Natural gene drives offer potential pathogen control strategies in plants

Gardiner, Donald M.; Rusu, Anca; Barrett, Luke G.; Hunter, Gavin C.; Kazan, Kemal

doi:10.1101/2020.04.05.026500

“…Following discussions with the European Commission (Directorate-General for Health and Food Safety), it was agreed to limit the scope of the mandate (see Section 1.1) to insects, as they represent the most likely cases of GDMOs moving to practical applications for deliberate release into the environment. Although the use of engineered gene drive systems is under consideration in mammals (Leitschuh et al, 2018;Conklin, 2019;Godwin et al, 2019;Grunwald et al, 2019;Manser et al, 2019;Faber et al, 2020) and plants (Neve, 2018;Barrett et al, 2019;Gardiner et al, 2020), basic technical challenges need to be overcome before an engineered gene drive will be possible in these taxa (NASEM, 2016;Godwin et al, 2019;Pixley et al, 2019;Scudellari, 2019). In the future, however, EFSA could be mandated by the European Commission to evaluate whether its guidelines for the risk assessment of genetically modified (GM) mammals and plants are adequate and sufficient for the risk assessment of GM mammals and plants containing engineered gene drives.…”

Section: Interpretation Of the Terms Of Referencementioning

confidence: 99%

Adequacy and sufficiency evaluation of existing EFSA guidelines for the molecular characterisation, environmental risk assessment and post‐market environmental monitoring of genetically modified insects containing engineered gene drives

Naegeli¹,

Bresson²,

Dalmay³

et al. 2020

EFS2

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Advances in molecular and synthetic biology are enabling the engineering of gene drives in insects for disease vector/pest control. Engineered gene drives (that bias their own inheritance) can be designed either to suppress interbreeding target populations or modify them with a new genotype. Depending on the engineered gene drive system, theoretically, a genetic modification of interest could spread through target populations and persist indefinitely, or be restricted in its spread or persistence. While research on engineered gene drives and their applications in insects is advancing at a fast pace, it will take several years for technological developments to move to practical applications for deliberate release into the environment. Some gene drive modified insects (GDMIs) have been tested experimentally in the laboratory, but none has been assessed in small-scale confined field trials or in open release trials as yet. There is concern that the deliberate release of GDMIs in the environment may have possible irreversible and unintended consequences. As a proactive measure, the European Food Safety Authority (EFSA) has been requested by the European Commission to review whether its previously published guidelines for the risk assessment of genetically modified animals (EFSA, 2012 and 2013), including insects (GMIs), are adequate and sufficient for GDMIs, primarily disease vectors, agricultural pests and invasive species, for deliberate release into the environment. Under this mandate, EFSA was not requested to develop risk assessment guidelines for GDMIs. In this Scientific Opinion, the Panel on Genetically Modified Organisms (GMO) concludes that EFSA's guidelines are adequate, but insufficient for the molecular characterisation (MC), environmental risk assessment (ERA) and post-market environmental monitoring (PMEM) of GDMIs. While the MC, ERA and PMEM of GDMIs can build on the existing risk assessment framework for GMIs that do not contain engineered gene drives, there are specific areas where further guidance is needed for GDMIs.

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“…In light of these limitations, a genomics approach to biosurveillance that is focused on discovering the genomic 'signatures' that FIAS use to successfully invade novel ecosystems outside of their taxonomic identity has been proposed [8][9][10]12 . Indeed, a more geneticsand genomics-centered approach to plant pathogen management is an increasingly prevalent theme in recent research [13][14][15][16] .…”

Section: Introductionmentioning

confidence: 99%

Large-scale genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits

Dort

¹

,

Layne

²

,

Feau³

et al. 2023

Preprint

0

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Invasive plant pathogenic fungi have a global impact, with devastating economic and environmental effects on crops and forests. Biosurveillance, a critical component of threat mitigation, requires risk prediction based on fungal lifestyles and traits. Using a machine learning approach that separates phylogenetic and genomic feature signals, we analyzed 387 fungal genomes to test the hypothesis that there are predictive signatures associated with phytopathogenic lifestyles and traits. Our results lend strong support to this hypothesis, and our analyses uncovered genomic patterns associated with phytopathogenic lifestyles and traits, identifying gene families that are potentially important in the evolution of plant pathogenic fungi. We found that the top-performing feature sets for predictions included carbohydrate-active enzymes (CAZymes), peptidases, and secondary metabolite clusters. Expansions in the number of CAZyme and peptidase genes, and of specific families such as CBM63, were revealed in the genomes of plant pathogens compared to non-phytopathogenic genomes (saprotrophs, endo- and ectomycorrhizal fungi) and were strong predictors of phytopathogenicity. Such genomic feature profiles could be useful to predict risks of fungal plant pathogens in future biosurveillance activities.

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“…In light of these limitations, a genomics approach to biosurveillance that is focused on discovering the genomic ‘signatures’ that FIAS use to successfully invade novel ecosystems outside of their taxonomic identity has been proposed 8 – 10 , 12 . Indeed, a more genetics- and genomics-centered approach to plant pathogen management is an increasingly prevalent theme in recent research 13 – 16 .…”

Section: Introductionmentioning

confidence: 99%

Large-scale genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits

Dort,

Layne,

Feau

et al. 2023

Sci Rep

5

0

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Invasive plant pathogenic fungi have a global impact, with devastating economic and environmental effects on crops and forests. Biosurveillance, a critical component of threat mitigation, requires risk prediction based on fungal lifestyles and traits. Recent studies have revealed distinct genomic patterns associated with specific groups of plant pathogenic fungi. We sought to establish whether these phytopathogenic genomic patterns hold across diverse taxonomic and ecological groups from the Ascomycota and Basidiomycota, and furthermore, if those patterns can be used in a predictive capacity for biosurveillance. Using a supervised machine learning approach that integrates phylogenetic and genomic data, we analyzed 387 fungal genomes to test a proof-of-concept for the use of genomic signatures in predicting fungal phytopathogenic lifestyles and traits during biosurveillance activities. Our machine learning feature sets were derived from genome annotation data of carbohydrate-active enzymes (CAZymes), peptidases, secondary metabolite clusters (SMCs), transporters, and transcription factors. We found that machine learning could successfully predict fungal lifestyles and traits across taxonomic groups, with the best predictive performance coming from feature sets comprising CAZyme, peptidase, and SMC data. While phylogeny was an important component in most predictions, the inclusion of genomic data improved prediction performance for every lifestyle and trait tested. Plant pathogenicity was one of the best-predicted traits, showing the promise of predictive genomics for biosurveillance applications. Furthermore, our machine learning approach revealed expansions in the number of genes from specific CAZyme and peptidase families in the genomes of plant pathogens compared to non-phytopathogenic genomes (saprotrophs, endo- and ectomycorrhizal fungi). Such genomic feature profiles give insight into the evolution of fungal phytopathogenicity and could be useful to predict the risks of unknown fungi in future biosurveillance activities.

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Natural gene drives offer potential pathogen control strategies in plants

Cited by 3 publications

References 41 publications

Adequacy and sufficiency evaluation of existing EFSA guidelines for the molecular characterisation, environmental risk assessment and post‐market environmental monitoring of genetically modified insects containing engineered gene drives

Adequacy and sufficiency evaluation of existing EFSA guidelines for the molecular characterisation, environmental risk assessment and post‐market environmental monitoring of genetically modified insects containing engineered gene drives

Large-scale genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits

Large-scale genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits

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