Both Epistasis and Diversifying Selection Drive the Structural Evolution of the Ebola Virus Glycoprotein Mucin-Like Domain

Ethanol Inactivation of Enveloped Viruses: Structural and Surface Chemistry Insights into Phi6

Lipid-enveloped viruses, such as Ebola, influenza, or coronaviruses, are a major threat to human health. Ethanol is an efficient disinfectant that is widely used to inactivate these viruses and prevent their transmission. However, the interactions between ethanol and enveloped viruses leading to their inactivation are not yet fully understood. This study demonstrates the link between ethanol-induced viral inactivation and the nanostructural and chemical transformations of the model virus Phi6, an 85 nm diameter lipid-enveloped bacterial virus that is commonly used as surrogate for human pathogenic viruses. The virus morphology was investigated using small-angle X-ray scattering and dynamic light scattering and was related to its infectivity. The Phi6's surface chemistry was characterized by cryogenic X-ray photoelectron spectroscopy, and the modifications in protein structure were assessed by circular dichroism and fluorescence spectroscopy. Ethanol-triggered structural modifications were found in the lipid envelope, detaching from the protein capsid and forming coexisting nanostructures.

Positive Selection of ORF1ab, ORF3a, and ORF8 Genes Drives the Early Evolutionary Trends of SARS-CoV-2 During the 2020 COVID-19 Pandemic

In this study, we analyzed full-length SARS-CoV-2 genomes from multiple countries to determine early trends in the evolutionary dynamics of the novel COVID-19 pandemic. Results indicated SARS-CoV-2 evolved early into at least three phylogenetic groups, characterized by positive selection at specific residues of the accessory proteins ORF3a and ORF8. Also, we are reporting potential relevant sites under positive selection at specific sites of non-structural proteins nsp6 and helicase. Our analysis of co-evolution showed evidence of epistatic interactions among sites in the genome that may be important in the generation of variants adapted to humans. These observations might impact not only public health but also suggest that more studies are needed to understand the genetic mechanisms that may affect the development of therapeutic and preventive tools, like antivirals and vaccines. Collectively, our results highlight the identification of ongoing selection even in a scenario of conserved sequences collected over the first 3 months of this pandemic.

Identifying the drivers of computationally detected correlated evolution among sites under antibiotic selection

The ultimate causes of correlated evolution among sites in a genome remain difficult to tease apart. To address this problem directly, we performed a high-throughput search for correlated evolution among sites associated with resistance to a fluoroquinolone antibiotic using whole-genome data from clinical strains of Pseudomonas aeruginosa, before validating our computational predictions experimentally. We show that for at least two sites, this correlation is underlain by epistasis. Our analysis also revealed eight additional pairs of synonymous substitutions displaying correlated evolution underlain by physical linkage, rather than selection associated with antibiotic resistance. Our results provide direct evidence that both epistasis and physical linkage among sites can drive the correlated evolution identified by high-throughput computational tools. In other words, the observation of correlated evolution is not by itself sufficient evidence to guarantee that the sites in question are epistatic; such a claim requires additional evidence, ideally coming from direct estimates of epistasis, based on experimental evidence.

Mucin-Like Domain of Ebola Virus Glycoprotein Enhances Selective Oncolytic Actions against Brain Tumors

Given that the Ebola virus (EBOV) infects a wide array of organs and cells yet displays a relative lack of neurotropism, we asked whether a chimeric vesicular stomatitis virus (VSV) expressing the EBOV glycoprotein (GP) might selectively target brain tumors. The mucin-like domain (MLD) of the EBOV GP may enhance virus immune system evasion. Here, we compared chimeric VSVs in which EBOV GP replaces the VSV glycoprotein, thereby reducing the neurotoxicity associated with wild-type VSV. A chimeric VSV expressing the full-length EBOV GP (VSV-EBOV) containing the MLD was substantially more effective and safer than a parallel construct with an EBOV GP lacking the MLD (VSV-EBOVΔMLD). One-step growth, reverse transcription-quantitative PCR, and Western blotting assessments showed that VSV-EBOVΔMLD produced substantially more progeny faster than VSV-EBOV. Using immunodeficient SCID mice, we focused on targeting human brain tumors with these VSV-EBOVs. Similar to the findings of our previous study in which we used an attenuated VSV-EBOV with no MLD that expressed green fluorescent protein (GFP) (VSV-EBOVΔMLD-GFP), VSV-EBOVΔMLD without GFP targeted glioma but yielded only a modest extension of survival. In contrast, VSV-EBOV containing the MLD showed substantially better targeting and elimination of brain tumors after intravenous delivery and increased the survival of brain tumor-bearing mice. Despite the apparent destruction of most tumor cells by VSV-EBOVΔMLD, the virus remained active within the SCID mouse brain and showed widespread infection of normal brain cells. In contrast, VSV-EBOV eliminated the tumors and showed relatively little infection of normal brain cells. Parallel experiments with direct intracranial virus infection generated similar results. Neither VSV-EBOV nor VSV-EBOVΔMLD showed substantive infection of the brains of normal immunocompetent mice.IMPORTANCE The Ebola virus glycoprotein contains a mucin-like domain which may play a role in immune evasion. Chimeric vesicular stomatitis viruses with the EBOV glycoprotein substituted for the VSV glycoprotein show greater safety and efficacy in targeting brain tumors in immunodeficient mice when the MLD was expressed within the EBOV glycoprotein than when EBOV lacked the mucin-like domain.

Viral Long-Term Evolutionary Strategies Favor Stability over Proliferation

Viruses are known to have some of the highest and most diverse mutation rates found in any biological replicator, with single-stranded (ss) RNA viruses evolving the fastest, and double-stranded (ds) DNA viruses having rates approaching those of bacteria. As mutation rates are tightly and negatively correlated with genome size, selection is a clear driver of viral evolution. However, the role of intragenomic interactions as drivers of viral evolution is still unclear. To understand how these two processes affect the long-term evolution of viruses infecting humans, we comprehensively analyzed ssRNA, ssDNA, dsRNA, and dsDNA viruses, to find which virus types and which functions show evidence for episodic diversifying selection and correlated evolution. We show that selection mostly affects single stranded viruses, that correlated evolution is more prevalent in DNA viruses, and that both processes, taken independently, mostly affect viral replication. However, the genes that are jointly affected by both processes are involved in key aspects of their life cycle, favoring viral stability over proliferation. We further show that both evolutionary processes are intimately linked at the amino acid level, which suggests that it is the joint action of selection and correlated evolution, and not just selection, that shapes the evolutionary trajectories of viruses—and possibly of their epidemiological potential.

Existing Host Range Mutations Constrain Further Emergence of RNA Viruses

RNA viruses are capable of rapid host shifting, typically due to a point mutation that confers expanded host range. As additional point mutations are necessary for further expansions, epistasis among host range mutations can potentially affect the mutational neighborhood and frequency of niche expansion. We mapped the mutational neighborhood of host range expansion using three genotypes of the double-stranded RNA (dsRNA) bacteriophage φ6 (wild type and two isogenic host range mutants) on the novel host Pseudomonas syringae pv. atrofaciens. Both Sanger sequencing of 50 P. syringae pv. atrofaciens mutant clones for each genotype and population Illumina sequencing revealed the same high-frequency mutations allowing infection of P. syringae pv. atrofaciens. Wild-type φ6 had at least nine different ways of mutating to enter the novel host, eight of which are in p3 (host attachment protein gene), and 13/50 clones had unchanged p3 genes. However, the two isogenic mutants had dramatically restricted neighborhoods: only one or two mutations, all in p3. Deep sequencing revealed that wild-type clones without mutations in p3 likely had changes in p12 (morphogenic protein), a region that was not polymorphic for the two isogenic host range mutants. Sanger sequencing confirmed that 10/13 of the wild-type φ6 clones had nonsynonymous mutations in p12, and 2 others had point mutations in p9 and p5. None of these genes had previously been associated with host range expansion in φ6. We demonstrate, for the first time, epistatic constraint in an RNA virus due to host range mutations themselves, which has implications for models of serial host range expansion.IMPORTANCE RNA viruses mutate rapidly and frequently expand their host ranges to infect novel hosts, leading to serial host shifts. Using an RNA bacteriophage model system (Pseudomonas phage φ6), we studied the impact of preexisting host range mutations on another host range expansion. Results from both clonal Sanger and Illumina sequencing show that extant host range mutations dramatically narrow the neighborhood of potential host range mutations compared to that of wild-type φ6. This research suggests that serial host-shifting viruses may follow a small number of molecular paths to enter additional novel hosts. We also identified new genes involved in φ6 host range expansion, expanding our knowledge of this important model system in experimental evolution.

Feature Learning of Virus Genome Evolution With the Nucleotide Skip-Gram Neural Network

Recent studies reveal that even the smallest genomes such as viruses evolve through complex and stochastic processes, and the assumption of independent alleles is not valid in most applications. Advances in sequencing technologies produce multiple time-point whole-genome data, which enable potential interactions between these alleles to be investigated empirically. To investigate these interactions, we represent alleles as distributed vectors that encode for relationships with other alleles in the course of evolution and apply artificial neural networks to time-sampled whole-genome datasets for feature learning. We build this platform using methods and algorithms derived from natural language processing (NLP), and we denote it as the nucleotide skip-gram neural network. We learn distributed vectors of alleles using the changes in allele frequency of echovirus 11 in the presence or absence of the disinfectant (ClO₂) from the experimental evolution data. Results from the training using a new open-source software TensorFlow show that the learned distributed vectors can be clustered using principal component analysis and hierarchical clustering to reveal a list of non-synonymous mutations that arise on the structural protein VP1 in connection to the candidate mutation for ClO₂ adaptation. Furthermore, this method can account for recombination rates by setting the extent of interactions as a biological hyper-parameter, and the results show that the most realistic scenario of mid-range interactions across the genome is most consistent with the previous studies.

Viral outbreaks involve destabilized evolutionary networks: evidence from Ebola, Influenza and Zika

Recent history has provided us with one pandemic (Influenza A/H1N1) and two severe viral outbreaks (Ebola and Zika). In all three cases, post-hoc analyses have given us deep insights into what triggered these outbreaks, their timing, evolutionary dynamics, and phylogeography, but the genomic characteristics of outbreak viruses are still unclear. To address this outstanding question, we searched for a common denominator between these recent outbreaks, positing that the genome of outbreak viruses is in an unstable evolutionary state, while that of non-outbreak viruses is stabilized by a network of correlated substitutions. Here, we show that during regular epidemics, viral genomes are indeed stabilized by a dense network of weakly correlated sites, and that these networks disappear during pandemics and outbreaks when rates of evolution increase transiently. Post-pandemic, these evolutionary networks are progressively re-established. We finally show that destabilization is not caused by substitutions targeting epitopes, but more likely by changes in the environment sensu lato. Our results prompt for a new interpretation of pandemics as being associated with evolutionary destabilized viruses.

HLA class I haplotype diversity is consistent with selection for frequent existing haplotypes

The major histocompatibility complex (MHC) contains the most polymorphic genetic system in humans, the human leukocyte antigen (HLA) genes of the adaptive immune system. High allelic diversity in HLA is argued to be maintained by balancing selection, such as negative frequency-dependent selection or heterozygote advantage. Selective pressure against immune escape by pathogens can maintain appreciable frequencies of many different HLA alleles. The selection pressures operating on combinations of HLA alleles across loci, or haplotypes, have not been extensively evaluated since the high HLA polymorphism necessitates very large sample sizes, which have not been available until recently. We aimed to evaluate the effect of selection operating at the HLA haplotype level by analyzing HLA A~C~B~DRB1~DQB1 haplotype frequencies derived from over six million individuals genotyped by the National Marrow Donor Program registry. In contrast with alleles, HLA haplotype diversity patterns suggest purifying selection, as certain HLA allele combinations co-occur in high linkage disequilibrium. Linkage disequilibrium is positive (D_ij'>0) among frequent haplotypes and negative (D_ij'<0) among rare haplotypes. Fitting the haplotype frequency distribution to several population dynamics models, we found that the best fit was obtained when significant positive frequency-dependent selection (FDS) was incorporated. Finally, the Ewens-Watterson test of homozygosity showed excess homozygosity for 5-locus haplotypes within 23 US populations studied, with an average Fnd of 28.43. Haplotype diversity is most consistent with purifying selection for HLA Class I haplotypes (HLA-A, -B, -C), and was not inferred for HLA Class II haplotypes (-DRB1 and—DQB1). We discuss our empirical results in the context of evolutionary theory, exploring potential mechanisms of selection that maintain high linkage disequilibrium in MHC haplotype blocks.

The adaptive immune system presents antigens derived from pathogenic and normal self proteins on the cell surface using human leukocyte antigen (HLA) molecules. The HLA loci coding for these molecules are found in major histocompatibility complex (MHC) region, the most polymorphic region in the human genome, with over 15,000 HLA alleles observed so far in the world population. A high frequency of many different HLA alleles is thought be sustained by balancing selection. New HLA alleles may have an advantage over existing frequent alleles since immune escape mutations in pathogens within a population are maintained primarily in epitopes presented on frequent HLA alleles. Host immune function is not determined by single HLA alleles, but by both copies of autosomal HLA genes together (genotypes). Complementarity in function across the two potentially-variant copies of HLA at each locus can result in overdominance and heterozygote advantage at the genotype level. Less explored are selection mechanisms that may be operating across combinations of HLA alleles across loci (haplotypes). Indeed, in addition to high allelic diversity, HLA also has distinctive patterns of haplotype diversity, as certain HLA alleles co-occur in high linkage disequilibrium across five classical HLA loci (HLA-A, -B, -C, -DRB1, -DQB1). We applied multiple population genetic models to a dataset of HLA haplotype frequencies derived from over six million individuals with the goal of determining what type of selection may impact HLA haplotype diversity. We found frequent haplotypes were preferentially maintained in the population across 23 US populations studied. Thus, balancing selection at the allele level and purifying selection at the haplotype level may together affect HLA diversity in human populations.

Widespread Historical Contingency in Influenza Viruses

In systems biology and genomics, epistasis characterizes the impact that a substitution at a particular location in a genome can have on a substitution at another location. This phenomenon is often implicated in the evolution of drug resistance or to explain why particular "disease-causing" mutations do not have the same outcome in all individuals. Hence, uncovering these mutations and their locations in a genome is a central question in biology. However, epistasis is notoriously difficult to uncover, especially in fast-evolving organisms. Here, we present a novel statistical approach that replies on a model developed in ecology and that we adapt to analyze genetic data in fast-evolving systems such as the influenza A virus. We validate the approach using a two-pronged strategy: extensive simulations demonstrate a low-to-moderate sensitivity with excellent specificity and precision, while analyses of experimentally validated data recover known interactions, including in a eukaryotic system. We further evaluate the ability of our approach to detect correlated evolution during antigenic shifts or at the emergence of drug resistance. We show that in all cases, correlated evolution is prevalent in influenza A viruses, involving many pairs of sites linked together in chains; a hallmark of historical contingency. Strikingly, interacting sites are separated by large physical distances, which entails either long-range conformational changes or functional tradeoffs, for which we find support with the emergence of drug resistance. Our work paves a new way for the unbiased detection of epistasis in a wide range of organisms by performing whole-genome scans.