Kappes, M. | bioRxiv Science

bioRxiv2024

Medicago truncatula CORYNE regulates inflorescence meristem branching, nutrient signaling, and arbuscular mycorrhizal symbiosis

The CLAVATA signaling pathway regulates plant development and plant-environment interactions. CLAVATA signaling consists of mobile, cell-type or environment-specific CLAVATA3/ESR-related (CLE) peptides, which are perceived by a receptor complex consisting of leucine-rich repeat receptor-like kinases such as CLAVATA1 and receptor-like proteins such as CLAVATA2, which often functions with the pseudokinase CORYNE (CRN). CLAVATA signaling has been extensively studied in various plant species for its role in meristem maintenance and in legumes for modulating root interactions with nitrogen-fixing rhizobia. Some signaling proteins involved in development and nodulation, including CLAVATA1, also regulate plant interactions with mutualistic arbuscular mycorrhizal (AM) fungi. However, our knowledge on AM symbiosis regulation by CLAVATA signaling remains limited and only a handful of genetic regulators have been identified. Here we report that Medicago truncatula CRN controls inflorescence meristem branching and negatively regulates root interactions with AM fungi. MtCRN functions partially independently of the AM autoregulation signal MtCLE53. Transcriptomic data revealed that crn roots display signs of perturbed nutrient, symbiosis, and stress signaling, suggesting that MtCRN plays various roles in plant development and interactions with the environment.

plant biology↗

bioRxiv2023

Fast and efficient root phenotyping via pose estimation

Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plants phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train), and error-prone (derived geometric features are sensitive to instance mask integrity). Here we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library (sleap-roots) for trait extraction directly comparable to existing segmentation-based analysis software. We show that landmark-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots, all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/.

plant biology↗