A single-amino-acid substitution in the HA protein changes the replication and pathogenicity of the 2009 pandemic A (H1N1) influenza viruses in vitro and in vivo

BACKGROUND: The novel pandemic A (H1N1) virus was first identified in Mexico in April 2009 and since then it spread world wide over a short period of time. Although the virus infection is generally associated with mild disease and a relatively low mortality, it is projected that mutations in specific regions of the viral genome, especially within the receptor binding domain of the hemagglutinin (HA) protein could result in more virulent virus stains, leading to a more severe pandemic. RESULTS: Here, we found that a single amino acid substitution of Asp-to-Gly at position 222 in the HA protein of the A (H1N1) virus occurred after two passage propagation in the allantoic cavities of chicken embryonated eggs, and this single residue variation dramatically increased the viral replication ability in MDCK cells and pathogenicity in BALB/c mice. CONCLUSIONS: A substitution of Asp-to-Gly at position 222 in the HA protein was prone to occur under positive selection pressures, and this single amino acid mutation could dramatically increase the virus replication ability in vitro and pathogenicity in vivo. Our finding offers a better understanding of the transmission and evolution of the 2009 pandemic A (H1N1) virus and brings attention to further potentially severe influenza pandemic that may result from cross-host evolution of the influenza viruses

High circulating CD39+ regulatory T cells predict poor survival for sepsis patients

SummaryBackgroundSepsis encompasses two phases, the ‘hyper’-reactive phase and the ‘hypo’-reactive phase. The initial inflammatory stage is quickly counterbalanced by an anti-inflammatory response, which compromises the immune system, leading to immune suppression. Regulatory T cells (Tregs) have been implicated in the pathogenesis of sepsis by inducing immunosuppression; however, the role of CD39+ Tregs in the process of sepsis is uncertain. This study investigated the dynamic levels of CD39+ Tregs and their phenotypic change in sepsis.MethodsFourteen patients with systemic inflammatory response syndrome (SIRS), 42 patients with sepsis, and 14 healthy controls were enrolled. Sequential blood samples were used to analyze the numbers of CD39+ Tregs and their phenotypic changes. Survival at 28 days was used to evaluate the capacity of CD39+ Treg levels to predict mortality in sepsis patients.ResultsSepsis patients displayed a high percentage (3.13%, 1.46%, and 0.35%, respectively) and mean fluorescence intensity (MFI) (59.65, 29.7, and 24.3, respectively) of CD39+ Tregs compared with SIRS patients and healthy subjects. High-level expression of CD39+ Tregs was correlated with the severity of sepsis, which was reflected by the sepsis-related organ failure assessment score (r=0.322 and r=0.31, respectively). In addition, the expression of CD39+ Tregs was associated with survival of sepsis patients (p<0.01). By receiver-operating characteristic (ROC) curve analysis, the percentage and MFI of CD39+ Tregs showed similar sensitivities and specificities to predict mortality (74.2% and 85.1%, and 73.9% and 84.1%, respectively). Using Kaplan–Meier curves to assess the impact of CD39+ Tregs percentage and MFI on overall survival, we found that a high CD39+ Tregs percentage (p<0.001; >4.1%) and MFI (p<0.001; >49.2) were significantly associated with mortality. Phenotypically, CD39+ Tregs from sepsis patients showed high expression of CD38 and PD-1 (p<0.01 and p<0.01 respectively).ConclusionsIncreased expression of CD39+ Tregs was associated with a poor prognosis for sepsis patients, which suggests that CD39+ Treg levels could be used as a biomarker to predict the outcome of sepsis patients

Genomic Polymorphism of the Pandemic A (H1N1) Influenza Viruses Correlates with Viral Replication, Virulence, and Pathogenicity In Vitro and In Vivo

The novel pandemic A (H1N1) virus was first identified in Mexico in April 2009 and quickly spread worldwide. Like all influenzas, the H1N1 strain-specific properties of replication, virulence, and pathogenicity are a result of the particular genomic sequence and concerted expression of multiple genes. Thus, specific mutations may support increased virulence and may be useful as biomarkers of potential threat to human health. We performed comparative genomic analysis of ten strains of the 2009 pandemic A (H1N1) influenza viruses to determine whether genotypes associated with clinical phenotypes, which ranged from mild to severe illness and up to lethal. Virus replication capacity was tested for each strain in vitro using cultured epithelial cells, while virulence and pathogenicity were investigated in vivo using the BALB/c mouse model. The results indicated that A/Sichuan/1/2009 strain had significantly higher replication ability and virulence than the other strains, and five unique non-synonymous mutations were identified in important gene-encoding sequences. These mutations led to amino acid substitutions in HA (L32I), PA (A343T), PB1 (K353R and T566A), and PB2 (T471M), and may be critical molecular determinants for replication, virulence, and pathogenicity. Our results suggested that the replication capacity in vitro and virulence in vivo of the 2009 pandemic A (H1N1) viruses were not associated with the clinical phenotypes. This study offers new insights into the transmission and evolution of the 2009 pandemic A (H1N1) virus

Adaption of Seasonal H1N1 Influenza Virus in Mice

The experimental infection of a mouse lung with influenza A virus has proven to be an invaluable model for studying the mechanisms of viral adaptation and virulence. The mouse adaption of human influenza A virus can result in mutations in the HA and other proteins, which is associated with increased virulence in mouse lungs. In this study, a mouse-adapted seasonal H1N1 virus was obtained through serial lung-to-lung passages and had significantly increased virulence and pathogenicity in mice. Genetic analysis indicated that the increased virulence of the mouse-adapted virus was attributed to incremental acquisition of three mutations in the HA protein (T89I, N125T, and D221G). However, the mouse adaption of influenza A virus did not change the specificity and affinity of receptor binding and the pH-dependent membrane fusion of HA, as well as the in vitro replication in MDCK cells. Notably, infection with the mouse adapted virus induced severe lymphopenia and modulated cytokine and chemokine responses in mice. Apparently, mouse adaption of human influenza A virus may change the ability to replicate in mouse lungs, which induces strong immune responses and inflammation in mice. Therefore, our findings may provide new insights into understanding the mechanisms underlying the mouse adaption and pathogenicity of highly virulent influenza viruses

Acoustic model topology optimization for large vocabulary speech recognition

Acoustic model topology selection work in constructing large vocabulary speech recognition systems is being done empirically or heuristically. In this paper, we propose two improved algorithms, which are based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) respectively, on the basis of our previously proposed algorithms to select and optimize model topologies for small or medium vocabulary speech recognition systems. Our improved algorithms attain the goal of optimizing acoustic model topologies for large vocabulary speech recognition systems mainly through modifying the encoding schemes of our previously proposed algorithms. Experiments on the dialogue corpus of Inner Mongolia University show that, compared with the conventional acoustic model topology selection method, our newly proposed algorithms are able to bring much higher recognition performance for large vocabulary speech recognition systems by optimizing their acoustic model topologies

Acoustic model topology optimization for large vocabulary speech recognition

Acoustic model topology selection work in constructing large vocabulary speech recognition systems is being done empirically or heuristically. In this paper, we propose two improved algorithms, which are based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) respectively, on the basis of our previously proposed algorithms to select and optimize model topologies for small or medium vocabulary speech recognition systems. Our improved algorithms attain the goal of optimizing acoustic model topologies for large vocabulary speech recognition systems mainly through modifying the encoding schemes of our previously proposed algorithms. Experiments on the dialogue corpus of Inner Mongolia University show that, compared with the conventional acoustic model topology selection method, our newly proposed algorithms are able to bring much higher recognition performance for large vocabulary speech recognition systems by optimizing their acoustic model topologies

Regularized numerical methods for the logarithmic Schrodinger equation

23 pages, 8 colored figuresInternational audienceWe present and analyze two numerical methods for the logarithmic Schrödinger equation (LogSE) consisting of a regularized splitting method and a regularized conservative Crank-Nicolson finite difference method (CNFD). In order to avoid numerical blow-up and/or to suppress round-off error due to the logarithmic nonlinearity in the LogSE, a regularized logarithmic Schrödinger equation (RLogSE) with a small regularized parameter 0 0 the time step, which implies an error bound at O(ε + τ 1/2 ln(ε −1)) for the LogSE by the Lie-Trotter splitting method. In addition, the CNFD is also applied to discretize the RLogSE, which conserves the mass and energy in the discretized level. Numerical results are reported to confirm our error bounds and to demonstrate rich and complicated dynamics of the LogSE