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.

Input: Consider Input: Early growth response gene-1 (EGR1) is a multifunctional transcription factor that is implicated in viral infection. In this study, we observed that foot-and-mouth disease virus (FMDV) infection significantly triggered EGR1 expression. Overexpression of EGR1 suppressed FMDV replication in porcine cells, and knockdown of EGR1 considerably promoted FMDV replication. A previously reported FMDV mutant virus (with two amino acids mutations in SAP domain) that displays a strong type I interferon (IFN) induction activity was used in this study. We found that SAP mutant FMDV infection induced a higher expression of EGR1 than wildtype FMDV infection, and also triggered higher IFNβ and IFN-stimulated genes (ISGs) expression than wildtype FMDV infection. This implied a link between EGR1 and type I IFN signaling. Further study showed that overexpression of EGR1 resulted in Sendai virus (SeV)-induced IFN-stimulated response element (ISRE) and NF-κB promoter activation. In addition, the SeV-induced ISGs expression was impaired in EGR1 knockdown cells. EGR1 upregulation promoted type I IFN signaling activation and suppressed FMDV and Seneca Valley virus replication. Suppression of the transcriptional activity of EGR1 did not affect its antiviral effect against FMDV. This study reveals a new mechanism evolved by EGR1 to enhance type I IFN signaling and suppress FMDV replication.

Output: Citation: Early Growth Response Gene-1 Suppresses Foot-and-Mouth Disease Virus Replication by Enhancing Type I Interferon Pathway Signal Transduction


Input: Consider Input: Motivation: Deep sequencing of clinical samples is now an established tool for the detection of infectious pathogens, with direct medical applications. The large amount of data generated produces an opportunity to detect species even at very low levels, provided that computational tools can effectively profile the relevant metagenomic communities. Data interpretation is complicated by the fact that short sequencing reads can match multiple organisms and by the lack of completeness of existing databases, in particular for viral pathogens. Here we present metaMix, a Bayesian mixture model framework for resolving complex metagenomic mixtures. We show that the use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species most likely to contribute to the mixture. Results: We demonstrate the greater accuracy of metaMix compared with relevant methods, particularly for profiling complex communities consisting of several related species. We designed metaMix specifically for the analysis of deep transcriptome sequencing datasets, with a focus on viral pathogen detection; however, the principles are generally applicable to all types of metagenomic mixtures. Availability and implementation: metaMix is implemented as a user friendly R package, freely available on CRAN: http://cran.r-project.org/web/packages/metaMix Contact: sofia.morfopoulou.10@ucl.ac.uk Supplementary information: Supplementary data are available at Bionformatics online.

Output: Bayesian mixture analysis for metagenomic community profiling


Input: Consider Input: The monoclonal antibody (mAb) revolution that currently provides many new options for the treatment of neoplastic and inflammatory diseases has largely bypassed the field of infectious diseases. Only one mAb is licensed for use against an infectious disease, although there are many in various stages of development. This situation is peculiar given that serum therapy was one of the first effective treatments for microbial diseases and that specific antibodies have numerous antimicrobial properties. The underdevelopment and underutilization of mAb therapies for microbial diseases has various complex explanations that include the current availability of antimicrobial drugs, small markets, high costs and microbial antigenic variation. However, there are signs that the climate for mAb therapeutics in infectious diseases is changing given increasing antibiotic drug resistance, the emergence of new pathogenic microbes for which no therapy is available, and development of mAb cocktail formulations. Currently, the major hurdle for the widespread introduction of mAb therapies for microbial diseases is economic, given the high costs of immunoglobulin preparations and relatively small markets. Despite these obstacles there are numerous opportunities for mAb development against microbial diseases and the development of radioimmunotherapy provides new options for enhancing the magic bullet. Hence, there is cautious optimism that the years ahead will see more mAbs in clinical use against microbial diseases.


Output: Monoclonal antibody-based therapies for microbial diseases