Evolution of viral genomes: interplay between selection, recombination, and other forces

APOBEC3-related mutations in the spike protein-encoding region facilitate SARS-CoV-2 evolution

SARS-CoV-2 evolves gradually to cause COVID-19 epidemic. One of driving forces of SARS-CoV-2 evolution might be activation of apolipoprotein B mRNA editing catalytic subunit-like protein 3 (APOBEC3) by inflammatory factors. Here, we aimed to elucidate the effect of the APOBEC3-related viral mutations on the infectivity and immune evasion of SARS-CoV-2. The APOBEC3-related C > U mutations ranked as the second most common mutation types in the SARS-CoV-2 genome. mRNA expression of APOBEC3A (A3A), APOBEC3B (A3B), and APOBEC3G (A3G) in peripheral blood cells increased with disease severity. A3B, a critical member of the APOBEC3 family, was significantly upregulated in both severe and moderate COVID-19 patients and positively associated with neutrophil proportion and COVID-19 severity. We identified USP18 protein, a key molecule centralizing the protein-protein interaction network of key APOBEC3 proteins. Furthermore, mRNA expression of USP18 was significantly correlated to ACE2 and TMPRSS2 expression in the tissue of upper airways. Knockdown of USP18 mRNA significantly decreased A3B expression. Ectopic expression of A3B gene increased SARS-CoV-2 infectivity. C > U mutations at S371F, S373L, and S375F significantly conferred with the immune escape of SARS-CoV-2. Thus, APOBEC3, whose expression are upregulated by inflammatory factors, might promote SARS-CoV-2 evolution and spread via upregulating USP18 level and facilitating the immune escape. A3B and USP18 might be therapeutic targets for interfering with SARS-CoV-2 evolution.

Accurate Detection of Convergent Mutations in Large Protein Alignments With ConDor

Evolutionary convergences are observed at all levels, from phenotype to DNA and protein sequences, and changes at these different levels tend to be correlated. Notably, convergent mutations can lead to convergent changes in phenotype, such as changes in metabolism, drug resistance, and other adaptations to changing environments. We propose a two-component approach to detect mutations subject to convergent evolution in protein alignments. The "Emergence" component selects mutations that emerge more often than expected, while the "Correlation" component selects mutations that correlate with the convergent phenotype under study. With regard to Emergence, a phylogeny deduced from the alignment is provided by the user and is used to simulate the evolution of each alignment position. These simulations allow us to estimate the expected number of mutations in a neutral model, which is compared to the observed number of mutations in the data studied. In Correlation, a comparative phylogenetic approach, is used to measure whether the presence of each of the observed mutations is correlated with the convergent phenotype. Each component can be used on its own, for example Emergence when no phenotype is available. Our method is implemented in a standalone workflow and a webserver, called ConDor. We evaluate the properties of ConDor using simulated data, and we apply it to three real datasets: sedge PEPC proteins, HIV reverse transcriptase, and fish rhodopsin. The results show that the two components of ConDor complement each other, with an overall accuracy that compares favorably to other available tools, especially on large datasets.

Spatio-temporal spread and evolution of Lassa virus in West Africa

BACKGROUND

Lassa fever is a hemorrhagic disease caused by Lassa virus (LASV), which has been classified by the World Health Organization as one of the top infectious diseases requiring prioritized research. Previous studies have provided insights into the classification and geographic characteristics of LASV lineages. However, the factor of the distribution and evolution characteristics and phylodynamics of the virus was still limited.

METHODS

To enhance comprehensive understanding of LASV, we employed phylogenetic analysis, reassortment and recombination detection, and variation evaluation utilizing publicly available viral genome sequences.

RESULTS

The results showed the estimated the root of time of the most recent common ancestor (TMRCA) for large (L) segment was approximately 634 (95% HPD: [385879]), whereas the TMRCA for small (S) segment was around 1224 (95% HPD: [10301401]). LASV primarily spread from east to west in West Africa through two routes, and in route 2, the virus independently spread to surrounding countries through Liberia, resulting in a wider spread of LASV. From 1969 to 2018, the effective population size experienced two significant increased, indicating the enhanced genetic diversity of LASV. We also found the evolution rate of L segment was faster than S segment, further results showed zinc-binding protein had the fastest evolution rate. Reassortment events were detected in multiple lineages including sub-lineage IIg, while recombination events were observed within lineage V. Significant amino acid changes in the glycoprotein precursor of LASV were identified, demonstrating sequence diversity among lineages in LASV.

CONCLUSION

This study comprehensively elucidated the transmission and evolution of LASV in West Africa, providing detailed insights into reassortment events, recombination events, and amino acid variations.

Recursive genome engineering decodes the evolutionary origin of an essential thymidylate kinase activity in Pseudomonas putida KT2440

IMPORTANCE

Investigating fundamental aspects of metabolism is vital for advancing our understanding of the diverse biochemical capabilities and biotechnological applications of bacteria. The origin of the essential thymidylate kinase function in the model bacterium Pseudomonas putida KT2440, seemingly interrupted due to the presence of a large genomic island that disrupts the cognate gene, eluded a satisfactory explanation thus far. This is a first-case example of an essential metabolic function, likely acquired by horizontal gene transfer, which "landed" in a locus encoding the same activity. As such, foreign DNA encoding an essential dNMPK could immediately adjust to the recipient host-instead of long-term accommodation and adaptation. Understanding how these functions evolve is a major biological question, and the work presented here is a decisive step toward this direction. Furthermore, identifying essential and accessory genes facilitates removing those deemed irrelevant in industrial settings-yielding genome-reduced cell factories with enhanced properties and genetic stability.

When Everything Becomes Bigger: Big Data for Big Poultry Production

In future decades, the demand for poultry meat and eggs is predicted to considerably increase in pace with human population growth. Although this expansion clearly represents a remarkable opportunity for the sector, it conceals a multitude of challenges. Pollution and land erosion, competition for limited resources between animal and human nutrition, animal welfare concerns, limitations on the use of growth promoters and antimicrobial agents, and increasing risks and effects of animal infectious diseases and zoonoses are several topics that have received attention from authorities and the public. The increase in poultry production must be achieved mainly through optimization and increased efficiency. The increasing ability to generate large amounts of data ("big data") is pervasive in both modern society and the farming industry. Information accessibility-coupled with the availability of tools and computational power to store, share, integrate, and analyze data with automatic and flexible algorithms-offers an unprecedented opportunity to develop tools to maximize farm profitability, reduce socio-environmental impacts, and increase animal and human health and welfare. A detailed description of all topics and applications of big data analysis in poultry farming would be infeasible. Therefore, the present work briefly reviews the application of sensor technologies, such as optical, acoustic, and wearable sensors, as well as infrared thermal imaging and optical flow, to poultry farming. The principles and benefits of advanced statistical techniques, such as machine learning and deep learning, and their use in developing effective and reliable classification and prediction models to benefit the farming system, are also discussed. Finally, recent progress in pathogen genome sequencing and analysis is discussed, highlighting practical applications in epidemiological tracking, and reconstruction of microorganisms' population dynamics, evolution, and spread. The benefits of the objective evaluation of the effectiveness of applied control strategies are also considered. Although human-artificial intelligence collaborations in the livestock sector can be frightening because they require farmers and employees in the sector to adapt to new roles, challenges, and competencies-and because several unknowns, limitations, and open-ended questions are inevitable-their overall benefits appear to be far greater than their drawbacks. As more farms and companies connect to technology, artificial intelligence (AI) and sensing technologies will begin to play a greater role in identifying patterns and solutions to pressing problems in modern animal farming, thus providing remarkable production-based and commercial advantages. Moreover, the combination of diverse sources and types of data will also become fundamental for the development of predictive models able to anticipate, rather than merely detect, disease occurrence. The increasing availability of sensors, infrastructures, and tools for big data collection, storage, sharing, and analysis-together with the use of open standards and integration with pathogen molecular epidemiology-have the potential to address the major challenge of producing higher-quality, more healthful food on a larger scale in a more sustainable manner, thereby protecting ecosystems, preserving natural resources, and improving animal and human welfare and health.

Identification of evolutionary trajectories shared across human betacoronaviruses

Comparing the evolution of distantly related viruses can provide insights into common adaptive processes related to shared ecological niches. Phylogenetic approaches, coupled with other molecular evolution tools, can help identify mutations informative on adaptation, whilst the structural contextualization of these to functional sites of proteins may help gain insight into their biological properties. Two zoonotic betacoronaviruses capable of sustained human-to-human transmission have caused pandemics in recent times (SARS-CoV-1 and SARS-CoV-2), whilst a third virus (MERS-CoV) is responsible for sporadic outbreaks linked to animal infections. Moreover, two other betacoronaviruses have circulated endemically in humans for decades (HKU1 and OC43). To search for evidence of adaptive convergence between established and emerging betacoronaviruses capable of sustained human-to-human transmission (HKU1, OC43, SARS-CoV-1 and SARS-CoV-2), we developed a methodological pipeline to classify shared non-synonymous mutations as putatively denoting homoplasy (repeated mutations that do not share direct common ancestry) or stepwise evolution (sequential mutations leading towards a novel genotype). In parallel, we look for evidence of positive selection, and draw upon protein structure data to identify potential biological implications. We find 30 mutations, with four of these [codon sites 18121 (nsp14/residue 28), 21623 (spike/21), 21635 (spike/25) and 23948 (spike/796); SARS-CoV-2 genome numbering] displaying evolution under positive selection and proximity to functional protein regions. Our findings shed light on potential mechanisms underlying betacoronavirus adaptation to the human host and pinpoint common mutational pathways that may occur during establishment of human endemicity.

RNA structure-altering mutations underlying positive selection on Spike protein reveal novel putative signatures to trace crossing host-species barriers in Betacoronavirus

Similar to other RNA viruses, the emergence of Betacoronavirus relies on cross-species viral transmission, which requires careful health surveillance monitoring of protein-coding information as well as genome-wide analysis. Although the evolutionary jump from natural reservoirs to humans may be mainly traced-back by studying the effect that hotspot mutations have on viral proteins, it is largely unexplored if other impacts might emerge on the structured RNA genome of Betacoronavirus. In this survey, the protein-coding and viral genome architecture were simultaneously studied to uncover novel insights into cross-species horizontal transmission events. We analysed 1,252,952 viral genomes of SARS-CoV, MERS-CoV, and SARS-CoV-2 distributed across the world in bats, intermediate animals, and humans to build a new landscape of changes in the RNA viral genome. Phylogenetic analyses suggest that bat viruses are the most closely related to the time of most recent common ancestor of Betacoronavirus, and missense mutations in viral proteins, mainly in the S protein S1 subunit: SARS-CoV (G > T; A577S); MERS-CoV (C > T; S746R and C > T; N762A); and SARS-CoV-2 (A > G; D614G) appear to have driven viral diversification. We also found that codon sites under positive selection on S protein overlap with non-compensatory mutations that disrupt secondary RNA structures in the RNA genome complement. These findings provide pivotal factors that might be underlying the eventual jumping the species barrier from bats to intermediate hosts. Lastly, we discovered that nearly half of the Betacoronavirus genomes carry highly conserved RNA structures, and more than 90% of these RNA structures show negative selection signals, suggesting essential functions in the biology of Betacoronavirus that have not been investigated to date. Further research is needed on negatively selected RNA structures to scan for emerging functions like the potential of coding virus-derived small RNAs and to develop new candidate antiviral therapeutic strategies.

Biological Significance of the Genomic Variation and Structural Dynamics of SARS-CoV-2 B.1.617

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have been emerging and circulating globally since the start of the COVID-19 pandemic, of which B.1.617 lineage that was first reported in India at the end of 2020, soon became predominant. Tracing genomic variations and understanding their impact on the viral properties are the foundations for the vaccine and drug development and for the mitigation measures to be taken or lifted. In this study, 1,051 near-complete genomes and 1,559 spike (S) sequences belonging to the B.1.617 were analyzed. A genome-wide spread of single nucleotide polymorphisms (SNPs) was identified. Of the high frequency mutations identified, 61% (11/18) involved structural proteins, despite two third of the viral genome encoding nonstructural proteins. There were 22 positive selection sites, mostly distributed across the S protein, of which 16 were led by non-C to U transition and should be of a special attention. Haplotype network revealed that a large number of daughter haplotypes were continually derived throughout the pandemic, of which H177, H181 H219 and H286 from the ancestor haplotype H176 of B.1.617.2 were widely prevalent. Besides the well known substitutions of L452R, P681R and deletions of E156 and F157, as well as the potential biological significance, structural analysis in this study still indicated that new amino acid changes in B.1.617, such as E484Q and N501Y, had reshaped the viral bonding network, and increasingly sequenced N501Y mutant with a potential enhanced binding ability was detected in many other countries in the follow-up monitoring. Although we can't conclude the properties of all the mutants including N501Y thoroughly, it merits focusing on their spread epidemically and biologically.

Parallel evolution in the emergence of highly pathogenic avian influenza A viruses

Parallel molecular evolution and adaptation are important phenomena commonly observed in viruses. Here, we exploit parallel molecular evolution to understand virulence evolution in avian influenza viruses (AIV). Highly-pathogenic AIVs evolve independently from low-pathogenic ancestors via acquisition of polybasic cleavage sites. Why some AIV lineages but not others evolve in this way is unknown. We hypothesise that the parallel emergence of highly-pathogenic AIV may be facilitated by permissive or compensatory mutations occurring across the viral genome. We combine phylogenetic, statistical and structural approaches to discover parallel mutations in AIV genomes associated with the highly-pathogenic phenotype. Parallel mutations were screened using a statistical test of mutation-phenotype association and further evaluated in the contexts of positive selection and protein structure. Our resulting mutational panel may help to reveal new links between virulence evolution and other traits, and raises the possibility of predicting aspects of AIV evolution.

Genomic Analysis of Early SARS-CoV-2 Variants Introduced in Mexico

The coronavirus disease 2019 (COVID-19) pandemic has affected most countries in the world. Studying the evolution and transmission patterns in different countries is crucial to enabling implementation of effective strategies for disease control and prevention. In this work, we present the full genome sequence for 17 SARS-CoV-2 isolates corresponding to the earliest sampled cases in Mexico. Global and local phylogenomics, coupled with mutational analysis, consistently revealed that these viral sequences are distributed within 2 known lineages, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage A/G, containing mostly sequences from North America, and lineage B/S, containing mainly sequences from Europe. Based on the exposure history of the cases and on the phylogenomic analysis, we characterized 14 independent introduction events. Additionally, three cases with no travel history were identified. We found evidence that two of these cases represented local transmission cases occurring in Mexico during mid-March 2020, denoting the earliest events described for the country. Within this local transmission cluster, we also identified an H49Y amino acid change in the Spike protein. This mutation represents a homoplasy occurring independently through time and space and may function as a molecular marker to follow any further spread of these viral variants throughout the country. Our results provide a general picture of the SARS-CoV-2 variants introduced at the beginning of the outbreak in Mexico, setting the foundation for future surveillance efforts.IMPORTANCE Understanding the introduction, spread, and establishment of SARS-CoV-2 within distinct human populations as well as the evolution of the pandemics is crucial to implement effective control strategies. In this work, we report that the initial virus strains introduced in Mexico came from Europe and the United States and that the virus was circulating locally in the country as early as mid-March. We also found evidence for early local transmission of strains with a H49Y mutation in the Spike protein, which could be further used as a molecular marker to follow viral spread within the country and the region.

Protein intrinsic disorder within the Potyvirus genus: from proteome-wide analysis to functional annotation

Within proteins, intrinsically disordered regions (IDRs) are devoid of stable secondary and tertiary structures under physiological conditions and rather exist as dynamic ensembles of inter-converting conformers. Although ubiquitous in all domains of life, the intrinsic disorder content is highly variable in viral genomes. Over the years, functional annotations of disordered regions at the scale of the whole proteome have been conducted for several animal viruses. But to date, similar studies applied to plant viruses are still missing. Based on disorder prediction tools combined with annotation programs and evolutionary studies, we analyzed the intrinsic disorder content in Potyvirus, using a 10-species dataset representative of this genus diversity. In this paper, we revealed that: (i) the Potyvirus proteome displays high disorder content, (ii) disorder is conserved during Potyvirus evolution, suggesting a functional advantage of IDRs, (iii) IDRs evolve faster than ordered regions, and (iv) IDRs may be associated with major biological functions required for the Potyvirus cycle. Notably, the proteins P1, Coat protein (CP) and Viral genome-linked protein (VPg) display a high content of conserved disorder, enriched in specific motifs mimicking eukaryotic functional modules and suggesting strategies of host machinery hijacking. In these three proteins, IDRs are particularly conserved despite their high amino acid polymorphism, indicating a link to adaptive processes. Through this comprehensive study, we further investigate the biological relevance of intrinsic disorder in Potyvirus biology and we propose a functional annotation of potyviral proteome IDRs.

Genome-wide detection of selection and other evolutionary forces

As is well known, pathogenic microbes evolve rapidly to escape from the host immune system and antibiotics. Genetic variations among microbial populations occur frequently during the long-term pathogen-host evolutionary arms race, and individual mutation beneficial for the fitness can be fixed preferentially. Many recent comparative genomics studies have pointed out the importance of selective forces in the molecular evolution of bacterial pathogens. The public availability of large-scale next-generation sequencing data and many state-of-the-art statistical methods of molecular evolution enable us to scan genome-wide alignments for evidence of positive Darwinian selection, recombination, and other evolutionary forces operating on the coding regions. In this chapter, we describe an integrative analysis pipeline and its application to tracking featured evolutionary trajectories on the genome of an animal pathogen. The evolutionary analysis of the protein-coding part of the genomes will provide a wide spectrum of genetic variations that play potential roles in adaptive evolution of bacteria.

Evolution of tertiary structure of viral RNA dependent polymerases

Viral RNA dependent polymerases (vRdPs) are present in all RNA viruses; unfortunately, their sequence similarity is too low for phylogenetic studies. Nevertheless, vRdP protein structures are remarkably conserved. In this study, we used the structural similarity of vRdPs to reconstruct their evolutionary history. The major strength of this work is in unifying sequence and structural data into a single quantitative phylogenetic analysis, using powerful a Bayesian approach.

The resulting phylogram of vRdPs demonstrates that RNA-dependent DNA polymerases (RdDPs) of viruses within Retroviridae family cluster in a clearly separated group of vRdPs, while RNA-dependent RNA polymerases (RdRPs) of dsRNA and +ssRNA viruses are mixed together. This evidence supports the hypothesis that RdRPs replicating +ssRNA viruses evolved multiple times from RdRPs replicating +dsRNA viruses, and vice versa. Moreover, our phylogram may be presented as a scheme for RNA virus evolution. The results are in concordance with the actual concept of RNA virus evolution. Finally, the methods used in our work provide a new direction for studying ancient virus evolution.

Hepatitis A virus: host interactions, molecular epidemiology and evolution

Infection with hepatitis A virus (HAV) is the commonest viral cause of liver disease and presents an important public health problem worldwide. Several unique HAV properties and molecular mechanisms of its interaction with host were recently discovered and should aid in clarifying the pathogenesis of hepatitis A. Genetic characterization of HAV strains have resulted in the identification of different genotypes and subtypes, which exhibit a characteristic worldwide distribution. Shifts in HAV endemicity occurring in different parts of the world, introduction of genetically diverse strains from geographically distant regions, genotype displacement observed in some countries and population expansion detected in the last decades of the 20th century using phylogenetic analysis are important factors contributing to the complex dynamics of HAV infections worldwide. Strong selection pressures, some of which, like usage of deoptimized codons, are unique to HAV, limit genetic variability of the virus. Analysis of subgenomic regions has been proven useful for outbreak investigations. However, sharing short sequences among epidemiologically unrelated strains indicates that specific identification of HAV strains for molecular surveillance can be achieved only using whole-genome sequences. Here, we present up-to-date information on the HAV molecular epidemiology and evolution, and highlight the most relevant features of the HAV-host interactions.

Molecular evolution of porcine reproductive and respiratory syndrome virus isolates from central China

To investigate the genetic diversity of prevailing porcine reproductive and respiratory syndrome virus (PRRSV) in Henan Province of China, 61 ORF5 gene sequences, originating from Henan Province during 2003-2010, were subjected to amino acid variation and phylogenetic analysis. The analyzed PRRSV ORF5 sequences carried evidence of one unique recombination event. Phylogenetic analysis revealed that all Henan isolates belonged to type 2 genotype and were divided into two subgroups. The dominant isolates had shifted from subgroup 1 to subgroup 2 during 2003-2010. Amino acid variation analysis of the glycoprotein 5 revealed that Henan PRRSV strains tended to accumulate more substitutions within the N-terminus and hypervariable region. Selective pressure analysis revealed evidence that some ORF5 sites have likely evolved in response to immune pressure.

High-throughput RNA interference screens integrative analysis: Towards a comprehensive understanding of the virus-host interplay

Viruses are extremely heterogeneous entities; the size and the nature of their genetic information, as well as the strategies employed to amplify and propagate their genomes, are highly variable. However, as obligatory intracellular parasites, replication of all viruses relies on the host cell. Having co-evolved with their host for several million years, viruses have developed very sophisticated strategies to hijack cellular factors that promote virus uptake, replication, and spread. Identification of host cell factors (HCFs) required for these processes is a major challenge for researchers, but it enables the identification of new, highly selective targets for anti viral therapeutics. To this end, the establishment of platforms enabling genome-wide high-throughput RNA interference (HT-RNAi) screens has led to the identification of several key factors involved in the viral life cycle. A number of genome-wide HT-RNAi screens have been performed for major human pathogens. These studies enable first inter-viral comparisons related to HCF requirements. Although several cellular functions appear to be uniformly required for the life cycle of most viruses tested (such as the proteasome and the Golgi-mediated secretory pathways), some factors, like the lipid kinase Phosphatidylinositol 4-kinase IIIα in the case of hepatitis C virus, are selectively required for individual viruses. However, despite the amount of data available, we are still far away from a comprehensive understanding of the interplay between viruses and host factors. Major limitations towards this goal are the low sensitivity and specificity of such screens, resulting in limited overlap between different screens performed with the same virus. This review focuses on how statistical and bioinformatic analysis methods applied to HT-RNAi screens can help overcoming these issues thus increasing the reliability and impact of such studies.

Genetic recombination in plant-infecting messenger-sense RNA viruses: overview and research perspectives

RNA recombination is one of the driving forces of genetic variability in (+)-strand RNA viruses. Various types of RNA-RNA crossovers were described including crosses between the same or different viral RNAs or between viral and cellular RNAs. Likewise, a variety of molecular mechanisms are known to support RNA recombination, such as replicative events (based on internal or end-to-end replicase switchings) along with non-replicative joining among RNA fragments of viral and/or cellular origin. Such mechanisms as RNA decay or RNA interference are responsible for RNA fragmentation and trans-esterification reactions which are likely accountable for ligation of RNA fragments. Numerous host factors were found to affect the profiles of viral RNA recombinants and significant differences in recombination frequency were observed among various RNA viruses. Comparative analyses of viral sequences allowed for the development of evolutionary models in order to explain adaptive phenotypic changes and co-evolving sites. Many questions remain to be answered by forthcoming RNA recombination research. (1) How various factors modulate the ability of viral replicase to switch templates, (2) What is the intracellular location of RNA-RNA template switchings, (3) Mechanisms and factors responsible for non-replicative RNA recombination, (4) Mechanisms of integration of RNA viral sequences with cellular genomic DNA, and (5) What is the role of RNA splicing and ribozyme activity. From an evolutionary stand point, it is not known how RNA viruses parasitize new host species via recombination, nor is it obvious what the contribution of RNA recombination is among other RNA modification pathways. We do not understand why the frequency of RNA recombination varies so much among RNA viruses and the status of RNA recombination as a form of sex is not well documented.