Untangling an insect's virome from its endogenous viral elements.

BACKGROUND: Insects are an important reservoir of viral biodiversity, but the vast majority of viruses associated with insects have not been discovered. Recent studies have employed high-throughput RNA sequencing, which has led to rapid advances in our understanding of insect viral diversity. However, insect genomes frequently contain transcribed endogenous viral elements (EVEs) with significant homology to exogenous viruses, complicating the use of RNAseq for viral discovery. METHODS: In this study, we used a multi-pronged sequencing approach to study the virome of an important agricultural pest and prolific vector of plant pathogens, the potato aphid Macrosiphum euphorbiae. We first used rRNA-depleted RNAseq to characterize the microbes found in individual insects. We then used PCR screening to measure the frequency of two heritable viruses in a local aphid population. Lastly, we generated a quality draft genome assembly for M. euphorbiae using Illumina-corrected Nanopore sequencing to identify transcriptionally active EVEs in the host genome. RESULTS: We found reads from two insect-specific viruses (a Flavivirus and an Ambidensovirus) in our RNAseq data, as well as a parasitoid virus (Bracovirus), a plant pathogenic virus (Tombusvirus), and two phages (Acinetobacter and APSE). However, our genome assembly showed that part of the 'virome' of this insect can be attributed to EVEs in the host genome. CONCLUSION: Our work shows that EVEs have led to the misidentification of aphid viruses from RNAseq data, and we argue that this is a widespread challenge for the study of viral diversity in insects.

Classification of fluorescent R-Band metaphase chromosomes using a convolutional neural network is precise and fast in generating karyograms of hematologic neoplastic cells.

Karyotype analysis has a great impact on the diagnosis, treatment and prognosis in hematologic neoplasms. The identification and characterization of chromosomes is a challenging process and needs experienced personal. Artificial intelligence provides novel support tools. However, their safe and reliable application in diagnostics needs to be evaluated. Here, we present a novel laboratory approach to identify chromosomes in cancer cells using a convolutional neural network (CNN). The CNN identified the correct chromosome class for 98.8% of chromosomes, which led to a time saving of 42% for the karyotyping workflow. These results demonstrate that the CNN has potential application value in chromosome classification of hematologic neoplasms. This study contributes to the development of an automatic karyotyping platform.

Viromics of extant insect orders unveil the evolution of the flavi-like superfamily.

Insects are the most diversified and species-rich group of animals and harbor an immense diversity of viruses. Several taxa in the flavi-like superfamily, such as the genus Flavivirus, are associated with insects; however, systematic studies on insect virus genetic diversity are lacking, limiting our understanding of the evolution of the flavi-like superfamily. Here, we examined the diversity of flavi-like viruses within the most complete and up-to-date insect transcriptome collection comprising 1,243 insect species by employing a Flaviviridae RdRp profile hidden Markov model search. We identified seventy-six viral sequences in sixty-one species belonging to seventeen insect, one entognathan, and one arachnidan orders. Phylogenetic analyses revealed that twenty-seven sequences fell within the Flaviviridae phylogeny but did not group with established genera. Despite the large diversity of insect hosts studied, we only detected one virus in a blood-feeding insect, which branched within the genus Flavivirus, indicating that this genus likely diversified only in hematophagous arthropods. Nine new jingmenviruses with novel host associations were identified. One of the jingmenviruses established a deep rooting lineage additional to the insect- and tick-associated clades. Segment co-segregation phylogenies support the separation of tick- and insect-associated groups within jingmenviruses, with evidence for segment reassortment. In addition, fourteen viruses grouped with unclassified flaviviruses encompassing genome length of up to 20 kb. Species-specific clades for Hymenopteran- and Orthopteran-associated viruses were identified. Forty-nine viruses populated three highly diversified clades in distant relationship to Tombusviridae, a plant-infecting virus family, suggesting the detection of three previously unknown insect-associated families that contributed to tombusvirus evolution.

Re-assessing the diversity of negative strand RNA viruses in insects.

The spectrum of viruses in insects is important for subjects as diverse as public health, veterinary medicine, food production, and biodiversity conservation. The traditional interest in vector-borne diseases of humans and livestock has drawn the attention of virus studies to hematophagous insect species. However, these represent only a tiny fraction of the broad diversity of Hexapoda, the most speciose group of animals. Here, we systematically probed the diversity of negative strand RNA viruses in the largest and most representative collection of insect transcriptomes from samples representing all 34 extant orders of Hexapoda and 3 orders of Entognatha, as well as outgroups, altogether representing 1243 species. Based on profile hidden Markov models we detected 488 viral RNA-directed RNA polymerase (RdRp) sequences with similarity to negative strand RNA viruses. These were identified in members of 324 arthropod species. Selection for length, quality, and uniqueness left 234 sequences for analyses, showing similarity to genomes of viruses classified in Bunyavirales (n = 86), Articulavirales (n = 54), and several orders within Haploviricotina (n = 94). Coding-complete genomes or nearly-complete subgenomic assemblies were obtained in 61 cases. Based on phylogenetic topology and the availability of coding-complete genomes we estimate that at least 20 novel viral genera in seven families need to be defined, only two of them monospecific. Seven additional viral clades emerge when adding sequences from the present study to formerly monospecific lineages, potentially requiring up to seven additional genera. One long sequence may indicate a novel family. For segmented viruses, cophylogenies between genome segments were generally improved by the inclusion of viruses from the present study, suggesting that in silico misassembly of segmented genomes is rare or absent. Contrary to previous assessments, significant virus-host codivergence was identified in major phylogenetic lineages based on two different approaches of codivergence analysis in a hypotheses testing framework. In spite of these additions to the known spectrum of viruses in insects, we caution that basing taxonomic decisions on genome information alone is challenging due to technical uncertainties, such as the inability to prove integrity of complete genome assemblies of segmented viruses.