The recent emergence of several new coronaviruses, including the etiological cause of severe acute respiratory syndrome, has significantly increased the importance of understanding virus-host cell interactions of this virus family. We used mouse hepatitis virus (MHV) A59 as a model to gain insight into how coronaviruses affect the type I alpha/beta interferon (IFN) system. We demonstrate that MHV is resistant to type I IFN. Protein kinase R (PKR) and the alpha subunit of eukaryotic translation initiation factor are not phosphorylated in infected cells. The RNase L activity associated with 2,5-oligoadenylate synthetase is not activated or is blocked, since cellular RNA is not degraded. These results are consistent with lack of protein translation shutoff early following infection. We used a well-established recombinant vaccinia virus (VV)-based expression system that lacks the viral IFN antagonist E3L to screen viral genes for their ability to rescue the IFN sensitivity of the mutant. The nucleocapsid (N) gene rescued VV⌬E3L from IFN sensitivity. N gene expression prevents cellular RNA degradation and partially rescues the dramatic translation shutoff characteristic of the VV⌬E3L virus. However, it does not prevent PKR phosphorylation. The results indicate that the MHV N protein is a type I IFN antagonist that likely plays a role in circumventing the innate immune response.

Coronavirus envelope (E) proteins are small (ϳ75-to 110-amino-acid) membrane proteins that have a short hydrophilic amino terminus, a relatively long hydrophobic membrane domain, and a long hydrophilic carboxyterminal domain. The protein is a minor virion structural component that plays an important, not fully understood role in virus production. It was recently demonstrated that the protein forms ion channels. We investigated the importance of the hydrophobic domain of the mouse hepatitis coronavirus (MHV) A59 E protein. Alanine scanning insertion mutagenesis was used to examine the effect of disruption of the domain on virus production in the context of the virus genome by using a MHV A59 infectious clone. Mutant viruses exhibited smaller plaque phenotypes, and virus production was significantly crippled. Analysis of recovered viruses suggested that the structure of the presumed ␣-helical structure and positioning of polar hydrophilic residues within the predicted transmembrane domain are important for virus production. Generation of viruses with restored wild-type helical pitch resulted in increased virus production, but some exhibited decreased virus release. Viruses with the restored helical pitch were more sensitive to treatment with the ion channel inhibitor hexamethylene amiloride than were the more crippled parental viruses with the single alanine insertions, suggesting that disruption of the transmembrane domain affects the functional activity of the protein. Overall the results indicate that the transmembrane domain plays a crucial role during biogenesis of virions.

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Mutations in the Fused in sarcoma/Translated in liposarcoma gene (FUS/TLS, FUS) have been identified among patients with amyotrophic lateral sclerosis (ALS). FUS protein aggregation is a major pathological hallmark of FUS proteinopathy, a group of neurodegenerative diseases characterized by FUS-immunoreactive inclusion bodies. We prepared transgenic Drosophila expressing either the wild type (Wt) or ALS-mutant human FUS protein (hFUS) using the UAS-Gal4 system. When expressing Wt, R524S or P525L mutant FUS in photoreceptors, mushroom bodies (MBs) or motor neurons (MNs), transgenic flies show age-dependent progressive neural damages, including axonal loss in MB neurons, morphological changes and functional impairment in MNs. The transgenic flies expressing the hFUS gene recapitulate key features of FUS proteinopathy, representing the first stable animal model for this group of devastating diseases.

Understanding the interaction of (-)-epigallocatechin-3-gallate (EGCG) and lipase is important for understanding EGCG's inhibition of lipase. In this paper, the interaction of EGCG and porcine lipase was characterized by fluorescence spectroscopy, circular dichroism (CD), isothermal titration calorimetry, and molecular docking. EGCG might act as a noncompetitive pancreatic lipase inhibiter. EGCG bound to lipase with affinity of K(a) = 2.70 × 10⁴ L mol⁻¹. Thermodynamic features suggested that the interaction process was spontaneous, with hydrogen bonds and electrostatic force perhaps primarily responsible for the interaction, with 1:1 interaction of lipase and EGCG. CD studies indicated conformation change of lipase on binding to EGCG. Furthermore, docking results supported experimental findings and revealed hydrogen-bonding interaction with Val21, Glu188, and Glu220. This noncovalent bonding between EGCG and lipase alters the molecular conformation of lipase, which decreases the enzyme catalytic activity. This study will help further understand the antiobesity mechanisms of green tea.

We show that the resolution of social dilemmas in random graphs and scale-free networks is facilitated by imitating not the strategy of better-performing players but, rather, their emotions. We assume sympathy and envy to be the two emotions that determine the strategy of each player in any given interaction, and we define them as the probabilities of cooperating with players having a lower and a higher payoff, respectively. Starting with a population where all possible combinations of the two emotions are available, the evolutionary process leads to a spontaneous fixation to a single emotional profile that is eventually adopted by all players. However, this emotional profile depends not only on the payoffs but also on the heterogeneity of the interaction network. Homogeneous networks, such as lattices and regular random graphs, lead to fixations that are characterized by high sympathy and high envy, while heterogeneous networks lead to low or modest sympathy but also low envy. Our results thus suggest that public emotions and the propensity to cooperate at large depend, and are in fact determined by, the properties of the interaction network.

Using a three-component evaluation method we demonstrated how one could elucidate the relative contributions of components under an integrated framework. To improve classification performance, this study encourages researchers to improve NLP accuracy, use a machine-parameterized classifier, and apply feature selection methods.

Influenza is a yearly recurrent disease that has the potential to become a pandemic. An effective biosurveillance system is required for early detection of the disease. In our previous studies, we have shown that electronic Emergency Department (ED) free-text reports can be of value to improve influenza detection in real time. This paper studies seven machine learning (ML) classifiers for influenza detection, compares their diagnostic capabilities against an expert-built influenza Bayesian classifier, and evaluates different ways of handling missing clinical information from the free-text reports. We randomly identified 31,268 ED reports from 4 hospitals between 2008 and 2011 to form two different datasets: training (468 cases, 29,004 controls), and test (176 cases and 1,620 controls). We employed Topaz, a natural language processing (NLP) tool, to extract influenza-related findings and to encode them into one of three values: Acute, Non-acute, and Missing. Results show that all ML classifiers had areas under ROCs (AUC) ranging from 0.88 to 0.93, and performed significantly better than the expert-built Bayesian model. Missing clinical information marked as a value of missing (not missing at random) had a consistently improved performance among 3 (out of 4) ML classifiers when it was compared with the configuration of not assigning a value of missing (missing completely at random). The case/control ratios did not affect the classification performance given the large number of training cases. Our study demonstrates ED reports in conjunction with the use of ML and NLP with the handling of missing value information have a great potential for the detection of infectious diseases.