Recombinant SARS-CoV-2 genomes are currently circulating at low levels

Viral recombination can generate novel genotypes with unique phenotypic characteristics, including transmissibility and virulence. Although the capacity for recombination among betacoronaviruses is well documented, there is limited evidence of recombination between SARS-CoV-2 strains. By identifying the mutations that primarily determine SARS-CoV-2 clade structure, we developed a lightweight approach for detecting recombinant genomes. Among the over 537,000 genomes queried, we detect 1175 putative recombinants that contain multiple mutational markers from distinct clades. Additional phylogenetic analysis and the observed co-circulation of predicted parent clades in the geographic regions of exposure further support the feasibility of recombination in these detected cases. An analysis of these detected cases did not reveal any evidence for recombination hotspots in the SARS-CoV-2 genome. Although most recombinant genotypes were detected a limited number of times, at least two recombinants are now widely transmitted. Recombinant genomes were also found to contain substitutions of concern for elevated transmissibility and lower vaccine efficacy, including D614G, N501Y, E484K, and L452R. Adjusting for an unequal probability of detecting recombinants derived from different parent clades, and for geographic variation in clade abundance, we estimate that at most 5% of circulating viruses in the USA and UK are recombinant. While the phenotypic characterization of detected recombinants was beyond the scope of our analysis, the identification of transmitted recombinants involving substitutions of concern underscores the need to sustain efforts to monitor the emergence of new genotypes generated through recombination.

The Effect of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Mitigation Strategies on Seasonal Respiratory Viruses: A Tale of 2 Large Metropolitan Centers in the United States

To assess the impact of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic on seasonal respiratory viruses, absolute case counts and viral reproductive rates from 2019-2020 were compared against previous seasons. Our findings suggest that the public health measures implemented to reduce SARS-CoV-2 transmission significantly reduced the transmission of other respiratory viruses.

Reanalysis of deep-sequencing data from Austria points towards a small SARS-COV-2 transmission bottleneck on the order of one to three virions.. [PMID: 0 DOI: 10.1101/2021.02.22.432096]

An early analysis of SARS-CoV-2 deep-sequencing data that combined epidemiological and genetic data to characterize the transmission dynamics of the virus in and beyond Austria concluded that the size of the virus’s transmission bottleneck was large – on the order of 1000 virions. We performed new computational analyses using these deep-sequenced samples from Austria. Our analyses included characterization of transmission bottleneck sizes across a range of variant calling thresholds and examination of patterns of shared low-frequency variants between transmission pairs in cases where de novo genetic variation was present in the recipient. From these analyses, among others, we found that SARS-CoV-2 transmission bottlenecks are instead likely to be very tight, on the order of 1-3 virions. These findings have important consequences for understanding how SARS-CoV-2 evolves between hosts and the processes shaping genetic variation observed at the population level.

Transmission of SARS-CoV-2 in domestic cats imposes a narrow bottleneck

The evolutionary mechanisms by which SARS-CoV-2 viruses adapt to mammalian hosts and, potentially, undergo antigenic evolution depend on the ways genetic variation is generated and selected within and between individual hosts. Using domestic cats as a model, we show that SARS-CoV-2 consensus sequences remain largely unchanged over time within hosts, while dynamic sub-consensus diversity reveals processes of genetic drift and weak purifying selection. We further identify a notable variant at amino acid position 655 in Spike (H655Y), which was previously shown to confer escape from human monoclonal antibodies. This variant arises rapidly and persists at intermediate frequencies in index cats. It also becomes fixed following transmission in two of three pairs. These dynamics suggest this site may be under positive selection in this system and illustrate how a variant can quickly arise and become fixed in parallel across multiple transmission pairs. Transmission of SARS-CoV-2 in cats involved a narrow bottleneck, with new infections founded by fewer than ten viruses. In RNA virus evolution, stochastic processes like narrow transmission bottlenecks and genetic drift typically act to constrain the overall pace of adaptive evolution. Our data suggest that here, positive selection in index cats followed by a narrow transmission bottleneck may have instead accelerated the fixation of S H655Y, a potentially beneficial SARS-CoV-2 variant. Overall, our study suggests species- and context-specific adaptations are likely to continue to emerge. This underscores the importance of continued genomic surveillance for new SARS-CoV-2 variants as well as heightened scrutiny for signatures of SARS-CoV-2 positive selection in humans and mammalian model systems.

Asymptomatic Infection and Transmission of Pertussis in Households: A Systematic Review

We conducted a systematic review to describe the frequency of mild, atypical, and asymptomatic infection among household contacts of pertussis cases and to explore the published literature for evidence of asymptomatic transmission. We included studies that obtained and tested laboratory specimens from household contacts regardless of symptom presentation and reported the proportion of cases with typical, mild/atypical, or asymptomatic infection. After screening 6789 articles, we included 26 studies. Fourteen studies reported household contacts with mild/atypical pertussis. These comprised up to 46.2% of all contacts tested. Twenty-four studies reported asymptomatic contacts with laboratory-confirmed pertussis, comprising up to 55.6% of those tested. Seven studies presented evidence consistent with asymptomatic pertussis transmission between household contacts. Our results demonstrate a high prevalence of subclinical infection in household contacts of pertussis cases, which may play a substantial role in the ongoing transmission of disease. Our review reveals a gap in our understanding of pertussis transmission.

Transmission of SARS-CoV-2 in domestic cats imposes a narrow bottleneck

The evolutionary mechanisms by which SARS-CoV-2 viruses adapt to mammalian hosts and, potentially, undergo antigenic evolution depend on the ways genetic variation is generated and selected within and between individual hosts. Using domestic cats as a model, we show that SARS-CoV-2 consensus sequences remain largely unchanged over time within hosts, while dynamic sub-consensus diversity reveals processes of genetic drift and weak purifying selection. We further identify a notable variant at amino acid position 655 in Spike (H655Y), which was previously shown to confer escape from human monoclonal antibodies. This variant arises rapidly and persists at intermediate frequencies in index cats. It also becomes fixed following transmission in two of three pairs. These dynamics suggest this site may be under positive selection in this system and illustrate how a variant can quickly arise and become fixed in parallel across multiple transmission pairs. Transmission of SARS-CoV-2 in cats involved a narrow bottleneck, with new infections founded by fewer than ten viruses. In RNA virus evolution, stochastic processes like narrow transmission bottlenecks and genetic drift typically act to constrain the overall pace of adaptive evolution. Our data suggest that here, positive selection in index cats followed by a narrow transmission bottleneck may have instead accelerated the fixation of S H655Y, a potentially beneficial SARS-CoV-2 variant. Overall, our study suggests species- and context-specific adaptations are likely to continue to emerge. This underscores the importance of continued genomic surveillance for new SARS-CoV-2 variants as well as heightened scrutiny for signatures of SARS-CoV-2 positive selection in humans and mammalian model systems.

484. The Impact of SARS-CoV-2 on Reproduction Rates of Seasonal Influenza and RSV

Background

The COVID-19 pandemic caused by SARS-CoV-2 has precipitated a global health crisis. In an effort to decrease person-to-person transmission, societal-level non-pharmacologic interventions (NPIs) to maintain social distancing have been enacted. As SARS-CoV-2 shares similar routes of transmission with other respiratory viruses, implementation of these NPIs may have decreased transmission for multiple viral pathogens. We compared influenza and respiratory syncytial (RSV) rates in prior seasons to rates during the 2019 - 2020 season at two large academic centers in Atlanta and Boston.

Methods

The clinical records were queried for adults with respiratory virus testing conducted at the Emory Healthcare system and associated clinics in Atlanta and the Mass General Brigham (MGB) Healthcare System in Boston. Total cases for influenza A and B, RSV and SARS-CoV-2 were analyzed for each week of the past 5 seasons (07/01/2015-05/30/2020) for the Atlanta and Boston sites. Systematic changes in viral infection rates were calculated using viral reproduction rates, R(t), between consecutive weeks. R(t) is the ratio of the number of positive cases in one week to the number of positive cases in the previous week. We used statistical bootstrapping to determine whether R(t) for influenza and RSV were lower in 2019–2020 following the introduction of SARS-CoV-2. Analyses were conducted using R (v 4.0.0).

Absolute respiratory virus activity by season, Boston (panel A) v. Atlanta (panel B)

Results

For the 2019–2020 Atlanta season, R(t) < 1 (which reflects steady decline in infection rates) occurred at week 28 for influenza A, week 33 for influenza B, and week 35 for RSV, which corresponded with the increase of SARS-Cov-2 cases. The R(t) of these viruses stayed at or near 1 during weeks 33–35 in prior seasons, and R(t) was greater than 1 up to week 47. Data from MGB sites showed similar trends with a sudden decline in R(t) to < 1 at the start of the SARS-CoV-2 pandemic.

Conclusion

We note decreased transmission of influenza and RSV during a time window where widespread movement restrictions and social distancing were imposed to control COVID-19. This trend was most pronounced for influenza A in Atlanta and influenza B in Boston. These data suggest that NPIs can have important effects across multiple pathogens.

Disclosures

Kraft Colleen, MD, MSc, Rebiotix (Advisor or Review Panel member)

Revealing fine-scale spatiotemporal differences in SARS-CoV-2 introduction and spread

Evidence-based public health approaches that minimize the introduction and spread of new SARS-CoV-2 transmission clusters are urgently needed in the United States and other countries struggling with expanding epidemics. Here we analyze 247 full-genome SARS-CoV-2 sequences from two nearby communities in Wisconsin, USA, and find surprisingly distinct patterns of viral spread. Dane County had the 12th known introduction of SARS-CoV-2 in the United States, but this did not lead to descendant community spread. Instead, the Dane County outbreak was seeded by multiple later introductions, followed by limited community spread. In contrast, relatively few introductions in Milwaukee County led to extensive community spread. We present evidence for reduced viral spread in both counties following the statewide "Safer at Home" order, which went into effect 25 March 2020. Our results suggest patterns of SARS-CoV-2 transmission may vary substantially even in nearby communities. Understanding these local patterns will enable better targeting of public health interventions.

Full genome viral sequences inform patterns of SARS-CoV-2 spread into and within Israel

Full genome sequences are increasingly used to track the geographic spread and transmission dynamics of viral pathogens. Here, with a focus on Israel, we sequence 212 SARS-CoV-2 sequences and use them to perform a comprehensive analysis to trace the origins and spread of the virus. We find that travelers returning from the United States of America significantly contributed to viral spread in Israel, more than their proportion in incoming infected travelers. Using phylodynamic analysis, we estimate that the basic reproduction number of the virus was initially around 2.5, dropping by more than two-thirds following the implementation of social distancing measures. We further report high levels of transmission heterogeneity in SARS-CoV-2 spread, with between 2-10% of infected individuals resulting in 80% of secondary infections. Overall, our findings demonstrate the effectiveness of social distancing measures for reducing viral spread.

Full genome viral sequences inform patterns of SARS-CoV-2 spread into and within Israel

Full genome sequences are increasingly used to track the geographic spread and transmission dynamics of viral pathogens. Here, with a focus on Israel, we sequence 212 SARS-CoV-2 sequences and use them to perform a comprehensive analysis to trace the origins and spread of the virus. We find that travelers returning from the United States of America significantly contributed to viral spread in Israel, more than their proportion in incoming infected travelers. Using phylodynamic analysis, we estimate that the basic reproduction number of the virus was initially around 2.5, dropping by more than two-thirds following the implementation of social distancing measures. We further report high levels of transmission heterogeneity in SARS-CoV-2 spread, with between 2-10% of infected individuals resulting in 80% of secondary infections. Overall, our findings demonstrate the effectiveness of social distancing measures for reducing viral spread.

Cellular co-infection can modulate the efficiency of influenza A virus production and shape the interferon response

During viral infection, the numbers of virions infecting individual cells can vary significantly over time and space. The functional consequences of this variation in cellular multiplicity of infection (MOI) remain poorly understood. Here, we rigorously quantify the phenotypic consequences of cellular MOI during influenza A virus (IAV) infection over a single round of replication in terms of cell death rates, viral output kinetics, interferon and antiviral effector gene transcription, and superinfection potential. By statistically fitting mathematical models to our data, we precisely define specific functional forms that quantitatively describe the modulation of these phenotypes by MOI at the single cell level. To determine the generality of these functional forms, we compare two distinct cell lines (MDCK cells and A549 cells), both infected with the H1N1 strain A/Puerto Rico/8/1934 (PR8). We find that a model assuming that infected cell death rates are independent of cellular MOI best fits the experimental data in both cell lines. We further observe that a model in which the rate and efficiency of virus production increase with cellular co-infection best fits our observations in MDCK cells, but not in A549 cells. In A549 cells, we also find that induction of type III interferon, but not type I interferon, is highly dependent on cellular MOI, especially at early timepoints. This finding identifies a role for cellular co-infection in shaping the innate immune response to IAV infection. Finally, we show that higher cellular MOI is associated with more potent superinfection exclusion, thus limiting the total number of virions capable of infecting a cell. Overall, this study suggests that the extent of cellular co-infection by influenza viruses may be a critical determinant of both viral production kinetics and cellular infection outcomes in a host cell type-dependent manner.

During influenza A virus (IAV) infection, the number of virions to enter individual cells can be highly variable. Cellular co-infection appears to be common and plays an essential role in facilitating reassortment for IAV, yet little is known about how cellular co-infection influences infection outcomes at the cellular level. Here, we combine quantitative in vitro infection experiments with statistical model fitting to precisely define the phenotypic consequences of cellular co-infection in two cell lines. We reveal that cellular co-infection can increase and accelerate the efficiency of IAV production in a cell line-dependent fashion, identifying it as a potential determinant of viral replication kinetics. We also show that induction of type III, but not type I, interferon is highly dependent upon the number of virions that infect a given cell, implicating cellular co-infection as an important determinant of the host innate immune response to infection. Altogether, our findings show that cellular co-infection plays a crucial role in determining infection outcome. The integration of experimental and statistical modeling approaches detailed here represents a significant advance in the quantitative study of influenza virus infection and should aid ongoing efforts focused on the construction of mathematical models of IAV infection.

Considering indirect benefits is critical when evaluating SARS-CoV-2 vaccine candidates

Significant progress has already been made in development and testing of SARS-CoV-2 vaccines, and Phase III clinical trials have begun for 6 novel vaccine candidates to date. These Phase III trials seek to demonstrate direct benefits of a vaccine on vaccine recipients. However, vaccination is also known to bring about indirect benefits to a population through the reduction of virus circulation. The indirect effects of SARS-CoV-2 vaccination can play a key role in reducing case counts and COVID-19 deaths. To illustrate this point, we show through simulation that a vaccine with strong indirect effects has the potential to reduce SARS-CoV-2 circulation and COVID-19 deaths to a greater extent than an alternative vaccine with stronger direct effects but weaker indirect effects. Protection via indirect effects may be of particular importance in the context of this virus, because elderly individuals are at an elevated risk of death but are also less likely to be directly protected by vaccination due to immune senescence. We therefore encourage ongoing data collection and model development aimed at evaluating the indirect effects of forthcoming SARS-CoV-2 vaccines.

Distinct patterns of SARS-CoV-2 transmission in two nearby communities in Wisconsin, USA

Evidence-based public health approaches that minimize the introduction and spread of new SARS-CoV-2 transmission clusters are urgently needed in the United States and other countries struggling with expanding epidemics. Here we analyze 247 full-genome SARS-CoV-2 sequences from two nearby communities in Wisconsin, USA, and find surprisingly distinct patterns of viral spread. Dane County had the 12th known introduction of SARS-CoV-2 in the United States, but this did not lead to descendant community spread. Instead, the Dane County outbreak was seeded by multiple later introductions, followed by limited community spread. In contrast, relatively few introductions in Milwaukee County led to extensive community spread. We present evidence for reduced viral spread in both counties, and limited viral transmission between counties, following the statewide Safer-at-Home public health order, which went into effect 25 March 2020. Our results suggest that early containment efforts suppressed the spread of SARS-CoV-2 within Wisconsin.

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.

Intrahost cytomegalovirus population genetics following antibody pretreatment in a monkey model of congenital transmission

Human cytomegalovirus (HCMV) infection is the leading non-genetic cause of congenital birth defects worldwide. While several studies have addressed the genetic composition of viral populations in newborns diagnosed with HCMV, little is known regarding mother-to-child viral transmission dynamics and how therapeutic interventions may impact within-host viral populations. Here, we investigate how preexisting CMV-specific antibodies shape the maternal viral population and intrauterine virus transmission. Specifically, we characterize the genetic composition of CMV populations in a monkey model of congenital CMV infection to examine the effects of passively-infused hyperimmune globulin (HIG) on viral population genetics in both maternal and fetal compartments. In this study, 11 seronegative, pregnant monkeys were challenged with rhesus CMV (RhCMV), including a group pretreated with a standard potency HIG preparation (n = 3), a group pretreated with a high-neutralizing potency HIG preparation (n = 3), and an untreated control group (n = 5). Targeted amplicon deep sequencing of RhCMV glycoprotein B and L genes revealed that one of the three strains present in the viral inoculum (UCD52) dominated maternal and fetal viral populations. We identified minor haplotypes of this strain and characterized their dynamics. Many of the identified haplotypes were consistently detected at multiple timepoints within sampled maternal tissues, as well as across tissue compartments, indicating haplotype persistence over time and transmission between maternal compartments. However, haplotype numbers and diversity levels were not appreciably different between control, standard-potency, and high-potency pretreatment groups. We found that while the presence of maternal antibodies reduced viral load and congenital infection, it had no apparent impact on intrahost viral genetic diversity at the investigated loci. Interestingly, some minor haplotypes present in fetal and maternal-fetal interface tissues were also identified as minor haplotypes in corresponding maternal tissues, providing evidence for a loose RhCMV mother-to-fetus transmission bottleneck even in the presence of preexisting antibodies.

Within-host infectious disease models accommodating cellular coinfection, with an application to influenza

Within-host models are useful tools for understanding the processes regulating viral load dynamics. While existing models have considered a wide range of within-host processes, at their core these models have shown remarkable structural similarity. Specifically, the structure of these models generally consider target cells to be either uninfected or infected, with the possibility of accommodating further resolution (e.g. cells that are in an eclipse phase). Recent findings, however, indicate that cellular coinfection is the norm rather than the exception for many viral infectious diseases, and that cells with high multiplicity of infection are present over at least some duration of an infection. The reality of these cellular coinfection dynamics is not accommodated in current within-host models although it may be critical for understanding within-host dynamics. This is particularly the case if multiplicity of infection impacts infected cell phenotypes such as their death rate and their viral production rates. Here, we present a new class of within-host disease models that allow for cellular coinfection in a scalable manner by retaining the low-dimensionality that is a desirable feature of many current within-host models. The models we propose adopt the general structure of epidemiological ‘macroparasite’ models that allow hosts to be variably infected by parasites such as nematodes and host phenotypes to flexibly depend on parasite burden. Specifically, our within-host models consider target cells as ‘hosts’ and viral particles as ‘macroparasites’, and allow viral output and infected cell lifespans, among other phenotypes, to depend on a cell’s multiplicity of infection. We show with an application to influenza that these models can be statistically fit to viral load and other within-host data, and demonstrate using model selection approaches that they have the ability to outperform traditional within-host viral dynamic models. Important in vivo quantities such as the mean multiplicity of cellular infection and time-evolving reassortant frequencies can also be quantified in a straightforward manner once these macroparasite models have been parameterized. The within-host model structure we develop here provides a mathematical way forward to address questions related to the roles of cellular coinfection, collective viral interactions, and viral complementation in within-host viral dynamics and evolution.

Causes and Consequences of Spatial Within-Host Viral Spread

The spread of viral pathogens both between and within hosts is inherently a spatial process. While the spatial aspects of viral spread at the epidemiological level have been increasingly well characterized, the spatial aspects of viral spread within infected hosts are still understudied. Here, with a focus on influenza A viruses (IAVs), we first review experimental studies that have shed light on the mechanisms and spatial dynamics of viral spread within hosts. These studies provide strong empirical evidence for highly localized IAV spread within hosts. Since mathematical and computational within-host models have been increasingly used to gain a quantitative understanding of observed viral dynamic patterns, we then review the (relatively few) computational modeling studies that have shed light on possible factors that structure the dynamics of spatial within-host IAV spread. These factors include the dispersal distance of virions, the localization of the immune response, and heterogeneity in host cell phenotypes across the respiratory tract. While informative, we find in these studies a striking absence of theoretical expectations of how spatial dynamics may impact the dynamics of viral populations. To mitigate this, we turn to the extensive ecological and evolutionary literature on range expansions to provide informed theoretical expectations. We find that factors such as the type of density dependence, the frequency of long-distance dispersal, specific life history characteristics, and the extent of spatial heterogeneity are critical factors affecting the speed of population spread and the genetic composition of spatially expanding populations. For each factor that we identified in the theoretical literature, we draw parallels to its analog in viral populations. We end by discussing current knowledge gaps related to the spatial component of within-host IAV spread and the potential for within-host spatial considerations to inform the development of disease control strategies.

Selection on non-antigenic gene segments of seasonal influenza A virus and its impact on adaptive evolution

Most studies on seasonal influenza A/H3N2 virus adaptation have focused on the main antigenic gene, hemagglutinin. However, there is increasing evidence that the genome-wide genetic background of novel antigenic variants can influence these variants’ emergence probabilities and impact their patterns of dominance in the population. This suggests that non-antigenic genes may be important in shaping the viral evolutionary dynamics. To better understand the role of selection on non-antigenic genes in the adaptive evolution of seasonal influenza viruses, we have developed a simple population genetic model that considers a virus with one antigenic and one non-antigenic gene segment. By simulating this model under different regimes of selection and reassortment, we find that the empirical patterns of lineage turnover for the antigenic and non-antigenic gene segments are best captured when there is both limited viral coinfection and selection operating on both gene segments. In contrast, under a scenario of only neutral evolution in the non-antigenic gene segment, we see persistence of multiple lineages for long periods of time in that segment, which is not compatible with observed molecular evolutionary patterns. Further, we find that reassortment, occurring in coinfected individuals, can increase the speed of viral adaptive evolution by primarily reducing selective interference and genetic linkage effects. Together, these findings suggest that, for influenza, with six internal or non-antigenic gene segments, the evolutionary dynamics of novel antigenic variants are likely to be influenced by the genome-wide genetic background as a result of linked selection among both beneficial and deleterious mutations.

Transmission Bottleneck Size Estimation from Pathogen Deep-Sequencing Data, with an Application to Human Influenza A Virus

The bottleneck governing infectious disease transmission describes the size of the pathogen population transferred from the donor to the recipient host. Accurate quantification of the bottleneck size is particularly important for rapidly evolving pathogens such as influenza virus, as narrow bottlenecks reduce the amount of transferred viral genetic diversity and, thus, may decrease the rate of viral adaptation. Previous studies have estimated bottleneck sizes governing viral transmission by using statistical analyses of variants identified in pathogen sequencing data. These analyses, however, did not account for variant calling thresholds and stochastic viral replication dynamics within recipient hosts. Because these factors can skew bottleneck size estimates, we introduce a new method for inferring bottleneck sizes that accounts for these factors. Through the use of a simulated data set, we first show that our method, based on beta-binomial sampling, accurately recovers transmission bottleneck sizes, whereas other methods fail to do so. We then apply our method to a data set of influenza A virus (IAV) infections for which viral deep-sequencing data from transmission pairs are available. We find that the IAV transmission bottleneck size estimates in this study are highly variable across transmission pairs, while the mean bottleneck size of 196 virions is consistent with a previous estimate for this data set. Furthermore, regression analysis shows a positive association between estimated bottleneck size and donor infection severity, as measured by temperature. These results support findings from experimental transmission studies showing that bottleneck sizes across transmission events can be variable and influenced in part by epidemiological factors.IMPORTANCE The transmission bottleneck size describes the size of the pathogen population transferred from the donor to the recipient host and may affect the rate of pathogen adaptation within host populations. Recent advances in sequencing technology have enabled bottleneck size estimation from pathogen genetic data, although there is not yet a consistency in the statistical methods used. Here, we introduce a new approach to infer the bottleneck size that accounts for variant identification protocols and noise during pathogen replication. We show that failing to account for these factors leads to an underestimation of bottleneck sizes. We apply this method to an existing data set of human influenza virus infections, showing that transmission is governed by a loose, but highly variable, transmission bottleneck whose size is positively associated with the severity of infection of the donor. Beyond advancing our understanding of influenza virus transmission, we hope that this work will provide a standardized statistical approach for bottleneck size estimation for viral pathogens.

Preexisting antibodies can protect against congenital cytomegalovirus infection in monkeys

Human cytomegalovirus (HCMV) is the most common congenital infection and a known cause of microcephaly, sensorineural hearing loss, and cognitive impairment among newborns worldwide. Natural maternal HCMV immunity reduces the incidence of congenital infection, but does not prevent the disease altogether. We employed a nonhuman primate model of congenital CMV infection to investigate the ability of preexisting antibodies to protect against placental CMV transmission in the setting of primary maternal infection and subsequent viremia, which is required for placental virus exposure. Pregnant, CD4+ T cell-depleted, rhesus CMV-seronegative (RhCMV-seronegative) rhesus monkeys were treated with either standardly produced hyperimmune globulin (HIG) from RhCMV-seropositive macaques or dose-optimized, potently RhCMV-neutralizing HIG prior to intravenous challenge with an RhCMV mixture. HIG passive infusion provided complete protection against fetal loss in both groups. The dose-optimized, RhCMV-neutralizing HIG additionally inhibited placental transmission of RhCMV and reduced viral replication and diversity. Our findings suggest that the presence of durable and potently neutralizing antibodies at the time of primary infection can prevent transmission of systemically replicating maternal RhCMV to the developing fetus, and therefore should be a primary target of vaccines to eliminate this neonatal infection.

The effective rate of influenza reassortment is limited during human infection

We characterise the evolutionary dynamics of influenza infection described by viral sequence data collected from two challenge studies conducted in human hosts. Viral sequence data were collected at regular intervals from infected hosts. Changes in the sequence data observed across time show that the within-host evolution of the virus was driven by the reversion of variants acquired during previous passaging of the virus. Treatment of some patients with oseltamivir on the first day of infection did not lead to the emergence of drug resistance variants in patients. Using an evolutionary model, we inferred the effective rate of reassortment between viral segments, measuring the extent to which randomly chosen viruses within the host exchange genetic material. We find strong evidence that the rate of effective reassortment is low, such that genetic associations between polymorphic loci in different segments are preserved during the course of an infection in a manner not compatible with epistasis. Combining our evidence with that of previous studies we suggest that spatial heterogeneity in the viral population may reduce the extent to which reassortment is observed. Our results do not contradict previous findings of high rates of viral reassortment in vitro and in small animal studies, but indicate that in human hosts the effective rate of reassortment may be substantially more limited.

The influenza virus is an important cause of disease in the human population. During the course of an infection the virus can evolve rapidly. An important mechanism of viral evolution is reassortment, whereby different segments of the influenza genome are shuffled with other segments, producing new viral combinations. Here we study natural selection and reassortment during the course of infections occurring in human hosts. Examining viral genome sequence data from these infections, we note that genetic variants that were acquired during the growth of viruses in culture are selected against in the human host. In addition, we find evidence that the effective rate of reassortment is low. We suggest that the spatial separation between viruses in different parts of the host airway may limit the extent to which genetically distinct segments reassort with one another. Within the global population of influenza viruses, reassortment remains an important factor. However, reassortment is not so rapid as to exclude the possibility of interactions between genome segments affecting the course of influenza evolution during a single infection.

The effects of a deleterious mutation load on patterns of influenza A/H3N2's antigenic evolution in humans

Recent phylogenetic analyses indicate that RNA virus populations carry a significant deleterious mutation load. This mutation load has the potential to shape patterns of adaptive evolution via genetic linkage to beneficial mutations. Here, we examine the effect of deleterious mutations on patterns of influenza A subtype H3N2's antigenic evolution in humans. By first analyzing simple models of influenza that incorporate a mutation load, we show that deleterious mutations, as expected, act to slow the virus's rate of antigenic evolution, while making it more punctuated in nature. These models further predict three distinct molecular pathways by which antigenic cluster transitions occur, and we find phylogenetic patterns consistent with each of these pathways in influenza virus sequences. Simulations of a more complex phylodynamic model further indicate that antigenic mutations act in concert with deleterious mutations to reproduce influenza's spindly hemagglutinin phylogeny, co-circulation of antigenic variants, and high annual attack rates.

DOI:http://dx.doi.org/10.7554/eLife.07361.001

Each year, up to 15% of the world's population experience symptoms of an influenza infection, also commonly known as flu. The most common culprit is a strain of the virus called influenza type A subtype H3N2. One reason that so many people become infected each year is that this virus evolves rapidly. Within a few years, proteins on the surface of the virus known as antigens become less recognizable to the immune system of a person who has been previously infected. This means that the person can become ill with the virus again because their immune system cannot mount an effective response to the evolved virus strain.

Influenza virus strains evolve rapidly because their genetic material accumulates mutations quickly. Although some of these mutations are beneficial to the virus, other mutations are harmful and reduce the ability of the virus to spread. Sometimes beneficial mutations may occur alongside harmful ones, but it is not known how the harmful mutations affect the evolution of the virus.

Here, Koelle and Rasmussen used computer models of H3N2 influenza to examine the effect of harmful mutations on the evolution of this virus population. The models show that harmful mutations limit how quickly the antigens can evolve. Also, the presence of these harmful mutations effectively acts as a sieve: they allow only large changes in the antigens to establish in the virus population.

The models suggest that there are three routes by which large changes in the antigens on H3N2 viruses may occur. The first is by a single mutation that has a big effect on the antigens in viruses that only carry a few harmful mutations, but these large mutations would not happen very often. Another route may be through more common mutations that have only a small or moderate benefit, which would allow the virus to become more common in the population before it acquires a beneficial mutation with a much greater effect. The third possibility is that a large beneficial mutation may arise in viruses that have many harmful mutations. These harmful mutations may initially limit the ability of the virus to spread, but over time, some of these harmful mutations may then be lost.

Koelle and Rasmussen found that the computer models could recreate the patterns of virus evolution that have been observed in real strains of H3N2. Researchers use predictions of influenza evolution to help them decide which virus strains should be included in flu vaccines each year. Koelle and Rasmussen findings indicate that harmful mutations should be considered when making these predictions.

DOI:http://dx.doi.org/10.7554/eLife.07361.002