively charged NA amino acid substitutions of N329K, K/S329R, S331R or S334R, being notably higher during the 2016–2017 season. Sialidase activity kinetics were determined for viruses of RI phenotype and contemporary wild-type (WT) viruses showing close genetic relatedness and displaying normal inhibition (NI). RI phenotypes resulted from reduced sialidase activity compared to relevant WT viruses. Those containing S329R or N329K or S331R showed markedly higher K_m for the substrate and K_i values for NAIs, while those with S334R showed smaller effects. Substitutions at N329 and S331 disrupt a glycosylation sequon (NDS), confirmed to be utilised by mass spectrometry. However, gain of positive charge at all three positions was the major factor influencing the kinetic effects, not loss of glycosylation. Because of the altered enzyme characteristics NAs carrying these substitutions cannot be assessed reliably for susceptibility to NAIs using standard MUNANA-based assays due to reductions in the affinity of the enzyme for its substrate and the concentration of the substrate usually used.