Bioassay-Guided Fractionation of Siparuna glycycarpa n-Butanol Extract with Inhibitory Activity against Influenza A(H1N1)pdm09 Virus by Centrifugal Partition Chromatography (CPC)

Siparuna glycycarpa occurs in the Amazon region, and some species of this genus are used in Brazilian folk medicine. A recent study showed the inhibitory effect of this species against influenza A(H1N1)pdm09 virus, and in order to acquire active fractions, a polar solvent system n-butanol-methanol-water (9:1:10, v/v) was selected and used for bioassay-guided fractionation of n-butanol extract by centrifugal partition chromatography (CPC). The upper phase was used as stationary phase and the lower phase as mobile (descending mode). Among the collected fractions, the ones coded SGA, SGC, SGD, and SGO showed the highest antiviral inhibition levels (above 74%) at 100 µg·mL−1 after 24 h of infection. The bioactive fractions chemical profiles were investigated by LC-HRMS/MS data in positive and negative ionization modes exploring the Global Natural Products Social Molecular Networking (GNPS) platform to build a molecular network. Benzylisoquinoline alkaloids were annotated in the fractions coded SGA, SGC, and SGD collected during elution step. Aporphine alkaloids, O-glycosylated flavonoids, and dihydrochalcones in SGO were acquired with the change of mobile phase from lower aqueous to upper organic. Benzylisoquinolinic and aporphine alkaloids as well as glycosylated flavonoids were annotated in the most bioactive fractions suggesting this group of compounds as responsible for antiviral activity.

TNFA and IL10 Polymorphisms and IL-6 and IL-10 Levels Influence Disease Severity in Influenza A(H1N1)pdm09 Virus Infected Patients

Cytokines are key modulators of immune response, and dysregulated production of proinflammatory and anti-inflammatory cytokines contributes to the pathogenesis of influenza A(H1N1)pdm09 virus infection. Cytokine production is impacted by single nucleotide polymorphisms (SNPs) in the genes coding for them. In the present study, SNPs in the IL6, TNFA, IFNG, IL17A, IL10, and TGFB were investigated for their association with disease severity and fatality in influenza A(H1N1)pdm09-affected patients with mild disease (n = 293) and severe disease (n = 86). Among those with severe disease, 41 patients had fatal outcomes. In a subset of the patients, levels of IL-2, IL-4, IL-6, IL-10, TNF, IFN-γ, and IL-17 were assayed in the plasma for their association with severe disease. The frequency of TNFA rs1800629 G/A allele was significantly higher in severe cases and survived severe cases group compared to that of those with mild infection (OR with 95% for mild vs. severe cases 2.95 (1.52–5.73); mild vs. survived severe cases 4.02 (1.84–8.82)). IL10 rs1800896-rs1800872 G-C haplotype was significantly lower (OR with 95% 0.34 (0.12–0.95)), while IL10 rs1800896-rs1800872 G-A haplotype was significantly higher (OR with 95% 12.11 (2.23–76.96)) in fatal cases group compared to that of the mild group. IL-6 and IL-10 levels were significantly higher in fatal cases compared to that of survived severe cases. IL-6 levels had greater discriminatory power than IL-10 to predict progression to fatal outcome in influenza A(H1N1)pdm09 virus-infected patients. To conclude, the present study reports the association of TNFA and IL10 SNPs with severe disease in Influenza A(H1N1)pdm09 virus-infected subjects. Furthermore, IL-6 levels can be a potential biomarker for predicting fatal outcomes in Influenza A(H1N1)pdm09 virus infected subjects.

Age-Specific Etiology of Severe Acute Respiratory Infections and Influenza Vaccine Effectivity in Prevention of Hospitalization in Russia, 2018–2019 Season

The expansion and standardization of clinical trials, as well as the use of sensitive and specific molecular diagnostics methods, provide new information on the age-specific roles of influenza and other respiratory viruses in development of severe acute respiratory infections (SARI). Here, we present the results of the multicenter hospital-based study aimed to detect age-specific impact of influenza and other respiratory viruses (ORV). The 2018–2019 influenza season in Russia was characterized by co-circulation of influenza A(H1N1)pdm09 and A(H3N2) virus subtypes which were detected among hospitalized patients with SARI in 19.3% and 16.4%, respectively. RSV dominated among ORV (15.1% of total cases and 26.8% in infants aged ≤ 2 years). The most significant SARI agents in intensive care units were RSV and influenza A(H1N1)pdm09 virus, (37.3% and 25.4%, respectively, of PCR-positive cases). Hyperthermia was the most frequently registered symptom for influenza cases. In contrast, hypoxia, decreased blood O2 concentration, and dyspnea were registered more often in RSV, rhinovirus, and metapneumovirus infection in young children. Influenza vaccine effectiveness (IVE) against hospitalization of patients with PCR-confirmed influenza was evaluated using test-negative case–control design. IVE for children and adults was estimated to be 57.0% and 62.0%, respectively. Subtype specific IVE was higher against influenza A(H1N1)pdm09, compared to influenza A(H3N2) (60.3% and 45.8%, respectively). This correlates with delayed antigenic drift of the influenza A(H1N1)pdm09 virus and genetic heterogeneity of the influenza A(H3N2) population. These studies demonstrate the need to improve seasonal influenza prevention and control in all countries as states by the WHO Global Influenza Strategy for 2019–2030 initiative.

Characterizing the Countrywide Epidemic Spread of Influenza A(H1N1)pdm09 Virus in Kenya between 2009 and 2018

The spatiotemporal patterns of spread of influenza A(H1N1)pdm09 viruses on a countrywide scale are unclear in many tropical/subtropical regions mainly because spatiotemporally representative sequence data are lacking. We isolated, sequenced, and analyzed 383 A(H1N1)pdm09 viral genomes from hospitalized patients between 2009 and 2018 from seven locations across Kenya. Using these genomes and contemporaneously sampled global sequences, we characterized the spread of the virus in Kenya over several seasons using phylodynamic methods. The transmission dynamics of A(H1N1)pdm09 virus in Kenya were characterized by (i) multiple virus introductions into Kenya over the study period, although only a few of those introductions instigated local seasonal epidemics that then established local transmission clusters, (ii) persistence of transmission clusters over several epidemic seasons across the country, (iii) seasonal fluctuations in effective reproduction number (Re) associated with lower number of infections and seasonal fluctuations in relative genetic diversity after an initial rapid increase during the early pandemic phase, which broadly corresponded to epidemic peaks in the northern and southern hemispheres, (iv) high virus genetic diversity with greater frequency of seasonal fluctuations in 2009–2011 and 2018 and low virus genetic diversity with relatively weaker seasonal fluctuations in 2012–2017, and (v) virus spread across Kenya. Considerable influenza virus diversity circulated within Kenya, including persistent viral lineages that were unique to the country, which may have been capable of dissemination to other continents through a globally migrating virus population. Further knowledge of the viral lineages that circulate within understudied low-to-middle-income tropical and subtropical regions is required to understand the full diversity and global ecology of influenza viruses in humans and to inform vaccination strategies within these regions.

Longitudinal Projection of Herd Prevalence of Influenza A(H1N1)pdm09 Virus Infection in the Norwegian Pig Population by Discrete-Time Markov Chain Modelling

In order to quantify projections of disease burden and to prioritise disease control strategies in the animal population, good mathematical modelling of infectious disease dynamics is required. This article investigates the suitability of discrete-time Markov chain (DTMC) as one such model for forecasting disease burden in the Norwegian pig population after the incursion of influenza A(H1N1)pdm09 virus (H1N1pdm09) in Norwegian pigs in 2009. By the year-end, Norway’s active surveillance further detected 20 positive herds from 54 random pig herds, giving an estimated initial population prevalence of 37% (95% CI 25–52). Since then, Norway’s yearly surveillance of pig herd prevalence has given this study 11 years of data from 2009 to 2020 to work with. Longitudinally, the pig herd prevalence for H1N1pdm09 rose sharply to >40% in three years and then fluctuated narrowly between 48% and 49% for 6 years before declining. This initial longitudinal pattern in herd prevalence from 2009 to 2016 inspired this study to test the steady-state discrete-time Markov chain model in forecasting disease prevalence. With the pig herd as the unit of analysis, the parameters for DTMC came from the initial two years of surveillance data after the outbreak, namely vector prevalence, first herd incidence and recovery rates. The latter two probabilities formed the fixed probability transition matrix for use in a discrete-time Markov chain (DTMC) that is quite similar to another compartmental model, the susceptible–infected–susceptible (SIS) model. These DTMC of predicted prevalence (DTMCP) showed good congruence (Pearson correlation = 0.88) with the subsequently observed herd prevalence for seven years from 2010 to 2016. While the DTMCP converged to the stationary (endemic) state of 48% in 2012, after three time steps, the observed prevalence declined instead from 48% after 2016 to 25% in 2018 before rising to 29% in 2020. A sudden plunge in H1N1pdm09 prevalence amongst Norwegians during the 2016/2017 human flu season may have had a knock-on effect in reducing the force of infection in pig herds in Norway. This paper endeavours to present the discrete-time Markov chain (DTMC) as a feasible but limited tool in forecasting the sequence of a predicted infectious disease’s prevalence after it’s incursion as an exotic disease.

Characterization of influenza A(H1N1)pdm09 viruses in Germany in season 2019-2020 – co-circulation of an antigenic drift variant

Background Influenza A(H1N1)pdm09 virus entered the human population in 2009 and evolved within this population for more than ten years. Despite genetic evolution no remarkable changes in the antigenic reactive pattern of these viruses were observed so far. Methods Primary respiratory samples of the German influenza virological sentinel were investigated by qPCR. Influenza virus-positive samples were characterized genetically and antigenetically. Results In December 2019, a antigenic drift variant characterized by an N156K substitution in the hemagglutinin of influenza A(H1N1)pdm09 virus emerged in Germany, which exhibited a reactivity to ferret antiserum that was an average 6 log2 lower than the vaccine virus A/Brisbane/02/2018 and the other A(H1N1)pdm09 viruses circulating in the influenza season 2019-2020. These viruses accounted for 20% of all A(H1N1)pdm09 viruses characterized in the German influenza sentinel. Patients infected with these viruses had a shorter median time period of medical consultation after onset of symptoms and were more frequently treated with neuraminidase inhibitors in comparison to patients infected with other A(H1N1)pdm09 viruses. Conclusions This parallel circulation of two antigenic variants of A(H1N1)pdm09 viruses which differ remarkably in their antigenic reactive pattern contributes to a greater variability in circulating influenza viruses and challenges vaccination.

Detection of H275Y Mutation Conferring Oseltamivir Drug Resistance in Influenza A (H1N1) pdm09 Virus

In the treatment of influenza, Neuraminidase inhibitors (NAIs) (Oseltamivir and Zanamivir) play a major role. The emergence of variants of influenza A (H1N1) pdm09 virus resistant to Oseltamivir is a matter of great concern as it limits its usage. Therefore, vigilant monitoring for Oseltamivir-resistant viruses has been recommended by the World Health Organization (WHO). Our study aimed to screen the influenza A (H1N1) pdm09 virus for NAI drug resistance during the outbreak of 2015-16 in North-Western India. A total of 640 H1N1pdm09 virus-positive samples were screened for drug resistance to Oseltamivir by WHO allelic discrimination real-time RT-PCR protocol. The allelic discrimination PCR protocol can detect the presence of single nucleotide polymorphisms (SNPs), the H275Y mutation is detected by this method which causes resistance to Oseltamivir. Sanger sequencing of partial fragment of NA gene (fragment IV), of 90 samples were performed to confirm the presence of NA-H275Y mutation. Neuraminidase susceptibility of 20 randomly selected isolates to Oseltamivir was tested using NA inhibition chemiluminiscence based assay. Among 640 H1N1pdm09 positive samples tested, H275Y mutation was detected in one sample (0.15%) by PCR and confirmed by Sanger sequencing also. All the 20 isolates tested for NAI susceptibility by NA star assay were found to be sensitive to Oseltamivir. WHO allelic discrimination PCR is an easy, rapid and sensitive method for high-throughput detection of resistance to Oseltamivir. Systematic regular drug resistance surveillance of Influenza A is essential to monitor the emergence and spread of drug-resistant strains.

SALIENT FEATURES OF CIRCULATING RESPIRATORY VIRUSES IN THE PRE– AND PANDEMIC INFLUENZA AND COVID–19 SEASONS

A wide variety of zoonotic viruses that can cross the interspecies barrier promote the emergence of new, potentially pandemic viruses in the human population that was often accompanied by the disappearance of existing circulating strains. Among the various reasons underlying this phenomenon is the strengthening of populational immunity by expanding the immune layer of the population and improving the means and methods of medical care. However, “Natura abhorret vacuum”, and new pathogens come to replace disappearing pathogens. In the past ten years, there have been two critical events – the pandemic spread of the swine influenza A (H1N1) pdm09 virus in 2009 and the novel SARS–CoV–2 coronavirus in 2019, providing scientists with a unique opportunity to learn more about a relationship between respiratory viruses and their pathogenesis. Together with viruses of pandemic significance, a large number of seasonal respiratory viruses circulate, which contribute to the structure of human morbidity, and co–infections aggravate the condition of the illness. In the conditions of the spread of new viruses with unexplored characteristics, in the absence of means of prevention and therapy, it is especially important to prevent the aggravation of morbidity due to mixed infections. Here we review the mutual involvement of pandemic influenza A(H1N1)pdm09 and SARS–CoV–2 coronavirus and seasonal respiratory viruses in the epidemic process, discuss some issues related to their spread, potential causes affecting the spread and severity of the morbidity. The given facts, testify to the existence of seasonality and temporal patterns of the beginning and end of the circulation of respiratory viruses. Interestingly, the beginning of the circulation of the pandemic influenza A(H1N1)pdm09 virus led to a shift in the timing and intensity of circulation of some respiratory viruses, which is probably caused by the existence of "replication conflicts" between them, and did not affect others. Co–infection with SARS–CoV–2–19 and other respiratory viruses, especially respiratory syncytial virus and rhinoviruses, was quite often observed. At the current stage, no aggravating effect of influenza on the course of COVID–19 in mixed infection has been established. Whether this is due to the mild course of influenza infection in the 2020 epidemic season, or the competitive impact of SARS–CoV–2 on influenza viruses is not yet clear. Experts are still at the stage of accumulating facts and working on creating means of effective prevention and treatment of the new coronavirus infection.