Metagenomic Nanopore Sequencing Versus Conventional Diagnosis for Identification of the Dieback Pathogens of Mango Trees

Hassan, Asmaa H; Bebawy, Abraam S; Saad, Mina T; Mosaad, Gamal S.; Saad, Bishoy T; Eltayeb, Wafaa N; Aboshanab, Khaled M.

doi:10.2144/btn-2022-0087

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…26 Additionally, it has been reported by studies that gene mutates the fastest among the eight genes of in uenza A virus. 27 Any changes in HA receptor binding sites affect the virulence, while the glycosylation in the vicinity of the cleavage site promotes the folding of HA. This can lead to masking or unmasking of proteolytic cleavage or antigenic sites and can affect viral pathogenicity.…”

Section: Discussionmentioning

confidence: 99%

Genomic Analysis of Circulating Influenza Virus from 2016 in Nepal

Jha,

Katuwal,

Manandhar

2024

Preprint

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Background Influenza constitutes a contagious viral infection primarily attributed to influenza viruses A or B, predominantly affecting the upper respiratory system. The rapid evolution of influenza viruses results in substantial variability. Type A encompasses 16 highly variable hemagglutinins (H1 to H16) and 9 distinct NAs (N1 to N9). Conversely, influenza type B lacks subtypes owing to fixed, albeit small, antigenic variabilities. Given the swift evolution and diversification, comprehendinggenomic epidemiology becomes imperative for discerning emerging strains and trackingtransmission. Objectives This study delves into the genomic analysis of various sequences of H1N1 and Influenza type B, isolated from Nepal and deposited in the Global Initiative on Sharing Avian Influenza Data (GISAID). Methods The viral genomic material, after extraction, were sent to National Institute of Infectious Disease, Japan for whole genome sequencing in 2016. The sequences were subsequently submitted to GISAID. The H1N1 virus genomes (n=18) in this study were investigated against the reference genome A/California/07/2009 (GenBank: CY121680). Similarly, Influenza Type B virus genomes (n=27) were examined against reference genome B/Brisbane/60/2008 (GenBank: KX058884) and B/Wisconsin/01/2010 (GenBank: JN993010). Mutational analyses were performed using Nextclade, followed by an investigation into the mutations present in the sequences. Finally, phylogenetic analysis was conducted using the Nextclade and CLUSTAL Omega. Results Out of the 18 HA genome segments of H1N1, all (n=12) except 6 isolates belonged to clade 6B, while the remainder were of clade 6B.1. For Influenza Type B, all 27 HA genome segments were classified under clade V1A when compared to B/Brisbane/60/2008. Similarly, in comparison to the reference genome B/Wisconsin/01/2010 (genbank: JN993010), all (n=23) except 4 isolates were of clade Y3, while the rest were of clade Y2. Conclusion The analysis of the HA1 (sub-segment of HA) proves valuable for strain identification based on mutations. The HA segment in influenza type A exhibits the fastest mutation rate, influencing virulence, while mutations in NA are associated with drug resistance. Given the heightened risk of influenza outbreaks due to emerging mutations, assortments, and drug resistance, vigilance in monitoring mutations in these segments are crucial.

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Section: Discussionmentioning

confidence: 99%

Genomic Analysis of Circulating Influenza Virus from 2016 in Nepal

Jha,

Katuwal,

Manandhar

2024

Preprint

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“…For instance, nanopore metagenomics quickly identified L. theobromae among other fungi from root-soil samples and infected plant tissues collected from diseased mango trees in Egypt [30]. Alternatively, genotyping by sequencing (GBS) may be useful for investigating the pathogen molecular genetic diversity in several orchards through genome-wide SNP discovery, enabling further genetic population studies for review see [30] . Knowledge about Lasiodiplodia spore production and propagation in orchards is also limited.…”

Section: Discussionmentioning

confidence: 99%

“…Taking adavantge of new sequencing methods could be useful for accurately detecting and analyzing the microbiota associated to diseased orchards. For instance, nanopore metagenomics quickly identified L. theobromae among other fungi from root-soil samples and infected plant tissues collected from diseased mango trees in Egypt [30]. Alternatively, genotyping by sequencing (GBS) may be useful for investigating the pathogen molecular genetic diversity in several orchards through genome-wide SNP discovery, enabling further genetic population studies for review see [30] .…”

Section: Discussionmentioning

confidence: 99%

Lasiodiplodiaspecies associated with mango (Mangifera indicaL.) decline in Burkina Faso and influence of climatic factors on the disease distribution

Dianda

Wonni

Ouédraogo

et al. 2023

Preprint

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Dieback or decline caused by Lasiodiplodia spp. is a major disease of mango trees (Mangifera indica L.). The main objectives of this study were to identify Lasiodiplodia species associated with mango decline in Burkina Faso, and to asses the climatic and edaphic factors affecting the geographic distribution of the disease in the country. The genetic diversity of 47 Lasiodiplodia isolates was studied based on sequence data of the translation elongation factor 1-alpha gene (tef1-a) and the rDNA internal transcribed spacer region (ITS). Phylogeny analyses grouped the isolates from Burkina Faso with 6 different Lasiodiplodia species, including L. euphorbicola that accounted for 36 of the 47 isolates. Lasiodiplodia isolates tested on mango seedlings induced the typical dieback symptoms. Disease incidence and severity were generally higher in the drier and warmer regions (eastern) of the country. This study provides the required information to establish control strategies against mango decline in Burkina Faso.

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“…These include the detection of different plant viruses in potato [14], tomato [15], cassava [10], wheat [16], yam [17] and various other plants [15,18,19] either following standard MinION protocols or elaborated cost-effective adaptations [18,20]. Several relevant studies provide reports on the identification of Xylella fastidiosa subspecies and sequence types from naturally infected plant material [21], tomato phytopathogenic bacteria identification [22], and mango dieback disease-associated fungal species [23]. Furthermore, there are studies on the detection of selected plant pathogens spanning bacteria, fungi, viruses and a phytoplasma in artificially inoculated plants via genomic DNA or RNA sequencing accordingly [15], and detection of plant and insect pathogens via induced meta-transcriptome analyses [24].…”

Section: Introductionmentioning

confidence: 99%

Nanopore-Sequencing Metabarcoding for Identification of Phytopathogenic and Endophytic Fungi in Olive (Olea europaea) Twigs

Theologidis,

Karamitros,

Vichou

et al. 2023

JoF

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Metabarcoding approaches for the identification of plant disease pathogens and characterization of plant microbial populations constitute a rapidly evolving research field. Fungal plant diseases are of major phytopathological concern; thus, the development of metabarcoding approaches for the detection of phytopathogenic fungi is becoming increasingly imperative in the context of plant disease prognosis. We developed a multiplex metabarcoding method for the identification of fungal phytopathogens and endophytes in olive young shoots, using the MinION sequencing platform (Oxford Nanopore Technologies). Selected fungal-specific primers were used to amplify three different genomic DNA loci (ITS, beta-tubulin, and 28S LSU) originating from olive twigs. A multiplex metabarcoding approach was initially evaluated using healthy olive twigs, and further assessed with naturally infected olive twig samples. Bioinformatic analysis of basecalled reads was carried out using MinKNOW, BLAST+ and R programming, and results were also evaluated using the BugSeq cloud platform. Data analysis highlighted the approaches based on ITS and their combination with beta-tubulin as the most informative ones according to diversity estimations. Subsequent implementation of the method on symptomatic samples identified major olive pathogens and endophytes including genera such as Cladosporium, Didymosphaeria, Paraconiothyrium, Penicillium, Phoma, Verticillium, and others.

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Metagenomic Nanopore Sequencing Versus Conventional Diagnosis for Identification of the Dieback Pathogens of Mango Trees

Cited by 6 publications

References 29 publications

Genomic Analysis of Circulating Influenza Virus from 2016 in Nepal

Genomic Analysis of Circulating Influenza Virus from 2016 in Nepal

Lasiodiplodiaspecies associated with mango (Mangifera indicaL.) decline in Burkina Faso and influence of climatic factors on the disease distribution

Nanopore-Sequencing Metabarcoding for Identification of Phytopathogenic and Endophytic Fungi in Olive (Olea europaea) Twigs

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