Virus Evolution最新文献

[This corrects the article DOI: 10.1093/ve/veae107.].

In rare individuals with a severely immunocompromised system, chronic infections of SARS-CoV-2 may develop, where the virus replicates in the body for months. Sequencing of some chronic infections has uncovered dramatic adaptive evolution and fixation of mutations reminiscent of lineage-defining mutations of variants of concern (VOCs). This has led to the prevailing hypothesis that VOCs emerged from chronic infections. To examine the mutation dynamics and intra-host genomic diversity of SARS-CoV-2 during chronic infections, we focused on a cohort of nine immunocompromised individuals with chronic infections and performed longitudinal sequencing of viral genomes. We showed that sequencing errors may cause erroneous inference of genetic variation, and to overcome this, we used duplicate sequencing across patients and time points, allowing us to distinguish errors from low-frequency mutations. We further found recurrent low-frequency mutations that we flagged as most likely sequencing errors. This stringent approach allowed us to reliably infer low-frequency mutations and their dynamics across time. We applied a generalized linear model that accounts for gradual mutation accumulation and episodic divergence shifts to infer a synonymous mutation rate of 1.9 × 10^-6 mutations/site/day. Using the same framework, we inferred patient-specific non-synonymous divergence rates that exhibited marked heterogeneity across individuals. This framework also uncovered episodes of high non-synonymous rates consistent with selective sweeps or subpopulation replacement. Overall, we observed diverse evolutionary dynamics across chronic infections, highlighting variation in patient-specific selection pressures and within-host demographic histories that shape intra-host viral evolution.

The evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for the COVID-19 pandemic, has produced unprecedented numbers of structures of the Spike protein. In this study, we present a comprehensive analysis of 1560 published structures, covering most major variants that emerged throughout the pandemic, diverse heteromerization, and interacting complexes. Using interaction-energy-informed geometric clustering, we identify 14 structurally distinct epitopes based on their conformational specificity, shared interface with angiotensin-converting enzyme 2 (ACE2), and glycosylation patterns. Our per-residue interaction evaluations accurately predict antibody recognition sites and correlate strongly with deep mutational scanning data, enabling immune escape predictions for future variants. To complement this structural analysis, we integrate longitudinal genomic data from nearly 3 million viral sequences, linking mutational patterns to changes in Spike's conformational dynamics. Our findings reveal two distinct evolutionary trade-offs driving immune escape. First, we confirm an enthalpic trade-off, where mutations in the receptor-binding motif (RBM) enhance immune escape at the cost of weakened ACE2 binding. Second, we introduce an entropic trade-off, showing that mutations outside the RBM modulate Spike's conformational equilibrium, reducing open-state occupancy to evade immune detection-without directly altering the ACE2-binding interface. With these analyses, this work not only highlights the different functional effects of mutations across SARS-CoV-2 Spike variants but also reveals the complex interplay of evolutionary forces shaping the evolution of the SARS-CoV-2 Spike protein over the course of the pandemic.

Identifying traits that make a host a good reservoir for virus emergence is central to understanding virus ecology, host range evolution and mitigating virus epidemics, but is often hindered by a lack of knowledge on the infection dynamics of the virus in the reservoir population. Here we analyse traits that determine the reservoir potential of the wild plant Nicotiana glauca for tobacco mild green mosaic virus (TMGMV), an important pathogen of pepper (Capsicum annuum) crops, using epidemiological, experimental and population genetic approaches. We show that TMGMV is maintained at high prevalence in N. glauca populations that share the space with pepper crops in South eastern Spain. High prevalence may be explained by low virulence associated with TMGMV behaving as a conditional mutualist, which is in part explained by increased survival of infected plants under drought conditions. We also show maintenance in N. glauca populations of TMGMV genotypes that have a within-host fitness advantage in pepper and a disadvantage in N. glauca. This is explained by pleiotropic effects of host range mutations that result in higher vertical transmission through the seeds of N. glauca of isolates adapted to pepper. Last, high migration from N. glauca prevents fixation of pepper-adapted genotypes in pepper populations. Our results underscore the need to analyse the effects of infection on a range of host life-history traits, and effects of host range mutations on different components of virus fitness, to understand dynamics of infection and virus host range evolution.

Cancer-inducing viruses (oncogenic viruses) are linked to over 10% of cancer cases. Although the molecular details of viral oncogenesis are well-documented, the evolutionary mechanisms by which viruses have acquired oncogenic properties remain poorly understood. Here, we investigate the evolutionary conditions affecting viral oncogenicity across both within- and between-host scales using mathematical models of oncovirus-immune system interactions, conceptualized as an extended shared enemy-victim relationship. We begin by examining how oncogenic traits impact within-host viral dynamics, focusing on the transformation rate of infected cells into pre-cancerous states and the pre-cancerous cell proliferation rate. In various scenarios reflecting different within-host conditions, we then identify the transformation and proliferation rates that maximize within- and between-host viral fitness. We find that the transformation rate maximizing the viral load depends on the viral production rate, immunogenicity, and the immune-mediated elimination rate of pre-cancerous cells. We also identify conditions under which an intermediate proliferation rate minimizes within- and between-host viral fitness: in that scenario, a lower or higher proliferation rate leads to a higher viral load, providing a possible explanation for the diversity of oncogenic viruses. The analyses presented here provide insights into the evolutionary drivers affecting viral oncogenicity and highlight the complexity of oncogenic virus-immune system interactions.

Shrews are primary hosts for mammalian hantaviruses and are thus considered to be important reservoirs for viruses, similar to rodents and bats. To explore the diversity of hantaviruses in Swedish common shrews (Sorex araneus), we investigated lung tissue from shrews collected between 2015 and 2017. The collection took place at three separate locations in south-central Sweden. Screening for hantaviruses was performed using two different approaches. (i) A total of 113 common shrews were investigated for hantaviruses by a pan-hantavirus L-gene reverse transcriptase PCR, and Sanger sequencing was performed on the 13 positive samples. (ii) In addition, 88 RNA samples were pooled into eight libraries subjected to RNA sequencing. The RNA sequencing data analysis, which focused specifically on identifying hantaviruses, revealed two divergent hantaviruses: the complete genome of an Altai virus (ALTV) and the partial genome of the Seewis virus. Evolutionary analysis revealed that Swedish ALTVs are closely related to Russian ALTVs but distinct from Finnish strains. On the contrary, the Swedish Seewis virus shares closer ancestry with Finnish Seewis virus strains. Given that these viruses were identified in several pools, Seewis virus and ALTV are likely circulating in Swedish common shrews. Supported by earlier studies, common shrews are probably a natural host for at least these two distinct hantaviruses.

Epidemics are often initiated by emerging and re-emerging infectious diseases caused by viruses of animal origin. It is thus important to identify the reservoirs of potentially zoonotic viruses and understand the dynamics of their host shifts. The flu viruses belong to the virus family Orthomyxoviridae, which also contains Isavirus, Quaranjavirus, and Thogotovirus. Many members of this virus family are known to be pathogenic to humans. For initial surveillance of animal-originated or zoonotic Orthomyxoviridae, unclassified viruses were screened by the use of high-throughput transcriptomes as a data source because of their wide species and lineage coverage. We identified 96 novel or unclassified Orthomyxoviridae members with the discovery of three new lineages of the virus, possibly new genera, one sister to Influenza + Thogotovirus, one to Influenza + Thogotovirus + Quaranjavirus, and another one to all orthomyxoviruses except Isavirus. Throughout the evolution of Orthomyxoviridae, there might be multiple host-shifting incidences, shifting between six different animal host phyla. The most common host shifts seemed to be between Arthropoda and Chordata; however, further evidence would be needed to fully support this statement. Nonetheless, Orthomyxoviridae viruses can infect a wide range of animal phyla, while some members hold a higher risk of shifting back to Chordates and humans that warrants surveillance.

Rodents and other non-volant small mammals (like shrews) maintain major ecological and epidemiological roles as reservoirs of zoonotic pathogens. Their presence within human-modified landscapes and interfaces with people, wildlife, and livestock create frequent opportunities for viral spillover. Despite this, the pathogen diversity and true risk of viral transmission are poorly understood by these hosts in Africa. Here, we explored the diversity and host association of paramyxoviruses and coronaviruses in non-volant small mammals from South Africa through longitudinal and opportunistic sample collection and molecular detection of viral RNA and host genetic barcoding. A high diversity of viruses was identified, with prevalences of 11.9% and 1.79% for paramyxoviruses and coronaviruses, respectively. Five instances of coinfections involving multiple paramyxoviruses and a coronavirus were detected, as well as nine Bayesian-supported paramyxovirus host genus, subfamily, and family switching, signifying frequent unrestrained viral sharing. Though the zoonotic potential of these identified viruses is unknown, the frequency of host switching suggests that these viruses may be more prone to adaptation to new host species or utilize highly conserved entry mechanisms. This highlights the risks for potential cross-species transmission events to livestock, domestic animals, and people, warranting continued surveillance.

Viruses impose a substantial disease burden on dogs, and the close relationship between dogs and humans may facilitate zoonotic disease emergence. Australia's geographic isolation, strict biosecurity measures, and native dingo populations present a unique model for understanding the spread and evolution of canine viruses. However, aside from a few well-characterized pathogens, genomic data are scarce for many common dog viruses, limiting our understanding of their evolution and disease ecology. Using a metatranscriptomic approach, we identified the viruses in dogs and dingoes from various geographical locations across mainland Australia and sample types, revealing 86 vertebrate-associated viruses belonging to 16 distinct species, including a new vesivirus-like species. Many of the viruses identified here have not previously been sequenced in Australia. We identified important dog pathogens associated with canine infectious respiratory disease syndrome-such as canine pneumovirus, canine herpesvirus, and canine respiratory coronavirus-and gastroenteritis, including canine parvovirus, canine coronavirus, and rotavirus A. The sequences of Australian canine viruses often occupied multiple distinct clades phylogenetically and had little geographic structure, suggesting multiple virus introductions and subsequent spread across the country. Notably, we identified the first RNA virus-rotavirus A-in a dingo. This virus was phylogenetically distinct from dog-associated rotavirus A sequences and more closely related to viruses found in humans and bats, indicative of the past cross-species transmission of a reassortant virus into dingoes, and shows dingoes and domestic dogs may have distinct viromes. Our findings expand the knowledge of viral diversity in Australian canines, improving our understanding of viral movement into and within Australia, as well as the potential zoonotic risks associated with dogs and dingoes.

Continuous phylogeographic inference is a popular method to estimate parameters of the dispersal process and to reconstruct the spatial location of ancestors of extant populations from samples viral of genome sequences. However, these models typically ignore that replication and population growth are tightly coupled to spatial location: populations expand in areas with abundant susceptible hosts and contract in regions with limited resources. Here, I first investigate the sampling consistency of popular summary statistics of dispersal and show that estimators of 'lineage velocities' are ill-defined. I then use simulations to investigate how local density regulation or shifting habitats perturb phylogeographic inference in continuous space and show that these can result in biassed and overconfident estimates of ancestral locations and dispersal parameters. These, sometimes dramatic, distortions depend in complicated ways on the past dynamics of habitats and underlying population dynamics and dispersal processes. Consequently, the validity of continuous phylogeographic inferences is hard to assess and confidence can be much lower than suggested by the inferred posterior distributions, in particular when involving poorly sampled locations or extrapolations far into the past.