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Background Vaccines based on live attenuated viruses are the most effective strategy for controlling infections, since they elicit long-lasting natural and effective immune response, but entail challenges for safety and virulence. Hepatitis C Virus (HCV) causes liver diseases and liver cancer, with millions infected each year and hundreds of thousands of annual fatalities; but no vaccine is currently available for the virus. Here, we present a novel computational approach for the accurate prediction of virus attenuation. Results We rationally design viral variants by inserting a large number of synonymous mutations in the NS5A/B coding region to disrupt the viral RNA’s secondary structure and regulatory sequences important for the viral life cycle. By measuring RNA levels and virus spread in an HCV infection model, we show that some variants have lower viral fitness relative to the wild-type virus, with gradient of attenuation in concordance with the prediction model. Deep sequencing of replicating viruses demonstrates relative genomic stability of the attenuated variant. Differential expression analysis and evaluation of cancer-related phenotypes reveal that some variants have a lower pathogenic influence on the host cells, compared to the wildtype virus. Conclusions These rationally designed variants reveal novel information on key functional elements in HCV RNA important for virus fitness, that may be further considered as a promising direction for a viable HCV vaccine. Importantly, the computational approach described here is based on the most fundamental viral regulatory motifs and therefore may be applied for almost all viruses as a new strategy for vaccine development.

Introns are key regulators of eukaryotic gene expression and present a potentially powerful tool for the design of synthetic eukaryotic gene expression systems. However, intronic control over gene expression is governed by a multitude of complex, incompletely understood, regulatory mechanisms. Despite this lack of detailed mechanistic understanding, here we show how a relatively simple model enables accurate and predictable tuning of synthetic gene expression system in yeast using several predictive intron features such as transcript folding and sequence motifs. Using only natural Saccharomyces cerevisiae introns as regulators, we demonstrate fine and accurate control over gene expression spanning a 100 fold expression range. These results broaden the engineering toolbox of synthetic gene expression systems and provide a framework in which precise and robust tuning of gene expression is accomplished.

Background Synthetic virology is an important multidisciplinary scientific field, with emerging applications in biotechnology and medicine, aiming at developing methods to generate and engineer synthetic viruses. In particular, many of the RNA viruses, including among others the Dengue and Zika, are widespread pathogens of significant importance to human health. The ability to design and synthesize such viruses may contribute to exploring novel approaches for developing vaccines and virus based therapies. Results Here we develop a full multidisciplinary pipeline for generation and analysis of synthetic RNA viruses and specifically apply it to Dengue virus serotype 2 (DENV-2). The major steps of the pipeline include comparative genomics of endogenous and synthetic viral strains. Specifically, we show that although the synthetic DENV-2 viruses were found to have lower nucleotide variability, their phenotype, as reflected in the study of the AG129 mouse model morbidity, RNA levels, and neutralization antibodies, is similar or even more pathogenic in comparison to the wildtype master strain. Additionally, the highly variable positions, identified in the analyzed DENV-2 population, were found to overlap with less conserved homologous positions in Zika virus and other Dengue serotypes. These results may suggest that synthetic DENV-2 could enhance virulence if the correct sequence is selected. Conclusions The approach reported in this study can be used to generate and analyze synthetic RNA viruses both on genotypic and on phenotypic level. It could be applied for understanding the functionality and the fitness effects of any set of mutations in viral RNA and for editing RNA viruses for various target applications.

Background The regulation of all gene expression steps (e.g., Transcription, RNA processing, Translation, and mRNA Degradation) is known to be primarily encoded in different parts of genes and in genomic regions in proximity to genes (e.g., promoters, untranslated regions, coding regions, introns, etc.). However, the entire gene expression codes and the genomic regions where they are encoded are still unknown. Results Here, we employ an unsupervised approach to estimate the concentration of gene expression codes in different non-coding parts of genes and transcripts, such as introns and untranslated regions, focusing on three model organisms ( Escherichia coli , Saccharomyces cerevisiae , and Schizosaccharomyces pombe ). Our analyses support the conjecture that regions adjacent to the beginning and end of ORFs and the beginning and end of introns tend to include higher concentration of gene expression information relatively to regions further away. In addition, we report the exact regions with elevated concentration of gene expression codes. Furthermore, we demonstrate that the concentration of these codes in different genetic regions is correlated with the expression levels of the corresponding genes, and with splicing efficiency measurements and meiotic stage gene expression measurements in S. cerevisiae . Conclusion We suggest that these discoveries improve our understanding of gene expression regulation and evolution; they can also be used for developing improved models of genome/gene evolution and for engineering gene expression in various biotechnological and synthetic biology applications.

Introns are key regulators of eukaryotic gene expression and present a potentially powerful tool for the design of synthetic eukaryotic gene expression systems. However, intronic control over gene expression is governed by a multitude of complex, incompletely understood, regulatory mechanisms. Despite this lack of detailed mechanistic understanding, here we show how a relatively simple model enables accurate and predictable tuning of synthetic gene expression system in yeast using several predictive intron features such as transcript folding and sequence motifs. Using only natural Saccharomyces cerevisiae introns as regulators, we demonstrate fine and accurate control over gene expression spanning a 100 fold expression range. These results broaden the engineering toolbox of synthetic gene expression systems and provide a framework in which precise and robust tuning of gene expression is accomplished.

Objective The aim of this study is to examine the experience of grandparenting a preterm grandchild up to first year after the birth, in the Israeli context. Background The birth of a preterm infant has an impact on the entire family, including grandparents. Although preterm birth is very common, thus far, there are only a few published studies about grandparents of premature infants. Method The study was designed and conducted according to the Interpretative Phenomenological Analysis method. In‐depth semi‐structured interviews were conducted with 13 grandparents ages 54–72 years. Results Results indicated that the issue of boundaries was the core category and yielded three subthemes: (1) internal boundaries—our experience or internalized prematurity, (2) interpersonal boundaries—my grandchild and me, and (3) external boundaries between the medical facility and the grandparents—the omnipotent NICU (neonatal intensive care unit). Conclusions The current research demonstrates the importance of focusing on grandparents of premature infants as individuals in need of support, as well as an important resource for the immediate family of the baby. Implications This should be taken into consideration in visiting policies and delivery of psychosocial services in NICUs.