Simultaneous detection of influenza A, B and respiratory syncytial virus in wastewater samples by one-step multiplex RT-ddPCR assay.

BACKGROUND: After the occurrence of the COVID-19 pandemic, detection of other disseminated respiratory viruses using highly sensitive molecular methods was declared essential for monitoring the spread of health-threatening viruses in communities. The development of multiplex molecular assays are essential for the simultaneous detection of such viruses even at low concentrations. In the present study, a highly sensitive and specific multiplex one-step droplet digital PCR (RT-ddPCR) assay was developed for the simultaneous detection and absolute quantification of influenza A (IAV), influenza B (IBV), respiratory syncytial virus (RSV), and beta-2-microglobulin transcript as an endogenous internal control (IC B2M). RESULTS: The assay was first evaluated for analytical sensitivity and specificity, linearity, reproducibility, and recovery rates with excellent performance characteristics and then applied to 37 wastewater samples previously evaluated with commercially available and in-house quantitative real-time reverse transcription PCR (RT-qPCR) assays. IAV was detected in 16/37 (43%), IBV in 19/37 (51%), and RSV in 10/37 (27%) of the wastewater samples. Direct comparison of the developed assay with real-time RT-qPCR assays showed statistically significant high agreement in the detection of IAV (kappa Cohen's correlation coefficient: 0.834, p = 0.001) and RSV (kappa: 0.773, p = 0.001) viruses between the two assays, while the results for the detection of IBV (kappa: 0.355, p = 0.27) showed good agreement without statistical significance. CONCLUSIONS: Overall, the developed one-step multiplex ddPCR assay is cost-effective, highly sensitive and specific, and can simultaneously detect three common respiratory viruses in the complex matrix of wastewater samples even at low concentrations. Due to its high sensitivity and resistance to PCR inhibitors, the developed assay could be further used as an early warning system for wastewater monitoring.

Exploring the Antiviral Potential of Natural Compounds against Influenza: A Combined Computational and Experimental Approach.

The influenza A virus nonstructural protein 1 (NS1), which is crucial for viral replication and immune evasion, has been identified as a significant drug target with substantial potential to contribute to the fight against influenza. The emergence of drug-resistant influenza A virus strains highlights the urgent need for novel therapeutics. This study proposes a combined theoretical criterion for the virtual screening of molecular libraries to identify candidate NS1 inhibitors. By applying the criterion to the ZINC Natural Product database, followed by ligand-based virtual screening and molecular docking, we proposed the most promising candidate as a potential NS1 inhibitor. Subsequently, the selected natural compound was experimentally evaluated, revealing measurable virus replication inhibition activity in cell culture. This approach offers a promising avenue for developing novel anti-influenza agents targeting the NS1 protein.

Genomic characterization of Influenza A (H1N1)pdm09 and SARS-CoV-2 from Influenza Like Illness (ILI) and Severe Acute Respiratory Illness (SARI) cases reported between July-December, 2022.

Influenza Like Illness (ILI) and Severe Acute Respiratory Infection (SARI) cases are more prone to Influenza and SARS-CoV-2 infection. Accordingly, we genetically characterized Influenza and SARS-CoV-2 in 633 ILI and SARI cases by rRT-PCR and WGS. ILI and SARI cases showed H1N1pdm09 prevalence of 20.9% and 23.2% respectively. 135 (21.3%) H1N1pdm09 and 23 (3.6%) H3N2 and 5 coinfection (0.78%) of H1N1pdm09 and SARS-CoV-2 were detected. Phylogenetic analysis revealed H1N1pdm09 resemblance to clade 6B.1A.5a.2 and their genetic relatedness to InfA/Perth/34/2020, InfA/Victoria/88/2020 and InfA/Victoria/2570/2019. Pan 24 HA and 26 NA nonsynonymous mutations and novel HA (G6D, Y7F, Y78H, P212L, G339R, T508K and S523T) and NA (S229A) mutations were observed. S74R, N129D, N156K, S162N, K163Q and S164T alter HA Cb and Sa antibody recognizing site. Similarly, M19T, V13T substitution and multiple mutations in transmembrane and NA head domain drive antigenic drift. SARS-CoV-2 strains genetically characterized to Omicron BA.2.75 lineage containing thirty nonsynonymous spike mutations exhibited enhanced virulence and transmission rates. Coinfection although detected very minimal, the mutational changes in H1N1pdm09 and SARS-CoV-2 virus infected individuals could alter antibody receptor binding sites, allowing the viruses to escape immune response resulting in better adaptability and transmission. Thus continuous genomic surveillance is required to tackle any future outbreak.

Triggering Degradation of Host Cellular Proteins for Robust Propagation of Influenza Viruses.

Following infection, influenza viruses strive to establish a new host cellular environment optimized for efficient viral replication and propagation. Influenza viruses use or hijack numerous host factors and machinery not only to fulfill their own replication process but also to constantly evade the host's antiviral and immune response. For this purpose, influenza viruses appear to have formulated diverse strategies to manipulate the host proteins or signaling pathways. One of the most effective tactics is to specifically induce the degradation of the cellular proteins that are detrimental to the virus life cycle. Here, we summarize the cellular factors that are deemed to have been purposefully degraded by influenza virus infection. The focus is laid on the mechanisms for the protein ubiquitination and degradation in association with facilitated viral amplification. The fate of influenza viral infection of hosts is heavily reliant on the outcomes of the interplay between the virus and the host antiviral immunity. Understanding the processes of how influenza viruses instigate the protein destruction pathways could provide a foundation for the development of advanced therapeutics to target host proteins and conquer influenza.

Ultrapotent influenza hemagglutinin fusion inhibitors developed through SuFEx-enabled high-throughput medicinal chemistry.

Seasonal and pandemic-associated influenza strains cause highly contagious viral respiratory infections that can lead to severe illness and excess mortality. Here, we report on the optimization of our small-molecule inhibitor F0045(S) targeting the influenza hemagglutinin (HA) stem with our Sulfur-Fluoride Exchange (SuFEx) click chemistry-based high-throughput medicinal chemistry (HTMC) strategy. A combination of SuFEx- and amide-based lead molecule diversification and structure-guided design led to identification and validation of ultrapotent influenza fusion inhibitors with subnanomolar EC50 cellular antiviral activity against several influenza A group 1 strains. X-ray structures of six of these compounds with HA indicate that the appended moieties occupy additional pockets on the HA surface and increase the binding interaction, where the accumulation of several polar interactions also contributes to the improved affinity. The compounds here represent the most potent HA small-molecule inhibitors to date. Our divergent HTMC platform is therefore a powerful, rapid, and cost-effective approach to develop bioactive chemical probes and drug-like candidates against viral targets.

Structures of H5N1 influenza polymerase with ANP32B reveal mechanisms of genome replication and host adaptation.

Avian influenza A viruses (IAVs) pose a public health threat, as they are capable of triggering pandemics by crossing species barriers. Replication of avian IAVs in mammalian cells is hindered by species-specific variation in acidic nuclear phosphoprotein 32 (ANP32) proteins, which are essential for viral RNA genome replication. Adaptive mutations enable the IAV RNA polymerase (FluPolA) to surmount this barrier. Here, we present cryo-electron microscopy structures of monomeric and dimeric avian H5N1 FluPolA with human ANP32B. ANP32B interacts with the PA subunit of FluPolA in the monomeric form, at the site used for its docking onto the C-terminal domain of host RNA polymerase II during viral transcription. ANP32B acts as a chaperone, guiding FluPolA towards a ribonucleoprotein-associated FluPolA to form an asymmetric dimer-the replication platform for the viral genome. These findings offer insights into the molecular mechanisms governing IAV genome replication, while enhancing our understanding of the molecular processes underpinning mammalian adaptations in avian-origin FluPolA.

Risk assessment of a highly pathogenic H5N1 influenza virus from mink.

Outbreaks of highly pathogenic H5N1 clade 2.3.4.4b viruses in farmed mink and seals combined with isolated human infections suggest these viruses pose a pandemic threat. To assess this threat, using the ferret model, we show an H5N1 isolate derived from mink transmits by direct contact to 75% of exposed ferrets and, in airborne transmission studies, the virus transmits to 37.5% of contacts. Sequence analyses show no mutations were associated with transmission. The H5N1 virus also has a low infectious dose and remains virulent at low doses. This isolate carries the adaptive mutation, PB2 T271A, and reversing this mutation reduces mortality and airborne transmission. This is the first report of a H5N1 clade 2.3.4.4b virus exhibiting direct contact and airborne transmissibility in ferrets. These data indicate heightened pandemic potential of the panzootic H5N1 viruses and emphasize the need for continued efforts to control outbreaks and monitor viral evolution.

Nanopore sequencing of influenza A and B in Oxfordshire and the United Kingdom, 2022-23.

OBJECTIVES: We evaluated Nanopore sequencing for influenza surveillance. METHODS: Influenza A and B PCR-positive samples from hospital patients in Oxfordshire, UK, and a UK-wide population survey from winter 2022-23 underwent Nanopore sequencing following targeted rt-PCR amplification. RESULTS: From 941 infections, successful sequencing was achieved in 292/388 (75 %) available Oxfordshire samples: 231 (79 %) A/H3N2, 53 (18 %) A/H1N1, and 8 (3 %) B/Victoria and in 53/113 (47 %) UK-wide samples. Sequencing was more successful at lower Ct values. Most same-sample replicate sequences had identical haemagglutinin segments (124/141, 88 %); 36/39 (92 %) Illumina vs. Nanopore comparisons were identical, and 3 (8 %) differed by 1 variant. Comparison of Oxfordshire and UK-wide sequences showed frequent inter-regional transmission. Infections were closely-related to 2022-23 vaccine strains. Only one sample had a neuraminidase inhibitor resistance mutation. 849/941 (90 %) Oxfordshire infections were community-acquired. 63/88 (72 %) potentially healthcare-associated cases shared a hospital ward with ≥ 1 known infectious case. 33 epidemiologically-plausible transmission links had sequencing data for both source and recipient: 8 were within ≤ 5 SNPs, of these, 5 (63 %) involved potential sources that were also hospital-acquired. CONCLUSIONS: Nanopore influenza sequencing was reproducible and antiviral resistance rare. Inter-regional transmission was common; most infections were genomically similar. Hospital-acquired infections are likely an important source of nosocomial transmission and should be prioritised for infection prevention and control.

Diet-induced obesity affects influenza disease severity and transmission dynamics in ferrets.

Obesity, and the associated metabolic syndrome, is a risk factor for increased disease severity with a variety of infectious agents, including influenza virus. Yet, the mechanisms are only partially understood. As the number of people, particularly children, living with obesity continues to rise, it is critical to understand the role of host status on disease pathogenesis. In these studies, we use a diet-induced obese ferret model and tools to demonstrate that, like humans, obesity resulted in notable changes to the lung microenvironment, leading to increased clinical disease and viral spread to the lower respiratory tract. The decreased antiviral responses also resulted in obese animals shedding higher infectious virus for a longer period, making them more likely to transmit to contacts. These data suggest that the obese ferret model may be crucial to understanding obesity's impact on influenza disease severity and community transmission and a key tool for therapeutic and intervention development for this high-risk population.

Airway epithelial CD47 plays a critical role in inducing influenza virus-mediated bacterial super-infection.

Respiratory viral infection increases host susceptibility to secondary bacterial infections, yet the precise dynamics within airway epithelia remain elusive. Here, we elucidate the pivotal role of CD47 in the airway epithelium during bacterial super-infection. We demonstrated that upon influenza virus infection, CD47 expression was upregulated and localized on the apical surface of ciliated cells within primary human nasal or bronchial epithelial cells. This induced CD47 exposure provided attachment sites for Staphylococcus aureus, thereby compromising the epithelial barrier integrity. Through bacterial adhesion assays and in vitro pull-down assays, we identified fibronectin-binding proteins (FnBP) of S. aureus as a key component that binds to CD47. Furthermore, we found that ciliated cell-specific CD47 deficiency or neutralizing antibody-mediated CD47 inactivation enhanced in vivo survival rates. These findings suggest that interfering with the interaction between airway epithelial CD47 and pathogenic bacterial FnBP holds promise for alleviating the adverse effects of super-infection.

Assessment of Illness Severity in Adults Hospitalized With Acute Respiratory Tract Infection due to Influenza, Respiratory Syncytial Virus, or Human Metapneumovirus.

BACKGROUND: Influenza, respiratory syncytial virus (RSV), and human metapneumovirus (hMPV) are common respiratory viruses causing similar symptoms. Optimal tools to assess illness severity for these viruses have not been defined. Using the Hospitalized Acute Respiratory Tract Infection (HARTI) study data, we report symptom severity by clinician-rated clinical severity scores (CSS) in adults with influenza, RSV, or hMPV and correlations between CSS and patient-reported outcomes (PROs). METHODS: HARTI was a global epidemiologic study in adults hospitalized with acute respiratory tract infections. Patients were assessed at enrollment within 24 h of admission with CSS and twice during hospitalization with CSS, Respiratory Infection Intensity and Impact Questionnaire™ (RiiQ™), and EQ-5D-5L. Data were summarized descriptively, stratified by pathogen and baseline and hospitalization characteristics. Domain (general, upper respiratory, and lower respiratory) and sign/symptom subscores are presented for CSS; sign/symptom subscores are presented for RiiQ™ results. RESULTS: Data from 635 patients with influenza, 248 with RSV, and 107 with hMPV were included. At enrollment, total CSS and general and lower respiratory signs/symptoms (LRS) scores were higher for RSV and hMPV than influenza. Between-pathogen differences were greatest for LRS scores. Dyspnea, rales/rhonchi, wheezing, and shortness of breath scores trended higher for RSV and hMPV than influenza. RiiQ™ scores for cough, fatigue, and short of breath were strongly correlated with corresponding clinician-rated symptoms. CONCLUSIONS: These findings support the use of PROs (e.g., the RiiQ™) correlating with clinician assessments to gauge patient well-being and aid patient management by accurately assessing respiratory illness severity due to RSV, hMPV, or influenza.

Analysis of the epidemiological situation of influenza in Guangzhou under the prevention and control of COVID-19 in June 2022.

OBJECTIVE: Under the prevention and control measures of COVID-19, the epidemiological situation of respiratory pathogens is not well known. Understanding the patterns of respiratory pathogens epidemiology under the prevention and control measures of COVID-19 is important to guide resource allocation for existing and future treatment and prevention strategies. METHODS: In total, 659 fever outpatients nasopharyngeal swabs were collected at fever illness onset during June in 2022 at the First Hospital of Guangzhou Medical University. Swabs were tested by real-time fluorescent single-tube multiplex polymerase chain reaction (PCR) for 12 respiratory pathogens. Moreover, 108 of the 659 swabs were tested for influenza virus antigen. RESULTS: At least one pathogen was detected in 477 (72.38%) of 659 fever outpatients with multiple pathogens identified in 25 (3.79%). The highest multiple infectious pattern is parainfluenza virus in combination with influenza (five cases). Influenza A virus (IFA), human rhinovirus (HRV), and parainfluenza virus are the three leading virus pathogens with proportions of 64.64%, 5.01%, and 2.88%. School-age children and adult groups have the highest pathogens positivity rate of 81.28% and 83.87%. CONCLUSION: A high proportion of adolescents and adults has respiratory pathogens detected during fever illnesses during June in 2022 under the prevention and control of COVID-19. These data indicate that diagnosis, prevention, and control of respiratory tract infection should be paid attention under the prevention and control of COVID-19.

Assessing the change in the epidemiology of seasonal respiratory viruses with the onset of the COVID-19 pandemic.

Background. Respiratory tract infections are among the most important causes of mortality and morbidity in children worldwide. The COVID-19 pandemic has affected the distribution of seasonal respiratory viruses as in all areas of life. In this study, we have aimed to evaluate the changes in the rates of seasonal respiratory viruses with the onset of the pandemic.Methods. This study included patients who were admitted to the Pediatrics Clinic of Eskisehir Osmangazi University Faculty of Medicine Hospital between December 2018 and February 2022 with respiratory tract infections and in whom pathogens were detected from nasopharyngeal swab samples analysed by multiplex PCR method.Results. A total of 833 respiratory tract pathogens were detected in 684 cases consisting of male (55.3 %), and female (44.7 %), patients with a total mean age of 42 months. Single pathogen was revealed in 550, and multiple pathogens in 134 cases. Intensive care was needed in 14 % of the cases. Most frequently influenza A/B, rhinovirus and respiratory syncytial virus (RSV) were detected during the pre-pandemic period, while rhinovirus, RSV, and adenovirus were observed during the lockdown period. In the post-lockdown period, the incidence rates of rhinovirus, RSV, human bocavirus (HboV) (12 %), influenza virus infections increased, and patients with RSV and bocavirus infections required intensive care hospitalization.Conclusion. It is thought that the COVID-9 pandemic lockdown measures may have an impact on the distribution of seasonal respiratory viruses, especially RSV and influenza. Current, prospective and large case series regarding the mechanism of action and dynamics are needed.

Ozone as an environmental driver of influenza.

Under long-standing threat of seasonal influenza outbreaks, it remains imperative to understand the drivers of influenza dynamics which can guide mitigation measures. While the role of absolute humidity and temperature is extensively studied, the possibility of ambient ozone (O3) as an environmental driver of influenza has received scant attention. Here, using state-level data in the USA during 2010-2015, we examined such research hypothesis. For rigorous causal inference by evidence triangulation, we applied 3 distinct methods for data analysis: Convergent Cross Mapping from state-space reconstruction theory, Peter-Clark-momentary-conditional-independence plus as graphical modeling algorithms, and regression-based Generalised Linear Model. The negative impact of ambient O3 on influenza activity at 1-week lag is consistently demonstrated by those 3 methods. With O3 commonly known as air pollutant, the novel findings here on the inhibition effect of O3 on influenza activity warrant further investigations to inform environmental management and public health protection.

Respiratory Syncytial Virus and Influenza Infections in Children in Ulaanbaatar, Mongolia, 2015-2021.

BACKGROUND: Data available for RSV and influenza infections among children < 2 years in Mongolia are limited. We present data from four districts of Ulaanbaatar from April 2015 to June 2021. METHODS: This study was nested in an enhanced surveillance project evaluating pneumococcal conjugate vaccine (PCV13) impact on the incidence of hospitalized lower respiratory tract infections (LRTIs). Our study was restricted to children aged < 2 years with arterial O2 saturation < 93% and children with radiological pneumonia. Nasopharyngeal (NP) swabs collected at admission were tested for RSV and influenza using qRT-PCR. NP swabs of all patients with radiological pneumonia and of a subset of randomly selected NP swabs were tested for S. pneumoniae (S.p.) by qPCR and for serotypes by culture and DNA microarray. RESULTS: Among 5705 patients, 2113 (37.0%) and 386 (6.8%) had RSV and influenza infections, respectively. Children aged 2-6 months had a higher percentage of very severe RSV infection compared to those older than 6 months (42.2% versus 31.4%, p-value Fisher's exact = 0.001). S.p. carriage was detected in 1073/2281 (47.0%) patients. Among S.p. carriage cases, 363/1073 (33.8%) had S.p. and RSV codetection, and 82/1073 (7.6%) had S.p. and influenza codetection. S.p. codetection with RSV/influenza was not associated with more severe LRTIs, compared to only RSV/influenza cases. CONCLUSION: In Mongolia, RSV is an important pathogen causing more severe LRTI in children under 6 months of age. Codetection of RSV or influenza virus and S.p. was not associated with increased severity.

The Detection of Influenza Virus Before and During the COVID-19 Pandemic in Cameroon.

BACKGROUND: Influenza and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are both respiratory viruses with similar clinical manifestations and modes of transmission. This study describes influenza data before and during the coronavirus disease pandemic (COVID-19) in Cameroon and SARS-CoV-2 data during the pandemic period. METHODS: The study ran from 2017 to 2022, and data were divided into two periods: before (2017-2019) and during (2020-2022) the COVID-19 pandemic. Nasopharyngeal samples collected from persons with respiratory illness were tested for influenza using the Centers for Disease Control and Prevention (CDC) typing and subtyping assays. During the COVID-19 pandemic, the respiratory specimens were simultaneously tested for SARS-CoV-2 using the DaAn gene protocol or the Abbott real-time SARS-CoV-2 assay. The WHO average curve method was used to compare influenza virus seasonality before and during the pandemic. RESULTS: A total of 6246 samples were tested. Influenza virus detection rates were significantly higher in the pre-pandemic period compared to the pandemic period (30.8% vs. 15.5%; p < 0.001). Meanwhile, the SARS-CoV-2 detection rate was 2.5%. A change in the seasonality of influenza viruses was observed from a bi-annual peak before the pandemic to no clear seasonal pattern during the pandemic. The age groups 2-4 and 5-14 years were significantly associated with higher influenza positivity rates in both pre-pandemic and pandemic periods. For SARS-CoV-2, all age groups above 15 years were the most affected population. CONCLUSION: The COVID-19 pandemic had a significant impact on the seasonal influenza by changing the seasonality of the virus and reducing its detection rates.

Characterizing the antigenic evolution of pandemic influenza A (H1N1) pdm09 from 2009 to 2023.

The H1N1pdm09 virus has been a persistent threat to public health since the 2009 pandemic. Particularly, since the relaxation of COVID-19 pandemic mitigation measures, the influenza virus and SARS-CoV-2 have been concurrently prevalent worldwide. To determine the antigenic evolution pattern of H1N1pdm09 and develop preventive countermeasures, we collected influenza sequence data and immunological data to establish a new antigenic evolution analysis framework. A machine learning model (XGBoost, accuracy = 0.86, area under the receiver operating characteristic curve = 0.89) was constructed using epitopes, physicochemical properties, receptor binding sites, and glycosylation sites as features to predict the antigenic similarity relationships between influenza strains. An antigenic correlation network was constructed, and the Markov clustering algorithm was used to identify antigenic clusters. Subsequently, the antigenic evolution pattern of H1N1pdm09 was analyzed at the global and regional scales across three continents. We found that H1N1pdm09 evolved into around five antigenic clusters between 2009 and 2023 and that their antigenic evolution trajectories were characterized by cocirculation of multiple clusters, low-level persistence of former dominant clusters, and local heterogeneity of cluster circulations. Furthermore, compared with the seasonal H1N1 virus, the potential cluster-transition determining sites of H1N1pdm09 were restricted to epitopes Sa and Sb. This study demonstrated the effectiveness of machine learning methods for characterizing antigenic evolution of viruses, developed a specific model to rapidly identify H1N1pdm09 antigenic variants, and elucidated their evolutionary patterns. Our findings may provide valuable support for the implementation of effective surveillance strategies and targeted prevention efforts to mitigate the impact of H1N1pdm09.

Seasonal antigenic prediction of influenza A H3N2 using machine learning.

Antigenic characterization of circulating influenza A virus (IAV) isolates is routinely assessed by using the hemagglutination inhibition (HI) assays for surveillance purposes. It is also used to determine the need for annual influenza vaccine updates as well as for pandemic preparedness. Performing antigenic characterization of IAV on a global scale is confronted with high costs, animal availability, and other practical challenges. Here we present a machine learning model that accurately predicts (normalized) outputs of HI assays involving circulating human IAV H3N2 viruses, using their hemagglutinin subunit 1 (HA1) sequences and associated metadata. Each season, the model learns an updated nonlinear mapping of genetic to antigenic changes using data from past seasons only. The model accurately distinguishes antigenic variants from non-variants and adaptively characterizes seasonal dynamics of HA1 sites having the strongest influence on antigenic change. Antigenic predictions produced by the model can aid influenza surveillance, public health management, and vaccine strain selection activities.