Hepatitis C virus cell-cell transmission and resistance to direct-acting antiviral agents

Hepatitis C virus (HCV) is transmitted between hepatocytes via classical cell entry but also uses direct cell-cell transfer to infect neighboring hepatocytes. Viral cell-cell transmission has been shown to play an important role in viral persistence allowing evasion from neutralizing antibodies. In contrast, the role of HCV cell-cell transmission for antiviral resistance is unknown. Aiming to address this question we investigated the phenotype of HCV strains exhibiting resistance to direct-acting antivirals (DAAs) in state-of-the-art model systems for cell-cell transmission and spread. Using HCV genotype 2 as a model virus, we show that cell-cell transmission is the main route of viral spread of DAA-resistant HCV. Cell-cell transmission of DAA-resistant viruses results in viral persistence and thus hampers viral eradication. We also show that blocking cell-cell transmission using host-targeting entry inhibitors (HTEIs) was highly effective in inhibiting viral dissemination of resistant genotype 2 viruses. Combining HTEIs with DAAs prevented antiviral resistance and led to rapid elimination of the virus in cell culture model. In conclusion, our work provides evidence that cell-cell transmission plays an important role in dissemination and maintenance of resistant variants in cell culture models. Blocking virus cell-cell transmission prevents emergence of drug resistance in persistent viral infection including resistance to HCV DAAs

The Cytosolic Tail of the Golgi Apyrase Ynd1 Mediates E4orf4-Induced Toxicity in Saccharomyces cerevisiae

The adenovirus E4 open reading frame 4 (E4orf4) protein contributes to regulation of the progression of virus infection. When expressed individually, E4orf4 was shown to induce non-classical transformed cell-specific apoptosis in mammalian cells. At least some of the mechanisms underlying E4orf4-induced toxicity are conserved from yeast to mammals, including the requirement for an interaction of E4orf4 with protein phosphatase 2A (PP2A). A genetic screen in yeast revealed that the Golgi apyrase Ynd1 associates with E4orf4 and contributes to E4orf4-induced toxicity, independently of Ynd1 apyrase activity. Ynd1 and PP2A were shown to contribute additively to E4orf4-induced toxicity in yeast, and to interact genetically and physically. A mammalian orthologue of Ynd1 was shown to bind E4orf4 in mammalian cells, confirming the evolutionary conservation of this interaction. Here, we use mutation analysis to identify the cytosolic tail of Ynd1 as the protein domain required for mediation of the E4orf4 toxic signal and for the interaction with E4orf4. We also show that E4orf4 associates with cellular membranes in yeast and is localized at their cytoplasmic face. However, E4orf4 is membrane-associated even in the absence of Ynd1, suggesting that additional membrane proteins may mediate E4orf4 localization. Based on our results and on a previous report describing a collection of Ynd1 protein partners, we propose that the Ynd1 cytoplasmic tail acts as a scaffold, interacting with a multi-protein complex, whose targeting by E4orf4 leads to cell death

A Common Origin for Neutrino Anarchy and Charged Hierarchies

The generation of exponential flavor hierarchies from extra-dimensional wavefunction overlaps is re-examined. We find, surprisingly, that coexistence of anarchic fermion mass matrices with such hierarchies is intrinsic and natural to this setting. The salient features of charged fermion and neutrino masses and mixings can thereby be captured within a single framework. Both Dirac and Majorana neutrinos can be realized. The minimal phenomenological consequences are discussed, including the need for a fundamental scale far above the weak scale to adequately suppress flavor-changing neutral currents. Two broad scenarios for stabilizing this electroweak hierarchy are studied, warped compactification and supersymmetry. In warped compactifications and "Flavorful Supersymmetry," where non-trivial flavor structure appears in the new TeV physics, Dirac neutrinos are strongly favored over Majorana by lepton flavor violation tests. We argue that this is part of a more general result for flavor-sensitive TeV-scale physics. Our scenario strongly suggests that the supersymmetric flavor problem is not solved locally in the extra dimension, but rather at or below the compactification scale. In the supersymmetric Dirac case, we discuss how the appearance of light right-handed sneutrinos considerably alters the physics of dark matter.Comment: Comparison with the Froggatt-Nielsen mechanism omitted. Some clarifications added. This is the version accepted by PRL with a longer abstract

Rods to discs in the study of mesomorphism in discotic liquid crystals

The rational design of calamitic liquid crystals is an area of research that has been intensively explored due to their extensive applications in various devices. The successful methods for design have been, to some extent, mapped on discotic systems such that certain features of the structures of calamitic phases have been superimposed upon those of nematic discotic and columnar phases. In this article, we explore the correlation between nematogenic behaviour of hard rod-like particles and that of hard disc-like systems. We show that for calamitics, nematic behaviour is observed, whereas for discotics this is not the case. Furthermore, we show that nematic discotic materials are miscible, whereas unlike smectics, columnar phases are less likely to be miscible. Indeed, it appears that columnar discotic phases are greater similarities with soft-solids than true liquid crystals

Deep learning-based polygenic risk analysis for Alzheimer's disease prediction

BACKGROUND: The polygenic nature of Alzheimer's disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual's genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk. METHODS: We constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model. RESULTS: The deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms. CONCLUSION: Our results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms

Protein C anticoagulant system—anti-inflammatory effects

Activated protein C (APC) plays active roles in preventing progression of a number of disease processes. These include thrombosis due to its direct anticoagulant activity which is likely augmented by its cytoprotective activity, thereby limiting exposure of procoagulant cellular membrane surfaces on cells. Beyond that, the pathway signals the cells to prevent apoptosis, to dampen inflammation, to increase endothelial barrier function, and to selectively downregulate some genes implicated in disease progression. Most of these functions are manifested to APC binding to endothelial protein C receptor (EPCR) allowing PAR1 activation, but activation of other PARS is also implicated in some cases. In addition to EPCR orchestrating these changes, CD11b is also capable of supporting APC signaling. Selective control of these pathways offers potential in new therapeutic approaches to disease

Generation of a Cell Culture-Adapted Hepatitis C Virus with Longer Half Life at Physiological Temperature

BACKGROUND: We previously reported infectious HCV clones that contain the convenient reporters, green fluorescent protein (GFP) and Renilla luciferase (Rluc), in the NS5a-coding sequence. Although these viruses were useful in monitoring viral proliferation and screening of anti-HCV drugs, the infectivity and yield of the viruses were low. METHODOLOGY/PRINCIPAL FINDINGS: In order to obtain a highly efficient HCV cultivation system, we transfected Huh7.5.1 cells [1] with JFH 5a-GFP RNA and then cultivated cells for 20 days. We found a highly infectious HCV clone containing two cell culture-adapted mutations. Two cell culture-adapted mutations which were responsible for the increased viral infectivity were located in E2 and p7 protein coding regions. The viral titer of the variant was ∼100-fold higher than that of the parental virus. The mutation in the E2 protein increased the viability of virus at 37°C by acquiring prolonged interaction capability with a HCV receptor CD81. The wild-type and p7-mutated virus had a half-life of ∼2.5 to 3 hours at 37°C. In contrast, the half-life of viruses, which contained E2 mutation singly and combination with the p7 mutation, was 5 to 6 hours at 37°C. The mutation in the p7 protein, either singly or in combination with the E2 mutation, enhanced infectious virus production about 10-50-fold by facilitating an early step of virion production. CONCLUSION/SIGNIFICANCE: The mutation in the E2 protein generated by the culture system increases virion viability at 37°C. The adaptive mutation in the p7 protein facilitates an earlier stage of virus production, such as virus assembly and/or morphogenesis. These reporter-containing HCV viruses harboring adaptive mutations are useful in investigations of the viral life cycle and for developing anti-viral agents against HCV

Analytic philosophy for biomedical research: the imperative of applying yesterday's timeless messages to today's impasses

The mantra that "the best way to predict the future is to invent it" (attributed to the computer scientist Alan Kay) exemplifies some of the expectations from the technical and innovative sides of biomedical research at present. However, for technical advancements to make real impacts both on patient health and genuine scientific understanding, quite a number of lingering challenges facing the entire spectrum from protein biology all the way to randomized controlled trials should start to be overcome. The proposal in this chapter is that philosophy is essential in this process. By reviewing select examples from the history of science and philosophy, disciplines which were indistinguishable until the mid-nineteenth century, I argue that progress toward the many impasses in biomedicine can be achieved by emphasizing theoretical work (in the true sense of the word 'theory') as a vital foundation for experimental biology. Furthermore, a philosophical biology program that could provide a framework for theoretical investigations is outlined

c-REDUCE: Incorporating sequence conservation to detect motifs that correlate with expression

<p>Abstract</p> <p>Background</p> <p>Computational methods for characterizing novel transcription factor binding sites search for sequence patterns or "motifs" that appear repeatedly in genomic regions of interest. Correlation-based motif finding strategies are used to identify motifs that correlate with expression data and do not rely on promoter sequences from a pre-determined set of genes.</p> <p>Results</p> <p>In this work, we describe a method for predicting motifs that combines the correlation-based strategy with phylogenetic footprinting, where motifs are identified by evaluating orthologous sequence regions from multiple species. Our method, c-REDUCE, can account for variability at a motif position inferred from evolutionary information. c-REDUCE has been tested on ChIP-chip data for yeast transcription factors and on gene expression data in <it>Drosophila</it>.</p> <p>Conclusion</p> <p>Our results indicate that utilizing sequence conservation information in addition to correlation-based methods improves the identification of known motifs.</p