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Abstract BackgroundArbuscular mycorrhizal fungi (AMF) are beneficial root symbionts contributing to improved plant growth and development and resistance to abiotic and biotic stresses. Commercial bioinoculants containing AMF are widely considered as an alternative to agrochemicals in vineyards. However, their effects on grapevine plants grown in soil containing native communities of AMF are still poorly understood. In a greenhouse experiment, we evaluated the influence of five different bioinoculants on the composition of native AMF communities of young Cabernet Sauvignon vines grown in a non-sterile soil. Root colonization, leaf nitrogen concentration, plant biomass and root morphology were assessed, and AMF communities of inoculated and non-inoculated grapevine roots were profiled using high-throughput sequencing. ResultsContrary to our predictions, no differences in the microbiome of plants exposed to native AMF communities versus commercial AMF bioinoculants + native AMF communities were detected in roots. However, inoculation induced positive changes in root traits as well as increased AMF colonization, plant biomass, and leaf nitrogen. Most of these desirable functional traits were positively correlated with the relative abundance of operational taxonomic units identified asGlomus,RhizophagusandClaroideoglomusgenera. ConclusionThese results suggest synergistic interactions between commercial AMF bioinoculants and native AMF communities of roots to promote grapevine growth. Long-term studies with further genomics, metabolomics and physiological research are needed to provide a deeper understanding of the symbiotic interaction among grapevine roots, bioinoculants and natural AMF communities and their role to promote plant adaptation to current environmental concerns.
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Computer vision models enable mixed linear modeling to predict arbuscular mycorrhizal fungal colonization using fungal morphology
Abstract The presence of Arbuscular Mycorrhizal Fungi (AMF) in vascular land plant roots is one of the most ancient of symbioses supporting nitrogen and phosphorus exchange for photosynthetically derived carbon. Here we provide a multi-scale modeling approach to predict AMF colonization of a worldwide crop from a Recombinant Inbred Line (RIL) population derived fromSorghum bicolorandS. propinquum. The high-throughput phenotyping methods of fungal structures here rely on a Mask Region-based Convolutional Neural Network (Mask R-CNN) in computer vision for pixel-wise fungal structure segmentations and mixed linear models to explore the relations of AMF colonization, root niche, and fungal structure allocation. Models proposed capture over 95% of the variation in AMF colonization as a function of root niche and relative abundance of fungal structures in each plant. Arbuscule allocation is a significant predictor of AMF colonization among sibling plants. Arbuscules and extraradical hyphae implicated in nutrient exchange predict highest AMF colonization in the top root section. Our work demonstrates that deep learning can be used by the community for the high-throughput phenotyping of AMF in plant roots. Mixed linear modeling provides a framework for testing hypotheses about AMF colonization phenotypes as a function of root niche and fungal structure allocations.
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- Award ID(s):
- 2311186
- PAR ID:
- 10506755
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 14
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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