The role of RNA folding free energy in the evolution of the polymerase genes of the influenza A virus

Decoding the Role of Temperature in RNA Virus Infections

RNA viruses include respiratory viruses, such as coronaviruses and influenza viruses, as well as vector-borne viruses, like dengue and West Nile virus. RNA viruses like these encounter various environments when they copy themselves and spread from cell to cell or host to host. Ex vivo differences, such as geographical location and humidity, affect their stability and transmission, while in vivo differences, such as pH and host gene expression, impact viral receptor binding, viral replication, and the host immune response against the viral infection. A critical factor affecting RNA viruses both ex vivo and in vivo, and defining the outcome of viral infections and the direction of viral evolution, is temperature. In this minireview, we discuss the impact of temperature on viral replication, stability, transmission, and adaptation, as well as the host innate immune response. Improving our understanding of how RNA viruses function, survive, and spread at different temperatures will improve our models of viral replication and transmission risk analyses.

The RNA structurome in the asexual blood stages of malaria pathogen plasmodium falciparum

Plasmodium falciparum is a deadly human pathogen responsible for the devastating disease called malaria. In this study, we measured the differential accumulation of RNA secondary structures in coding and non-coding transcripts from the asexual developmental cycle in P. falciparum in human red blood cells. Our comprehensive analysis that combined high-throughput nuclease mapping of RNA structures by duplex RNA-seq, SHAPE-directed RNA structure validation, immunoaffinity purification and characterization of antisense RNAs collectively measured differentially base-paired RNA regions throughout the parasite's asexual RBC cycle. Our mapping data not only aligned to a diverse pool of RNAs with known structures but also enabled us to identify new structural RNA regions in the malaria genome. On average, approximately 71% of the genes with secondary structures are found to be protein coding mRNAs. The mapping pattern of these base-paired RNAs corresponded to all regions of mRNAs, including the 5' UTR, CDS and 3' UTR as well as the start and stop codons. Histone family genes which are known to form secondary structures in their mRNAs and transcripts from genes which are important for transcriptional and post-transcriptional control, such as the unique plant-like transcription factor family, ApiAP2, DNA-/RNA-binding protein, Alba3 and proteins important for RBC invasion and malaria cytoadherence also showed strong accumulation of duplex RNA reads in various asexual stages in P. falciparum. Intriguingly, our study determined stage-specific, dynamic relationships between mRNA structural contents and translation efficiency in P. falciparum asexual blood stages, suggesting an essential role of RNA structural changes in malaria gene expression programs.

Mutations designed to modify the NS gene mRNA secondary structure affect influenza A pathogenicity <em>in vivo</em>

The influenza A virus genome consists of eight segments of negative-sense RNA that encode up to 18 proteins. During the process of viral replication, positive-sense (+)RNA (cRNA) or messenger RNA (mRNA) is synthesized. Today, there is only a partial understanding of the function of several secondary structures within vRNA and cRNA promoters, and splice sites in the M and NS genes. The most precise secondary structure of (+)RNA has been determined for the NS segment of influenza A virus.  The influenza A virus NS gene features two regions with a conserved mRNA secondary structure located near splice sites. Here, we compared 4 variants of the A/Puerto Rico/8/1934 strain featuring different combinations of secondary structures at the NS segment (+)RNA regions 82-148 and 497-564. We found that RNA structures did not affect viral replication in cell culture. However, one of the viruses demonstrated lower NS1 and NEP expression levels during early stage cell infection as well as reduced pathogenicity in mice compared to other variants. In particular, this virus is characterized by an RNA hairpin in the 82-148 region and a stable hairpin in the 497-564 region.

Insights from the comparison of genomic variants from two influenza B viruses grown in the presence of human antibodies in cell culture

Understanding the extent and limitation of viral genome evolution can provide insight about potential drug and vaccine targets. Influenza B Viruses (IBVs) infect humans in a seasonal manner and causes significant morbidity and mortality. IBVs are negative-sense single-stranded RNA viruses with a segmented genome and can be divided into two antigenically distinct lineages. The two lineages have been circulating and further evolving for almost four decades. The immune response to IBV infection can lead to antibodies that target the strain causing the infection. Some antibodies are cross-reactive and are able to bind strains from both lineages but, because of antigenic drift and immunodominance, both lineages continue to evolve and challenge human health. Here we investigate changes in the genomes of an IBVs from each lineage after passage in tissue culture in the presence of human sera containing polyclonal antibodies directed toward antigenically and temporally distinct viruses. Our previous analysis of the fourth segment, which encodes the major surface protein HA, revealed a pattern of change in which signature sequences from one lineage mutated to the signature sequences of the other lineage. Here we analyze genes from the other genomic segments and observe that most of the quasispecies’ heterogeneity occurs at the same loci in each lineage. The nature of the variants at these loci are investigated and possible reasons for this pattern are discussed. This work expands our understanding of the extent and limitations of genomic change in IBV.

The fit of codon usage of human-isolated avian influenza A viruses to human

Avian influenza A viruses (AIVs) classify into 18 hemagglutinin (HA) and 11 neuraminidase (NA) subtypes. Even though H1N1 and H3N2 subtypes usually circulate among humans leading to infection, occasionally, H5, H6, H7, H9, and H10 that circulate in poultry also infect humans, and especially H5N1 and H7N9. Efficient virus replication is a critical factor that influences infection. Codon usage of a virus must coevolve with its host for efficient viral replication, therefore, we conduct a comprehensive analysis of codon usage bias in human-isolated AIVs to test their adaptation to host expression system. The relative synonymous codon usage (RSCU) pattern, and the codon adaptation index (CAI) are calculated for this purpose. We find that all human-isolated AIVs tend to eliminate GC and CpG compositions, which may prevent activation of the host innate immune system. Although codon usage differs between AIV subtypes, our data support the conclusion that natural selection has played a major role and mutation pressure a minor role in shaping codon usage bias in all AIVs. Our efforts discover that codon usage of genes encoding surface proteins of H5N1, and the polymerase genes of H7N9 has better fit to the human expression system. This may associate with their better replication and infection in human.

The effective rate of influenza reassortment is limited during human infection

We characterise the evolutionary dynamics of influenza infection described by viral sequence data collected from two challenge studies conducted in human hosts. Viral sequence data were collected at regular intervals from infected hosts. Changes in the sequence data observed across time show that the within-host evolution of the virus was driven by the reversion of variants acquired during previous passaging of the virus. Treatment of some patients with oseltamivir on the first day of infection did not lead to the emergence of drug resistance variants in patients. Using an evolutionary model, we inferred the effective rate of reassortment between viral segments, measuring the extent to which randomly chosen viruses within the host exchange genetic material. We find strong evidence that the rate of effective reassortment is low, such that genetic associations between polymorphic loci in different segments are preserved during the course of an infection in a manner not compatible with epistasis. Combining our evidence with that of previous studies we suggest that spatial heterogeneity in the viral population may reduce the extent to which reassortment is observed. Our results do not contradict previous findings of high rates of viral reassortment in vitro and in small animal studies, but indicate that in human hosts the effective rate of reassortment may be substantially more limited.

The influenza virus is an important cause of disease in the human population. During the course of an infection the virus can evolve rapidly. An important mechanism of viral evolution is reassortment, whereby different segments of the influenza genome are shuffled with other segments, producing new viral combinations. Here we study natural selection and reassortment during the course of infections occurring in human hosts. Examining viral genome sequence data from these infections, we note that genetic variants that were acquired during the growth of viruses in culture are selected against in the human host. In addition, we find evidence that the effective rate of reassortment is low. We suggest that the spatial separation between viruses in different parts of the host airway may limit the extent to which genetically distinct segments reassort with one another. Within the global population of influenza viruses, reassortment remains an important factor. However, reassortment is not so rapid as to exclude the possibility of interactions between genome segments affecting the course of influenza evolution during a single infection.

Computational and molecular analysis of conserved influenza A virus RNA secondary structures involved in infectious virion production

As well as encoding viral proteins, genomes of RNA viruses harbor secondary and tertiary RNA structures that have been associated with functions essential for successful replication and propagation. Here, we identified stem-loop structures that are extremely conserved among 1,884 M segment sequences of influenza A virus (IAV) strains from various subtypes and host species using computational and evolutionary methods. These structures were predicted within the 3' and 5' ends of the coding regions of M1 and M2, respectively, where packaging signals have been previously proposed to exist. These signals are thought to be required for the incorporation of a single copy of 8 different negative-strand RNA segments (vRNAs) into an IAV particle. To directly test the functionality of conserved stem-loop structures, we undertook reverse genetic experiments to introduce synonymous mutations designed to disrupt secondary structures predicted at 3 locations and found them to attenuate infectivity of recombinant virus. In one mutant, predicted to disrupt stem loop structure at nucleotide positions 219-240, attenuation was more evident at increased temperature and was accompanied by an increase in the production of defective virus particles. Our results suggest that the conserved secondary structures predicted in the M segment are involved in the production of infectious viral particles during IAV replication.

Innate immune system activation by viral RNA: How to predict it?

The immune system is able to identify foreign pathogens via different pathways. In the case of viral infection, recognition of the viral RNA is a crucial step, and many efforts have been made to understand which features of viral RNA are detected by the immune system. The biased viral RNA composition, measured as host-virus nucleotidic divergence, or CpG enrichment, has been proposed as salient signal. Peculiar structural features of these RNA could also be related to the immune system activation. Here, we gather multiple datasets and proceed to a meta-analysis to uncover the best predictors of immune system activation by viral RNA. "A" nucleotide content and Minimum Folding Energy are good predictors, and are more easily generalized than more complex indicators suggested previously. As RNA composition and structure are highly correlated, we suggest further experiments on synthetic sequences to identify the viral RNA sensing mechanisms by immune system receptors.

Viral evolution: beyond drift and shift

Technological advances have allowed aspects of viral evolution to be explored at unprecedented scales. As a consequence, new quantitative approaches are needed to investigate features of viral evolution that fall outside traditional areas of study, such as antigenic evolution. We examine three areas of viral evolution where tools from disciplines such as statistical physics, topology, and information theory have been used recently as quantitative frameworks for large-scale studies and, in some cases, suggest a novel theoretical approach to a problem. Ongoing interaction among these disciplines with biology is necessary so that experimental researchers can determine which quantitative tools are right for them and quantitative researchers can learn which aspects of viral evolution can be understood and advanced with their approaches.

RNA structural constraints in the evolution of the influenza A virus genome NP segment

Conserved RNA secondary structures were predicted in the nucleoprotein (NP) segment of the influenza A virus genome using comparative sequence and structure analysis. A number of structural elements exhibiting nucleotide covariations were identified over the whole segment length, including protein-coding regions. Calculations of mutual information values at the paired nucleotide positions demonstrate that these structures impose considerable constraints on the virus genome evolution. Functional importance of a pseudoknot structure, predicted in the NP packaging signal region, was confirmed by plaque assays of the mutant viruses with disrupted structure and those with restored folding using compensatory substitutions. Possible functions of the conserved RNA folding patterns in the influenza A virus genome are discussed.

Quantitative theory of entropic forces acting on constrained nucleotide sequences applied to viruses

We outline a theory to quantify the interplay of entropic and selective forces on nucleotide organization and apply it to the genomes of single-stranded RNA viruses. We quantify these forces as intensive variables that can easily be compared between sequences, outline a computationally efficient transfer-matrix method for their calculation, and apply this method to influenza and HIV viruses. We find viruses altering their dinucleotide motif use under selective forces, with these forces on CpG dinucleotides growing stronger in influenza the longer it replicates in humans. For a subset of genes in the human genome, many involved in antiviral innate immunity, the forces acting on CpG dinucleotides are even greater than the forces observed in viruses, suggesting that both effects are in response to similar selective forces involving the innate immune system. We further find that the dynamics of entropic forces balancing selective forces can be used to predict how long it will take a virus to adapt to a new host, and that it would take H1N1 several centuries to adapt to humans from birds, typically contributing many of its synonymous substitutions to the forcible removal of CpG dinucleotides. By examining the probability landscape of dinucleotide motifs, we predict where motifs are likely to appear using only a single-force parameter and uncover the localization of UpU motifs in HIV. Essentially, we extend the natural language and concepts of statistical physics, such as entropy and conjugated forces, to understanding viral sequences and, more generally, constrained genome evolution.

Predicting the dynamics of protein abundance

Protein synthesis is finely regulated across all organisms, from bacteria to humans, and its integrity underpins many important processes. Emerging evidence suggests that the dynamic range of protein abundance is greater than that observed at the transcript level. Technological breakthroughs now mean that sequencing-based measurement of mRNA levels is routine, but protocols for measuring protein abundance remain both complex and expensive. This paper introduces a Bayesian network that integrates transcriptomic and proteomic data to predict protein abundance and to model the effects of its determinants. We aim to use this model to follow a molecular response over time, from condition-specific data, in order to understand adaptation during processes such as the cell cycle. With microarray data now available for many conditions, the general utility of a protein abundance predictor is broad. Whereas most quantitative proteomics studies have focused on higher organisms, we developed a predictive model of protein abundance for both Saccharomyces cerevisiae and Schizosaccharomyces pombe to explore the latitude at the protein level. Our predictor primarily relies on mRNA level, mRNA-protein interaction, mRNA folding energy and half-life, and tRNA adaptation. The combination of key features, allowing for the low certainty and uneven coverage of experimental observations, gives comparatively minor but robust prediction accuracy. The model substantially improved the analysis of protein regulation during the cell cycle: predicted protein abundance identified twice as many cell-cycle-associated proteins as experimental mRNA levels. Predicted protein abundance was more dynamic than observed mRNA expression, agreeing with experimental protein abundance from a human cell line. We illustrate how the same model can be used to predict the folding energy of mRNA when protein abundance is available, lending credence to the emerging view that mRNA folding affects translation efficiency. The software and data used in this research are available at http://bioinf.scmb.uq.edu.au/proteinabundance/.

Stress-induced reversion to virulence of infectious pancreatic necrosis virus in naïve fry of Atlantic salmon (Salmo salar L.)

We have studied stress-induced reversion to virulence of infectious pancreatic necrosis virus (IPNV) in persistently infected Atlantic salmon (Salmo salar L.) fry. Naïve fry were persistently infected with a virulent strain (T₂₁₇A₂₂₁ of major structural virus protein 2, VP2) or a low virulent (T₂₁₇T₂₂₁) variant of IPNV. The fry were infected prior to immunocompetence as documented by lack of recombination activating gene-1, T-cell receptor and B-cell receptor mRNA expression at time of challenge. The fish were followed over 6 months and monitored monthly for presence of virus and viral genome mutations. No mutation was identified in the TA or TT group over the 6 months period post infection. Six months post infection TA and TT infected groups were subject to daily stress for 7 days and then sampled weekly for an additional period of 28 days post stress. Stress-responses were documented by down-regulation of mRNA expression of IFN-α1 and concomitant increase of replication levels of T₂₁₇T₂₂₁ infected fish at day 1 post stress. By 28 days post stress a T221A reversion was found in 3 of 6 fish in the T₂₁₇T₂₂₁ infected group. Sequencing of reverted isolates showed single nucleotide peaks on chromatograms for residue 221 for all three isolates and no mix of TA and TT strains. Replication fitness of reverted (TA) and non-reverted (TT) variants was studied in vitro under an antiviral state induced by recombinant IFN-α1. The T₂₁₇A₂₂₁ reverted variant replicated to levels 23-fold higher than the T₂₁₇T₂₂₁ strain in IFN-α1 treated cells. Finally, reverted TA strains were virulent when tested in an in vivo trial in susceptible salmon fry. In conclusion, these results indicate that stress plays a key role in viral replication in vivo and can facilitate conditions that will allow reversion from attenuated virus variants of IPNV.

Novel modeling of combinatorial miRNA targeting identifies SNP with potential role in bone density

MicroRNAs (miRNAs) are post-transcriptional regulators that bind to their target mRNAs through base complementarity. Predicting miRNA targets is a challenging task and various studies showed that existing algorithms suffer from high number of false predictions and low to moderate overlap in their predictions. Until recently, very few algorithms considered the dynamic nature of the interactions, including the effect of less specific interactions, the miRNA expression level, and the effect of combinatorial miRNA binding. Addressing these issues can result in a more accurate miRNA:mRNA modeling with many applications, including efficient miRNA-related SNP evaluation. We present a novel thermodynamic model based on the Fermi-Dirac equation that incorporates miRNA expression in the prediction of target occupancy and we show that it improves the performance of two popular single miRNA target finders. Modeling combinatorial miRNA targeting is a natural extension of this model. Two other algorithms show improved prediction efficiency when combinatorial binding models were considered. ComiR (Combinatorial miRNA targeting), a novel algorithm we developed, incorporates the improved predictions of the four target finders into a single probabilistic score using ensemble learning. Combining target scores of multiple miRNAs using ComiR improves predictions over the naïve method for target combination. ComiR scoring scheme can be used for identification of SNPs affecting miRNA binding. As proof of principle, ComiR identified rs17737058 as disruptive to the miR-488-5p:NCOA1 interaction, which we confirmed in vitro. We also found rs17737058 to be significantly associated with decreased bone mineral density (BMD) in two independent cohorts indicating that the miR-488-5p/NCOA1 regulatory axis is likely critical in maintaining BMD in women. With increasing availability of comprehensive high-throughput datasets from patients ComiR is expected to become an essential tool for miRNA-related studies.

Interplay between RNA structure and protein evolution in HIV-1

The genomes of many RNA viruses contain abundant secondary structures that have been shown to be important for understanding the evolution of noncoding regions and synonymous sites. However, the consequences for protein evolution are less well understood. Recently, the secondary structure of the HIV-1 RNA genome has been experimentally determined. Using this information, here we show that RNA structure and proteins do not evolve independently. A negative correlation exists between the extent of base pairing in the genomic RNA and amino acid variability. Relaxed RNA structures may favor the accumulation of genetic variation in proteins and, conversely, sequence changes driven by positive selection at the protein level may disrupt existing RNA structures. We also find that breakage of RNA base pairs might impose a fitness cost to drug resistance mutations in the protease and reverse transcriptase genes, thereby limiting their spread among untreated patients. Characterizing the evolutionary trade-offs between the selective pressures acting at the RNA and protein levels will help us to better understand the variability and evolution of HIV-1.

Identifying genetic markers of adaptation for surveillance of viral host jumps

Adaptation is often thought to affect the likelihood that a virus will be able to successfully emerge in a new host species. If so, surveillance for genetic markers of adaptation could help to predict the risk of disease emergence. However, adaptation is difficult to distinguish conclusively from the other processes that generate genetic change. In this Review we survey the research on the host jumps of influenza A, severe acute respiratory syndrome-coronavirus, canine parvovirus and Venezuelan equine encephalitis virus to illustrate the insights that can arise from combining genetic surveillance with microbiological experimentation in the context of epidemiological data. We argue that using a multidisciplinary approach for surveillance will provide a better understanding of when adaptations are required for host jumps and thus when predictive genetic markers may be present.

Influenza virus RNA structure: unique and common features

The influenza A virus genome consists of eight negative-sense RNA segments. Here we review the currently available data on structure-function relationships in influenza virus RNAs. Various ideas and hypotheses about the roles of influenza virus RNA folding in the virus replication are also discussed in relation to other viruses.

Codon usage bias and the evolution of influenza A viruses. Codon Usage Biases of Influenza Virus

BACKGROUND

The influenza A virus is an important infectious cause of morbidity and mortality in humans and was responsible for 3 pandemics in the 20th century. As the replication of the influenza virus is based on its host's machinery, codon usage of its viral genes might be subject to host selection pressures, especially after interspecies transmission. A better understanding of viral evolution and host adaptive responses might help control this disease.

RESULTS

Relative Synonymous Codon Usage (RSCU) values of the genes from segment 1 to segment 6 of avian and human influenza viruses, including pandemic H1N1, were studied via Correspondence Analysis (CA). The codon usage patterns of seasonal human influenza viruses were distinct among their subtypes and different from those of avian viruses. Newly isolated viruses could be added to the CA results, creating a tool to investigate the host origin and evolution of viral genes. It was found that the 1918 pandemic H1N1 virus contained genes with mammalian-like viral codon usage patterns, indicating that the introduction of this virus to humans was not through in toto transfer of an avian influenza virus.Many human viral genes had directional changes in codon usage over time of viral isolation, indicating the effect of host selection pressures. These changes reduced the overall GC content and the usage of G at the third codon position in the viral genome. Limited evidence of translational selection pressure was found in a few viral genes.

CONCLUSIONS

Codon usage patterns from CA allowed identification of host origin and evolutionary trends in influenza viruses, providing an alternative method and a tool to understand the evolution of influenza viruses. Human influenza viruses are subject to selection pressure on codon usage which might assist in understanding the characteristics of newly emerging viruses.

Comparative pathogenesis and systems biology for biodefense virus vaccine development

Developing vaccines to biothreat agents presents a number of challenges for discovery, preclinical development, and licensure. The need for high containment to work with live agents limits the amount and types of research that can be done using complete pathogens, and small markets reduce potential returns for industry. However, a number of tools, from comparative pathogenesis of viral strains at the molecular level to novel computational approaches, are being used to understand the basis of viral attenuation and characterize protective immune responses. As the amount of basic molecular knowledge grows, we will be able to take advantage of these tools not only to rationally attenuate virus strains for candidate vaccines, but also to assess immunogenicity and safety in silico. This review discusses how a basic understanding of pathogenesis, allied with systems biology and machine learning methods, can impact biodefense vaccinology.

Viral adaptation to host: a proteome-based analysis of codon usage and amino acid preferences

Viruses differ markedly in their specificity toward host organisms. Here, we test the level of general sequence adaptation that viruses display toward their hosts. We compiled a representative data set of viruses that infect hosts ranging from bacteria to humans. We consider their respective amino acid and codon usages and compare them among the viruses and their hosts. We show that bacteria-infecting viruses are strongly adapted to their specific hosts, but that they differ from other unrelated bacterial hosts. Viruses that infect humans, but not those that infect other mammals or aves, show a strong resemblance to most mammalian and avian hosts, in terms of both amino acid and codon preferences. In groups of viruses that infect humans or other mammals, the highest observed level of adaptation of viral proteins to host codon usages is for those proteins that appear abundantly in the virion. In contrast, proteins that are known to participate in host-specific recognition do not necessarily adapt to their respective hosts. The implication for the potential of viral infectivity is discussed.