In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

[Q]: The endoplasmic reticulum (ER) is an architecturally diverse organelle that serves as a membrane source for the replication of multiple viruses. Flaviviruses, including yellow fever virus, West Nile virus, dengue virus, and Zika virus, induce unique single-membrane ER invaginations that house the viral replication machinery 1 . Whether this virus-induced ER remodeling is vulnerable to antiviral pathways is unknown. Here, we show that flavivirus replication at the ER is targeted by the interferon (IFN) response. Through genome-scale CRISPR screening, we uncovered an antiviral mechanism mediated by a functional gene pairing between IFI6, an interferon-stimulated gene (ISG) cloned over 30 years ago 2 , and HSPA5, which encodes the ER-resident HSP70 chaperone BiP. We reveal that IFI6 is an ERlocalized integral membrane effector that is stabilized through interactions with BiP. Mechanistically, IFI6 prophylactically protects uninfected cells by preventing formation of virus-induced ER membrane invaginations. Notably, IFI6 has little effect on other mammalian RNA viruses, including the related Flaviviridae member hepatitis C virus, which replicates in double-membrane vesicles that protrude outward from the ER. These findings support a model in which the IFN response is armed with a membrane-targeted effector that discriminately blocks the establishment of virus-specific ER microenvironments required for replication. Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:
[A]: A CRISPR screen identifies IFI6 as an ER-resident interferon effector that blocks flavivirus replication HHS Public Access Author manuscript

[Q]: According to recent statistics, 96 million apparent dengue infections were estimated worldwide in 2010. This figure is by far greater than the WHO prediction which indicates the rapid spread of this disease posing a growing threat to the economy and a major challenge to clinicians and health care services across the globe particularly in the affected areas. This article aims at bringing to light the current epidemiological and clinical status of the dengue fever. The relationship between genetic mutations, single nucleotide polymorphism (SNP) and the pathophysiology of disease progression will be put into perspective. It will also highlight the recent advances in dengue vaccine development. Thus far, a significant progress has been made in unraveling the risk factors and understanding the molecular pathogenesis associated with the disease. However, further insights in molecular features of the disease and the development of animal models will enormously help improving the therapeutic interventions and potentially contribute to finding new preventive measures for population at risk.
[A]: Tackling dengue fever: Current status and challenges

[Q]: Systems medicine promotes a range of approaches and strategies to study human health and disease at a systems level with the aim of improving the overall well-being of (healthy) individuals, and preventing, diagnosing, or curing disease. In this chapter we discuss how bioinformatics critically contributes to systems medicine. First, we explain the role of bioinformatics in the management and analysis of data. In particular we show the importance of publicly available biological and clinical repositories to support systems medicine studies. Second, we discuss how the integration and analysis of multiple types of omics data through integrative bioinformatics may facilitate the determination of more predictive and robust disease signatures, lead to a better understanding of (patho)physiological molecular mechanisms, and facilitate personalized medicine. Third, we focus on network analysis and discuss how gene networks can be constructed from omics data and how these networks can be decomposed into smaller modules. We discuss how the resulting modules can be used to generate experimentally testable hypotheses, provide insight into disease mechanisms, and lead to predictive models. Throughout, we provide several examples demonstrating how bioinformatics contributes to systems medicine and discuss future challenges in bioinformatics that need to be addressed to enable the advancement of systems medicine.
[A]:
Chapter 2 Taking Bioinformatics to Systems Medicine