Dataset Information

Mechanistic modeling of light-induced chemotactic infiltration of bacteria into leaf stomata.

ABSTRACT: Light is one of the factors that can play a role in bacterial infiltration into leafy greens by keeping stomata open and providing photosynthetic products for microorganisms. We model chemotactic transport of bacteria within a leaf tissue in response to photosynthesis occurring within plant mesophyll. The model includes transport of carbon dioxide, oxygen, bicarbonate, sucrose/glucose, bacteria, and autoinducer-2 within the leaf tissue. Biological processes of carbon fixation in chloroplasts, and respiration in mitochondria of the plant cells, as well as motility, chemotaxis, nutrient consumption and communication in the bacterial community are considered. We show that presence of light is enough to boost bacterial chemotaxis through the stomatal opening and toward photosynthetic products within the leaf tissue. Bacterial chemotactic ability is a major player in infiltration, and plant stomatal defense in closing the stomata as a perception of microbe-associated molecular patterns is an effective way to inhibit the infiltration.

SUBMITTER: Ranjbaran M

PROVIDER: S-EPMC7209104 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Mechanistic modeling of light-induced chemotactic infiltration of bacteria into leaf stomata.

Ranjbaran Mohsen M Solhtalab Mina M Datta Ashim K AK

PLoS computational biology 20200508 5

Light is one of the factors that can play a role in bacterial infiltration into leafy greens by keeping stomata open and providing photosynthetic products for microorganisms. We model chemotactic transport of bacteria within a leaf tissue in response to photosynthesis occurring within plant mesophyll. The model includes transport of carbon dioxide, oxygen, bicarbonate, sucrose/glucose, bacteria, and autoinducer-2 within the leaf tissue. Biological processes of carbon fixation in chloroplasts, an ...[more]

PMID: 32384085

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Project description:Plant leaf stomata are the gatekeepers of the atmosphere-plant interface and are essential building blocks of land surface models as they control transpiration and photosynthesis. Although more stomatal trait data are needed to significantly reduce the error in these model predictions, recording these traits is time-consuming, and no standardized protocol is currently available. Some attempts were made to automate stomatal detection from photomicrographs; however, these approaches have the disadvantage of using classic image processing or targeting a narrow taxonomic entity which makes these technologies less robust and generalizable to other plant species. We propose an easy-to-use and adaptable workflow from leaf to label. A methodology for automatic stomata detection was developed using deep neural networks according to the state of the art and its applicability demonstrated across the phylogeny of the angiosperms.We used a patch-based approach for training/tuning three different deep learning architectures. For training, we used 431 micrographs taken from leaf prints made according to the nail polish method from herbarium specimens of 19 species. The best-performing architecture was tested on 595 images of 16 additional species spread across the angiosperm phylogeny.The nail polish method was successfully applied in 78% of the species sampled here. The VGG19 architecture slightly outperformed the basic shallow and deep architectures, with a confidence threshold equal to 0.7 resulting in an optimal trade-off between precision and recall. Applying this threshold, the VGG19 architecture obtained an average F-score of 0.87, 0.89, and 0.67 on the training, validation, and unseen test set, respectively. The average accuracy was very high (94%) for computed stomatal counts on unseen images of species used for training.The leaf-to-label pipeline is an easy-to-use workflow for researchers of different areas of expertise interested in detecting stomata more efficiently. The described methodology was based on multiple species and well-established methods so that it can serve as a reference for future work.

Project description:BACKGROUND AND AIMS: Leaf transpiration is a key parameter for understanding land surface-climate interactions, plant stress and plant structure–function relationships. Transpiration takes place at the microscale level, namely via stomata that are distributed discretely over the leaf surface with a very low surface coverage (approx. 0·2-5%). The present study aims to shed more light on the dependency of the leaf boundary-layer conductance (BLC) on stomatal surface coverage and air speed. METHODS: An innovative three-dimensional cross-scale modelling approach was applied to investigate convective mass transport from leaves, using computational fluid dynamics. The gap between stomatal and leaf scale was bridged by including all these scales in the same computational model (10??-10?¹ m), which implies explicitly modelling individual stomata. KEY RESULTS: BLC was strongly dependent on stomatal surface coverage and air speed. Leaf BLC at low surface coverage ratios (CR), typical for stomata, was still relatively high, compared with BLC of a fully wet leaf (hypothetical CR of 100%). Nevertheless, these conventional BLCs (CR of 100%), as obtained from experiments or simulations on leaf models, were found to overpredict the convective exchange. In addition, small variations in stomatal CR were found to result in large variations in BLCs. Furthermore, stomata of a certain size exhibited a higher mass transfer rate at lower CRs. CONCLUSIONS: The proposed cross-scale modelling approach allows us to increase our understanding of transpiration at the sub-leaf level as well as the boundary-layer microclimate in a way currently not feasible experimentally. The influence of stomatal size, aperture and surface density, and also flow-field parameters can be studied using the model, and prospects for further improvement of the model are presented. An important conclusion of the study is that existing measures of conductances (e.g. from artificial leaves) can be significantly erroneous because they do not account for microscopic stomata, but instead assume a uniform distribution of evaporation such as found for a fully-wet leaf. The model output can be used to correct or upgrade existing BLCs or to feed into higher-scale models, for example within a multiscale framework.

Dataset Information

Mechanistic modeling of light-induced chemotactic infiltration of bacteria into leaf stomata.

Publications

Mechanistic modeling of light-induced chemotactic infiltration of bacteria into leaf stomata.

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