A novel picorna-like virus identified in the cotton boll weevil Anthonomus grandis (Coleoptera: Curculionidae)

Silva, Leonardo Assis da; Basso, Marcos Fernando; Ribeiro, Bergmann Morais

doi:10.1007/s00705-022-05672-8

Arch Virol

2023

DOI: 10.1007/s00705-022-05672-8

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A novel picorna-like virus identified in the cotton boll weevil Anthonomus grandis (Coleoptera: Curculionidae)

Leonardo Assis da Silva

Marcos Fernando Basso²,

Bergmann Morais Ribeiro

Abstract: Cotton boll weevil (CBW; Anthonomus grandis; Coleoptera: Curculionidae), is considered the major cotton insect-pest causing considerable losses in yield and ber quality. The increase in boll weevil population due to the increasingly ine cient chemical control measures are of great concern among cotton producers. The absence of conventional or transgenic cultivars with minimal resistance to CBW has stimulated the search for new molecular and biological tools for e cient control of this insect-pest. Herein, were… Show more

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2024

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Cited by 3 publications

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Cotton plants overexpressing the Bacillus thuringiensis Cry23Aa and Cry37Aa binary-like toxins exhibit high resistance to the cotton boll weevil (Anthonomus grandis)

Ribeiro,

Martins-de-Sa,

Macedo

et al. 2024

Plant Science

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Cotton plants overexpressing the Bacillus thuringiensis Cry23Aa and Cry37Aa binary-like toxins exhibit high resistance to the cotton boll weevil (Anthonomus grandis)

Ribeiro,

Martins-de-Sa,

Macedo

et al. 2024

Plant Science

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RNAVirHost: a machine learning–based method for predicting hosts of RNA viruses through viral genomes

Chen,

Jiang,

Sun

2024

GigaScience

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Background The high-throughput sequencing technologies have revolutionized the identification of novel RNA viruses. Given that viruses are infectious agents, identifying hosts of these new viruses carries significant implications for public health and provides valuable insights into the dynamics of the microbiome. However, determining the hosts of these newly discovered viruses is not always straightforward, especially in the case of viruses detected in environmental samples. Even for host-associated samples, it is not always correct to assign the sample origin as the host of the identified viruses. The process of assigning hosts to RNA viruses remains challenging due to their high mutation rates and vast diversity. Results In this study, we introduce RNAVirHost, a machine learning–based tool that predicts the hosts of RNA viruses solely based on viral genomes. RNAVirHost is a hierarchical classification framework that predicts hosts at different taxonomic levels. We demonstrate the superior accuracy of RNAVirHost in predicting hosts of RNA viruses through comprehensive comparisons with various state-of-the-art techniques. When applying to viruses from novel genera, RNAVirHost achieved the highest accuracy of 84.3%, outperforming the alignment-based strategy by 12.1%. Conclusions The application of machine learning models has proven beneficial in predicting hosts of RNA viruses. By integrating genomic traits and sequence homologies, RNAVirHost provides a cost-effective and efficient strategy for host prediction. We believe that RNAVirHost can greatly assist in RNA virus analyses and contribute to pandemic surveillance.

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Genetic Characterization of Two Novel Insect-Infecting Negative-Sense RNA Viruses Identified in a Leaf Beetle, Aulacophora indica

Chen,

Ye,

Feng

et al. 2024

Insects

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Herbivorous insects harbor a variety of insect-specific viruses (ISVs) some of which are considered to be valuable biological agents for potential applications in biological defense and control strategies. Leaf beetles with chewing mouthparts are particularly known for their capacity to disrupt plant tissue while feeding, often creating openings that can act as entry points for plant pathogens. In this study, we have identified two new negative-sense RNA viruses infecting the leaf beetle Aulacophora indica, an important member of the Chrysomelidae family. These recently discovered viruses belong to the viral families Nyamiviridae and Chuviridae and have been preliminarily named Aulacophora indica nyami-like virus 1 (AINlV1) and Aulacophora indica chu-like virus 1 (AIClV1), respectively. The complete genomic sequences of these viruses were obtained using rapid amplification of cDNA ends (RACE) techniques. Detailed analysis of their genomic structures has confirmed their similarity to other members within their respective families. Furthermore, analysis of virus-derived small interfering RNA (vsiRNA) demonstrated a high abundance and typical vsiRNA pattern of AINlV1 and AIClV1, offering substantial evidence to support their classification as ISVs. This research enhances our understanding of viral diversity within insects.

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A novel picorna-like virus identified in the cotton boll weevil Anthonomus grandis (Coleoptera: Curculionidae)

Cited by 3 publications

References 20 publications

Cotton plants overexpressing the Bacillus thuringiensis Cry23Aa and Cry37Aa binary-like toxins exhibit high resistance to the cotton boll weevil (Anthonomus grandis)

Cotton plants overexpressing the Bacillus thuringiensis Cry23Aa and Cry37Aa binary-like toxins exhibit high resistance to the cotton boll weevil (Anthonomus grandis)

RNAVirHost: a machine learning–based method for predicting hosts of RNA viruses through viral genomes

Genetic Characterization of Two Novel Insect-Infecting Negative-Sense RNA Viruses Identified in a Leaf Beetle, Aulacophora indica

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