Integrating patient and whole-genome sequencing data to provide insights into the epidemiology of seasonal influenza A(H3N2) viruses

Evolutionary analysis of seasonal influenza A viruses in Pakistan 2020-2023

INTRODUCTION

Influenza A viruses cause global health concerns due to their high amino acid substitution rates. They are linked to yearly seasonal epidemics and occasional pandemics. This study focused on sequencing influenza A virus strains in Pakistan.

MATERIALS AND METHODS

We analyzed the genetic characteristics of influenza A(H1N1)pdm09 and A(H3N2) viruses circulating in Pakistan from January 2020 to January 2023. Whole genome sequences from influenza A (n = 126) virus isolates were amplified and sequenced by the Oxford Nanopore (MinION) platform.

RESULTS

The HA genes of influenza A(H1N1)pdm09 underwent amino acid substitutions at positions K54Q, A186T, Q189E, E224A, R259K, and K308R in sequenced samples. The HA genes of influenza A(H3N2) had amino acid substitutions at G53D, E83K, D104G, I140M, S205F, A212T, and K276R in the sequenced samples. Furthermore, the HA gene sequences of influenza A(H1N1)pdm09 in this study belonged to subclade 6B.1A.5a.2a. Similarly, the HA gene sequences of influenza A(H3N2) were classified under six subclades (3C.3a.1 and 3C.2a1b.2a [2, 2a.1, 2b, 2c, and 2a.3b]). Notably, amino acid substitutions in other gene segments of influenza A(H1N1)pdm09 and A(H3N2) were also found.

CONCLUSION

These findings indicate influenza A(H1N1)pdm09 and A(H3N2) viruses co-circulated during the 2020-2023 influenza season in Pakistan. Continued surveillance is crucial for real-time monitoring of possible high-virulence variation and their relevance to existing vaccine strains.

Genetic and potential antigenic evolution of influenza A(H1N1)pdm09 viruses circulating in Kenya during 2009-2018 influenza seasons

Influenza viruses undergo rapid evolutionary changes, which requires continuous surveillance to monitor for genetic and potential antigenic changes in circulating viruses that can guide control and prevention decision making. We sequenced and phylogenetically analyzed A(H1N1)pdm09 virus genome sequences obtained from specimens collected from hospitalized patients of all ages with or without pneumonia between 2009 and 2018 from seven sentinel surveillance sites across Kenya. We compared these sequences with recommended vaccine strains during the study period to infer genetic and potential antigenic changes in circulating viruses and associations of clinical outcome. We generated and analyzed a total of 383 A(H1N1)pdm09 virus genome sequences. Phylogenetic analyses of HA protein revealed that multiple genetic groups (clades, subclades, and subgroups) of A(H1N1)pdm09 virus circulated in Kenya over the study period; these evolved away from their vaccine strain, forming clades 7 and 6, subclades 6C, 6B, and 6B.1, and subgroups 6B.1A and 6B.1A1 through acquisition of additional substitutions. Several amino acid substitutions among circulating viruses were associated with continued evolution of the viruses, especially in antigenic epitopes and receptor binding sites (RBS) of circulating viruses. Disease severity declined with an increase in age among children aged < 5 years. Our study highlights the necessity of timely genomic surveillance to monitor the evolutionary changes of influenza viruses. Routine influenza surveillance with broad geographic representation and whole genome sequencing capacity to inform on prioritization of antigenic analysis and the severity of circulating strains are critical to improved selection of influenza strains for inclusion in vaccines.

Phylogenomic analysis uncovers a 9-year variation of Uganda influenza type-A strains from the WHO-recommended vaccines and other Africa strains

Genetic characterisation of circulating influenza viruses directs annual vaccine strain selection and mitigation of infection spread. We used next-generation sequencing to locally generate whole genomes from 116 A(H1N1)pdm09 and 118 A(H3N2) positive patient swabs collected across Uganda between 2010 and 2018. We recovered sequences from 92% (215/234) of the swabs, 90% (193/215) of which were whole genomes. The newly-generated sequences were genetically and phylogenetically compared to the WHO-recommended vaccines and other Africa strains sampled since 1994. Uganda strain hemagglutinin (n = 206), neuraminidase (n = 207), and matrix protein (MP, n = 213) sequences had 95.23-99.65%, 95.31-99.79%, and 95.46-100% amino acid similarity to the 2010-2020 season vaccines, respectively, with several mutated hemagglutinin antigenic, receptor binding, and N-linked glycosylation sites. Uganda influenza type-A virus strains sequenced before 2016 clustered uniquely while later strains mixed with other Africa and global strains. We are the first to report novel A(H1N1)pdm09 subclades 6B.1A.3, 6B.1A.5(a,b), and 6B.1A.6 (± T120A) that circulated in Eastern, Western, and Southern Africa in 2017-2019. Africa forms part of the global influenza ecology with high viral genetic diversity, progressive antigenic drift, and local transmissions. For a continent with inadequate health resources and where social distancing is unsustainable, vaccination is the best option. Hence, African stakeholders should prioritise routine genome sequencing and analysis to direct vaccine selection and virus control.

Evolutionary Dynamics of Whole-Genome Influenza A/H3N2 Viruses Isolated in Myanmar from 2015 to 2019

This study aimed to analyze the genetic and evolutionary characteristics of the influenza A/H3N2 viruses circulating in Myanmar from 2015 to 2019. Whole genomes from 79 virus isolates were amplified using real-time polymerase chain reaction and successfully sequenced using the Illumina iSeq100 platforms. Eight individual phylogenetic trees were retrieved for each segment along with those of the World Health Organization (WHO)-recommended Southern Hemisphere vaccine strains for the respective years. Based on the WHO clades classification, the A/H3N2 strains in Myanmar from 2015 to 2019 collectively belonged to clade 3c.2. These strains were further defined based on hemagglutinin substitutions as follows: clade 3C.2a (n = 39), 3C.2a1 (n = 2), and 3C.2a1b (n = 38). Genetic analysis revealed that the Myanmar strains differed from the Southern Hemisphere vaccine strains each year, indicating that the vaccine strains did not match the circulating strains. The highest rates of nucleotide substitution were estimated for hemagglutinin (3.37 × 10^-3 substitutions/site/year) and neuraminidase (2.89 × 10^-3 substitutions/site/year). The lowest rate was for non-structural protein segments (4.19 × 10^-5 substitutions/site/year). The substantial genetic diversity that was revealed improved phylogenetic classification. This information will be particularly relevant for improving vaccine strain selection.

Whole-genome-based phylogenomic analysis of the Belgian 2016-2017 influenza A(H3N2) outbreak season allows improved surveillance

Seasonal influenza epidemics are associated with high mortality and morbidity in the human population. Influenza surveillance is critical for providing information to national influenza programmes and for making vaccine composition predictions. Vaccination prevents viral infections, but rapid influenza evolution results in emerging mutants that differ antigenically from vaccine strains. Current influenza surveillance relies on Sanger sequencing of the haemagglutinin (HA) gene. Its classification according to World Health Organization (WHO) and European Centre for Disease Prevention and Control (ECDC) guidelines is based on combining certain genotypic amino acid mutations and phylogenetic analysis. Next-generation sequencing technologies enable a shift to whole-genome sequencing (WGS) for influenza surveillance, but this requires laboratory workflow adaptations and advanced bioinformatics workflows. In this study, 253 influenza A(H3N2) positive clinical specimens from the 2016–2017 Belgian season underwent WGS using the Illumina MiSeq system. HA-based classification according to WHO/ECDC guidelines did not allow classification of all samples. A new approach, considering the whole genome, was investigated based on using powerful phylogenomic tools including beast and Nextstrain, which substantially improved phylogenetic classification. Moreover, Bayesian inference via beast facilitated reassortment detection by both manual inspection and computational methods, detecting intra-subtype reassortants at an estimated rate of 15 %. Real-time analysis (i.e. as an outbreak is ongoing) via Nextstrain allowed positioning of the Belgian isolates into the globally circulating context. Finally, integration of patient data with phylogenetic groups and reassortment status allowed detection of several associations that would have been missed when solely considering HA, such as hospitalized patients being more likely to be infected with A(H3N2) reassortants, and the possibility to link several phylogenetic groups to disease severity indicators could be relevant for epidemiological monitoring. Our study demonstrates that WGS offers multiple advantages for influenza monitoring in (inter)national influenza surveillance, and proposes an improved methodology. This allows leveraging all information contained in influenza genomes, and allows for more accurate genetic characterization and reassortment detection.

Evaluation of the added value of viral genomic information for predicting severity of influenza infection

BACKGROUND

The severity of an influenza infection is influenced by both host and viral characteristics. This study aims to assess the relevance of viral genomic data for the prediction of severe influenza A(H3N2) infections among patients hospitalized for severe acute respiratory infection (SARI), in view of risk assessment and patient management.

METHODS

160 A(H3N2) influenza positive samples from the 2016-2017 season originating from the Belgian SARI surveillance were selected for whole genome sequencing. Predictor variables for severity were selected using a penalized elastic net logistic regression model from a combined host and genomic dataset, including patient information and nucleotide mutations identified in the viral genome. The goodness-of-fit of the model combining host and genomic data was compared using a likelihood-ratio test with the model including host data only. Internal validation of model discrimination was conducted by calculating the optimism-adjusted area under the Receiver Operating Characteristic curve (AUC) for both models.

RESULTS

The model including viral mutations in addition to the host characteristics had an improved fit ([Formula: see text]=12.03, df = 3, p = 0.007). The optimism-adjusted AUC increased from 0.671 to 0.732.

CONCLUSIONS

Adding genomic data (selected season-specific mutations in the viral genome) to the model containing host characteristics improved the prediction of severe influenza infection among hospitalized SARI patients, thereby offering the potential for translation into a prospective strategy to perform early season risk assessment or to guide individual patient management.

A host-based whole genome sequencing study reveals novel risk loci associated with severity of influenza A(H1N1)pdm09 infection

Influenza A(H1N1)pdm09 virus has remained in a seasonal circulation since being recognized in 2009. Although it followed a mild course in most patients, in others it caused a series of severe clinical illnesses. Epidemiologic studies have implicated that host factors have a major influence on the disease severity of influenza A(H1N1)pdm09 infection. However, an understanding of relevant genetic variations and the underlying mechanisms is still limited. In this present study, we used a host-based whole genome sequencing (WGS) method to comprehensively explore the genetic risk loci associated with severity of influenza A(H1N1)pdm09 infection. From the common single-nucleotide variants (SNVs) analysis, we identified the abnormal nominally significant (P < 1 × 10⁻⁴) common SNVs enriched in PTBP3 gene. The results of rare functional SNVs analysis supported that there were several novel candidate genes might confer risk of severe influenza A(H1N1)pdm09 diseases, such as FTSJ3, CPVL, BST2, NOD2 and MAVS. Moreover, our results of gene set based analysis indicated that the HIF-1 transcription factor and IFN-γ pathway might play an important role in the underlying mechanism of severe influenza A(H1N1)pdm09. These findings will increase our knowledge about biological mechanism underlying the severe influenza A(H1N1)pdm09 and facilitate to design novel personalized treatments.

Randomly primed, strand-switching, MinION-based sequencing for the detection and characterization of cultured RNA viruses

RNA viruses rapidly mutate, which can result in increased virulence, increased escape from vaccine protection, and false-negative detection results. Targeted detection methods have a limited ability to detect unknown viruses and often provide insufficient data to detect coinfections or identify antigenic variants. Random, deep sequencing is a method that can more fully detect and characterize RNA viruses and is often coupled with molecular techniques or culture methods for viral enrichment. We tested viral culture coupled with third-generation sequencing for the ability to detect and characterize RNA viruses. Cultures of bovine viral diarrhea virus, canine distemper virus (CDV), epizootic hemorrhagic disease virus, infectious bronchitis virus, 2 influenza A viruses, and porcine respiratory and reproductive syndrome virus were sequenced on the MinION platform using a random, reverse primer in a strand-switching reaction, coupled with PCR-based barcoding. Reads were taxonomically classified and used for reference-based sequence building using a stock personal computer. This method accurately detected and identified complete coding sequence genomes with a minimum of 20× coverage depth for all 7 viruses, including a sample containing 2 viruses. Each lineage-typing region had at least 26× coverage depth for all viruses. Furthermore, analyzing the CDV sample through a pipeline devoid of CDV reference sequences modeled the ability of this protocol to detect unknown viruses. Our results show the ability of this technique to detect and characterize dsRNA, negative- and positive-sense ssRNA, and nonsegmented and segmented RNA viruses.

Influenza classification from short reads with VAPOR facilitates robust mapping pipelines and zoonotic strain detection for routine surveillance applications

Motivation

Influenza viruses represent a global public health burden due to annual epidemics and pandemic potential. Due to a rapidly evolving RNA genome, inter-species transmission, intra-host variation, and noise in short-read data, reads can be lost during mapping, and de novo assembly can be time consuming and result in misassembly. We assessed read loss during mapping and designed a graph-based classifier, VAPOR, for selecting mapping references, assembly validation and detection of strains of non-human origin.

Results

Standard human reference viruses were insufficient for mapping diverse influenza samples in simulation. VAPOR retrieved references for 257 real whole-genome sequencing samples with a mean of >99.8% identity to assemblies, and increased the proportion of mapped reads by up to 13.3% compared to standard references. VAPOR has the potential to improve the robustness of bioinformatics pipelines for surveillance and could be adapted to other RNA viruses.

Availability and implementation

VAPOR is available at https://github.com/connor-lab/vapor.

Supplementary information

Supplementary data are available at Bioinformatics online.

Epidemiological and evolutionary dynamics of influenza B virus in coastal Kenya as revealed by genomic analysis of strains sampled over a single season

The genomic epidemiology of influenza B virus (IBV) remains understudied in Africa despite significance to design of effective local and global control strategies. We undertook surveillance throughout 2016 in coastal Kenya, recruiting individuals presenting with acute respiratory illness at nine outpatient health facilities (any age) or admitted to the Kilifi County Hospital (<5 years old). Whole genomes were sequenced for a selected 111 positives; 94 (84.7%) of B/Victoria lineage and 17 (15.3%) of B/Yamagata lineage. Inter-lineage reassortment was detected in ten viruses; nine with B/Yamagata backbone but B/Victoria NA and NP segments and one with a B/Victoria backbone but B/Yamagata PB2, PB1, PA, and MP segments. Five phylogenomic clusters were identified among the sequenced viruses; (i), pure B/Victoria clade 1A (n = 93, 83.8%), (ii), reassortant B/Victoria clade 1A (n = 1, 0.9%), (iii), pure B/Yamagata clade 2 (n = 2, 1.8%), (iv), pure B/Yamagata clade 3 (n = 6, 5.4%), and (v), reassortant B/Yamagata clade 3 (n = 9, 8.1%). Using divergence dates and clustering patterns in the presence of global background sequences, we counted up to twenty-nine independent IBV strain introductions into the study area (∼900 km2) in 2016. Local viruses, including the reassortant B/Yamagata strains, clustered closely with viruses from neighbouring Tanzania and Uganda. Our study demonstrated that genomic analysis provides a clearer picture of locally circulating IBV diversity. The high number of IBV introductions highlights the challenge in controlling local influenza epidemics by targeted approaches, for example, sub-population vaccination or patient quarantine. The finding of divergent IBV strains co-circulating within a single season emphasises why broad immunity vaccines are the most ideal for influenza control in Kenya.

INSaFLU: an automated open web-based bioinformatics suite "from-reads" for influenza whole-genome-sequencing-based surveillance

BACKGROUND

A new era of flu surveillance has already started based on the genetic characterization and exploration of influenza virus evolution at whole-genome scale. Although this has been prioritized by national and international health authorities, the demanded technological transition to whole-genome sequencing (WGS)-based flu surveillance has been particularly delayed by the lack of bioinformatics infrastructures and/or expertise to deal with primary next-generation sequencing (NGS) data.

RESULTS

We developed and implemented INSaFLU ("INSide the FLU"), which is the first influenza-oriented bioinformatics free web-based suite that deals with primary NGS data (reads) towards the automatic generation of the output data that are actually the core first-line "genetic requests" for effective and timely influenza laboratory surveillance (e.g., type and sub-type, gene and whole-genome consensus sequences, variants' annotation, alignments and phylogenetic trees). By handling NGS data collected from any amplicon-based schema, the implemented pipeline enables any laboratory to perform multi-step software intensive analyses in a user-friendly manner without previous advanced training in bioinformatics. INSaFLU gives access to user-restricted sample databases and projects management, being a transparent and flexible tool specifically designed to automatically update project outputs as more samples are uploaded. Data integration is thus cumulative and scalable, fitting the need for a continuous epidemiological surveillance during the flu epidemics. Multiple outputs are provided in nomenclature-stable and standardized formats that can be explored in situ or through multiple compatible downstream applications for fine-tuned data analysis. This platform additionally flags samples as "putative mixed infections" if the population admixture enrolls influenza viruses with clearly distinct genetic backgrounds, and enriches the traditional "consensus-based" influenza genetic characterization with relevant data on influenza sub-population diversification through a depth analysis of intra-patient minor variants. This dual approach is expected to strengthen our ability not only to detect the emergence of antigenic and drug resistance variants but also to decode alternative pathways of influenza evolution and to unveil intricate routes of transmission.

CONCLUSIONS

In summary, INSaFLU supplies public health laboratories and influenza researchers with an open "one size fits all" framework, potentiating the operationalization of a harmonized multi-country WGS-based surveillance for influenza virus. INSaFLU can be accessed through https://insaflu.insa.pt .

A Multiplex Asymmetric Reverse Transcription-PCR Assay Combined With an Electrochemical DNA Sensor for Simultaneously Detecting and Subtyping Influenza A Viruses

The reliable and rapid detection of viral pathogens that cause respiratory infections provide physicians several advantages in treating patients and managing outbreaks. The Luminex respiratory virus panel (RVP) assay has been shown to be comparable to or superior to culture/direct fluorescent-antibody assays (DFAs) and nucleic acid tests that are used to diagnose respiratory viral infections. We developed a multiplex asymmetric reverse transcription (RT)-PCR assay that can simultaneously differentiate all influenza A virus epidemic subtypes. The amplified products were hybridized with an electrochemical DNA sensor, and the results were automatically acquired. The limits of detection (LoDs) of both the Luminex RVP assay and the multiplex RT-PCR-electrochemical DNA sensor were 101 TCID50 for H1N1 virus and 102 TCID50 for H3N2 virus. The specificity assessment of the multiplex RT-PCR-electrochemical DNA sensor showed no cross-reactivity among different influenza A subtypes or with other non-influenza respiratory viruses. In total, 3098 respiratory tract specimens collected from padiatric patients diagnosed with pneumonia were tested. More than half (43, 53.75%) of the specimens positive for influenza A viruses could not be further subtyped using the Luminex RVP assay. Among the remaining 15 specimens that were not subtyped, not degraded, and in sufficient amounts for the multiplex RT-PCR-electrochemical DNA sensor test, all (100%) were H3N2 positive. Therefore, the sensitivity of the Luminex RVP assay for influenza A virus was 46.25%, whereas the sensitivity of the multiplex RT-PCR-electrochemical DNA sensor for the clinical H1N1 and H3N2 specimens was 100%. The sensitivities of the multiplex RT-PCR-electrochemical DNA sensor for the avian H5N1, H5N6, H9N2, and H10N8 viruses were 100%, whereas that for H7N9 virus was 85.19%. We conclude that the multiplex RT-PCR-electrochemical DNA sensor is a reliable method for the rapid and accurate detection of highly variable influenza A viruses in respiratory infections with greater detection sensitivity than that of the Luminex xTAG assay. The high mutation rate of influenza A viruses, particularly H3N2 during the 2014 to 2016 epidemic seasons, has a strong impact on diagnosis. A study involving more positive specimens from all influenza A virus epidemic subtypes is required to fully assess the performance of the assay.