A speed limit on serial strain replacement from original antigenic sin

Many pathogens evolve to escape immunity, yet it remains difficult to predict whether immune pressure will lead to diversification, serial replacement of one variant by another, or more complex patterns. Pathogen strain dynamics are mediated by cross-protective immunity, whereby exposure to one strain partially protects against infection by antigenically diverged strains. There is growing evidence that this protection is influenced by early exposures, a phenomenon referred to as original antigenic sin (OAS) or imprinting. In this paper, we derive new constraints on the emergence of the pattern of successive strain replacements demonstrated by influenza, SARS-CoV-2, seasonal coronaviruses, and other pathogens. We find that OAS implies that the limited diversity found with successive strain replacement can only be maintained if R 0 is less than a threshold set by the characteristic antigenic distances for cross-protection and for the creation of new immune memory. This bound implies a "speed limit" on the evolution of new strains and a minimum variance of the distribution of infecting strains in antigenic space at any time. To carry out this analysis, we develop a theoretical model of pathogen evolution in antigenic space that implements OAS by decoupling the antigenic distances required for protection from infection and strain-specific memory creation. Our results demonstrate that OAS can play an integral role in the emergence of strain structure from host immune dynamics, preventing highly transmissible pathogens from maintaining serial strain replacement without diversification.

Reduced effectiveness of repeat influenza vaccination: distinguishing among within-season waning, recent clinical infection, and subclinical infection

Studies have reported that prior-season influenza vaccination is associated with higher risk of clinical influenza infection among vaccinees. This effect might arise from incomplete consideration of within-season waning and recent infection. Using data from the US Flu Vaccine Effectiveness (VE) Network (2011-2012 to 2018-2019 seasons), we found that repeat vaccinees were vaccinated earlier in a season by one week. After accounting for waning VE, repeat vaccinees were still more likely to test positive for A(H3N2) (OR=1.11, 95%CI:1.02-1.21) but not for influenza B or A(H1N1). We found that clinical infection influenced individuals' decision to vaccinate in the following season while protecting against clinical infection of the same (sub)type. However, adjusting for recent clinical infections did not strongly influence the estimated effect of prior-season vaccination. In contrast, we found that adjusting for subclinical infection could theoretically attenuate this effect. Additional investigation is needed to determine the impact of subclinical infections on VE.

High-throughput sequencing-based neutralization assay reveals how repeated vaccinations impact titers to recent human H1N1 influenza strains

The high genetic diversity of influenza viruses means that traditional serological assays have too low throughput to measure serum antibody neutralization titers against all relevant strains. To overcome this challenge, we have developed a sequencing-based neutralization assay that simultaneously measures titers against many viral strains using small serum volumes via a workflow similar to traditional neutralization assays. The key innovation is to incorporate unique nucleotide barcodes into the hemagglutinin (HA) genomic segment, and then pool viruses with numerous different barcoded HA variants and quantify infectivity of all of them simultaneously using next-generation sequencing. With this approach, a single researcher performed the equivalent of 2,880 traditional neutralization assays (80 serum samples against 36 viral strains) in approximately one month. We applied the sequencing-based assay to quantify the impact of influenza vaccination on neutralization titers against recent human H1N1 strains for individuals who had or had not also received a vaccine in the previous year. We found that the viral strain specificities of the neutralizing antibodies elicited by vaccination vary among individuals, and that vaccination induced a smaller increase in titers for individuals who had also received a vaccine the previous year-although the titers six months after vaccination were similar in individuals with and without the previous-year vaccination. We also identified a subset of individuals with low titers to a subclade of recent H1N1 even after vaccination. This study demonstrates the utility of high-throughput sequencing-based neutralization assays that enable titers to be simultaneously measured against many different viral strains. We provide a detailed experimental protocol (DOI: https://dx.doi.org/10.17504/protocols.io.kqdg3xdmpg25/v1) and a computational pipeline (https://github.com/jbloomlab/seqneut-pipeline) for the sequencing-based neutralization assays to facilitate the use of this method by others.

Measures of population immunity can predict the dominant clade of influenza A (H3N2) and reveal age-associated differences in susceptibility and specificity

For antigenically variable pathogens such as influenza, strain fitness is partly determined by the relative availability of hosts susceptible to infection with that strain compared to others. Antibodies to the hemagglutinin (HA) and neuraminidase (NA) confer substantial protection against influenza infection. We asked if a cross-sectional antibody-derived estimate of population susceptibility to different clades of influenza A (H3N2) could predict the success of clades in the following season. We collected sera from 483 healthy individuals aged 1 to 90 years in the summer of 2017 and analyzed neutralizing responses to the HA and NA of representative strains. The clade to which neutralizing antibody titers were lowest, indicating greater population susceptibility, dominated the next season. Titers to different HA and NA clades varied dramatically between individuals but showed significant associations with age, suggesting dependence on correlated past exposures. Despite this correlation, inter-individual variability in antibody titers to H3N2 strains increased gradually with age. This study indicates how representative measures of population immunity might improve evolutionary forecasts and inform selective pressures on influenza.

Germline-encoded specificities and the predictability of the B cell response

Antibodies result from the competition of B cell lineages evolving under selection for improved antigen recognition, a process known as affinity maturation. High-affinity antibodies to pathogens such as HIV, influenza, and SARS-CoV-2 are frequently reported to arise from B cells whose receptors, the precursors to antibodies, are encoded by particular immunoglobulin alleles. This raises the possibility that the presence of particular germline alleles in the B cell repertoire is a major determinant of the quality of the antibody response. Alternatively, initial differences in germline alleles' propensities to form high-affinity receptors might be overcome by chance events during affinity maturation. We first investigate these scenarios in simulations: when germline-encoded fitness differences are large relative to the rate and effect size variation of somatic mutations, the same germline alleles persistently dominate the response of different individuals. In contrast, if germline-encoded advantages can be easily overcome by subsequent mutations, allele usage becomes increasingly divergent over time, a pattern we then observe in mice experimentally infected with influenza virus. We investigated whether affinity maturation might nonetheless strongly select for particular amino acid motifs across diverse genetic backgrounds, but we found no evidence of convergence to similar CDR3 sequences or amino acid substitutions. These results suggest that although germline-encoded specificities can lead to similar immune responses between individuals, diverse evolutionary routes to high affinity limit the genetic predictability of responses to infection and vaccination.

Investigation of the Impact of Childhood Immune Imprinting on Birth Year-Specific Risk of Clinical Infection During Influenza A Virus Epidemics in Hong Kong

Influenza imprinting reduces risks of influenza A virus clinical infection by 40%-90%, estimated from surveillance data in western countries. We analyzed surveillance data from 2010 to 2019 in Hong Kong. Based on the best model, which included hemagglutinin group-level imprinting, we estimated that individuals imprinted to H1N1 or H2N2 had a 17% (95% confidence interval [CI], 3%-28%) lower risk of H1N1 clinical infection, and individuals imprinted to H3N2 would have 12% (95% CI, -3% to 26%) lower risk of H3N2 clinical infection. These estimated imprinting protections were weaker than estimates in western countries. Identifying factors affecting imprinting protections is important for control policies and disease modeling.

Antigen-driven colonic inflammation is associated with development of dysplasia in primary sclerosing cholangitis

Primary sclerosing cholangitis (PSC) is an immune-mediated disease of the bile ducts that co-occurs with inflammatory bowel disease (IBD) in almost 90% of cases. Colorectal cancer is a major complication of patients with PSC and IBD, and these patients are at a much greater risk compared to patients with IBD without concomitant PSC. Combining flow cytometry, bulk and single-cell transcriptomics, and T and B cell receptor repertoire analysis of right colon tissue from 65 patients with PSC, 108 patients with IBD and 48 healthy individuals we identified a unique adaptive inflammatory transcriptional signature associated with greater risk and shorter time to dysplasia in patients with PSC. This inflammatory signature is characterized by antigen-driven interleukin-17A (IL-17A)+ forkhead box P3 (FOXP3)+ CD4 T cells that express a pathogenic IL-17 signature, as well as an expansion of IgG-secreting plasma cells. These results suggest that the mechanisms that drive the emergence of dysplasia in PSC and IBD are distinct and provide molecular insights that could guide prevention of colorectal cancer in individuals with PSC.

COVID-19 and children

There has been substantial research on adult COVID-19 and how to treat it. But how do severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections afflict children? The COVID-19 pandemic has yielded many surprises, not least that children generally develop less severe disease than older adults, which is unusual for a respiratory disease. However, some children can develop serious complications from COVID-19, such as multisystem inflammatory syndrome in children (MIS-C) and Long Covid, even after mild or asymptomatic COVID-19. Why this occurs in some and not others is an important question. Moreover, when children do contract COVID-19, understanding their role in transmission, especially in schools and at home, is crucial to ensuring effective mitigation measures. Therefore, in addition to nonpharmaceutical interventions, such as improved ventilation, there is a strong case to vaccinate children so as to reduce possible long-term effects from infection and to decrease transmission. But questions remain about whether vaccination might skew immune responses to variants in the long term. As the experts discuss below, more is being learned about these important issues, but much more research is needed to understand the long-term effects of COVID-19 in children.

PARIS and SPARTA: Finding the Achilles' Heel of SARS-CoV-2

To understand reinfection rates and correlates of protection for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we established eight different longitudinal cohorts in 2020 under the umbrella of the PARIS (Protection Associated with Rapid Immunity to SARS-CoV-2)/SPARTA (SARS SeroPrevalence And Respiratory Tract Assessment) studies. Here, we describe the PARIS/SPARTA cohorts, the harmonized assays and analysis that are performed across the cohorts, as well as case definitions for SARS-CoV-2 infection and reinfection that have been established by the team of PARIS/SPARTA investigators. IMPORTANCE Determining reinfection rates and correlates of protection against SARS-CoV-2 infection induced by both natural infection and vaccination is of high significance for the prevention and control of coronavirus disease 2019 (COVID-19). Furthermore, understanding reinfections or infection after vaccination and the role immune escape plays in these scenarios will inform the need for updates of the current SARS-CoV-2 vaccines and help update guidelines suitable for the postpandemic world.

SARS-CoV-2 Infection Among Pregnant People at Labor and Delivery and Changes in Infection Rates in the General Population: Lessons Learned From Illinois

Objectives:

The Illinois Department of Public Health (IDPH) assessed whether increases in the SARS-CoV-2 test positivity rate among pregnant people at labor and delivery (L&D) could signal increases in SARS-CoV-2 prevalence in the general Illinois population earlier than current state metrics.

Materials and Methods:

Twenty-six birthing hospitals universally testing for SARS-CoV-2 at L&D voluntarily submitted data from June 21, 2020 through January 23, 2021, to IDPH. Hospitals reported the daily number of people who delivered, SARS-CoV-2 tests, and test results as well as symptom status. We compared the test positivity rate at L&D with the test positivity rate of the general population and the number of hospital admissions for COVID-19–like illness by quantifying correlations in trends and identifying a lead time.

Results:

Of 26 633 reported pregnant people who delivered, 96.8% (n = 25 772) were tested for SARS-CoV-2. The overall test positivity rate was 2.4% (n = 615); 77.7% (n = 478) were asymptomatic. In Chicago, the only region with a sufficient sample size for analysis, the test positivity rate at L&D (peak of 5% on December 7, 2020) was lower and more stable than the test positivity rate of the general population (peak of 14% on November 13, 2020) and lagged hospital admissions for COVID-19–like illness (peak of 118 on November 15, 2020) and the test positivity rate of the general population by about 10 days (Pearson correlation = 0.73 and 0.75, respectively).

Practice Implications:

Trends in the test positivity rate at L&D did not provide an earlier signal of increases in Illinois’s SARS-CoV-2 prevalence than current state metrics did. Nonetheless, the role of universal testing protocols in identifying asymptomatic infection is important for clinical decision making and patient education about infection prevention and control.

Incorporating temporal distribution of population-level viral load enables real-time estimation of COVID-19 transmission

Many locations around the world have used real-time estimates of the time-varying effective reproductive number (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}_{t}$$\end{document}Rt) of COVID-19 to provide evidence of transmission intensity to inform control strategies. Estimates of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}_{t}$$\end{document}Rt are typically based on statistical models applied to case counts and typically suffer lags of more than a week because of the latent period and reporting delays. Noting that viral loads tend to decline over time since illness onset, analysis of the distribution of viral loads among confirmed cases can provide insights into epidemic trajectory. Here, we analyzed viral load data on confirmed cases during two local epidemics in Hong Kong, identifying a strong correlation between temporal changes in the distribution of viral loads (measured by RT-qPCR cycle threshold values) and estimates of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}_{t}$$\end{document}Rt based on case counts. We demonstrate that cycle threshold values could be used to improve real-time \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}_{t}$$\end{document}Rt estimation, enabling more timely tracking of epidemic dynamics.

The time-varying effective reproductive number (Rt) is useful for monitoring transmission of infections such as COVID-19, but reporting delays impact case count-based estimation methods. Here, the authors demonstrate and validate a method for estimation of Rt based on viral load data from Hong Kong that does not require accurate daily counts.

Of variants and vaccines

In this issue of Cell, Bushman et al. show how more transmissible variants, even if they do not escape immunity, can be strongly selected during the early pandemic. This explains the dynamics of past SARS-CoV-2 variants, but as immunity increases, it is difficult to predict what will emerge next.

Correction: Practical considerations for measuring the effective reproductive number, Rt

[This corrects the article DOI: 10.1371/journal.pcbi.1008409.].

Improvements in SARS-CoV-2 Testing Cascade in the US: Data from Serial Cross-sectional Assessments

Background

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing is critical for monitoring case counts, early detection and containment of infection, clinical management, and surveillance of variants. However, community-based data on the access, uptake, and barriers to testing have been lacking.

Methods

We conducted serial cross-sectional online surveys covering demographics, coronavirus disease 2019 symptoms, and experiences around SARS-CoV-2 diagnostic testing to characterize the SARS-CoV-2 testing cascade and associated barriers across 10 US states (California, Florida, Illinois, Maryland, Massachusetts, Nebraska, North Dakota, South Dakota, Texas, and Wisconsin), from July 2020 to February 2021.

Results

In February 2021, across 10 US states, 895 respondents (11%) reported wanting a diagnostic test in the prior 2 weeks, 63% of whom were tested, with limited variability across states. Almost all (97%) who were tested received their results; 56% received their results within 2 days. In Maryland, Florida, and Illinois, where serial data were available at 4 time points, 56% were tested the same day they wanted or needed a test in February 2021, compared with 28% in July 2020, and 45% received results the same day, compared with 17% in July 2020. Wanting a test was significantly more common among younger, nonwhite respondents and participants with a history of symptoms or exposure. Logistical challenges, including not knowing where to go, were the most frequently cited barriers.

Conclusions

There were significant improvements in access and turnaround times across US states, yet barriers to testing remained consistent across states, underscoring the importance of a continued focus on testing, even amidst mass vaccination campaigns.

Lineage-specific protection and immune imprinting shape the age distributions of influenza B cases

How a history of influenza virus infections contributes to protection is not fully understood, but such protection might explain the contrasting age distributions of cases of the two lineages of influenza B, B/Victoria and B/Yamagata. Fitting a statistical model to those distributions using surveillance data from New Zealand, we found they could be explained by historical changes in lineage frequencies combined with cross-protection between strains of the same lineage. We found additional protection against B/Yamagata in people for whom it was their first influenza B infection, similar to the immune imprinting observed in influenza A. While the data were not informative about B/Victoria imprinting, B/Yamagata imprinting could explain the fewer B/Yamagata than B/Victoria cases in cohorts born in the 1990s and the bimodal age distribution of B/Yamagata cases. Longitudinal studies can test if these forms of protection inferred from historical data extend to more recent strains and other populations.

Preexisting immunity shapes distinct antibody landscapes after influenza virus infection and vaccination in humans

Humans are repeatedly exposed to variants of influenza virus throughout their lifetime. As a result, preexisting influenza-specific memory B cells can dominate the response after infection or vaccination. Memory B cells recalled by adulthood exposure are largely reactive to conserved viral epitopes present in childhood strains, posing unclear consequences on the ability of B cells to adapt to and neutralize newly emerged strains. We sought to investigate the impact of preexisting immunity on generation of protective antibody responses to conserved viral epitopes upon influenza virus infection and vaccination in humans. We accomplished this by characterizing monoclonal antibodies (mAbs) from plasmablasts, which are predominantly derived from preexisting memory B cells. We found that, whereas some influenza infection-induced mAbs bound conserved and neutralizing epitopes on the hemagglutinin (HA) stalk domain or neuraminidase, most of the mAbs elicited by infection targeted non-neutralizing epitopes on nucleoprotein and other unknown antigens. Furthermore, most infection-induced mAbs had equal or stronger affinity to childhood strains, indicating recall of memory B cells from childhood exposures. Vaccination-induced mAbs were similarly induced from past exposures and exhibited substantial breadth of viral binding, although, in contrast to infection-induced mAbs, they targeted neutralizing HA head epitopes. Last, cocktails of infection-induced mAbs displayed reduced protective ability in mice compared to vaccination-induced mAbs. These findings reveal that both preexisting immunity and exposure type shape protective antibody responses to conserved influenza virus epitopes in humans. Natural infection largely recalls cross-reactive memory B cells against non-neutralizing epitopes, whereas vaccination harnesses preexisting immunity to target protective HA epitopes.

Geographic and demographic heterogeneity of SARS-CoV-2 diagnostic testing in Illinois, USA, March to December 2020

BACKGROUND

Availability of SARS-CoV-2 testing in the United States (U.S.) has fluctuated through the course of the COVID-19 pandemic, including in the U.S. state of Illinois. Despite substantial ramp-up in test volume, access to SARS-CoV-2 testing remains limited, heterogeneous, and insufficient to control spread.

METHODS

We compared SARS-CoV-2 testing rates across geographic regions, over time, and by demographic characteristics (i.e., age and racial/ethnic groups) in Illinois during March through December 2020. We compared age-matched case fatality ratios and infection fatality ratios through time to estimate the fraction of SARS-CoV-2 infections that have been detected through diagnostic testing.

RESULTS

By the end of 2020, initial geographic differences in testing rates had closed substantially. Case fatality ratios were higher in non-Hispanic Black and Hispanic/Latino populations in Illinois relative to non-Hispanic White populations, suggesting that tests were insufficient to accurately capture the true burden of COVID-19 disease in the minority populations during the initial epidemic wave. While testing disparities decreased during 2020, Hispanic/Latino populations consistently remained the least tested at 1.87 tests per 1000 population per day compared with 2.58 and 2.87 for non-Hispanic Black and non-Hispanic White populations, respectively, at the end of 2020. Despite a large expansion in testing since the beginning of the first wave of the epidemic, we estimated that over half (50-80%) of all SARS-CoV-2 infections were not detected by diagnostic testing and continued to evade surveillance.

CONCLUSIONS

Systematic methods for identifying relatively under-tested geographic regions and demographic groups may enable policymakers to regularly monitor and evaluate the shifting landscape of diagnostic testing, allowing officials to prioritize allocation of testing resources to reduce disparities in COVID-19 burden and eventually reduce SARS-CoV-2 transmission.

COVID-19 Infection, Reinfection, and Vaccine Effectiveness in a Prospective Cohort of Arizona Frontline/Essential Workers: The AZ HEROES Research Protocol

BACKGROUND

The Arizona Healthcare, Emergency Response, and Other Essential workers Study (AZ HEROES) aims to examine the epidemiology of SARS-CoV-2 infection and COVID-19 illness among adults with high occupational exposure risk.

OBJECTIVE

Study objectives include estimating incidence of SARS-CoV-2 infection in essential workers by symptom presentation and demographic factors, determining independent effects of occupational and community exposures on incidence of SARS-CoV-2 infection, establishing molecular and immunologic characteristics of SARS-CoV-2 infection in essential workers, describing the duration and patterns of rRT-PCR-positivity, and examining post-vaccine immunologic response.

METHODS

Eligible participants include Arizona residents aged 18-85 years who work at least 20 hours per week in an occupation involving regular direct contact (within three feet) with others. Recruitment goals are stratified by demographic characteristics (50% aged 40 or older, 50% women, and 50% Hispanic or American Indian), by occupation (40% healthcare personnel, 30% first responders, and 30% other essential workers), and by prior SARS-CoV-2 infection (with up to 50% seropositive at baseline). Information on sociodemographics, health and medical history, vaccination status, exposures to individuals with suspected or confirmed SARS-CoV-2 infection, use of personal protective equipment, and perceived risks are collected at enrollment and updated through quarterly surveys. Every week, participants complete active surveillance for COVID-19-like illness (CLI) and self-collect nasal swabs. Additional self-collected nasal swab and saliva specimens are collected in the event of CLI onset. Respiratory specimens are sent to Marshfield Laboratories and tested for SARS-CoV-2 by real-time reverse transcription polymerase chain reaction (rRT-PCR) assay. CLI symptoms and impact on work and productivity are followed through illness resolution. Serum specimens are collected every 3 months and additional sera are collected following incident rRT-PCR positivity and after each COVID-19 vaccine dose. Incidence of SARS-CoV-2 infections will be calculated by person-weeks at risk and compared by occupation and demographic characteristics and by seropositivity status and infection and vaccination history.

RESULTS

The AZ HEROES study was funded by the Centers for Disease Control and Prevention. Enrollment began July 27, 2020 and as of May 1, 2021 a total of 3,165 participants have been enrolled in the study.

CONCLUSIONS

AZ HEROES is unique in aiming to recruit a diverse sample of essential workers and prospectively following strata of SARS-CoV-2 seronegative and seropositive adults. Survey results combined with active surveillance data on exposure, CLI, weekly molecular diagnostic testing, and periodic serology will be used to estimate the incidence of symptomatic and asymptomatic SARS-CoV-2 infection, assess the intensity and durability of immune responses to natural infection and COVID-19 vaccination, and contribute to the evaluation of COVID-19 vaccine effectiveness.

CLINICALTRIAL

INTERNATIONAL REGISTERED REPORT

DERR1-10.2196/28925.

Concerns about SARS-CoV-2 evolution should not hold back efforts to expand vaccination

When vaccines are in limited supply, expanding the number of people who receive some vaccine, such as by halving doses or increasing the interval between doses, can reduce disease and mortality compared with concentrating available vaccine doses in a subset of the population. A corollary of such dose-sparing strategies is that the vaccinated individuals may have less protective immunity. Concerns have been raised that expanding the fraction of the population with partial immunity to SARS-CoV-2 could increase selection for vaccine-escape variants, ultimately undermining vaccine effectiveness. We argue that, although this is possible, preliminary evidence instead suggests such strategies should slow the rate of viral escape from vaccine or naturally induced immunity. As long as vaccination provides some protection against escape variants, the corresponding reduction in prevalence and incidence should reduce the rate at which new variants are generated and the speed of adaptation. Because there is little evidence of efficient immune selection of SARS-CoV-2 during typical infections, these population-level effects are likely to dominate vaccine-induced evolution.

Geographic and demographic heterogeneity of SARS-CoV-2 diagnostic testing in Illinois, USA, March to December 2020

Background

Availability of SARS-CoV-2 testing in the United States (U.S.) has fluctuated through the course of the COVID-19 pandemic, including in the U.S. state of Illinois. Despite substantial ramp-up in test volume, access to SARS-CoV-2 testing remains limited, heterogeneous, and insufficient to control spread.

Methods

We compared SARS-CoV-2 testing rates across geographic regions, over time, and by demographic characteristics (i.e., age and racial/ethnic groups) in Illinois during March through December 2020. We compared age-matched case fatality ratios and infection fatality ratios through time to estimate the fraction of SARS-CoV-2 infections that have been detected through diagnostic testing.

Results

By the end of 2020, initial geographic differences in testing rates had closed substantially. Case fatality ratios were higher in non-Hispanic Black and Hispanic/Latino populations in Illinois relative to non-Hispanic White populations, suggesting that tests were insufficient to accurately capture the true burden of COVID-19 disease in the minority populations during the initial epidemic wave. While testing disparities decreased during 2020, Hispanic/Latino populations consistently remained the least tested at 1.87 tests per 1000 population per day compared with 2.58 and 2.87 for non-Hispanic Black and non-Hispanic White populations, respectively, at the end of 2020. Despite a large expansion in testing since the beginning of the first wave of the epidemic, we estimated that over half (50-80%) of all SARS-CoV-2 infections were not detected by diagnostic testing and continued to evade surveillance.

Conclusions

Systematic methods for identifying relatively under-tested geographic regions and demographic groups may enable policymakers to regularly monitor and evaluate the shifting landscape of diagnostic testing, allowing officials to prioritize allocation of testing resources to reduce disparities in COVID-19 burden and eventually reduce SARS-CoV-2 transmission.

Model-informed COVID-19 vaccine prioritization strategies by age and serostatus

Limited initial supply of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine raises the question of how to prioritize available doses. We used a mathematical model to compare five age-stratified prioritization strategies. A highly effective transmission-blocking vaccine prioritized to adults ages 20 to 49 years minimized cumulative incidence, but mortality and years of life lost were minimized in most scenarios when the vaccine was prioritized to adults greater than 60 years old. Use of individual-level serological tests to redirect doses to seronegative individuals improved the marginal impact of each dose while potentially reducing existing inequities in COVID-19 impact. Although maximum impact prioritization strategies were broadly consistent across countries, transmission rates, vaccination rollout speeds, and estimates of naturally acquired immunity, this framework can be used to compare impacts of prioritization strategies across contexts.

Model-informed COVID-19 vaccine prioritization strategies by age and serostatus

Limited initial supply of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine raises the question of how to prioritize available doses. We used a mathematical model to compare five age-stratified prioritization strategies. A highly effective transmission-blocking vaccine prioritized to adults ages 20 to 49 years minimized cumulative incidence, but mortality and years of life lost were minimized in most scenarios when the vaccine was prioritized to adults greater than 60 years old. Use of individual-level serological tests to redirect doses to seronegative individuals improved the marginal impact of each dose while potentially reducing existing inequities in COVID-19 impact. Although maximum impact prioritization strategies were broadly consistent across countries, transmission rates, vaccination rollout speeds, and estimates of naturally acquired immunity, this framework can be used to compare impacts of prioritization strategies across contexts.

Model-informed COVID-19 vaccine prioritization strategies by age and serostatus

Limited initial supply of SARS-CoV-2 vaccine raises the question of how to prioritize available doses. Here, we used a mathematical model to compare five age-stratified prioritization strategies. A highly effective transmission-blocking vaccine prioritized to adults ages 20-49 years minimized cumulative incidence, but mortality and years of life lost were minimized in most scenarios when the vaccine was prioritized to adults over 60 years old. Use of individual-level serological tests to redirect doses to seronegative individuals improved the marginal impact of each dose while potentially reducing existing inequities in COVID-19 impact. While maximum impact prioritization strategies were broadly consistent across countries, transmission rates, vaccination rollout speeds, and estimates of naturally acquired immunity, this framework can be used to compare impacts of prioritization strategies across contexts.

Practical considerations for measuring the effective reproductive number, Rt

Estimation of the effective reproductive number Rt is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of Rt, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation.

Comparison of Human H3N2 Antibody Responses Elicited by Egg-Based, Cell-Based, and Recombinant Protein-Based Influenza Vaccines During the 2017-2018 Season

BACKGROUND

The H3N2 component of egg-based 2017-2018 influenza vaccines possessed an adaptive substitution that alters antigenicity. Several influenza vaccines include antigens that are produced through alternative systems, but a systematic comparison of different vaccines used during the 2017-2018 season has not been completed.

METHODS

We compared antibody responses in humans vaccinated with Fluzone (egg-based, n = 23), Fluzone High-Dose (egg-based, n = 16), Flublok (recombinant protein-based, n = 23), or Flucelvax (cell-based, n = 23) during the 2017-2018 season. We completed neutralization assays using an egg-adapted H3N2 virus, a cell-based H3N2 virus, wild-type 3c2.A and 3c2.A2 H3N2 viruses, and the H1N1 vaccine strain. We also performed enzyme-linked immunosorbent assays using a recombinant wild-type 3c2.A hemagglutinin. Antibody responses were compared in adjusted analysis.

RESULTS

Postvaccination neutralizing antibody titers to 3c2.A and 3c2.A2 were higher in Flublok recipients compared with Flucelvax or Fluzone recipients (P < .01). Postvaccination titers to 3c2.A and 3c2.A2 were similar in Flublok and Fluzone High-Dose recipients, though seroconversion rates trended higher in Flublok recipients. Postvaccination titers in Flucelvax recipients were low to all H3N2 viruses tested, including the cell-based H3N2 strain. Postvaccination neutralizing antibody titers to H1N1 were similar among the different vaccine groups.

CONCLUSIONS

These data suggest that influenza vaccine antigen match and dose are both important for eliciting optimal H3N2 antibody responses in humans. Future studies should be designed to determine if our findings directly impact vaccine effectiveness.

CLINICAL TRIALS REGISTRATION

NCT03068949.

Middle-aged individuals may be in a perpetual state of H3N2 influenza virus susceptibility

Influenza virus exposures in childhood can establish long-lived memory B cell responses that can be recalled later in life. Here, we complete a large serological survey to elucidate the specificity of antibodies against contemporary H3N2 viruses in differently aged individuals who were likely primed with different H3N2 strains in childhood. We find that most humans who were first infected in childhood with H3N2 viral strains from the 1960s and 1970s possess non-neutralizing antibodies against contemporary 3c2.A H3N2 viruses. We find that 3c2.A H3N2 virus infections boost non-neutralizing H3N2 antibodies in middle-aged individuals, potentially leaving many of them in a perpetual state of 3c2.A H3N2 viral susceptibility.

Practical considerations for measuring the effective reproductive number, R t

Estimation of the effective reproductive number, R t , is important for detecting changes in disease transmission over time. During the COVID-19 pandemic, policymakers and public health officials are using R t to assess the effectiveness of interventions and to inform policy. However, estimation of R t from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of R t , we recommend the approach of Cori et al. (2013), which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis (2004), are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to spread. We advise against using methods derived from Bettencourt and Ribeiro (2008), as the resulting R t estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in R t estimation.

Earliest infections predict the age distribution of seasonal influenza A cases

Seasonal variation in the age distribution of influenza A cases suggests that factors other than age shape susceptibility to medically attended infection. We ask whether these differences can be partly explained by protection conferred by childhood influenza infection, which has lasting impacts on immune responses to influenza and protection against new influenza A subtypes (phenomena known as original antigenic sin and immune imprinting). Fitting a statistical model to data from studies of influenza vaccine effectiveness (VE), we find that primary infection appears to reduce the risk of medically attended infection with that subtype throughout life. This effect is stronger for H1N1 compared to H3N2. Additionally, we find evidence that VE varies with both age and birth year, suggesting that VE is sensitive to early exposures. Our findings may improve estimates of age-specific risk and VE in similarly vaccinated populations and thus improve forecasting and vaccination strategies to combat seasonal influenza.

CpG-creating mutations are costly in many human viruses

Mutations can occur throughout the virus genome and may be beneficial, neutral or deleterious. We are interested in mutations that yield a C next to a G, producing CpG sites. CpG sites are rare in eukaryotic and viral genomes. For the eukaryotes, it is thought that CpG sites are rare because they are prone to mutation when methylated. In viruses, we know less about why CpG sites are rare. A previous study in HIV suggested that CpG-creating transition mutations are more costly than similar non-CpG-creating mutations. To determine if this is the case in other viruses, we analyzed the allele frequencies of CpG-creating and non-CpG-creating mutations across various strains, subtypes, and genes of viruses using existing data obtained from Genbank, HIV Databases, and Virus Pathogen Resource. Our results suggest that CpG sites are indeed costly for most viruses. By understanding the cost of CpG sites, we can obtain further insights into the evolution and adaptation of viruses.

Poor Immunogenicity, Not Vaccine Strain Egg Adaptation, May Explain the Low H3N2 Influenza Vaccine Effectiveness in 2012-2013

Background

Influenza vaccination aims to prevent infection by influenza virus and reduce associated morbidity and mortality; however, vaccine effectiveness (VE) can be modest, especially for subtype A(H3N2). Low VE has been attributed to mismatches between the vaccine and circulating influenza strains and to the vaccine’s elicitation of protective immunity in only a subset of the population. The low H3N2 VE in the 2012–2013 season was attributed to egg-adaptive mutations that created antigenic mismatch between the actual vaccine strain (IVR-165) and both the intended vaccine strain (A/Victoria/361/2011) and the predominant circulating strains (clades 3C.2 and 3C.3).

Methods

We investigated the basis of low VE in 2012–2013 by determining whether vaccinated and unvaccinated individuals were infected by different viral strains and by assessing the serologic responses to IVR-165, A/Victoria/361/2011, and 3C.2 and 3C.3 strains in an adult cohort before and after vaccination.

Results

We found no significant genetic differences between the strains that infected vaccinated and unvaccinated individuals. Vaccination increased titers to A/Victoria/361/2011 and 3C.2 and 3C.3 representative strains as much as to IVR-165. These results are consistent with the hypothesis that vaccination boosted cross-reactive immune responses instead of specific responses against unique vaccine epitopes. Only approximately one-third of the cohort achieved a ≥4-fold increase in titer.

Conclusions

In contrast to analyses based on ferret studies, low H3N2 VE in 2012–2013 in adults does not appear to be due to egg adaptation of the vaccine strain. Instead, low VE might have been caused by low vaccine immunogenicity in a subset of the population.

1203. Epidemiology of Respiratory Viruses in Influenza-Vaccinated Healthcare Workers During an H1N1-Dominant Season

Background

Respiratory pathogens are an important cause of morbidity and mortality in hospitalized patients and nosocomial spread of such pathogens is known to occur. However, little is known about the epidemiology of respiratory viruses in healthcare workers (HCW).

Methods

Between December 28, 2018 and April 26, 2019 enrolled HCW completed a weekly symptom diary, including presence or absence of respiratory symptoms, flu exposure history and whether they received medical attention. Vaccination and flu infection history were collected on enrollment. Participants self-collected flocked nasopharyngeal (NP) swabs every other week and if they reported any symptom on the weekly diary. These were tested using a multiplex PCR platform (Biofire, Salt Lake City, UT) with targets for 14 respiratory viruses. Symptomatic HCW with influenza or respiratory syncytial virus (RSV) were notified and followed policy regarding work restriction.

Results

66 HCWs provided baseline data and 57 continued data submission (9 withdrew). The active participants included 13 nurses (22.8%), 7 advanced practice providers (12.3%), 18 physicians (31.6%), and 19 other (33.3%). Participants received quadrivalent inactivated flu vaccine this season (2 self-reported/unknown type). Compliance was 89.8% (749 of 834) with weekly diary completion and 83.3% (378/454) for biweekly NP swabs. Thirty-nine unique participants reported symptoms on weekly diaries 100 times and submitted 88 total “symptomatic” NP swabs (88% compliance). Of these, 16 swabs revealed any pathogen (18.2%) and 3 had influenza H3N2 (18.8%) (only one reported fever). Other pathogens identified are detailed in Figure 1. 12 of the 366 asymptomatic swabs were positive for respiratory viruses (23.3%, see Figure 1). No participant had asymptomatic influenza.

Conclusion

Pauci-symptomatic influenza has been previously described by our group and others and is noted even in this small cohort. While asymptomatic flu was not found, there were several cases of other asymptomatic respiratory viruses in HCW. Analysis of the impact on patients is still underway from this cohort but the initial data suggest that patients are at risk of contracting healthcare-acquired respiratory infection even from health care providers.

Disclosures

All authors: No reported disclosures,

Selection and Neutral Mutations Drive Pervasive Mutability Losses in Long-Lived Anti-HIV B-Cell Lineages

High-affinity antibodies arise within weeks of infection from the evolution of B-cell receptors under selection to improve antigen recognition. This rapid adaptation is enabled by the distribution of highly mutable "hotspot" motifs in B-cell receptor genes. High mutability in antigen-binding regions (complementarity determining regions [CDRs]) creates variation in binding affinity, whereas low mutability in structurally important regions (framework regions [FRs]) may reduce the frequency of destabilizing mutations. During the response, loss of mutational hotspots and changes in their distribution across CDRs and FRs are predicted to compromise the adaptability of B-cell receptors, yet the contributions of different mechanisms to gains and losses of hotspots remain unclear. We reconstructed changes in anti-HIV B-cell receptor sequences and show that mutability losses were ∼56% more frequent than gains in both CDRs and FRs, with the higher relative mutability of CDRs maintained throughout the response. At least 21% of the total mutability loss was caused by synonymous mutations. However, nonsynonymous substitutions caused most (79%) of the mutability loss in CDRs. Because CDRs also show strong positive selection, this result suggests that selection for mutations that increase binding affinity contributed to loss of mutability in antigen-binding regions. Although recurrent adaptation to evolving viruses could indirectly select for high mutation rates, we found no evidence of indirect selection to increase or retain hotspots. Our results suggest mutability losses are intrinsic to both the neutral and adaptive evolution of B-cell populations and might constrain their adaptation to rapidly evolving pathogens such as HIV and influenza.

Spec-seq unveils transcriptional subpopulations of antibody-secreting cells following influenza vaccination

Vaccines are among the most effective public health tools for combating certain infectious diseases such as influenza. The role of the humoral immune system in vaccine-induced protection is widely appreciated; however, our understanding of how antibody specificities relate to B cell function remains limited due to the complexity of polyclonal antibody responses. To address this, we developed the Spec-seq framework, which allows for simultaneous monoclonal antibody (mAb) characterization and transcriptional profiling from the same single cell. Here, we present the first application of the Spec-seq framework, which we applied to human plasmablasts after influenza vaccination in order to characterize transcriptional differences governed by B cell receptor (BCR) isotype and vaccine reactivity. Our analysis did not find evidence of long-term transcriptional specialization between plasmablasts of different isotypes. However, we did find enhanced transcriptional similarity between clonally related B cells, as well as distinct transcriptional signatures ascribed by BCR vaccine recognition. These data suggest IgG and IgA vaccine-positive plasmablasts are largely similar, whereas IgA vaccine-negative cells appear to be transcriptionally distinct from conventional, terminally differentiated, antigen-induced peripheral blood plasmablasts.

Use of an individual-based model of pneumococcal carriage for planning a randomized trial of a whole-cell vaccine

For encapsulated bacteria such as Streptococcus pneumoniae, asymptomatic carriage is more common and longer in duration than disease, and hence is often a more convenient endpoint for clinical trials of vaccines against these bacteria. However, using a carriage endpoint entails specific challenges. Carriage is almost always measured as prevalence, whereas the vaccine may act by reducing incidence or duration. Thus, to determine sample size requirements, its impact on prevalence must first be estimated. The relationship between incidence and prevalence (or duration and prevalence) is convex, saturating at 100% prevalence. For this reason, the proportional effect of a vaccine on prevalence is typically less than its proportional effect on incidence or duration. This relationship is further complicated in the presence of multiple pathogen strains. In addition, host immunity to carriage accumulates rapidly with frequent exposures in early years of life, creating potentially complex interactions with the vaccine’s effect. We conducted a simulation study to predict the impact of an inactivated whole cell pneumococcal vaccine—believed to reduce carriage duration—on carriage prevalence in different age groups and trial settings. We used an individual-based model of pneumococcal carriage that incorporates relevant immunological processes, both vaccine-induced and naturally acquired. Our simulations showed that for a wide range of vaccine efficacies, sampling time and age at vaccination are important determinants of sample size. There is a window of favorable sampling times during which the required sample size is relatively low, and this window is prolonged with a younger age at vaccination, and in a trial setting with lower transmission intensity. These results illustrate the ability of simulation studies to inform the planning of vaccine trials with carriage endpoints, and the methods we present here can be applied to trials evaluating other pneumococcal vaccine candidates or comparing alternative dosing schedules for the existing conjugate vaccines.

Streptococcus pneumoniae, a bacterium carried in the nasopharynx of many healthy people, is also a leading cause of bacterial pneumonia, sepsis, and ear infections in children aged five years and younger. Vaccines targeting select strains of S. pneumoniae have been effective, and the development of new vaccines, particularly those that target all strains, can further lower disease burden. For clinical trials of these vaccines, the number of study participants needed depends on the expected effect of the vaccine on a conveniently measured outcome: asymptomatic carriage. The most economical way to test a vaccine for its effect on carriage is by measuring prevalence at a specific time, and comparing vaccinated to unvaccinated participants. The relationship between incidence (or duration) and prevalence is complex, and changes with time as children develop natural immunity. We explored this relationship using a mathematical model. Given a vaccine efficacy, our computer simulations predict that fewer study participants are needed if they are vaccinated at a younger age, taken from a population with intermediate levels of transmission, and sampled for carriage at a certain time window: 9 to 18 months after vaccination. Our study illustrates how simulation studies can help plan more efficient vaccine trials.

Immune History and Influenza Vaccine Effectiveness

The imperfect effectiveness of seasonal influenza vaccines is often blamed on antigenic mismatch, but even when the match appears good, effectiveness can be surprisingly low. Seasonal influenza vaccines also stand out for their variable effectiveness by age group from year to year and by recent vaccination status. These patterns suggest a role for immune history in influenza vaccine effectiveness, but inference is complicated by uncertainty about the contributions of bias to the estimates themselves. In this review, we describe unexpected patterns in the effectiveness of seasonal influenza vaccination and explain how these patterns might arise as consequences of study design, the dynamics of immune memory, or both. Resolving this uncertainty could lead to improvements in vaccination strategy, including the use of universal vaccines in experienced populations, and the evaluation of vaccine efficacy against influenza and other antigenically variable pathogens.

Host population structure and treatment frequency maintain balancing selection on drug resistance

It is a truism that antimicrobial drugs select for resistance, but explaining pathogen- and population-specific variation in patterns of resistance remains an open problem. Like other common commensals, Streptococcus pneumoniae has demonstrated persistent coexistence of drug-sensitive and drug-resistant strains. Theoretically, this outcome is unlikely. We modelled the dynamics of competing strains of S. pneumoniae to investigate the impact of transmission dynamics and treatment-induced selective pressures on the probability of stable coexistence. We find that the outcome of competition is extremely sensitive to structure in the host population, although coexistence can arise from age-assortative transmission models with age-varying rates of antibiotic use. Moreover, we find that the selective pressure from antibiotics arises not so much from the rate of antibiotic use per se but from the frequency of treatment: frequent antibiotic therapy disproportionately impacts the fitness of sensitive strains. This same phenomenon explains why serotypes with longer durations of carriage tend to be more resistant. These dynamics may apply to other potentially pathogenic, microbial commensals and highlight how population structure, which is often omitted from models, can have a large impact.

Explaining the geographical origins of seasonal influenza A (H3N2)

Most antigenically novel and evolutionarily successful strains of seasonal influenza A (H3N2) originate in East, South and Southeast Asia. To understand this pattern, we simulated the ecological and evolutionary dynamics of influenza in a host metapopulation representing the temperate north, tropics and temperate south. Although seasonality and air traffic are frequently used to explain global migratory patterns of influenza, we find that other factors may have a comparable or greater impact. Notably, a region's basic reproductive number (R0) strongly affects the antigenic evolution of its viral population and the probability that its strains will spread and fix globally: a 17-28% higher R0 in one region can explain the observed patterns. Seasonality, in contrast, increases the probability that a tropical (less seasonal) population will export evolutionarily successful strains but alone does not predict that these strains will be antigenically advanced. The relative sizes of different host populations, their birth and death rates, and the region in which H3N2 first appears affect influenza's phylogeography in different but relatively minor ways. These results suggest general principles that dictate the spatial dynamics of antigenically evolving pathogens and offer predictions for how changes in human ecology might affect influenza evolution.

Immune history and influenza virus susceptibility

Antibody responses to influenza viruses are critical for protection, but the ways in which repeated viral exposures shape antibody evolution and effectiveness over time remain controversial. Early observations demonstrated that viral exposure history has a profound effect on the specificity and magnitude of antibody responses to a new viral strain, a phenomenon called 'original antigenic sin.' Although 'sin' might suppress some aspects of the immune response, so far there is little indication that hosts with pre-existing immunity are more susceptible to viral infections compared to naïve hosts. However, the tendency of the immune response to focus on previously recognized conserved epitopes when encountering new viral strains can create an opportunity cost when mutations arise in these conserved epitopes. Hosts with different exposure histories may continue to experience distinct patterns of infection over time, which may influence influenza viruses' continued antigenic evolution. Understanding the dynamics of B cell competition that underlie the development of antibody responses might help explain the low effectiveness of current influenza vaccines and lead to better vaccination strategies.

Limits to Causal Inference with State-Space Reconstruction for Infectious Disease

Infectious diseases are notorious for their complex dynamics, which make it difficult to fit models to test hypotheses. Methods based on state-space reconstruction have been proposed to infer causal interactions in noisy, nonlinear dynamical systems. These “model-free” methods are collectively known as convergent cross-mapping (CCM). Although CCM has theoretical support, natural systems routinely violate its assumptions. To identify the practical limits of causal inference under CCM, we simulated the dynamics of two pathogen strains with varying interaction strengths. The original method of CCM is extremely sensitive to periodic fluctuations, inferring interactions between independent strains that oscillate with similar frequencies. This sensitivity vanishes with alternative criteria for inferring causality. However, CCM remains sensitive to high levels of process noise and changes to the deterministic attractor. This sensitivity is problematic because it remains challenging to gauge noise and dynamical changes in natural systems, including the quality of reconstructed attractors that underlie cross-mapping. We illustrate these challenges by analyzing time series of reportable childhood infections in New York City and Chicago during the pre-vaccine era. We comment on the statistical and conceptual challenges that currently limit the use of state-space reconstruction in causal inference.

Viral factors in influenza pandemic risk assessment

The threat of an influenza A virus pandemic stems from continual virus spillovers from reservoir species, a tiny fraction of which spark sustained transmission in humans. To date, no pandemic emergence of a new influenza strain has been preceded by detection of a closely related precursor in an animal or human. Nonetheless, influenza surveillance efforts are expanding, prompting a need for tools to assess the pandemic risk posed by a detected virus. The goal would be to use genetic sequence and/or biological assays of viral traits to identify those non-human influenza viruses with the greatest risk of evolving into pandemic threats, and/or to understand drivers of such evolution, to prioritize pandemic prevention or response measures. We describe such efforts, identify progress and ongoing challenges, and discuss three specific traits of influenza viruses (hemagglutinin receptor binding specificity, hemagglutinin pH of activation, and polymerase complex efficiency) that contribute to pandemic risk.

Trade-offs in antibody repertoires to complex antigens

Pathogens vary in their antigenic complexity. While some pathogens such as measles present a few relatively invariant targets to the immune system, others such as malaria display considerable antigenic diversity. How the immune response copes in the presence of multiple antigens, and whether a trade-off exists between the breadth and efficacy of antibody (Ab)-mediated immune responses, are unsolved problems. We present a theoretical model of affinity maturation of B-cell receptors (BCRs) during a primary infection and examine how variation in the number of accessible antigenic sites alters the Ab repertoire. Naive B cells with randomly generated receptor sequences initiate the germinal centre (GC) reaction. The binding affinity of a BCR to an antigen is quantified via a genotype-phenotype map, based on a random energy landscape, that combines local and distant interactions between residues. In the presence of numerous antigens or epitopes, B-cell clones with different specificities compete for stimulation during rounds of mutation within GCs. We find that the availability of many epitopes reduces the affinity and relative breadth of the Ab repertoire. Despite the stochasticity of somatic hypermutation, patterns of immunodominance are strongly shaped by chance selection of naive B cells with specificities for particular epitopes. Our model provides a mechanistic basis for the diversity of Ab repertoires and the evolutionary advantage of antigenically complex pathogens.

The evolution within us

The B-cell immune response is a remarkable evolutionary system found in jawed vertebrates. B-cell receptors, the membrane-bound form of antibodies, are capable of evolving high affinity to almost any foreign protein. High germline diversity and rapid evolution upon encounter with antigen explain the general adaptability of B-cell populations, but the dynamics of repertoires are less well understood. These dynamics are scientifically and clinically important. After highlighting the remarkable characteristics of naive and experienced B-cell repertoires, especially biased usage of genes encoding the B-cell receptors, we contrast methods of sequence analysis and their attempts to explain patterns of B-cell evolution. These phylogenetic approaches are currently unlinked to explicit models of B-cell competition, which analyse repertoire evolution at the level of phenotype, the affinities and specificities to particular antigenic sites. The models, in turn, suggest how chance, infection history and other factors contribute to different patterns of immunodominance and protection between people. Challenges in rational vaccine design, specifically vaccines to induce broadly neutralizing antibodies to HIV, underscore critical gaps in our understanding of B cells' evolutionary and ecological dynamics.