scholarly journals Reconciling disparate estimates of viral genetic diversity during human influenza infections

2019 ◽  
Vol 51 (9) ◽  
pp. 1298-1301 ◽  
Author(s):  
Katherine S. Xue ◽  
Jesse D. Bloom
2018 ◽  
Author(s):  
Katherine S. Xue ◽  
Jesse D. Bloom

AbstractDeep sequencing can measure viral genetic diversity within human influenza infections, but published studies disagree in their estimates of how much genetic diversity is typically present. One large-scale deep-sequencing study of human influenza reported high levels of shared viral genetic diversity among infected individuals in Hong Kong, but subsequent studies of other cohorts have reported little shared viral diversity. We re-analyze sequencing data from four studies of within-host genetic diversity encompassing more than 500 acute human influenza infections. We identify an anomaly in the Hong Kong data that provides a technical explanation for these discrepancies: read pairs from this study are often split between different biological samples, indicating that some reads are incorrectly assigned. These technical abnormalities explain the high levels of within-host variation and loose transmission bottlenecks reported by this study. Studies without these anomalies consistently report low levels of genetic diversity in acute human influenza infections.


2019 ◽  
Vol 51 (9) ◽  
pp. 1301-1303 ◽  
Author(s):  
Leo L. M. Poon ◽  
Timothy Song ◽  
David E. Wentworth ◽  
Edward C. Holmes ◽  
Benjamin D. Greenbaum ◽  
...  

2021 ◽  
Vol 49 ◽  
pp. 157-163
Author(s):  
Lara Fuhrmann ◽  
Kim Philipp Jablonski ◽  
Niko Beerenwinkel

Viruses ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 104
Author(s):  
Adam A. Capoferri ◽  
Wei Shao ◽  
Jon Spindler ◽  
John M. Coffin ◽  
Jason W. Rausch ◽  
...  

COVID-19 vaccines were first administered on 15 December 2020, marking an important transition point for the spread of SARS-CoV-2 in the United States (U.S.). Prior to this point in time, the virus spread to an almost completely immunologically naïve population, whereas subsequently, vaccine-induced immune pressure and prior infections might be expected to influence viral evolution. Accordingly, we conducted a study to characterize the spread of SARS-CoV-2 in the U.S. pre-vaccination, investigate the depth and uniformity of genetic surveillance during this period, and measure and otherwise characterize changing viral genetic diversity, including by comparison with more recently emergent variants of concern (VOCs). In 2020, SARS-CoV-2 spread across the U.S. in three phases distinguishable by peaks in the numbers of infections and shifting geographical distributions. Virus was genetically sampled during this period at an overall rate of ~1.2%, though there was a substantial mismatch between case rates and genetic sampling nationwide. Viral genetic diversity tripled over this period but remained low in comparison to other widespread RNA virus pathogens, and although 54 amino acid changes were detected at frequencies exceeding 5%, linkage among them was not observed. Based on our collective observations, our analysis supports a targeted strategy for worldwide genetic surveillance as perhaps the most sensitive and efficient means of detecting new VOCs.


2021 ◽  
Vol 291 ◽  
pp. 198201
Author(s):  
Alexandre Flageul ◽  
Pierrick Lucas ◽  
Edouard Hirchaud ◽  
Fabrice Touzain ◽  
Yannick Blanchard ◽  
...  

2020 ◽  
Vol 117 (29) ◽  
pp. 17104-17111
Author(s):  
Nicola F. Müller ◽  
Ugnė Stolz ◽  
Gytis Dudas ◽  
Tanja Stadler ◽  
Timothy G. Vaughan

Reassortment is an important source of genetic diversity in segmented viruses and is the main source of novel pathogenic influenza viruses. Despite this, studying the reassortment process has been constrained by the lack of a coherent, model-based inference framework. Here, we introduce a coalescent-based model that allows us to explicitly model the joint coalescent and reassortment process. In order to perform inference under this model, we present an efficient Markov chain Monte Carlo algorithm to sample rooted networks and the embedding of phylogenetic trees within networks. This algorithm provides the means to jointly infer coalescent and reassortment rates with the reassortment network and the embedding of segments in that network from full-genome sequence data. Studying reassortment patterns of different human influenza datasets, we find large differences in reassortment rates across different human influenza viruses. Additionally, we find that reassortment events predominantly occur on selectively fitter parts of reassortment networks showing that on a population level, reassortment positively contributes to the fitness of human influenza viruses.


2019 ◽  
Author(s):  
Nicola F. Müller ◽  
Ugnė Stolz ◽  
Gytis Dudas ◽  
Tanja Stadler ◽  
Timothy G. Vaughan

AbstractReassortment is an important source of genetic diversity in segmented viruses and is the main source of novel pathogenic influenza viruses. Despite this, studying the reassortment process has been constrained by the lack of a coherent, model-based inference framework. We here introduce a novel coalescent based model that allows us to explicitly model the joint coalescent and reassortment process. In order to perform inference under this model, we present an efficient Markov chain Monte Carlo algorithm to sample rooted networks and the embedding of phylogenetic trees within networks. Together, these provide the means to jointly infer coalescent and reassortment rates with the reassortment network and the embedding of segments in that network from full genome sequence data. Studying reassortment patterns of different human influenza datasets, we find large differences in reassortment rates across different human influenza viruses. Additionally, we find that reassortment events predominantly occur on selectively fitter parts of reassortment networks showing that on a population level, reassortment positively contributes to the fitness of human influenza viruses.


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