An unbiased genetic screen reveals the polygenic nature of the influenza virus anti-interferon response

UNLABELLED: Influenza A viruses counteract the cellular innate immune response at several steps, including blocking RIG I-dependent activation of interferon (IFN) transcription, interferon (IFN)-dependent upregulation of IFN-stimulated genes (ISGs), and the activity of various ISG products; the multifunctional NS1 protein is responsible for most of these activities. To determine the importance of other viral genes in the interplay between the virus and the host IFN response, we characterized populations and selected mutants of wild-type viruses selected by passage through non-IFN-responsive cells. We reasoned that, by allowing replication to occur in the absence of the selection pressure exerted by IFN, the virus could mutate at positions that would normally be restricted and could thus find new optimal sequence solutions. Deep sequencing of selected virus populations and individual virus mutants indicated that nonsynonymous mutations occurred at many phylogenetically conserved positions in nearly all virus genes. Most individual mutants selected for further characterization induced IFN and ISGs and were unable to counteract the effects of exogenous IFN, yet only one contained a mutation in NS1. The relevance of these mutations for the virus phenotype was verified by reverse genetics. Of note, several virus mutants expressing intact NS1 proteins exhibited alterations in the M1/M2 proteins and accumulated large amounts of deleted genomic RNAs but nonetheless replicated to high titers. This suggests that the overproduction of IFN inducers by these viruses can override NS1-mediated IFN modulation. Altogether, the results suggest that influenza viruses replicating in IFN-competent cells have tuned their complete genomes to evade the cellular innate immune system and that serial replication in non-IFN-responsive cells allows the virus to relax from these constraints and find a new genome consensus within its sequence space.

IMPORTANCE: In natural virus infections, the production of interferons leads to an antiviral state in cells that effectively limits virus replication. The interferon response places considerable selection pressure on viruses, and they have evolved a variety of ways to evade it. Although the influenza virus NS1 protein is a powerful interferon antagonist, the contributions of other viral genes to interferon evasion have not been well characterized. Here, we examined the effects of alleviating the selection pressure exerted by interferon by serially passaging influenza viruses in cells unable to respond to interferon. Viruses that grew to high titers had mutations at many normally conserved positions in nearly all genes and were not restricted to the NS1 gene. Our results demonstrate that influenza viruses have fine-tuned their entire genomes to evade the interferon response, and by removing interferon-mediated constraints, viruses can mutate at genome positions normally restricted by the interferon response.

A New Android Malware Detection Method Using Bayesian Classification

Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android app marketplaces remain at risk of hosting malicious apps that could evade detection before being downloaded by unsuspecting users. Hence, in this paper we present an effective approach to alleviate this problem based on Bayesian classification models obtained from static code analysis. The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities. The models are evaluated with real malware samples in the wild and results of experiments are presented to demonstrate the effectiveness of the proposed approach.

A sting in the spit: widespread cross-infection of multiple RNA viruses across wild and managed bees

Declining populations of bee pollinators are a cause of concern, with major repercussions for biodiversity loss and food security. RNA viruses associated with honeybees represent a potential threat to other insect pollinators, but the extent of this threat is poorly understood. This study aims to attain a detailed understanding of the current and ongoing risk of emerging infectious disease (EID) transmission between managed and wild pollinator species across a wide range of RNA viruses. Within a structured large-scale national survey across 26 independent sites, we quantify the prevalence and pathogen loads of multiple RNA viruses in co-occurring managed honeybee (Apis mellifera) and wild bumblebee (Bombus spp.) populations. We then construct models that compare virus prevalence between wild and managed pollinators. Multiple RNA viruses associated with honeybees are widespread in sympatric wild bumblebee populations. Virus prevalence in honeybees is a significant predictor of virus prevalence in bumblebees, but we remain cautious in speculating over the principle direction of pathogen transmission. We demonstrate species-specific differences in prevalence, indicating significant variation in disease susceptibility or tolerance. Pathogen loads within individual bumblebees may be high and in the case of at least one RNA virus, prevalence is higher in wild bumblebees than in managed honeybee populations. Our findings indicate widespread transmission of RNA viruses between managed and wild bee pollinators, pointing to an interconnected network of potential disease pressures within and among pollinator species. In the context of the biodiversity crisis, our study emphasizes the importance of targeting a wide range of pathogens and defining host associations when considering potential drivers of population decline.

Fusion of Static and Temporal Information for Threat Evaluation in Sensor Networks

In many CCTV and sensor network based intelligent surveillance systems, a number of attributes or criteria are used to individually evaluate the degree of potential threat of a suspect. The outcomes for these attributes are in general from analytical algorithms where data are often pervaded with uncertainty and incompleteness. As a result, such individual threat evaluations are often inconsistent, and individual evaluations can change as time elapses. Therefore, integrating heterogeneous threat evaluations with temporal influence to obtain a better overall evaluation is a challenging issue. So far, this issue has rarely be considered by existing event reasoning frameworks under uncertainty in sensor network based surveillance. In this paper, we first propose a weighted aggregation operator based on a set of principles that constraints the fusion of individual threat evaluations. Then, we propose a method to integrate the temporal influence on threat evaluation changes. Finally, we demonstrate the usefulness of our system with a decision support event modeling framework using an airport security surveillance scenario.

Effects of Parasitism and Morphology on Squirrelpox Virus Seroprevalence in Grey Squirrels (Sciurus carolinensis)

Invasive species have been cited as major causes of population extinctions in several animal and plant classes worldwide. The North American grey squirrel (Sciurus carolinensis) has a major detrimental effect on native red squirrel (Sciurus vulgaris) populations across Britain and Ireland, in part because it can be a reservoir host for the deadly squirrelpox virus (SQPV). Whilst various researchers have investigated the epizootiology of SQPV disease in grey squirrels and have modelled the consequent effects on red squirrel populations, less work has examined morphological and physiological characteristics that might make individual grey squirrels more susceptible to contracting SQPV. The current study investigated the putative relationships between morphology, parasitism, and SQPV exposure in grey squirrels. We found geographical, sex, and morphological differences in SQPV seroprevalence. In particular, larger animals, those with wide zygomatic arch widths (ZAW), males with large testes, and individuals with concurrent nematode and/or coccidial infections had an increased seroprevalence of SQPV. In addition, males with larger spleens, particularly those with narrow ZAW, were more likely to be exposed to SQPV. Overall these results show that there is variation in SQPV seroprevalence in grey squirrels and that, consequently, certain individual, or populations of, grey squirrels might be more responsible for transmitting SQPV to native red squirrel populations.

Infection of donor lymphocytes with human T lymphotrophic virus type 1 (HTLV-I) following allogeneic bone marrow transplantation for HTLV-I positive adult T-cell leukaemia

Human T lymphotrophic virus type 1 (HTLV-I) associated leukaemia has a poor prognosis even with chemotherapy. We describe a patient with adult T-cell leukaemia treated with allogeneic bone marrow transplantation from an HTLV-I negative identical sibling donor. During follow-up after bone marrow transplantation, HTLV-I could be repeatedly isolated inspite of anti-viral prophylaxis. The patient died of an acute encephalitis and HTLV-I could be detected in autopsy material from the brain. By a PCR-based technique using short tandem repeats (STRs) it was shown that the patient's haemopoiesis was of donor origin. This shows the infection of donor cells in vivo by an aetiological agent which has been implicated in the leukaemogenic process for adult T-cell leukaemia.

High accuracy android malware detection using ensemble learning

With over 50 billion downloads and more than 1.3 million apps in Google’s official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus this paper proposes an approach that utilizes ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3 % to 99% detection accuracy with very low false positive rates.

Android Malware Detection: an Eigenspace Analysis Approach

The battle to mitigate Android malware has become more critical with the emergence of new strains incorporating increasingly sophisticated evasion techniques, in turn necessitating more advanced detection capabilities. Hence, in this paper we propose and evaluate a machine learning based approach based on eigenspace analysis for Android malware detection using features derived from static analysis characterization of Android applications. Empirical evaluation with a dataset of real malware and benign samples show that detection rate of over 96% with a very low false positive rate is achievable using the proposed method.

Dengue Virus Inhibition of Autophagic Flux and Dependency of Viral Replication on Proteasomal Degradation of the Autophagy Receptor p62

Autophagic flux involves formation of autophagosomes and their degradation by lysosomes. Autophagy can either promote or restrict viral replication. In the case of Dengue virus (DENV) several studies report that autophagy supports the viral replication cycle, and describe an increase of autophagic vesicles (AVs) following infection. However, it is unknown how autophagic flux is altered to result in increased AVs. To address this question, and gain insight into the role of autophagy during DENV infection, we established an unbiased, image-based flow cytometry approach to quantify autophagic flux under normal growth conditions and in response to activation by nutrient deprivation or the mTOR inhibitor Torin1. We found that DENV induced an initial activation of autophagic flux, followed by inhibition of general and specific autophagy. Early after infection, basal and activated autophagic flux was enhanced. However, during established replication, basal and Torin1-activated autophagic flux was blocked, while autophagic flux activated by nutrient deprivation was reduced, indicating a block to AV formation and reduced AV degradation capacity. During late infection AV levels increased as a result of inefficient fusion of autophagosomes with lysosomes. Additionally, endo-lysosomal trafficking was suppressed, while lysosomal activities were increased. We further determined that DENV infection progressively reduced levels of the autophagy receptor SQSTM1/p62 via proteasomal degradation. Importantly, stable over-expression of p62 significantly suppressed DENV replication suggesting a novel role for p62 as viral restriction factor. Overall our findings indicate that in the course of DENV infection, autophagy shifts from a supporting to an anti-viral role, which is countered by DENV.

IMPORTANCE: Autophagic flux is a dynamic process starting with the formation of autophagosomes and ending with their degradation after fusion with lysosomes. Autophagy impacts the replication cycle of many viruses. However, thus far the dynamics of autophagy in case of Dengue virus (DENV) infections has not been systematically quantified. Therefore, we employed high-content, imaging-based flow cytometry to quantify autophagic flux and endo-lysosomal trafficking in response to DENV infection. We report that DENV induced an initial activation of autophagic flux, followed by inhibition of general and specific autophagy. Further, lysosomal activity was increased, but endo-lysosomal trafficking was suppressed confirming the block of autophagic flux. Importantly, we provide evidence that p62, an autophagy receptor, restrict DENV replication and was specifically depleted in DENV-infected cells via increased proteasomal degradation. These results suggest that during DENV infection autophagy shifts from a pro- to an antiviral cellular process, which is counteracted by the virus.

Induction and antagonism of antiviral responses in respiratory syncytial virus-infected pediatric airway epithelium.

Airway epithelium is the primary target of many respiratory viruses. However, virus induction and antagonism of host responses by human airway epithelium remains poorly understood. To address this, we developed a model of respiratory syncytial virus (RSV) infection based on well- differentiated pediatric primary bronchial epithelial cell cultures (WD-PBECs) that mimics hallmarks of RSV disease in infants. RSV is the most important respiratory viral pathogen in young infants worldwide. We found that RSV induces a potent antiviral state in WD-PBECs that was mediated in part by secreted factors, including interferon lambda-1 (IFNλ1)/IL-29. In contrast, type I interferons were not detected following RSV infection of WD-PBECs., Interferon (IFN) responses in RSV-infected WD-PBECs reflected those in lower airway samples from RSV-hospitalized infants. In view of the prominence of IL-29, we determined whether recombinant IL-29 treatment of WD-PBECs before or after infection abrogated RSV replication. Interestingly, IL-29 demonstrated prophylactic, but not therapeutic, potential against RSV. The absence of therapeutic potential reflected effective RSV antagonism of IFN-mediated antiviral responses in infected cells. Our data are consistent with RSV non-structural proteins 1 and/or 2 perturbing the Jak-STAT signaling pathway, with concomitant reduced expression of antiviral effector molecules, such as MxA/B. Antagonism of Jak-STAT signaling was restricted to RSV-infected cells in WD-PBEC cultures. Importantly, our study provides the rationale to further explore IL-29 as a novel RSV prophylactic.

Generation and Characterization of ALX-0171, a Potent Novel Therapeutic Nanobody for the Treatment of Respiratory Syncytial Virus Infection

Respiratory Syncytial Virus (RSV) is an important causative agent of lower respiratory tract infections in infants and elderly. Its fusion (F) protein is critical for virus infection. It is targeted by several investigational antivirals and by palivizumab, a humanised monoclonal antibody used prophylactically in infants considered at high risk of severe RSV disease. ALX-0171 is a trimeric Nanobody that binds the antigenic site II of RSV F-protein with subnanomolar affinity. ALX-0171 demonstrated superior in vitro neutralisation compared to palivizumab against prototypic RSV A and B strains. Moreover, ALX-0171 completely blocked replication below limit of detection in 87% of the viruses tested versus 18% for palivizumab at a fixed concentration. Importantly, ALX-0171 was highly effective in reducing both nasal and lung RSV titers when delivered prophylactically or therapeutically directly to the lungs of cotton rats. ALX-0171 represents a potent novel antiviral compound with significant potential to treat RSV-mediated disease.

Squirrelpox virus in Northern Ireland: quantifying the risk to red squirrels.

Information and collated statistical analysis on the decline of red squirrels.