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Computer vision models enable mixed linear modeling to predict arbuscular mycorrhizal fungal colonization using fungal morphology.
Zhang, Shufan; Wu, Yue; Skaro, Michael; Cheong, Jia-Hwei; Bouffier-Landrum, Amanda; Torrres, Isaac; Guo, Yinping; Stupp, Lauren; Lincoln, Brooke; Prestel, Anna; Felt, Camryn; Spann, Sedona; Mandal, Abhyuday; Johnson, Nancy; Arnold, Jonathan.
Afiliación
  • Zhang S; Institute of Bioinformatics, University of Georgia, Athens, GA, USA.
  • Wu Y; Institute of Bioinformatics, University of Georgia, Athens, GA, USA.
  • Skaro M; Institute of Bioinformatics, University of Georgia, Athens, GA, USA.
  • Cheong JH; Genetics Department, University of Georgia, Athens, GA, USA.
  • Bouffier-Landrum A; Institute of Bioinformatics, University of Georgia, Athens, GA, USA.
  • Torrres I; Institute of Bioinformatics, University of Georgia, Athens, GA, USA.
  • Guo Y; Genetics Department, University of Georgia, Athens, GA, USA.
  • Stupp L; Genetics Department, University of Georgia, Athens, GA, USA.
  • Lincoln B; Genetics Department, University of Georgia, Athens, GA, USA.
  • Prestel A; Genetics Department, University of Georgia, Athens, GA, USA.
  • Felt C; Genetics Department, University of Georgia, Athens, GA, USA.
  • Spann S; School of Earth and Sustainability and Department of Biological Sciences, North Arizona University, Flagstaff, AZ, USA.
  • Mandal A; Statistics Department, University of Georgia, Athens, GA, USA.
  • Johnson N; School of Earth and Sustainability and Department of Biological Sciences, North Arizona University, Flagstaff, AZ, USA.
  • Arnold J; Genetics Department, University of Georgia, Athens, GA, USA. arnold@uga.edu.
Sci Rep ; 14(1): 10866, 2024 05 13.
Article en En | MEDLINE | ID: mdl-38740920
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 from Sorghum bicolor and S. 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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Raíces de Plantas / Micorrizas / Sorghum Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Raíces de Plantas / Micorrizas / Sorghum Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido