Dataset Information

The Clinical Application of MicroRNAs in Infectious Disease.

ABSTRACT: MicroRNAs (miRNAs) are short single-stranded non-coding RNA sequences that posttranscriptionally regulate up to 60% of protein encoding genes. Evidence is emerging that miRNAs are key mediators of the host response to infection, predominantly by regulating proteins involved in innate and adaptive immune pathways. miRNAs can govern the cellular tropism of some viruses, are implicated in the resistance of some individuals to infections like HIV, and are associated with impaired vaccine response in older people. Not surprisingly, pathogens have evolved ways to undermine the effects of miRNAs on immunity. Recognition of this has led to new experimental treatments, RG-101 and Miravirsen-hepatitis C treatments which target host miRNA. miRNAs are being investigated as novel infection biomarkers, and they are being used to design attenuated vaccines, e.g., against Dengue virus. This comprehensive review synthesizes current knowledge of miRNA in host response to infection with emphasis on potential clinical applications, along with an evaluation of the challenges still to be overcome.

SUBMITTER: Drury RE

PROVIDER: S-EPMC5622146 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The Clinical Application of MicroRNAs in Infectious Disease.

Drury Ruth E RE O'Connor Daniel D Pollard Andrew J AJ

Frontiers in immunology 20170925

MicroRNAs (miRNAs) are short single-stranded non-coding RNA sequences that posttranscriptionally regulate up to 60% of protein encoding genes. Evidence is emerging that miRNAs are key mediators of the host response to infection, predominantly by regulating proteins involved in innate and adaptive immune pathways. miRNAs can govern the cellular tropism of some viruses, are implicated in the resistance of some individuals to infections like HIV, and are associated with impaired vaccine response in ...[more]

PMID: 28993774

Similar Datasets

Project description:Within-host models are useful tools for understanding the processes regulating viral load dynamics. While existing models have considered a wide range of within-host processes, at their core these models have shown remarkable structural similarity. Specifically, the structure of these models generally consider target cells to be either uninfected or infected, with the possibility of accommodating further resolution (e.g. cells that are in an eclipse phase). Recent findings, however, indicate that cellular coinfection is the norm rather than the exception for many viral infectious diseases, and that cells with high multiplicity of infection are present over at least some duration of an infection. The reality of these cellular coinfection dynamics is not accommodated in current within-host models although it may be critical for understanding within-host dynamics. This is particularly the case if multiplicity of infection impacts infected cell phenotypes such as their death rate and their viral production rates. Here, we present a new class of within-host disease models that allow for cellular coinfection in a scalable manner by retaining the low-dimensionality that is a desirable feature of many current within-host models. The models we propose adopt the general structure of epidemiological 'macroparasite' models that allow hosts to be variably infected by parasites such as nematodes and host phenotypes to flexibly depend on parasite burden. Specifically, our within-host models consider target cells as 'hosts' and viral particles as 'macroparasites', and allow viral output and infected cell lifespans, among other phenotypes, to depend on a cell's multiplicity of infection. We show with an application to influenza that these models can be statistically fit to viral load and other within-host data, and demonstrate using model selection approaches that they have the ability to outperform traditional within-host viral dynamic models. Important in vivo quantities such as the mean multiplicity of cellular infection and time-evolving reassortant frequencies can also be quantified in a straightforward manner once these macroparasite models have been parameterized. The within-host model structure we develop here provides a mathematical way forward to address questions related to the roles of cellular coinfection, collective viral interactions, and viral complementation in within-host viral dynamics and evolution.

Dataset Information

The Clinical Application of MicroRNAs in Infectious Disease.

Publications

The Clinical Application of MicroRNAs in Infectious Disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets