Dataset Information

Dynamic network approach for the modelling of genomic sub-complexes in multi-segmented viruses.

ABSTRACT: Viruses with segmented genomes, including pathogens such as influenza virus, Rotavirus and Bluetongue virus (BTV), face the collective challenge of packaging their genetic material in terms of the correct number and types of segments. Here we develop a novel network approach to predict RNA-RNA interactions between different genomic segments. Experimental data on RNA complex formation in the multi-segmented BTV genome are used to establish proof-of-concept of this technique. In particular, we show that trans interactions between segments occur at multiple specific sites, termed segment assortment signals (SASs) that are dispersed across each segment. In order to validate the putative trans acting networks, we used various biochemical and molecular techniques which confirmed predictions of the RNA network approach. A combination of mutagenesis and reverse genetics systems revealed that the RNA-RNA interacting sites identified are indeed responsible for segment assortment and complex formation, which are essential criteria for genome packaging. This paves the way for their exploitation as novel types of drug target, either to inhibit assembly, or for designing defective interfering particles containing an incomplete set of genomic segments.

SUBMITTER: AlShaikhahmed K

PROVIDER: S-EPMC6294558 | biostudies-other | 2018 Dec

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Dynamic network approach for the modelling of genomic sub-complexes in multi-segmented viruses.

AlShaikhahmed Kinda K Leonov German G Sung Po-Yu PY Bingham Richard J RJ Twarock Reidun R Roy Polly P

Nucleic acids research 20181201 22

Viruses with segmented genomes, including pathogens such as influenza virus, Rotavirus and Bluetongue virus (BTV), face the collective challenge of packaging their genetic material in terms of the correct number and types of segments. Here we develop a novel network approach to predict RNA-RNA interactions between different genomic segments. Experimental data on RNA complex formation in the multi-segmented BTV genome are used to establish proof-of-concept of this technique. In particular, we sho ...[more]

PMID: 30299495

Similar Datasets

Project description:Jingmenviruses are a group of viruses identified recently, in 2014, and currently classified by the International Committee on Taxonomy of Viruses as unclassified Flaviviridae. These viruses closely related to flaviviruses are unique due to the segmented nature of their genome. The prototype jingmenvirus, Jingmen tick virus (JMTV), was discovered in Rhipicephalus microplus ticks collected from China in 2010. Jingmenviruses genomes are composed of four to five segments, encoding for up to seven structural proteins and two non-structural proteins, both of which display strong similarities with flaviviral non-structural proteins (NS2B/NS3 and NS5). Jingmenviruses are currently separated into two phylogenetic clades. One clade includes tick- and vertebrate-associated jingmenviruses, which have been detected in ticks and mosquitoes, as well as in humans, cattle, monkeys, bats, rodents, sheep, and tortoises. In addition to these molecular and serological detections, over a hundred human patients tested positive for jingmenviruses after developing febrile illness and flu-like symptoms in China and Serbia. The second phylogenetic clade includes insect-associated jingmenvirus sequences, which have been detected in a wide range of insect species, as well as in crustaceans, plants, and fungi. In addition to being found in various types of hosts, jingmenviruses are endemic, as they have been detected in a wide range of environments, all over the world. Taken together, all of these elements show that jingmenviruses correspond exactly to the definition of emerging viruses at risk of causing a pandemic, since they are already endemic, have a close association with arthropods, are found in animals in close contact with humans, and have caused sporadic cases of febrile illness in multiple patients. Despite these arguments, the vast majority of published data is from metagenomics studies and many aspects of jingmenvirus replication remain to be elucidated, such as their tropism, cycle of transmission, structure, and mechanisms of replication and restriction or epidemiology. It is therefore crucial to prioritize jingmenvirus research in the years to come, to be prepared for their emergence as human or veterinary pathogens.

Project description:Genetic interactions occur when a combination of mutations results in a surprising phenotype. These interactions capture functional redundancy, and thus are important for predicting function, dissecting protein complexes into functional pathways, and exploring the mechanistic underpinnings of common human diseases. Synthetic sickness and lethality are the most studied types of genetic interactions in yeast. However, even in yeast, only a small proportion of gene pairs have been tested for genetic interactions due to the large number of possible combinations of gene pairs. To expand the set of known synthetic lethal (SL) interactions, we have devised an integrative, multi-network approach for predicting these interactions that significantly improves upon the existing approaches. First, we defined a large number of features for characterizing the relationships between pairs of genes from various data sources. In particular, these features are independent of the known SL interactions, in contrast to some previous approaches. Using these features, we developed a non-parametric multi-classifier system for predicting SL interactions that enabled the simultaneous use of multiple classification procedures. Several comprehensive experiments demonstrated that the SL-independent features in conjunction with the advanced classification scheme led to an improved performance when compared to the current state of the art method. Using this approach, we derived the first yeast transcription factor genetic interaction network, part of which was well supported by literature. We also used this approach to predict SL interactions between all non-essential gene pairs in yeast (http://sage.fhcrc.org/downloads/downloads/predicted_yeast_genetic_interactions.zip). This integrative approach is expected to be more effective and robust in uncovering new genetic interactions from the tens of millions of unknown gene pairs in yeast and from the hundreds of millions of gene pairs in higher organisms like mouse and human, in which very few genetic interactions have been identified to date.

Dataset Information

Dynamic network approach for the modelling of genomic sub-complexes in multi-segmented viruses.

Publications

Dynamic network approach for the modelling of genomic sub-complexes in multi-segmented viruses.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets