scholarly journals Virome Analysis of Signal Crayfish (Pacifastacus leniusculus) along Its Invasion Range Reveals Diverse and Divergent RNA Viruses

Viruses ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2259
Author(s):  
Katarina Bačnik ◽  
Denis Kutnjak ◽  
Silvija Černi ◽  
Ana Bielen ◽  
Sandra Hudina
Keyword(s):  
High Throughput Sequencing ◽  
Rna Viruses ◽  
Sequence Data ◽  
Keystone Species ◽  
Freshwater Ecosystems ◽  
Pacifastacus Leniusculus ◽  
Signal Crayfish ◽  
Random Dispersal ◽  
Invasion Fronts ◽  
Viral Sequences

Crayfish are a keystone species of freshwater ecosystems and a successful invasive species. However, their pathogens, including viruses, remain understudied. The aim of this study was to analyze the virome of the invasive signal crayfish (Pacifastacus leniusculus) and to elucidate the potential differences in viral composition and abundance along its invasion range in the Korana River, Croatia. By the high-throughput sequencing of ribosomal RNA, depleted total RNA isolated from the crayfish hepatopancreas, and subsequent sequence data analysis, we identified novel and divergent RNA viruses, including signal crayfish-associated reo-like, hepe-like, toti-like, and picorna-like viruses, phylogenetically related to viruses previously associated with crustacean hosts. The patterns of reads abundance and calculated nucleotide diversities of the detected viral sequences varied along the invasion range. This could indicate the possible influence of different factors and processes on signal crayfish virome composition: e.g., the differences in signal crayfish population density, the non-random dispersal of host individuals from the core to the invasion fronts, and the transfer of viruses from the native co-occurring and phylogenetically related crayfish species. The study reveals a high, previously undiscovered diversity of divergent RNA viruses associated with signal crayfish, and sets foundations for understanding the potential risk of virus transmissions as a result of this invader’s dispersal.

scholarly journals Viromes of Ten Alfalfa Plants in Australia Reveal Diverse Known Viruses and a Novel RNA Virus

Pathogens ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 214
Author(s):  
Samira Samarfard ◽  
Alistair R. McTaggart ◽  
Murray Sharman ◽  
Nicolás E. Bejerman ◽  
Ralf G. Dietzgen
Keyword(s):  
Complete Genome ◽  
High Throughput Sequencing ◽  
Rna Viruses ◽  
Sequence Data ◽  
Rna Virus ◽  
Rolling Circle Amplification ◽  
French Bean ◽  
Cdna Libraries ◽  
Restriction Enzyme Digestion ◽  
Rolling Circle

Alfalfa plants in the field can display a range of virus-like symptoms, especially when grown over many years for seed production. Most known alfalfa viruses have RNA genomes, some of which can be detected using diagnostic assays, but many viruses of alfalfa are not well characterized. This study aims to identify the RNA and DNA virus complexes associated with alfalfa plants in Australia. To maximize the detection of RNA viruses, we purified double-stranded RNA (dsRNA) for high throughput sequencing and characterized the viromes of ten alfalfa samples that showed diverse virus-like symptoms. Using Illumina sequencing of tagged cDNA libraries from immune-captured dsRNA, we identified sequences of the single-stranded RNA viruses, alfalfa mosaic virus (AMV), bean leafroll virus, a new emaravirus tentatively named alfalfa ringspot-associated virus, and persistent dsRNA viruses belonging to the families Amalgaviridae and Partitiviridae. Furthermore, rolling circle amplification and restriction enzyme digestion revealed the complete genome of chickpea chlorosis Australia virus, a mastrevirus (family Geminiviridae) previously reported only from chickpea and French bean that was 97% identical to the chickpea isolate. The sequence data also enabled the assembly of the first complete genome (RNAs 1–3) of an Australian AMV isolate from alfalfa.

scholarly journals Viral Sequences Detection by High-Throughput Sequencing in Cerebrospinal Fluid of Individuals with and without Central Nervous System Disease

Genes ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 625 ◽  
Author(s):  
Schibler ◽  
Brito ◽  
Zanella ◽  
Zdobnov ◽  
Laubscher ◽  
...  
Keyword(s):  
Central Nervous System ◽  
Cerebrospinal Fluid ◽  
Nervous System ◽  
High Throughput ◽  
Clinical Microbiology ◽  
High Throughput Sequencing ◽  
Sequence Data ◽  
Unknown Origin ◽  
Cns Inflammation ◽  
Viral Sequences

: Meningitis, encephalitis, and myelitis are various forms of acute central nervous system (CNS) inflammation, which can coexist and lead to serious sequelae. Known aetiologies include infections and immune-mediated processes. Despite advances in clinical microbiology over the past decades, the cause of acute CNS inflammation remains unknown in approximately 50% of cases. High-throughput sequencing was performed to search for viral sequences in cerebrospinal fluid (CSF) samples collected from 26 patients considered to have acute CNS inflammation of unknown origin, and 10 patients with defined causes of CNS diseases. In order to better grasp the clinical significance of viral sequence data obtained in CSF, 30 patients without CNS disease who had a lumbar puncture performed during elective spinal anaesthesia were also analysed. One case of human astrovirus (HAstV)-MLB2-related meningitis and disseminated infection was identified. No other viral sequences that can easily be linked to CNS inflammation were detected. Viral sequences obtained in all patient groups are discussed. While some of them reflect harmless viral infections, others result from reagent or sample contamination, as well as index hopping. Altogether, this study highlights the potential of high-throughput sequencing in identifying previously unknown viral neuropathogens, as well as the interpretation issues related to its application in clinical microbiology.

Detection of nobel crayfish Astacus astacus v1

2020 ◽  
Author(s):  
Omneya Ahmed ◽  
Alexander Eiler
Keyword(s):  
Freshwater Ecosystems ◽  
Pacifastacus Leniusculus ◽  
Indigenous Species ◽  
Astacus Astacus ◽  
Signal Crayfish ◽  
Noble Crayfish ◽  
Non Indigenous Species

Crayfish play important role in freshwater ecosystems. Noble crayfish "Astacus astacus" is threatened by non-indigenous species such as signal crayfish "Pacifastacus leniusculus".

Detection of signal crayfish Pacifastacus leniusculus v1

2020 ◽  
Author(s):  
Omneya Ahmed ◽  
Alexander Eiler ◽  
Mats Töpel
Keyword(s):  
Freshwater Ecosystems ◽  
Pacifastacus Leniusculus ◽  
Indigenous Species ◽  
Astacus Astacus ◽  
Signal Crayfish ◽  
Specific Assay ◽  
Noble Crayfish ◽  
Species Specific ◽  
Non Indigenous Species

A species-specific assay was developed and tested by Agersnap et al. (2017) Crayfish play important role in freshwater ecosystems. Noble crayfish "Astacus astacus" is threatened to extinct by non-indigenous species such as signal crayfish "Pacifastacus leniusculus".

scholarly journals Short k-mer Abundance Profiles Yield Robust Machine Learning Features and Accurate Classifiers for RNA Viruses

2020 ◽  
Author(s):  
Md. Nafis Ul Alam ◽  
Umar Faruq Chowdhury
Keyword(s):  
Machine Learning ◽  
Sequence Alignment ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Rna Viruses ◽  
Sequence Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Automated Annotation

AbstractHigh throughout sequencing technologies have greatly enabled the study of genomics, transcriptomics and metagenomics. Automated annotation and classification of the vast amounts of generated sequence data has become paramount for facilitating biological sciences. Genomes of viruses can be radically different from all life, both in terms of molecular structure and primary sequence. Alignment-based and profile-based searches are commonly employed for characterization of assembled viral contigs from high-throughput sequencing data. Recent attempts have highlighted the use of machine learning models for the task but these models rely entirely on DNA genomes and owing to the intrinsic genomic complexity of viruses, RNA viruses have gone completely overlooked. Here, we present a novel short k-mer based sequence scoring method that generates robust sequence information for training machine learning classifiers. We trained 18 classifiers for the task of distinguishing viral RNA from human transcripts. We challenged our models with very stringent testing protocols across different species and evaluated performance against BLASTn, BLASTx and HMMER3 searches. For clean sequence data retrieved from curated databases, our models display near perfect accuracy, outperforming all similar attempts previously reported. On de-novo assemblies of raw RNA-Seq data from cells subjected to Ebola virus, the area under the ROC curve varied from 0.6 to 0.86 depending on the software used for assembly. Our classifier was able to properly classify the majority of the false hits generated by BLAST and HMMER3 searches on the same data. The outstanding performance metrics of our model lays the groundwork for robust machine learning methods for the automated annotation of sequence data.Author SummaryIn this age of high-throughput sequencing, proper classification of copious amounts of sequence data remains to be a daunting challenge. Presently, sequence alignment methods are immediately assigned to the task. Owing to the selection forces of nature, there is considerable homology even between the sequences of different species which draws ambiguity to the results of alignment-based searches. Machine Learning methods are becoming more reliable for characterizing sequence data, but virus genomes are more variable than all forms of life and viruses with RNA-based genomes have gone overlooked in previous machine learning attempts. We designed a novel short k-mer based scoring criteria whereby a large number of highly robust numerical feature sets can be derived from sequence data. These features were able to accurately distinguish virus RNA from human transcripts with performance scores better than all previous reports. Our models were able to generalize well to distant species of viruses and mouse transcripts. The model correctly classifies the majority of false hits generated by current standard alignment tools. These findings strongly imply that this k-mer score based computational pipeline forges a highly informative, rich set of numerical machine learning features and similar pipelines can greatly advance the field of computational biology.

scholarly journals Virus Surveys in Olive Orchards in Greece Identify Olive Virus T, a Novel Member of the Genus Tepovirus

Pathogens ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 574
Author(s):  
Evanthia Xylogianni ◽  
Paolo Margaria ◽  
Dennis Knierim ◽  
Kyriaki Sareli ◽  
Stephan Winter ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Leaf Roll ◽  
Virus Infections ◽  
Northern Greece ◽  
Coat Protein Region ◽  
Cherry Leaf Roll Virus ◽  
Leaf Yellowing ◽  
Olive Trees ◽  
Olive Varieties ◽  
Viral Sequences

Field surveys were conducted in Greek olive orchards from 2017 to 2020 to collect information on the sanitary status of the trees. Using a high-throughput sequencing approach, viral sequences were identified in total RNA extracts from several trees and assembled to reconstruct the complete genomes of two isolates of a new viral species of the genus Tepovirus (Betaflexiviridae), for which the name olive virus T (OlVT) is proposed. A reverse transcription–polymerase chain reaction assay was developed which detected OlVT in samples collected in olive growing regions in Central and Northern Greece, showing a virus prevalence of 4.4% in the olive trees screened. Sequences of amplified fragments from the movement–coat protein region of OlVT isolates varied from 75.64% to 99.35%. Three olive varieties (Koroneiki, Arbequina and Frantoio) were infected with OlVT via grafting to confirm a graft-transmissible agent, but virus infections remained latent. In addition, cucumber mosaic virus, olive leaf yellowing-associated virus and cherry leaf roll virus were identified.

scholarly journals Urbanization pressures alter tree rhizosphere microbiomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Carl L. Rosier ◽  
Shawn W. Polson ◽  
Vincent D’Amico ◽  
Jinjun Kan ◽  
Tara L. E. Trammell
Keyword(s):  
Organic Matter ◽  
Relative Abundance ◽  
Tree Species ◽  
High Throughput Sequencing ◽  
Regional Climate ◽  
Keystone Species ◽  
Fagus Grandifolia ◽  
Yellow Poplar ◽  
Individual Tree ◽  
Structural Formation

AbstractThe soil microbial community (SMC) provides critical ecosystem services including organic matter decomposition, soil structural formation, and nutrient cycling. Studies suggest plants, specifically trees, act as soil keystone species controlling SMC structure via multiple mechanisms (e.g., litter chemistry, root exudates, and canopy alteration of precipitation). Tree influence on SMC is shaped by local/regional climate effects on forested environments and the connection of forests to surrounding landscapes (e.g., urbanization). Urban soils offer an ideal analog to assess the influence of environmental conditions versus plant species-specific controls on SMC. We used next generation high throughput sequencing to characterize the SMC of specific tree species (Fagus grandifolia [beech] vs Liriodendron tulipifera [yellow poplar]) across an urban–rural gradient. Results indicate SMC dissimilarity within rural forests suggests the SMC is unique to individual tree species. However, greater urbanization pressure increased SMC similarity between tree species. Relative abundance, species richness, and evenness suggest that increases in similarity within urban forests is not the result of biodiversity loss, but rather due to greater overlap of shared taxa. Evaluation of soil chemistry across the rural–urban gradient indicate pH, Ca+, and organic matter are largely responsible for driving relative abundance of specific SMC members.

scholarly journals Illuminating the Plant Rhabdovirus Landscape through Metatranscriptomics Data

Viruses ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1304
Author(s):  
Nicolás Bejerman ◽  
Ralf G. Dietzgen ◽  
Humberto Debat
Keyword(s):  
Plant Species ◽  
High Throughput Sequencing ◽  
Plant Viruses ◽  
The Novel ◽  
Coding Regions ◽  
Public Data ◽  
Invaluable Tool ◽  
Sequencing Platforms ◽  
Viral Sequences ◽  
Plant Rhabdovirus

Rhabdoviruses infect a large number of plant species and cause significant crop diseases. They have a negative-sense, single-stranded unsegmented or bisegmented RNA genome. The number of plant-associated rhabdovirid sequences has grown in the last few years in concert with the extensive use of high-throughput sequencing platforms. Here, we report the discovery of 27 novel rhabdovirus genomes associated with 25 different host plant species and one insect, which were hidden in public databases. These viral sequences were identified through homology searches in more than 3000 plant and insect transcriptomes from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) using known plant rhabdovirus sequences as the query. The identification, assembly and curation of raw SRA reads resulted in sixteen viral genome sequences with full-length coding regions and ten partial genomes. Highlights of the obtained sequences include viruses with unique and novel genome organizations among known plant rhabdoviruses. Phylogenetic analysis showed that thirteen of the novel viruses were related to cytorhabdoviruses, one to alphanucleorhabdoviruses, five to betanucleorhabdoviruses, one to dichorhaviruses and seven to varicosaviruses. These findings resulted in the most complete phylogeny of plant rhabdoviruses to date and shed new light on the phylogenetic relationships and evolutionary landscape of this group of plant viruses. Furthermore, this study provided additional evidence for the complexity and diversity of plant rhabdovirus genomes and demonstrated that analyzing SRA public data provides an invaluable tool to accelerate virus discovery, gain evolutionary insights and refine virus taxonomy.

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