Metaviromics reveals unknown viral diversity in the biting midge Culicoides impunctatus

Biting midges (Culicoides species) are vectors of arboviruses and were responsible for the emergence and spread of Schmallenberg virus (SBV) in Europe in 2011 and are likely to be involved in the emergence of other arboviruses in Europe. Improved surveillance and better understanding of risks require a better understanding of the circulating viral diversity in these biting insects. In this study, we expand the sequence space of RNA viruses by identifying a number of novel RNA viruses from Culicoides impunctatus (biting midge) using a meta-transcriptomic approach. A novel metaviromic pipeline called MetaViC was developed specifically to identify novel virus sequence signatures from high throughput sequencing (HTS) datasets in the absence of a known host genome. MetaViC is a protein centric pipeline that looks for specific protein signatures in the reads and contigs generated as part of the pipeline. Several novel viruses, including an alphanodavirus with both segments, a novel relative of the Hubei sobemo-like virus 49, two rhabdo-like viruses and a chuvirus, were identified in the Scottish midge samples. The newly identified viruses were found to be phylogenetically distinct to those previous known. These findings expand our current knowledge of viral diversity in arthropods and especially in these understudied disease vectors

Integration and analysis of virome NGS data

Type 1 Diabetes is a growing disease impacting young children. The disease is caused by the pancreases producing insufficient amounts of insulin required for the body to convert sugar and starches into required energy. There is no known cause to Type 1 Diabetes though there are cited genetic and environment associations. An environmental implication is viruses disrupting host immune and regulatory systems using molecular mimicry inducing islet autoimmunity. One observed diabetes correlation is the presences of Picornavirus family, including enterovirus and poliovirus. Taking ad-vantage of advances in the sensitivity and accuracy of Next Generation Sequencing, Diabetes Prediction and Prevention researchers have started a study to investigate the viral genome diversity and whether viral genomes can accelerate islet autoimmunity, when the host fails to produce insulin, triggering diabetes. This application of Next Generation sequencing provides an unbiased study and essentially applying metagenomics techniques to disease research. Sequence analysis requires processing and storing of vast amount of information. The other challenges are data integration, interpretation and lack of reference viral genomes. This thesis describes an open sourced Web 2.0 project to integrate and manage analysis results upon application of Velvet, for de novo assembly and BLAST for viral strain identification. Because of the lack of reference, majority of next generation reads are unmapped. Ac-counting for these unmapped reads and also investigating molecular mimicry, additional computational analysis includes translating raw sequence reads into protein fragments. Frames of these fragments were matched with islet autoimmunity protein markers. The preliminary matched results appear valuable with case affected samples in clear majority. The assembled results can be dynamically plotted on the web as aligned protein tracks for visual pattern and density inspection

Systems Biology of the human microbiome

© The Author(s), 2017. This is the author's version of the work and is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Current Opinion in Biotechnology 51 (2018): 146-153, doi:10.1016/j.copbio.2018.01.018.Recent research has shown that the microbiome—a collection of microorganisms, including bacteria, fungi, and viruses, living on and in a host—are of extraordinary importance in human health, even from conception and development in the uterus. Therefore, to further our ability to diagnose disease, to predict treatment outcomes, and to identify novel therapeutics, it is essential to include microbiome and microbial metabolic biomarkers in Systems Biology investigations. In clinical studies or, more precisely, Systems Medicine approaches, we can use the diversity and individual characteristics of the personal microbiome to enhance our resolution for patient stratification. In this review, we explore several Systems Medicine approaches, including Microbiome Wide Association Studies to understand the role of the human microbiome in health and disease, with a focus on ‘preventive medicine’ or P4 (i.e., personalized, predictive, preventive, participatory) medicine.BPB is funded by the Arnold and Mabel Beckman Foundation (Arnold O. Beckman Postdoctoral Fellow)2019-02-1