tetano
Editor, Senior Moderator
J Virol. 2017 May 3. pii: JVI.00171-17. doi: 10.1128/JVI.00171-17. [Epub ahead of print]
[h=1]Transmission Bottleneck Size Estimation from Pathogen Deep-Sequencing Data, with an Application to Human Influenza A Virus.[/h] Sobel Leonard A[SUP]1[/SUP], Weissman D[SUP]2[/SUP], Greenbaum B[SUP]3[/SUP], Ghedin E[SUP]4[/SUP], Koelle K[SUP]5[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] The bottleneck governing infectious disease transmission describes the size of the pathogen population transferred from donor to 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 slow the rate of viral adaptation. Previous studies have estimated bottleneck sizes governing viral transmission 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 dataset, 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 dataset of influenza A 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 the previous estimate for this dataset. Further, 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 in part influenced by epidemiological factors.IMPORTANCE The transmission bottleneck size describes the size of the pathogen population transferred from donor to 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, though 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 underestimation of bottleneck sizes. We apply this method to an existing dataset of human influenza infections, showing that transmission is governed by a loose, but highly variable, transmission bottleneck whose size is positively associated with donor infection severity. Beyond advancing our understanding of influenza transmission, we hope this work will provide a standardized statistical approach for bottleneck size estimation for viral pathogens.
Copyright ? 2017 Sobel Leonard et al.
PMID: 28468874 DOI: 10.1128/JVI.00171-17
[h=1]Transmission Bottleneck Size Estimation from Pathogen Deep-Sequencing Data, with an Application to Human Influenza A Virus.[/h] Sobel Leonard A[SUP]1[/SUP], Weissman D[SUP]2[/SUP], Greenbaum B[SUP]3[/SUP], Ghedin E[SUP]4[/SUP], Koelle K[SUP]5[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] The bottleneck governing infectious disease transmission describes the size of the pathogen population transferred from donor to 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 slow the rate of viral adaptation. Previous studies have estimated bottleneck sizes governing viral transmission 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 dataset, 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 dataset of influenza A 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 the previous estimate for this dataset. Further, 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 in part influenced by epidemiological factors.IMPORTANCE The transmission bottleneck size describes the size of the pathogen population transferred from donor to 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, though 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 underestimation of bottleneck sizes. We apply this method to an existing dataset of human influenza infections, showing that transmission is governed by a loose, but highly variable, transmission bottleneck whose size is positively associated with donor infection severity. Beyond advancing our understanding of influenza transmission, we hope this work will provide a standardized statistical approach for bottleneck size estimation for viral pathogens.
Copyright ? 2017 Sobel Leonard et al.
PMID: 28468874 DOI: 10.1128/JVI.00171-17