The myriad roles of RNA structure in the flavivirus life cycle.

As positive-sense RNA viruses, the genomes of flaviviruses serve as the template for all stages of the viral life cycle, including translation, replication, and infectious particle production. Yet, they encode just 10 proteins, suggesting that the structure and dynamics of the viral RNA itself helps shepherd the viral genome through these stages. Herein, we highlight advances in our understanding of flavivirus RNA structural elements through the lens of their impact on the viral life cycle.

Zika virus NS3 and NS5 proteins determine strain-dependent differences in dsRNA accumulation in a host cell type-dependent manner.

For positive-sense RNA viruses, initiation of viral RNA replication represents a major target of antiviral responses to infection. Despite this, the interplay between viral replication and the innate antiviral response at early steps in the Zika virus (ZIKV) life cycle is not well understood. We have previously identified ZIKV isolates with differing levels of dsRNA accumulation, ZIKV (high dsRNA per infected cell) and ZIKV (low dsRNA per infected cell), and we hypothesized that we could use reverse genetics to investigate how host and viral factors contribute to the establishment of viral RNA replication.

A highly sensitive strand-specific multiplex RT-qPCR assay for quantitation of Zika virus replication.

Reverse-transcription quantitative polymerase chain reaction (RT-qPCR) is widely used to quantify viral RNA genomes for diagnostics and research, yet conventional RT-qPCR protocols are unable to accurately distinguish between the different viral RNA species that exist during infection. Here we show that false-priming and self-priming occur during reverse transcription with several published Zika virus (ZIKV) primer sets. We developed a RT-qPCR assay using tagged primers and thermostable reverse transcriptase, which greatly reduced the occurrence of nonspecific cDNA products.

Molecular Determinants of Flavivirus Virion Assembly.

Virion assembly is an important step in the life cycle of all viruses. For viruses of the Flavivirus genus, a group of enveloped positive-sense RNA viruses, the assembly step represents one of the least understood processes in the viral life cycle. While assembly is primarily driven by the viral structural proteins, recent studies suggest that several nonstructural proteins also play key roles in coordinating the assembly and packaging of the viral genome.

Gata6 promotes hair follicle progenitor cell renewal by genome maintenance during proliferation.

Cell proliferation is essential to rapid tissue growth and repair, but can result in replication-associated genome damage. Here, we implicate the transcription factor Gata6 in adult mouse hair follicle regeneration where it controls the renewal of rapidly proliferating epithelial (matrix) progenitors and hence the extent of production of terminally differentiated lineages. We find that Gata6 protects against DNA damage associated with proliferation, thus preventing cell cycle arrest and apoptosis.

Imprinted DNA methylation reconstituted at a non-imprinted locus.

Background: In mammals, tight regulation of cytosine methylation is required for embryonic development and cellular differentiation. The trans-acting DNA methyltransferases that catalyze this modification have been identified and characterized; however, these proteins lack sequence specificity, leaving the mechanism of targeting unknown. A cis-acting regulator within the Rasgrf1 imprinting control region (ICR) is necessary for establishment and maintenance of local imprinted methylation.

Efficient population assignment and outlier detection in human populations using biallelic markers chosen by principal component-based rankings.

Whole-genome studies of genetic variation are now performed routinely and have accelerated the identification of disease-associated allelic variants, positive selection, recombination, and structural variation. However, these studies are sensitive to the presence of outlier data from individuals of different ancestry than the rest of the sample. Currently, the most common method of excluding outlier individuals is to collect a population sample and exclude outliers after genome-wide data have been collected.