Evolutionary dynamics of influenza A/H1N1 virus circulating in India from 2011 to 2021

Rapid evolution leads to extensive genetic diversification of cattle flu Influenza D virus

Influenza D virus (IDV), the cattle flu virus, is a novel multi-host RNA virus, circulating silently worldwide, with widespread seropositivity among US cattle, reaching up to 80% in some areas raising a potential threat of cattle-to-human transmission. Currently, five genetic lineages of IDV have been described, but their evolutionary dynamics have not been studied. Although IDV was first identified in 2011, our comprehensive analysis of all known IDV genomes suggests that the earliest ancestors of IDV likely to have evolved towards the end of the 20th century and D/OK lineage appears to have emerged in 2005. We confirmed a significantly higher substitution rate in IDV than in Influenza C virus, which is consistent with their global distribution and multi-host tropism. We identified multiple sub-populations within the D/OK lineage, highlighting extensive diversification and dissemination. Other findings are evidence for potential reassortment among IDV strains in the USA and transboundary circulation of IDV in Europe with introductions into Danish cattle, some of which potentially originated from France. IDV, an emerging virus with a higher rate of evolution and uncontrolled circulation, could facilitate its adaptation to humans. Our findings underscore the importance of targeted surveillance for IDV in humans and at-risk animal populations.

Phylodynamic and Evolution of the Hemagglutinin (HA) and Neuraminidase (NA) Genes of Influenza A(H1N1) pdm09 Viruses Circulating in the 2009 and 2023 Seasons in Italy

The influenza A(H1N1) pdm09 virus, which emerged in 2009, has been circulating seasonally since then. In this study, we conducted a comprehensive genome-based investigation to gain a detailed understanding of the genetic and evolutionary characteristics of the hemagglutinin (HA) and neuraminidase (NA) surface proteins of A/H1N1pdm09 strains circulating in Italy over a fourteen-year period from 2009 to 2023 in relation to global strains. Phylogenetic analysis revealed rapid transmission and diversification of viral variants during the early pandemic that clustered in clade 6B.1. In contrast, limited genetic diversity was observed during the 2023 season, probably due to the genetic drift, which provides the virus with a constant adaptability to the host; furthermore, all isolates were split into two main groups representing two clades, i.e., 6B.1A.5a.2a and its descendant 6B.1A.5a.2a.1. The HA gene showed a faster rate of evolution compared to the NA gene. Using FUBAR, we identified positively selected sites 41 and 177 for HA and 248, 286, and 455 for NA in 2009, as well as sites 22, 123, and 513 for HA and 339 for NA in 2023, all of which may be important sites related to the host immune response. Changes in glycosylation acquisition/loss at prominent sites, i.e., 177 in HA and 248 in NA, should be considered as a predictive tool for early warning signs of emerging pandemics, and for vaccine and drug development.

Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models

Birth-death models play a key role in phylodynamic analysis for their interpretation in terms of key epidemiological parameters. In particular, models with piecewise-constant rates varying at different epochs in time, to which we refer as episodic birth-death-sampling (EBDS) models, are valuable for their reflection of changing transmission dynamics over time. A challenge, however, that persists with current time-varying model inference procedures is their lack of computational efficiency. This limitation hinders the full utilization of these models in large-scale phylodynamic analyses, especially when dealing with high-dimensional parameter vectors that exhibit strong correlations. We present here a linear-time algorithm to compute the gradient of the birth-death model sampling density with respect to all time-varying parameters, and we implement this algorithm within a gradient-based Hamiltonian Monte Carlo (HMC) sampler to alleviate the computational burden of conducting inference under a wide variety of structures of, as well as priors for, EBDS processes. We assess this approach using three different real world data examples, including the HIV epidemic in Odesa, Ukraine, seasonal influenza A/H3N2 virus dynamics in New York state, America, and Ebola outbreak in West Africa. HMC sampling exhibits a substantial efficiency boost, delivering a 10- to 200-fold increase in minimum effective sample size per unit-time, in comparison to a Metropolis-Hastings-based approach. Additionally, we show the robustness of our implementation in both allowing for flexible prior choices and in modeling the transmission dynamics of various pathogens by accurately capturing the changing trend of viral effective reproductive number.

Global Prevalence and Hemagglutinin Evolution of H7N9 Avian Influenza Viruses from 2013 to 2022

H7N9 avian influenza viruses have caused severe harm to the global aquaculture industry and human health. For further understanding of the characteristics of prevalence and hemagglutinin evolution of H7N9 avian influenza viruses, we generated the global epidemic map of H7N9 viruses from 2013 to 2022, constructed a phylogenetic tree, predicted the glycosylation sites and compared the selection pressure of the hemagglutinin. The results showed that although H7N9 avian influenza appeared sporadically in other regions worldwide, China had concentrated outbreaks from 2013 to 2017. The hemagglutinin genes were classified into six distinct lineages: A, B, C, D, E and F. After 2019, H7N9 viruses from the lineages B, E and F persisted, with the lineage B being the dominant. The hemagglutinin of highly pathogenic viruses in the B lineage has an additional predicted glycosylation site, which may account for their persistent pandemic, and is under more positive selection pressure. The most recent ancestor of the H7N9 avian influenza viruses originated in September 1991. The continuous evolution of hemagglutinin has led to an increase in virus pathogenicity in both poultry and humans, and sustained human-to-human transmission. This study provides a theoretical basis for better prediction and control of H7N9 avian influenza.

Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models

Birth-death models play a key role in phylodynamic analysis for their interpretation in terms of key epidemiological parameters. In particular, models with piecewise-constant rates varying at different epochs in time, to which we refer as episodic birth-death-sampling (EBDS) models, are valuable for their reflection of changing transmission dynamics over time. A challenge, however, that persists with current time-varying model inference procedures is their lack of computational efficiency. This limitation hinders the full utilization of these models in large-scale phylodynamic analyses, especially when dealing with high-dimensional parameter vectors that exhibit strong correlations. We present here a linear-time algorithm to compute the gradient of the birth-death model sampling density with respect to all time-varying parameters, and we implement this algorithm within a gradient-based Hamiltonian Monte Carlo (HMC) sampler to alleviate the computational burden of conducting inference under a wide variety of structures of, as well as priors for, EBDS processes. We assess this approach using three different real world data examples, including the HIV epidemic in Odesa, Ukraine, seasonal influenza A/H3N2 virus dynamics in New York state, America, and Ebola outbreak in West Africa. HMC sampling exhibits a substantial efficiency boost, delivering a 10- to 200-fold increase in minimum effective sample size per unit-time, in comparison to a Metropolis-Hastings-based approach. Additionally, we show the robustness of our implementation in both allowing for flexible prior choices and in modeling the transmission dynamics of various pathogens by accurately capturing the changing trend of viral effective reproductive number.