Diverse RNA viruses in a parasitic flowering plant (spruce dwarf mistletoe) revealed through RNA-seq data mining

Author(s):  
Venkidusamy Kavi Sidharthan ◽  
Krishna Kumar Chaturvedi ◽  
Virendra Kumar Baranwal
2020 ◽  
Author(s):  
Romain Daveu ◽  
Caroline Hervet ◽  
Louane Sigrist ◽  
Davide Sassera ◽  
Aaron Jex ◽  
...  

AbstractWe studied a family of iflaviruses, a group of RNA viruses frequently found in arthropods, focusing on viruses associated with ticks. Our aim was to bring insight on the evolutionary dynamics of this group of viruses, which may interact with the biology of ticks. We explored systematically de novo RNA-Seq assemblies available for species of ticks which allowed to identify nine new genomes of iflaviruses. The phylogeny of virus sequences was not congruent with that of the tick hosts, suggesting recurrent host changes across tick genera along evolution. We identified five different variants with a complete or near-complete genome in Ixodes ricinus. These sequences were closely related, which allowed a fine-scale estimation of patterns of substitutions: we detected a strong excess of synonymous mutations suggesting evolution under strong positive selection. ISIV, a sequence found in the ISE6 cell line of Ixodes scapularis, was unexpectedly nearidentical with I. ricinus variants, suggesting a contamination of this cell line by I. ricinus material. Overall, our work constitutes a step in the understanding of the interactions between this family of viruses and ticks.


2021 ◽  
Author(s):  
Rodrigo R. D. Goitia ◽  
Diego M. Riaño-Pachón ◽  
Alexandre Victor Fassio ◽  
Flavia V. Winck

AbstractPhycoMine is data warehouse system created to fostering the analysis of complex and integrated data from microalgae species in a single computational environment. The PhycoMine was developed on top of the InterMine software system, and it has implemented an extended database model, containing a series of tools that help the users in the analysis and mining of individual data and group data. The platform has widgets created to facilitate simultaneous data mining of different datasets. Among the widgets implemented in PhycoMine, there are options for mining chromosome distribution, gene expression variation via transcriptomics, proteomics sets, Gene Onthology enrichment, KEGG enrichment, publication enrichment, EggNOG, Transcription factors and transcriptional regulators enrichment and phenotypical data. These widgets were created to facilitate data visualization of the gene expression levels in different experimental setups, for which RNA-seq experimental data is available in data repositories. For comparative purposes, we have reanalyzed 200 RNA-seq datasets from Chlamydomonas reinhardtii, a model unicellular microalga, for optimizing the performance and accuracy of data comparisons. We have also implemented widgets for metabolic pathway analysis of selected genes and proteins and options for biological network analysis. The option for analysis of orthologue genes was also included. With this platform, the users can perform data mining for a list of genes or proteins of interest in an integrated way through accessing the data from different sources and visualizing them in graphics and by exporting the data into table formats. The PhycoMine platform is freely available and can be visited through the URL https://PhycoMine.iq.usp.br.


2020 ◽  
Author(s):  
Yuto Chiba ◽  
Takashi Yaguchi ◽  
Syun-ichi Urayama ◽  
Daisuke Hagiwara

AbstractBy identifying variations in viral RNA genomes, cutting-edge metagenome technology has potential to reshape current concepts about the evolution of RNA viruses. This technology, however, cannot process low-homology genomic regions properly, leaving the true diversity of RNA viruses unappreciated. To overcome this technological limitation we applied an advanced method, Fragmented and Primer-Ligated Double-stranded (ds) RNA Sequencing (FLDS), to screen RNA viruses from 155 fungal isolates, which allowed us to obtain complete viral genomes in a homology-independent manner. We created a high-quality catalog of 19 RNA viruses (12 viral species) that infect Aspergillus isolates. Among them, nine viruses were not detectable by the conventional methodology involving agarose gel electrophoresis of dsRNA, a hallmark of RNA virus infections. Segmented genome structures were determined in 42% of the viruses. Some RNA viruses had novel genome architectures; one contained a dual methyltransferase domain and another had a separated RNA-dependent RNA polymerase (RdRp) gene. A virus from a different fungal taxon (Pyricularia) had an RdRp sequence that was separated on different segments, suggesting that a divided RdRp is widely present among fungal viruses, despite the belief that all RNA viruses encode RdRp as a single gene. These findings illustrate the previously hidden diversity and evolution of RNA viruses, and prompt reconsideration of the structural plasticity of RdRp. By highlighting the limitations of conventional surveillance methods for RNA viruses, we showcase the potential of FLDS technology to broaden current knowledge about these viruses.Author SummaryThe development of RNA-seq technology has facilitated the discovery of RNA viruses in all types of biological samples. However, it is technically difficult to detect highly novel viruses using RNA-seq. We successfully reconstructed the genomes of multiple novel fungal RNA viruses by screening host fungi using a new technology, FLDS. Surprisingly, we identified two viral species whose RNA-dependent RNA polymerase (RdRp) proteins were separately encoded on different genome segments, overturning the commonly accepted view of the positional unity of RdRp proteins in viral genomes. This new perspective on divided RdRp proteins should hasten the discovery of viruses with unique RdRp structures that have been overlooked, and further advance current knowledge and understanding of the diversity and evolution of RNA viruses.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jimmy Vandel ◽  
Céline Gheeraert ◽  
Bart Staels ◽  
Jérôme Eeckhoute ◽  
Philippe Lefebvre ◽  
...  

AbstractTranscriptomic analyses are broadly used in biomedical research calling for tools allowing biologists to be directly involved in data mining and interpretation. We present here GIANT, a Galaxy-based tool for Interactive ANalysis of Transcriptomic data, which consists of biologist-friendly tools dedicated to analyses of transcriptomic data from microarray or RNA-seq analyses. GIANT is organized into modules allowing researchers to tailor their analyses by choosing the specific set of tool(s) to analyse any type of preprocessed transcriptomic data. It also includes a series of tools dedicated to the handling of raw Affymetrix microarray data. GIANT brings easy-to-use solutions to biologists for transcriptomic data mining and interpretation.


2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Rolena A. J. deBruyn ◽  
Mark Paetkau ◽  
Kelly A. Ross ◽  
David V. Godfrey ◽  
John S. Church ◽  
...  

Abstract Lodgepole pine dwarf mistletoe (DM), Arceuthobium americanum, is a parasitic flowering plant and forest pathogen in North America. Seed dispersal in DM occurs by explosive discharge. Notably, slight warming of ripe DM fruit in the laboratory can trigger explosions. Previously, we showed that alternative oxidase, a protein involved in endogenous heat production (thermogenesis) in plants, is present in DM fruit. These observations have led us to investigate if thermogenesis induces discharge. Here, infrared thermographs reveal that ripe DM fruits display an anomalous increase in surface temperature by an average of 2.1±0.8 °C over an average time of 103±29 s (n=9, 95% confidence interval) before dehiscence. Furthermore, both non-isothermal and isothermal modulated differential scanning calorimetry consistently show an exothermic event (~1 J g−1) in the non-reversible heat flow just prior to discharge. These results support thermogenesis-triggered seed discharge, never before observed in any plant.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pablo Acera Mateos ◽  
Renzo F. Balboa ◽  
Simon Easteal ◽  
Eduardo Eyras ◽  
Hardip R. Patel

AbstractViral co-infections occur in COVID-19 patients, potentially impacting disease progression and severity. However, there is currently no dedicated method to identify viral co-infections in patient RNA-seq data. We developed PACIFIC, a deep-learning algorithm that accurately detects SARS-CoV-2 and other common RNA respiratory viruses from RNA-seq data. Using in silico data, PACIFIC recovers the presence and relative concentrations of viruses with > 99% precision and recall. PACIFIC accurately detects SARS-CoV-2 and other viral infections in 63 independent in vitro cell culture and patient datasets. PACIFIC is an end-to-end tool that enables the systematic monitoring of viral infections in the current global pandemic.


2015 ◽  
Vol 7 (1) ◽  
pp. 66-68 ◽  
Author(s):  
Jun-ichi Satoh ◽  
Mika Takitani ◽  
Junko Miyoshi ◽  
Yoshihiro Kino

Author(s):  
Yanhui Hu ◽  
Sudhir Gopal Tattikota ◽  
Yifang Liu ◽  
Aram Comjean ◽  
Yue Gao ◽  
...  

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