DNA Metabarcoding and Isolation by Baiting Complement Each Other in Revealing Phytophthora Diversity in Anthropized and Natural Ecosystems

Spada, Federico La; Cock, Peter J. A.; Randall, Eva; Pane, A.; Cooke, D. E. L.; Cacciola, S. O.

doi:10.3390/jof8040330

Cited by 21 publications

(20 citation statements)

References 91 publications

(169 reference statements)

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“…The major advantage of this combined approach is that baiting allows to obtain Phytophthora cultures useful for a better knowledge of isolates and Koch’s postulates validation. As expected, DNA metabarcoding revealed a higher number of Phytophthora taxa than baiting [ 85 , 88 ]. Sometimes, the identified taxa were not the same between the two methods [ 88 ], in other cases the two approaches revealed very similar Phytophthora communities [ 87 ].…”

Section: Metabarcodingsupporting

confidence: 67%

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New-Generation Sequencing Technology in Diagnosis of Fungal Plant Pathogens: A Dream Comes True?

Aragona

Haegi

Valente

et al. 2022

JoF

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The fast and continued progress of high-throughput sequencing (HTS) and the drastic reduction of its costs have boosted new and unpredictable developments in the field of plant pathology. The cost of whole-genome sequencing, which, until few years ago, was prohibitive for many projects, is now so affordable that a new branch, phylogenomics, is being developed. Fungal taxonomy is being deeply influenced by genome comparison, too. It is now easier to discover new genes as potential targets for an accurate diagnosis of new or emerging pathogens, notably those of quarantine concern. Similarly, with the development of metabarcoding and metagenomics techniques, it is now possible to unravel complex diseases or answer crucial questions, such as “What’s in my soil?”, to a good approximation, including fungi, bacteria, nematodes, etc. The new technologies allow to redraw the approach for disease control strategies considering the pathogens within their environment and deciphering the complex interactions between microorganisms and the cultivated crops. This kind of analysis usually generates big data that need sophisticated bioinformatic tools (machine learning, artificial intelligence) for their management. Herein, examples of the use of new technologies for research in fungal diversity and diagnosis of some fungal pathogens are reported.

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Section: Metabarcodingsupporting

confidence: 67%

“…As expected, DNA metabarcoding revealed a higher number of Phytophthora taxa than baiting [ 85 , 88 ]. Sometimes, the identified taxa were not the same between the two methods [ 88 ], in other cases the two approaches revealed very similar Phytophthora communities [ 87 ].…”

Section: Metabarcodingsupporting

confidence: 67%

New-Generation Sequencing Technology in Diagnosis of Fungal Plant Pathogens: A Dream Comes True?

Aragona

Haegi

Valente

et al. 2022

JoF

View full text Add to dashboard Cite

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“…‘ kelmania ’), a riddle that cannot be solved without examination of pure cultures. Comparative studies on Phytophthora population compositions obtained by culture-dependent and metabarcoding methods showed that both techniques complemented each other [ 5 , 94 , 95 , 96 ]. Currently, several web-accessible databases have been developed to support accurate and rapid identification of Phytophthora species ( , and , accessed on 19 December 2022).…”

Section: Phytophthora Species Diversity In Plant Nurseriesmentioning

confidence: 99%

The Never-Ending Presence of Phytophthora Species in Italian Nurseries

et al. 2022

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Plant trade coupled with climate change has led to the increased spread of well-known and new Phytophthora species, a group of fungus-like organisms placed in the Kingdom Chromista. Their presence in plant nurseries is of particular concern because they are responsible for many plant diseases, with high environmental, economic and social impacts. This paper offers a brief overview of the current status of Phytophthora species in European plant nurseries. Focus was placed on Italian sites. Despite the increasing awareness of the risk of Phytophthora spread and the management strategies applied for controlling it, the complexity of the Phytophthora community in the horticulture industry is increasing over time. Since the survey carried out by Jung et al. (2016), new Phytophthora taxa and Phytophthora-host associations were identified. Phytophthorahydropathica, P. crassamura, P. pseudocryptogea and P. meadii were reported for the first time in European plant nurseries, while P. pistaciae, P. mediterranea and P. heterospora were isolated from Italian ornamental nurseries. Knowledge of Phytophthora diversity in plant nurseries and the potential damage caused by them will help to contribute to the development of early detection methods and sustainable management strategies to control Phytophthora spread in the future.

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“…The Phyto-Threats project's metabarcoding protocol used nested PCR primers designed to target the Internal Transcribed Spacer 1 marker sequence (ITS1; a genomic region located between 18S and 5.8S rRNA genes in eukaryotes) of Phytophthora and related plant pathogenic oomycete (Scibetta et al, 2012). This approach is the current de facto standard within the oomycete community (Robideau et al, 2011), and these primers have been used in conjunction with THAPBI PICT in recent Phytophthora surveys (Vélez et al, 2020;La Spada et al, 2022). PICT was short for Phytophthora ITS1 Classification Tool.…”

Section: Introductionmentioning

confidence: 99%

THAPBI PICT - a fast, cautious, and accurate metabarcoding analysis pipeline

Cock

Cooke

Thorpe

et al. 2023

Preprint

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THAPBI PICT is an open source software pipeline for metabarcoding analysis with multiplexed Illumina paired-end reads, including where different amplicons are sequenced together. We demonstrate using worked examples with our own and public data sets how, with appropriate primer settings and a custom database, THAPBI PICT can be applied to other amplicons and organisms, and used for reanalysis of existing datasets. The core dataflow of the implementation is (i) data reduction to unique marker sequences, often called amplicon sequence variants (ASVs), (ii) dynamic thresholds for discarding low abundance sequences to remove noise and artifacts (rather than error correction by default), before (iii) classification using a curated reference database. The default classifier assigns a label to each query sequence based on a database match that is either perfect, or a single base pair edit away (substitution, deletion or insertion). Abundance thresholds for inclusion can be set by the user or automatically using per-batch negative or synthetic control samples. Output is designed for practical interpretation by non-specialists and includes a read report (ASVs with classification and counts per sample), sample report (samples with counts per species classification), and a topological graph of ASVs as nodes with short edit distances as edges. Source code available from https://github.com/peterjc/thapbi-pict/ with documentation including installation instructions.

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DNA Metabarcoding and Isolation by Baiting Complement Each Other in Revealing Phytophthora Diversity in Anthropized and Natural Ecosystems

Cited by 21 publications

References 91 publications

New-Generation Sequencing Technology in Diagnosis of Fungal Plant Pathogens: A Dream Comes True?

New-Generation Sequencing Technology in Diagnosis of Fungal Plant Pathogens: A Dream Comes True?

The Never-Ending Presence of Phytophthora Species in Italian Nurseries

THAPBI PICT - a fast, cautious, and accurate metabarcoding analysis pipeline

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