Models of RNA virus evolution and their roles in vaccine design

Haloperoxidase-mimicking CeO2-x nanorods for the deactivation of human coronavirus OC43

Despite the excellent antibacterial and antifouling effects of haloperoxidase (HPO)-mimicking CeO_2-x nanorods, their antiviral efficiency has not been explored. Herein, we designed and synthesized CeO_2-x nanorods with varying aspect ratios via the hydrothermal method. CeO_2-x nanorods catalysed the oxidative bromination of Br^- and H₂O₂ to HOBr, the kinetics of which were studied systematically using a phenol red assay. The CeO_2-x nanorods with the optimized aspect ratio (i.e., 4.5) demonstrated strong antiviral efficacies against the human coronavirus OC43, with no visible toxicity to the HCT-8 host cells.

Genome-wide identification and prediction of SARS-CoV-2 mutations show an abundance of variants: Integrated study of bioinformatics and deep neural learning

Genomic data analysis is a fundamental system for monitoring pathogen evolution and the outbreak of infectious diseases. Based on bioinformatics and deep learning, this study was designed to identify the genomic variability of SARS-CoV-2 worldwide and predict the impending mutation rate. Analysis of 259044 SARS-CoV-2 isolates identified 3334545 mutations with an average of 14.01 mutations per isolate. Globally, single nucleotide polymorphism (SNP) is the most prevalent mutational event. The prevalence of C > T (52.67%) was noticed as a major alteration across the world followed by the G > T (14.59%) and A > G (11.13%). Strains from India showed the highest number of mutations (48) followed by Scotland, USA, Netherlands, Norway, and France having up to 36 mutations. D416G, F106F, P314L, UTR:C241T, L93L, A222V, A199A, V30L, and A220V mutations were found as the most frequent mutations. D1118H, S194L, R262H, M809L, P314L, A8D, S220G, A890D, G1433C, T1456I, R233C, F263S, L111K, A54T, A74V, L183A, A316T, V212F, L46C, V48G, Q57H, W131R, G172V, Q185H, and Y206S missense mutations were found to largely decrease the structural stability of the corresponding proteins. Conversely, D3L, L5F, and S97I were found to largely increase the structural stability of the corresponding proteins. Multi-nucleotide mutations GGG > AAC, CC > TT, TG > CA, and AT > TA have come up in our analysis which are in the top 20 mutational cohort. Future mutation rate analysis predicts a 17%, 7%, and 3% increment of C > T, A > G, and A > T, respectively in the future. Conversely, 7%, 7%, and 6% decrement is estimated for T > C, G > A, and G > T mutations, respectively. T > G\A, C > G\A, and A > T\C are not anticipated in the future. Since SARS-CoV-2 is mutating continuously, our findings will facilitate the tracking of mutations and help to map the progression of the COVID-19 intensity worldwide.

Phylodynamic analysis in the understanding of the current COVID-19 pandemic and its utility in vaccine and antiviral design and assessment

Over the last decades, the use of phylogenetic methods in the study of emerging infectious diseases has gained considerable traction in public health. Particularly, the integration of phylogenetic analyses with the understanding of the pathogen dynamics at the population level has provided powerful tools for epidemiological surveillance systems. In the same way, the development of statistical methods and theory, as well as improvement of computational efficiency for evolutionary analysis, has expanded the use of these tools for vaccine and antiviral development. Today with the Coronavirus Disease 2019 (COVID-19), this seems to be critical. In this article, we discuss how the application of phylodynamic analysis can improve the understanding of current pandemic dynamics as well as the design, selection, and evaluation of vaccine candidates and antivirals.

Remdesivir MD Simulations Suggest a More Favourable Binding to SARS-CoV-2 RNA Dependent RNA Polymerase Mutant P323L Than Wild-Type

SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) protein is the target for the antiviral drug Remdesivir (RDV). With RDV clinical trials on COVID-19 patients showing a reduced hospitalisation time. During the spread of the virus, the RdRp has developed several mutations, with the most frequent being A97V and P323L. The current study sought to investigate whether A97V and P323L mutations influence the binding of RDV to the RdRp of SARS-CoV-2 compared to wild-type (WT). The interaction of RDV with WT-, A97V-, and P323L-RdRp were measured using molecular dynamic (MD) simulations, and the free binding energies were extracted. Results showed that RDV that bound to WT- and A97V-RdRp had a similar dynamic motion and internal residue fluctuations, whereas RDV interaction with P323L-RdRp exhibited a tighter molecular conformation, with a high internal motion near the active site. This was further corroborated with RDV showing a higher binding affinity to P323L-RdRp (-24.1 kcal/mol) in comparison to WT-RdRp (-17.3 kcal/mol). This study provides insight into the potential significance of administering RDV to patients carrying the SARS-CoV-2 P323L-RdRp mutation, which may have a more favourable chance of alleviating the SARS-CoV-2 illness in comparison to WT-RdRp carriers, thereby suggesting further scientific consensus for the usage of Remdesivir as clinical candidate against COVID-19.

Human neutralising antibodies elicited by SARS-CoV-2 non-D614G variants offer cross-protection against the SARS-CoV-2 D614G variant

OBJECTIVES

The emergence of a SARS-CoV-2 variant with a point mutation in the spike (S) protein, D614G, has taken precedence over the original Wuhan isolate by May 2020. With an increased infection and transmission rate, it is imperative to determine whether antibodies induced against the D614 isolate may cross-neutralise against the G614 variant.

METHODS

Antibody profiling against the SARS-CoV-2 S protein of the D614 variant by flow cytometry and assessment of neutralising antibody titres using pseudotyped lentiviruses expressing the SARS-CoV-2 S protein of either the D614 or G614 variant tagged with a luciferase reporter were performed on plasma samples from COVID-19 patients with known D614G status (n = 44 infected with D614, n = 6 infected with G614, n = 7 containing all other clades: O, S, L, V, G, GH or GR).

RESULTS

Profiling of the anti-SARS-CoV-2 humoral immunity reveals similar neutralisation profiles against both S protein variants, albeit waning neutralising antibody capacity at the later phase of infection. Of clinical importance, patients infected with either the D614 or G614 clade elicited a similar degree of neutralisation against both pseudoviruses, suggesting that the D614G mutation does not impact the neutralisation capacity of the elicited antibodies.

CONCLUSIONS

Cross-reactivity occurs at the functional level of the humoral response on both the S protein variants, which suggests that existing serological assays will be able to detect both D614 and G614 clades of SARS-CoV-2. More importantly, there should be negligible impact towards the efficacy of antibody-based therapies and vaccines that are currently being developed.

Role of artificial intelligence in peptide vaccine design against RNA viruses

RNA viruses have high rate of replication and mutation that help them adapt and change according to their environmental conditions. Many viral mutants are the cause of various severe and lethal diseases. Vaccines, on the other hand have the capacity to protect us from infectious diseases by eliciting antibody or cell-mediated immune responses that are pathogen-specific. While there are a few reviews pertaining to the use of artificial intelligence (AI) for SARS-COV-2 vaccine development, none focus on peptide vaccination for RNA viruses and the important role played by AI in it. Peptide vaccine which is slowly coming to be recognized as a safe and effective vaccination strategy has the capacity to overcome the mutant escape problem which is also being currently faced by SARS-COV-2 vaccines in circulation.Here we review the present scenario of peptide vaccines which are developed using mathematical and computational statistics methods to prevent the spread of disease caused by RNA viruses. We also focus on the importance and current stage of AI and mathematical evolutionary modeling using machine learning tools in the establishment of these new peptide vaccines for the control of viral disease.

Decoding SARS-CoV-2 Transmission and Evolution and Ramifications for COVID-19 Diagnosis, Vaccine, and Medicine

Tremendous effort has been given to the development of diagnostic tests, preventive vaccines, and therapeutic medicines for coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Much of this development has been based on the reference genome collected on January 5, 2020. Based on the genotyping of 15 140 genome samples collected up to June 1, 2020, we report that SARS-CoV-2 has undergone 8309 single mutations which can be clustered into six subtypes. We introduce mutation ratio and mutation h-index to characterize the protein conservativeness and unveil that SARS-CoV-2 envelope protein, main protease, and endoribonuclease protein are relatively conservative, while SARS-CoV-2 nucleocapsid protein, spike protein, and papain-like protease are relatively nonconservative. In particular, we have identified mutations on 40% of nucleotides in the nucleocapsid gene in the population level, signaling potential impacts on the ongoing development of COVID-19 diagnosis, vaccines, and antibody and small-molecular drugs.

Population genomics of louping ill virus provide new insights into the evolution of tick-borne flaviviruses

The emergence and spread of tick-borne arboviruses pose an increased challenge to human and animal health. In Europe this is demonstrated by the increasingly wide distribution of tick-borne encephalitis virus (TBEV, Flavivirus, Flaviviridae), which has recently been found in the United Kingdom (UK). However, much less is known about other tick-borne flaviviruses (TBFV), such as the closely related louping ill virus (LIV), an animal pathogen which is endemic to the UK and Ireland, but which has been detected in other parts of Europe including Scandinavia and Russia. The emergence and potential spatial overlap of these viruses necessitates improved understanding of LIV genomic diversity, geographic spread and evolutionary history. We sequenced a virus archive composed of 22 LIV isolates which had been sampled throughout the UK over a period of over 80 years. Combining this dataset with published virus sequences, we detected no sign of recombination and found low diversity and limited evidence for positive selection in the LIV genome. Phylogenetic analysis provided evidence of geographic clustering as well as long-distance movement, including movement events that appear recent. However, despite genomic data and an 80-year time span, we found that the data contained insufficient temporal signal to reliably estimate a molecular clock rate for LIV. Additional analyses revealed that this also applied to TBEV, albeit to a lesser extent, pointing to a general problem with phylogenetic dating for TBFV. The 22 LIV genomes generated during this study provide a more reliable LIV phylogeny, improving our knowledge of the evolution of tick-borne flaviviruses. Our inability to estimate a molecular clock rate for both LIV and TBEV suggests that temporal calibration of tick-borne flavivirus evolution should be interpreted with caution and highlight a unique aspect of these viruses which may be explained by their reliance on tick vectors.

Tick-borne pathogens represent a major emerging threat to public health and in recent years have been expanding into new areas. LIV is a neglected virus endemic to the UK and Ireland (though it has been detected in Scandinavia and Russia) which is closely related to the major human pathogen TBEV, but predominantly causes disease in sheep and grouse. The recent detection of TBEV in the UK, which has also emerged elsewhere in Europe, requires more detailed understanding of the spread and sequence diversity of LIV. This could be important for diagnosis and vaccination, but also to improve our understanding of the evolution and emergence of these tick-borne viruses. Here we describe the sequencing of 22 LIV isolates which have been sampled from several host species across the past century. We have utilised this dataset to investigate the evolutionary pressures that LIV is subjected to and have explored the evolution of LIV using phylogenetic analysis. Crucially we were unable to estimate a reliable molecular clock rate for LIV and found that this problem also extends to a larger phylogeny of TBEV sequences. This work highlights a previously unknown caveat of tick-borne flavivirus evolutionary analysis which may be important for understanding the evolution of these important pathogens.

Evolutionary trajectory of fish Piscine novirhabdovirus (=Viral Hemorrhagic Septicemia Virus) across its Laurentian Great Lakes history: Spatial and temporal diversification

Piscine novirhabdovirus = Viral Hemorrhagic Septicemia Virus (VHSV) first appeared in the Laurentian Great Lakes with large outbreaks from 2005 to 2006, as a new and novel RNA rhabdovirus subgenogroup (IVb) that killed >30 fish species. Interlude periods punctuated smaller more localized outbreaks in 2007, 2010, and 2017, although some fishes tested positive in the intervals. There have not been reports of outbreaks or positives from 2018, 2019, or 2020. Here, we employ a combined population genetics and phylogenetic approach to evaluate spatial and temporal evolutionary trajectory on its G-gene sequence variation, in comparison with whole-genome sequences (11,083 bp) from a subset of 44 individual isolates (including 40 newly sequenced ones). Our results show that IVb (N = 184 individual fish isolates) diversified into 36 G-gene haplotypes from 2003 to 2017, stemming from two originals ("a" and "b"). G-gene haplotypes "a" and "b" differed by just one synonymous single-nucleotide polymorphism (SNP) substitution, remained the most abundant until 2011, then disappeared. Group "a" descendants (14 haplotypes) remained most prevalent in the Upper and Central Great Lakes, with eight (51%) having nonsynonymous substitutions. Group "b" descendants primarily have occurred in the Lower Great Lakes, including 22 haplotypes, of which 15 (68%) contained nonsynonymous changes. Evolutionary patterns of the whole-genome sequences (which had 34 haplotypes among 44 isolates) appear congruent with those from the G-gene. Virus populations significantly diverged among the Upper, Central, and Lower Great Lakes, diversifying over time. Spatial divergence was apparent in the overall patterns of nucleotide substitutions, while amino acid changes increased temporally. VHSV-IVb thus significantly differentiated across its less than two decades in the Great Lakes, accompanied by declining outbreaks and virulence. Continuing diversification likely allowed the virus to persist at low levels in resident fish populations, and may facilitate its potential for further and future spread to new habitats and nonacclimated hosts.

Time series prediction of COVID-19 by mutation rate analysis using recurrent neural network-based LSTM model

SARS-CoV-2, a novel coronavirus mostly known as COVID-19 has created a global pandemic. The world is now immobilized by this infectious RNA virus. As of June 15, already more than 7.9 million people have been infected and 432k people died. This RNA virus has the ability to do the mutation in the human body. Accurate determination of mutation rates is essential to comprehend the evolution of this virus and to determine the risk of emergent infectious disease. This study explores the mutation rate of the whole genomic sequence gathered from the patient's dataset of different countries. The collected dataset is processed to determine the nucleotide mutation and codon mutation separately. Furthermore, based on the size of the dataset, the determined mutation rate is categorized for four different regions: China, Australia, the United States, and the rest of the World. It has been found that a huge amount of Thymine (T) and Adenine (A) are mutated to other nucleotides for all regions, but codons are not frequently mutating like nucleotides. A recurrent neural network-based Long Short Term Memory (LSTM) model has been applied to predict the future mutation rate of this virus. The LSTM model gives Root Mean Square Error (RMSE) of 0.06 in testing and 0.04 in training, which is an optimized value. Using this train and testing process, the nucleotide mutation rate of 400^th patient in future time has been predicted. About 0.1% increment in mutation rate is found for mutating of nucleotides from T to C and G, C to G and G to T. While a decrement of 0.1% is seen for mutating of T to A, and A to C. It is found that this model can be used to predict day basis mutation rates if more patient data is available in updated time.

COVID-19: A Review of Emerging Preventative Vaccines and Treatment Strategies

COVID-19, which was first detected in the Hubei province of China, has become a global phenomenon. The effects and devastation on both health and economy have been global. At present, there is a substantial amount of research being done to discover suitable treatment modalities. Efforts have been made on the development of potential efficacious vaccines. The development of a vaccine can be complex, expensive as well as time-consuming. Currently, various ongoing clinical trials are in progress that are investigating either pharmacologic therapies or vaccines against this virus. We, in this brief review have tried to address the process and current development efforts of vaccine in progress.

The phylogenetic approach for viral infectious disease evolution and epidemiology: An updating review

In the last decade, the phylogenetic approach is recurrent in molecular evolutionary analysis. On 12 May, 2019, about 2 296 213 papers are found, but typing "phylogeny" or "epidemiology AND phylogeny" only 199 804 and 20 133 are retrieved, respectively. Molecular epidemiology in infectious diseases is widely used to define the source of infection as so as the ancestral relationships of individuals sampled from a population. Coalescent theory and phylogeographic analysis have had scientific application in several, recent pandemic events, and nosocomial outbreaks. Hepatitis viruses and immunodeficiency virus (human immunodeficiency virus) have been largely studied. Phylogenetic analysis has been recently applied on Polyomaviruses so as in the more recent outbreaks due to different arboviruses type as Zika and chikungunya viruses discovering the source of infection and the geographic spread. Data on sequences isolated by the microorganism are essential to apply the phylogenetic tools and research in the field of infectious disease phylodinamics is growing up. There is the need to apply molecular phylogenetic and evolutionary methods in areas out of infectious diseases, as translational genomics and personalized medicine. Lastly, the application of these tools in vaccine strategy so as in antibiotic and antiviral researchers are encouraged.

A new twist in measuring mutation rates

The influenza virus mutates faster than we previously thought.

Evidence for genetic variation in Natterer's bats (Myotis nattereri) across three regions in Germany but no evidence for co-variation with their associated astroviruses

Background

As bats have recently been described to harbor many different viruses, several studies have investigated the genetic co-variation between viruses and different bat species. However, little is known about the genetic co-variation of viruses and different populations of the same bat species, although such information is needed for an understanding of virus transmission dynamics within a given host species. We hypothesized that if virus transmission between host populations depends on events linked to gene flow in the bats, genetic co-variation should exist between host populations and astroviruses.

Results

We used 19 nuclear and one mitochondrial microsatellite loci to analyze the genetic population structure of the Natterer’s bat (Myotis nattereri) within and among populations at different geographical scales in Germany. Further, we correlated the observed bat population structure to that of partial astrovirus sequences (323–394 nt fragments of the RNA-dependent RNA polymerase gene) obtained from the same bat populations. Our analyses revealed that the studied bat colonies can be grouped into three distinct genetic clusters, corresponding to the three geographic regions sampled. Furthermore, we observed an overall isolation-by-distance pattern, while no significant pattern was observed within a geographic region. Moreover, we found no correlation between the genetic distances among the bat populations and the astrovirus sequences they harbored. Even though high genetic similarity of some of the astrovirus haplotypes found in several different regions was detected, identical astrovirus haplotypes were not shared between different sampled regions.

Conclusions

The genetic population structure of the bat host suggests that mating sites where several local breeding colonies meet act as stepping-stones for gene flow. Identical astrovirus haplotypes were not shared between different sampled regions suggesting that astroviruses are mostly transmitted among host colonies at the local scale. Nevertheless, high genetic similarity of some of the astrovirus haplotypes found in several different regions implies that occasional transmission across regions with subsequent mutations of the virus haplotypes does occur.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-016-0856-0) contains supplementary material, which is available to authorized users.

Transcriptomics and venomics: implications for medicinal chemistry

Over the last three decades, transcriptomic studies of venom gland cells have continuously evolved, opening up new possibilities for exploring the molecular diversity of animal venoms, a prerequisite for the discovery of new drug candidates and molecular phylogenetics. The molecular complexity of animal venoms is much greater than initially thought. In this review, we describe the different technologies available for transcriptomic studies of venom, from the original individual cloning approaches to the more recent global Next Generation Sequencing strategies. Our understanding of animal venoms is evolving, with the discovery of complex and diverse bio-optimized cocktails of compounds, including mostly peptides and proteins, which are now beginning to be studied by academic and industrial researchers.

Computational peptide vaccinology

Immunoinformatics focuses on modeling immune responses for better understanding of the immune system and in many cases for proposing agents able to modify the immune system. The most classical of these agents are vaccines derived from living organisms such as smallpox or polio. More modern vaccines comprise recombinant proteins, protein domains, and in some cases peptides. Generating a vaccine from peptides however requires technologies and concepts very different from classical vaccinology. Immunoinformatics therefore provides the computational tools to propose peptides suitable for formulation into vaccines. This chapter introduces the essential biological concepts affecting design and efficacy of peptide vaccines and discusses current methods and workflows applied to design successful peptide vaccines using computers.

Next generation deep sequencing and vaccine design: today and tomorrow

Next generation sequencing (NGS) technologies have redefined the modus operandi in both human and microbial genetics research, allowing the unprecedented generation of very large sequencing datasets on a short time scale and at affordable costs. Vaccine development research is rapidly taking full advantage of the advent of NGS. This review provides a concise summary of the current applications of NGS in relation to research seeking to develop vaccines for human infectious diseases, incorporating studies of both the pathogen and the host. We focus on rapidly mutating viral pathogens, which are major targets in current vaccine research. NGS is unraveling the complex dynamics of viral evolution and host responses against these viruses, thus contributing substantially to the likelihood of successful vaccine development.