Genetic variations on 31 and 450 residues of influenza A nucleoprotein affect viral replication and translation.

BACKGROUND: Influenza A viruses cause epidemics/severe pandemics that pose a great global health threat. Among eight viral RNA segments, the multiple functions of nucleoprotein (NP) play important roles in viral replication and transcription. METHODS: To understand how NP contributes to the virus evolution, we analyzed the NP gene of H3N2 viruses in Taiwan and 14,220 NP sequences collected from Influenza Research Database. The identified genetic variations were further analyzed by mini-genome assay, virus growth assay, viral RNA and protein expression as well as ferret model to analyze their impacts on viral replication properties. RESULTS: The NP genetic analysis by Taiwan and global sequences showed similar evolution pattern that the NP backbones changed through time accompanied with specific residue substitutions from 1999 to 2018. Other than the conserved residues, fifteen sporadic substitutions were observed in which the 31R, 377G and 450S showed higher frequency. We found 31R and 450S decreased polymerase activity while the dominant residues (31 K and 450G) had higher activity. The 31 K and 450G showed better viral translation and replication in vitro and in vivo. CONCLUSIONS: These findings indicated variations identified in evolution have roles in modulating viral replication in vitro and in vivo. This study demonstrates that the interaction between variations of NP during virus evolution deserves future attention.

The molecular epidemiology of a dengue virus outbreak in Taiwan: population wide versus infrapopulation mutation analysis.

Dengue virus (DENV) causes approximately 390 million dengue infections worldwide every year. There were 22,777 reported DENV infections in Tainan, Taiwan in 2015. In this study, we sequenced the C-prM-E genes from 45 DENV 2015 strains, and phylogenetic analysis based on C-prM-E genes revealed that all strains were classified as DENV serotype 2 Cosmopolitan genotype. Sequence analysis comparing different DENV-2 genotypes and Cosmopolitan DENV-2 sequences prior to 2015 showed a clade replacement event in the DENV-2 Cosmopolitan genotype. Additionally, a major substitution C-A314G (K73R) was found in the capsid region which may have contributed to the clade replacement event. Reverse genetics virus rgC-A314G (K73R) showed slower replication in BHK-21 and C6/36 cells compared to wildtype virus, as well as a decrease in NS1 production in BHK-21-infected cells. After a series of passaging, the C-A314G (K73R) mutation reverted to wildtype and was thus considered to be unstable. Next generation sequencing (NGS) of three sera collected from a single DENV2-infected patient at 1-, 2-, and 5-days post-admission was employed to examine the genetic diversity over-time and mutations that may work in conjunction with C-A314G (K73R). Results showed that the number of haplotypes decreased with time in the DENV-infected patient. On the fifth day after admission, two new haplotypes emerged, and a single non-synonymous NS4A-L115I mutation was identified. Therefore, we have identified a persistent mutation C-A314G (K73R) in all of the DENV-2 isolates, and during the course of an infection, a single new non-synonymous mutation in the NS4A region appears in the virus population within a single host. The C-A314G (K73R) thus may have played a role in the DENV-2 2015 outbreak while the NS4A-L115I may be advantageous during DENV infection within the host.

Assessment of the Risk of Severe Dengue Using Intrahost Viral Population in Dengue Virus Serotype 2 Patients via Machine Learning.

Dengue virus, a positive-sense single-stranded RNA virus, continuously threatens human health. Although several criteria for evaluation of severe dengue have been recently established, the ability to prognose the risk of severe outcomes for dengue patients remains limited. Mutant spectra of RNA viruses, including single nucleotide variants (SNVs) and defective virus genomes (DVGs), contribute to viral virulence and growth. Here, we determine the potency of intrahost viral population in dengue patients with primary infection that progresses into severe dengue. A total of 65 dengue virus serotype 2 infected patients in primary infection including 17 severe cases were enrolled. We utilized deep sequencing to directly define the frequency of SNVs and detection times of DVGs in sera of dengue patients and analyzed their associations with severe dengue. Among the detected SNVs and DVGs, the frequencies of 9 SNVs and the detection time of 1 DVG exhibited statistically significant differences between patients with dengue fever and those with severe dengue. By utilizing the detected frequencies/times of the selected SNVs/DVG as features, the machine learning model showed high average with a value of area under the receiver operating characteristic curve (AUROC, 0.966 ± 0.064). The elevation of the frequency of SNVs at E (nucleotide position 995 and 2216), NS2A (nucleotide position 4105), NS3 (nucleotide position 4536, 4606), and NS5 protein (nucleotide position 7643 and 10067) and the detection times of the selected DVG that had a deletion junction in the E protein region (nucleotide positions of the junction: between 969 and 1022) increased the possibility of dengue patients for severe dengue. In summary, we demonstrated the detected frequencies/times of SNVs/DVG in dengue patients associated with severe disease and successfully utilized them to discriminate severe patients using machine learning algorithm. The identified SNVs and DVGs that are associated with severe dengue will expand our understanding of intrahost viral population in dengue pathogenesis.

Impact of Intrahost NS5 Nucleotide Variations on Dengue Virus Replication.

Due to the nature of RNA viruses, their high mutation rates produce a population of closely related but genetically diverse viruses, termed quasispecies. To determine the role of quasispecies in DENV disease severity, 22 isolates (10 from mild cases, 12 from fatal cases) were obtained, amplified, and sequenced with Next Generation Sequencing using the Illumina MiSeq platform. Using variation calling, unique wildtype nucleotide positions were selected and analyzed for variant nucleotides between mild and fatal cases. The analysis of variant nucleotides between mild and fatal cases showed 6 positions with a significant difference of p < 0.05 with 1 position in the structural region, and 5 positions in the non-structural (NS) regions. All variations were found to have a higher percentage in fatal cases. To further investigate the genetic changes that affect the virus's properties, reverse genetics (rg) viruses containing substitutions with the variations were generated and viral growth properties were examined. We found that the virus variant rgNS5-T7812G (G81G) had higher replication rates in both Baby hamster kidney cells (BHK-21) and Vero cells while rgNS5-C9420A (A617A) had a higher replication rate only in BHK-21 cells compared to wildtype virus. Both variants were considered temperature sensitive whereby the viral titers of the variants were relatively lower at 39°C, but was higher at 35 and 37°C. Additionally, the variants were thermally stable compared to wildtype at temperatures of 29, 37, and 39°C. In conclusion, viral quasispecies found in isolates from the 2015 DENV epidemic, resulted in variations with significant difference between mild and fatal cases. These variations, NS5-T7812G (G81G) and NS5-C9420A (A617A), affect viral properties which may play a role in the virulence of DENV.

Contributions of Genetic Evolution to Zika Virus Emergence.

Mosquito-borne Zika virus (ZIKV) was considered an obscure virus causing only mild or self-limited symptoms until the explosive outbreaks in French Polynesia in 2013-2014 and in the Americas in 2015-2016, resulting in more than 700,000 cases of the disease, with occasional miscarriage and severe congenital birth defects, such as intrauterine growth restriction, fetal microcephaly, and other neurodevelopmental malformations. In this review, we summarized the evolution of ZIKV from a mundane virus to an epidemic virus. ZIKV has acquired a panel of amino acid substitutions during evolution when the virus spread from Africa, Asia, Pacific, through to the Americas. Robust occurrence of mutations in the evolution of ZIKV has increased its epidemic potential. Here we discussed the contributions of these evolutionary mutations to the enhancement of viral pathogenicity and host-mosquito transmission. We further explored the potential hypotheses for the increase in ZIKV activity in recent decades. Through this review, we also explored the hypotheses for the occurrence of the recent ZIKV epidemics and highlighted the potential roles of various factors including pathogen-, host-, vector-related, and environmental factors, which may have synergistically contributed to the ZIKV epidemics.

Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning.

BACKGROUND: Dengue virus causes a wide spectrum of disease, which ranges from subclinical disease to severe dengue shock syndrome. However, estimating the risk of severe outcomes using clinical presentation or laboratory test results for rapid patient triage remains a challenge. Here, we aimed to develop prognostic models for severe dengue using machine learning, according to demographic information and clinical laboratory data of patients with dengue. METHODOLOGY/PRINCIPAL FINDINGS: Out of 1,581 patients in the National Cheng Kung University Hospital with suspected dengue infections and subjected to NS1 antigen, IgM and IgG, and qRT-PCR tests, 798 patients including 138 severe cases were enrolled in the study. The primary target outcome was severe dengue. Machine learning models were trained and tested using the patient dataset that included demographic information and qualitative laboratory test results collected on day 1 when they sought medical advice. To develop prognostic models, we applied various machine learning methods, including logistic regression, random forest, gradient boosting machine, support vector classifier, and artificial neural network, and compared the performance of the methods. The artificial neural network showed the highest average discrimination area under the receiver operating characteristic curve (0.8324 ± 0.0268) and balance accuracy (0.7523 ± 0.0273). According to the model explainer that analyzed the contributions/co-contributions of the different factors, patient age and dengue NS1 antigenemia were the two most important risk factors associated with severe dengue. Additionally, co-existence of anti-dengue IgM and IgG in patients with dengue increased the probability of severe dengue. CONCLUSIONS/SIGNIFICANCE: We developed prognostic models for the prediction of dengue severity in patients, using machine learning. The discriminative ability of the artificial neural network exhibited good performance for severe dengue prognosis. This model could help clinicians obtain a rapid prognosis during dengue outbreaks. However, the model requires further validation using external cohorts in future studies.

Assessing the application of a pseudovirus system for emerging SARS-CoV-2 and re-emerging avian influenza virus H5 subtypes in vaccine development.

BACKGROUND: Highly pathogenic emerging and re-emerging viruses continuously threaten lives worldwide. In order to provide prophylactic prevention from the emerging and re-emerging viruses, vaccine is suggested as the most efficient way to prevent individuals from the threat of viral infection. Nonetheless, the highly pathogenic viruses need to be handled in a high level of biosafety containment, which hinders vaccine development. To shorten the timeframe of vaccine development, the pseudovirus system has been widely applied to examine vaccine efficacy or immunogenicity in the emerging and re-emerging viruses. METHODS: We developed pseudovirus systems for emerging SARS coronavirus 2 (SARS-CoV-2) and re-emerging avian influenza virus H5 subtypes which can be handled in the biosafety level 2 facility. Through the generated pseudovirus of SARS-CoV-2 and avian influenza virus H5 subtypes, we successfully established a neutralization assay to quantify the neutralizing activity of antisera against the viruses. RESULTS: The result of re-emerging avian influenza virus H5Nx pseudoviruses provided valuable information for antigenic evolution and immunogenicity analysis in vaccine candidate selection. Together, our study assessed the potency of pseudovirus systems in vaccine efficacy, antigenic analysis, and immunogenicity in the vaccine development of emerging and re-emerging viruses. CONCLUSION: Instead of handling live highly pathogenic viruses in a high biosafety level facility, using pseudovirus systems would speed up the process of vaccine development to provide community protection against emerging and re-emerging viral diseases with high pathogenicity.

Application of deep sequencing methods for inferring viral population diversity.

The first deep sequencing method was announced in 2005. Due to an increasing number of sequencing data and a reduction in the costs of each sequencing dataset, this innovative technique was soon applied to genetic investigations of viral genome diversity in various viruses, particularly RNA viruses. These deep sequencing findings documented viral epidemiology and evolution and provided high-resolution data on the genetic changes in viral populations. Here, we review deep sequencing platforms that have been applied in viral quasispecies studies. Further, we discuss recent deep sequencing studies on viral inter- and intrahost evolution, drug resistance, and humoral immune selection, especially in emerging and re-emerging viruses. Deep sequencing methods are becoming the standard for providing comprehensive results of viral population diversity, and their applications are discussed.