Avian H7N9 influenza viruses are evolutionarily constrained by stochastic processes during replication and transmission in mammals

Abstract

H7N9 avian influenza viruses (AIV) have caused over 1,500 documented human infections since emerging in 2013. Although wild type H7N9 AIV can transmit by respiratory droplets in ferrets, they have not yet caused widespread outbreaks in humans. Previous studies have revealed molecular determinants of H7N9 AIV virus host-switching, but little is known about potential evolutionary constraints on this process. Here we compare patterns of sequence evolution for H7N9 AIV and mammalian H1N1 viruses during replication and transmission in ferrets. We show that three main factors – purifying selection, stochasticity, and very narrow transmission bottlenecks – combine to severely constrain the ability of H7N9 AIV to effectively adapt to mammalian hosts in isolated, acute spillover events. We find rare evidence of natural selection favoring new or mammalian-adapting mutations within ferrets, but no evidence of natural selection acting during transmission. We conclude that human-adapted H7N9 viruses are unlikely to emerge during typical spillover infections. Our findings are instead consistent with a model in which the emergence of a human-transmissible virus would be a rare and unpredictable, though highly consequential, “jackpot” event. Strategies to limit the total number of spillover infections will limit opportunities for the virus to win this evolutionary lottery.

A Computer Simulation of SARS-CoV-2 Mutation Spectra for Empirical Data Characterization and Analysis

“…To replace the fixed threshold during data filtering in the previous algorithms [22][23][24], we develop a nucleobase filtering algorithm with dynamic threshold. The algorithm filters the low-quality base qualities during data mapping to determine the dynamic threshold interval by calculating the distribution specificity of sequencing data through the moving of pointers, which are detailed by Figure 2.…”

“…After data preprocessing illustrated in Section 2.1, the positive-and negative-sense strand of this project that mapped on the reference genome include approximately 6,000,000 SARS-CoV-2 sequencing reads in total. Then, we carried out sequencing data filtering algorithms respectively with the following five approaches: unfiltered, fastp [22], seqtk [23], sickle [24] and the nucleobase filtering algorithm with dynamic threshold, which are detailed in Figure 2. Figure 4 shows the comparison results.…”