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.

Knowledge of influenza virus evolution at the point of transmission and at the intrahost level remains limited, particularly for human hosts. Here, we analyze a unique viral data set of next-generation sequencing (NGS) samples generated from a human influenza challenge study wherein 17 healthy subjects were inoculated with cell- and egg-passaged virus. Nasal wash samples collected from 7 of these subjects were successfully deep sequenced. From these, we characterized changes in the subjects' viral populations during infection and identified differences between the virus in these samples and the viral stock used to inoculate the subjects. We first calculated pairwise genetic distances between the subjects' nasal wash samples, the viral stock, and the influenza virus A/Wisconsin/67/2005 (H3N2) reference strain used to generate the stock virus. These distances revealed that considerable viral evolution occurred at various points in the human challenge study. Further quantitative analyses indicated that (i) the viral stock contained genetic variants that originated and likely were selected for during the passaging process, (ii) direct intranasal inoculation with the viral stock resulted in a selective bottleneck that reduced nonsynonymous genetic diversity in the viral hemagglutinin and nucleoprotein, and (iii) intrahost viral evolution continued over the course of infection. These intrahost evolutionary dynamics were dominated by purifying selection. Our findings indicate that rapid viral evolution can occur during acute influenza infection in otherwise healthy human hosts when the founding population size of the virus is large, as is the case with direct intranasal inoculation.IMPORTANCE Influenza viruses circulating among humans are known to rapidly evolve over time. However, little is known about how influenza virus evolves across single transmission events and over the course of a single infection. To address these issues, we analyze influenza virus sequences from a human challenge experiment that initiated infection with a cell- and egg-passaged viral stock, which appeared to have adapted during its preparation. We find that the subjects' viral populations differ genetically from the viral stock, with subjects' viral populations having lower representation of the amino-acid-changing variants that arose during viral preparation. We also find that most of the viral evolution occurring over single infections is characterized by further decreases in the frequencies of these amino-acid-changing variants and that only limited intrahost genetic diversification through new mutations is apparent. Our findings indicate that influenza virus populations can undergo rapid genetic changes during acute human infections.

44The bottleneck governing infectious disease transmission describes the size of the 45 pathogen population transferred from a donor to a recipient host. Accurate quantification 46 of the bottleneck size is of particular importance for rapidly evolving pathogens such as 47 influenza virus, as narrow bottlenecks would limit the extent of transferred viral genetic 48 diversity and, thus, have the potential to slow the rate of viral adaptation. Previous studies 49 have estimated the transmission bottleneck size governing viral transmission through 50 statistical analyses of variants identified in pathogen sequencing data. The methods used 51 by these studies, however, did not account for variant calling thresholds and stochastic 52 dynamics of the viral population within recipient hosts. Because these factors can skew 53 bottleneck size estimates, we here introduce a new method for inferring transmission 54 bottleneck sizes that explicitly takes these factors into account. We compare our method, 55 based on beta-binomial sampling, with existing methods in the literature for their ability to 56 recover the transmission bottleneck size of a simulated dataset. This comparison 57 demonstrates that the beta-binomial sampling method is best able to accurately infer the 58 simulated bottleneck size. We then apply our method to a recently published dataset of 59 influenza A H1N1p and H3N2 infections, for which viral deep sequencing data from 60 inferred donor-recipient transmission pairs are available. Our results indicate that 61 transmission bottleneck sizes across transmission pairs are variable, yet that there is no 62 significant difference in the overall bottleneck sizes inferred for H1N1p and H3N2. The 63 mean bottleneck size for influenza virus in this study, considering all transmission pairs, 64was Nb = 196 (95% confidence interval 66-392) virions. While this estimate is consistent 65 peer-reviewed)

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.

Elevated blood pressure presents a global health threat, with rates of hypertension increasing in low and middle-income countries. Lifestyle changes may be an important driver of these increases in blood pressure. Hypertension is particularly prevalent in African countries, though the majority of studies have focused on mainland Africa. We collected demographic and health data from 513 adults living in a community in rural Madagascar. We used generalized linear mixed models to assess body mass index (BMI), age, sex, and attributes related to household composition and lifestyle as predictors of blood pressure and hypertension. The prevalence of hypertension in this cohort was 49.1% (both sexes combined: N = 513; females: 50.3%, N = 290; males: 47.5%, N = 223). Blood pressure, as well as hypertensive state, was positively associated with age and BMI. Lifestyle and household factors had no significant relationships with blood pressure. The prevalence of hypertension was similar to that found in urban centers of other African countries, yet almost double what has been previously found in Madagascar. Future research should investigate the drivers of hypertension in rural communities worldwide, as well as the lifestyle, cultural, and genetic factors that underlie variation in hypertension across space and time.