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.

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.

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.

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.

Molecular characterization of influenza virus circulating in Nepal in the year 2019.

Influenza (sometimes referred to as "flu") is a contagious viral infection of the airways in the lungs that affects a significant portion of the world's population. Clinical symptoms of influenza virus infections can range widely, from severe pneumonia to moderate or even asymptomatic sickness. If left untreated, influenza can have more severe effects on the heart, brain, and lungs than on the respiratory tract and can necessitate hospitalization. This study was aimed to investigate and characterize all types of influenza cases prevailing in Nepal and to analyze seasonal occurrence of Influenza in Nepal in the year 2019. A cross sectional, retrospective and descriptive study was carried out at National Influenza Center (NIC), National Public Health Laboratory Kathmandu Nepal for the period of one year (Jan-Dec 2019). A total of 3606 throat swab samples from various age groups and sexes were processed at the NIC. The specimens were primarily stored at 4 °C and processed using ABI 7500 RT PCR system for the identification of Influenza virus types and subtypes. Data accessed for research purpose were retrieved from National Influenza Centre (NIC) on 1st Jan 2020. Of the total 3606 patients suspected of having influenza infection, influenza viruses were isolated from 1213 (33.6%) patients with male predominance. The highest number of infection was caused by Influenza A/Pdm09 strain 739 (60.9%) followed by Influenza B 304 (25.1%) and Influenza A/H3 169 (13.9%) and most remarkable finding of this study was the detection of H5N1 in human which is the first ever case of such infection in human from Nepal. Similar to other tropical nations, influenza viruses were detected year-round in various geographical locations of Nepal. The influenza virus type and subtypes that were in circulation in Nepal were comparable to vaccine candidate viruses, which the currently available influenza vaccine may prevent.

The antigenic landscape of human influenza N2 neuraminidases from 2009 until 2017.

Human H3N2 influenza viruses are subject to rapid antigenic evolution which translates into frequent updates of the composition of seasonal influenza vaccines. Despite these updates, the effectiveness of influenza vaccines against H3N2-associated disease is suboptimal. Seasonal influenza vaccines primarily induce hemagglutinin-specific antibody responses. However, antibodies directed against influenza neuraminidase (NA) also contribute to protection. Here, we analysed the antigenic diversity of a panel of N2 NAs derived from human H3N2 viruses that circulated between 2009 and 2017. The antigenic breadth of these NAs was determined based on the NA inhibition (NAI) of a broad panel of ferret and mouse immune sera that were raised by infection and recombinant N2 NA immunisation. This assessment allowed us to distinguish at least four antigenic groups in the N2 NAs derived from human H3N2 viruses that circulated between 2009 and 2017. Computational analysis further revealed that the amino acid residues in N2 NA that have a major impact on susceptibility to NAI by immune sera are in proximity of the catalytic site. Finally, a machine learning method was developed that allowed to accurately predict the impact of mutations that are present in our N2 NA panel on NAI. These findings have important implications for the renewed interest to develop improved influenza vaccines based on the inclusion of a protective NA antigen formulation.

Two proteins, the hemagglutinin and the neuraminidase, protrude from the surface of the influenza virus. Their detection by the immune system allows the host organism to mount defences against the viral threat. The virus evolves in response to this pressure, which manifests as changes in the appearance of its hemagglutinin and neuraminidase. This process, known as antigenic drift, leads to the proteins evading detection. It is also why flu vaccines require frequent updates, as they rely on 'training' the immune system to recognise the most important strains in circulation ­ primarily by exposing it to appropriate versions of hemagglutinin. While the antigenic drift of hemagglutinin has been extensively studied, much less is known about how the neuraminidase accumulates mutations, and how these affect the immune response. To investigate this question, Catani et al. selected 43 genetically distant neuraminidases from human viral samples isolated between 2009 and 2017. Statistical analyses were applied to define their relatedness, revealing that a group of closely related neuraminidases predominated from 2009 to 2015, before they were being taken over by a second group. A third group, which was identified in viruses isolated in 2013, was remarkably close to the neuraminidase of strains that circulated in the late 1990s. The fourth and final group of neuraminidases was derived from influenza viruses that normally circulate in pigs but can also occasionally infect humans. Next, Catani et al. examined the immune response that these 43 neuraminidases could elicit in mice, as well as in ferrets ­ the animal most traditionally used in influenza research. This allowed them to pinpoint which changes in the neuraminidase sequences were important to escape recognition by the host. Data obtained from the two model species were comparable, suggesting that these experiments could be conducted on mice going forward, which are easier to work with than ferrets. Finally, Catani et al. used machine learning to build a computational model that could predict how strongly the immune system would respond to a specific neuraminidase variant. These findings could help guide the development of new vaccines that include neuraminidases tailored to best prime and train the immune system against a larger variety of strains. This may aid the development of 'supra-seasonal' vaccines that protect against a broad range of influenza viruses, reducing the need for yearly updates.

Effects and mechanism of Qingke Pingchuan granules against influenza virus infection.

Qingke Pingchuan granules (QPGs), which contain Houttuynia cordata Thunb, Fritillaria cirrhosa, fired licorice, and fired bitter almonds, among other components, can clear heat and ventilate the lungs, relieving cough and asthma. Clinically, QPGs are mainly used to treat cough, asthma, fever and other discomforts caused by acute or chronic bronchitis. In this study, the antiviral activity of QPGs against respiratory syncytial virus (RSV), influenza A virus A/FM/1/47 (H1N1), oseltamivir-resistant H1N1, A/Beijing/32/92 (H3N2), Sendai virus, and human adenovirus type 3 in Hep-2 or MDCK cells was evaluated using the CCK-8 method, and the cytotoxicity of QPGs to these two cell lines was tested. The effect of QPGs on mice infected with influenza A virus A/FM/1/47 (H1N1) was evaluated by measuring body weight, survival time, and survival rate, as well as virus titers and lesions in the lungs and levels of inflammatory factors in serum. In addition, the expression of TLR-7-My88-NF-κB signaling pathway-related proteins in lung tissues was analyzed by Western blotting and qRT-PCR. The results showed that QPGs had a potent inhibitory effect on the six viruses tested in vitro. Interestingly, QPGs also displayed particularly pronounced antiviral activity against H1N1-OC, similar to that of oseltamivir, a well-known antiviral drug. QPGs effectively protected mice from infection by H1N1, as indicated by significantly increased body weights, survival times, and survival rates and reduced lung virus titers of inflammatory factors and lung tissue injury. The levels of TLR-7-MyD88-NF-κB-pathway-related proteins in the lung tissue of infected mice were found to be decreased after QPG treatment, thereby alleviating lung injury caused by excessive release of inflammatory factors. Taken together, these findings indicate that QPGs have satisfactory activity against influenza virus infection.

MUC5AC: A potential biomarker of severity in pediatric patients infected with influenza.

Numerous factors can increase the risk of severe influenza; however, a majority of severe cases occur in previously healthy children. Identification of high-risk children is important for targeted preventive interventions and prompt treatment. The aim of this study was to evaluate MUC5AC as a biomarker for influenza disease severity in children. For this, a prospective cohort study was conducted in 2019. Children hospitalized with acute respiratory infection (ARI) with confirmed positive influenza infection were enrolled. Influenza cases were identified by reverse transcriptase-polymerase chain reaction. Life-threatening disease (LTD) was defined by the need for intensive care and ventilatory support. MUC5AC, epidemiologic, and clinical risk factors were assessed. Three hundred and forty-two patients were hospitalized with ARI, of which 49 (14%) had confirmed influenza infection and 6 (12%) of them developed LTD. MUC5AC levels were higher in those patients with mild disease compared to cases with poorer outcomes. Our results show that the severity of influenza infection in children is significantly associated with low levels of MUC5AC. These findings suggest its potential as a suitable biomarker for predicting disease severity.

Electronic Health Record-Based Algorithm for Monitoring Respiratory Virus-Like Illness.

Viral respiratory illness surveillance has traditionally focused on single pathogens (e.g., influenza) and required fever to identify influenza-like illness (ILI). We developed an automated system applying both laboratory test and syndrome criteria to electronic health records from 3 practice groups in Massachusetts, USA, to monitor trends in respiratory viral-like illness (RAVIOLI) across multiple pathogens. We identified RAVIOLI syndrome using diagnosis codes associated with respiratory viral testing or positive respiratory viral assays or fever. After retrospectively applying RAVIOLI criteria to electronic health records, we observed annual winter peaks during 2015-2019, predominantly caused by influenza, followed by cyclic peaks corresponding to SARS-CoV-2 surges during 2020-2024, spikes in RSV in mid-2021 and late 2022, and recrudescent influenza in late 2022 and 2023. RAVIOLI rates were higher and fluctuations more pronounced compared with traditional ILI surveillance. RAVIOLI broadens the scope, granularity, sensitivity, and specificity of respiratory viral illness surveillance compared with traditional ILI surveillance.

Influenza A virus infection disrupts the function of syncytiotrophoblast cells and contributes to adverse pregnancy outcomes.

Pregnancy heightens susceptibility to influenza A virus (IAV) infection, thereby increasing the risk of severe pneumonia and maternal mortality. It also raises the chances of adverse outcomes in offspring, such as fetal growth restriction, preterm birth, miscarriage, and stillbirth in offsprings. However, the underlying mechanisms behind these effects remain largely unknown. Syncytiotrophoblast cells, crucial in forming the placental barrier, nutrient exchange and hormone secretion, have not been extensively studied for their responses to IAV. In our experiment, we used Forskolin-treated BeWo cells to mimic syncytiotrophoblast cells in vitro, and infected them with H1N1, H5N1 and H7N9 virus stains. Our results showed that syncytiotrophoblast cells, with their higher intensity of sialic acid receptors, strongly support IAV infection and replication. Notably, high-dose viral infection and prolonged exposure resulted in a significant decrease in fusion index, as well as gene and protein expression levels associated with trophoblast differentiation, ß-human chorionic gonadotropin secretion, estrogen and progesterone biosynthesis, and nutrient transport. In pregnant BALB/c mice infected with the H1N1 virus, we observed significant decreases in trophoblast differentiation and hormone secretion gene expression levels. IAV infection also resulted in preterm labor, fetal growth restriction, and increased maternal and fetal morbidity and mortality. Our findings indicate that IAV infection in syncytiotrophoblastic cells can result in adverse pregnancy outcomes by altering trophoblast differentiation, suppressing of ß-hCG secretion, and disrupting placental barrier function.

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.

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.

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.

Prevalence of circulating antibodies against hemagglutinin of influenza viruses in epidemic season 2021/2022 in Poland.

The aim of the study was to determine the level of anti-hemagglutinin antibodies in the serum of patients during the 2021/2022 epidemic season in Poland. A total of 700 sera samples were tested, divided according to the age of the patients into 7 age groups: 0-4 years of age, 5-9 years of age, 10-14 years of age, 15-25 years of age, 26-44 years of age, 45-64 years of age and ≥65 years of age, 100 samples were collected from each age group. Anti-hemagglutinin antibody levels was determined using the haemagglutination inhibition assay (OZHA). The results obtained confirm the presence of anti-hemagglutinin antibodies for the antigens A/Victoria/2570/2019 (H1N1) pdm09, A/Cambodia/e0826360/2020 (H3N2), B/Washington/02/2019 and B/Phuket/3073/2013 recommended by World Health Organization (WHO) for the 2021/2022 epidemic season. The analysis of the results shows differences in the levels of individual anti-hemagglutinin antibodies in the considered age groups. In view of very low percentage of the vaccinated population in Poland, which was 6.90% in the 2021/2022 epidemic season, the results obtained in the study would have to be interpreted as the immune system response in patients after a previous influenza virus infection.

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.