Raman-Deuterium Isotope Probing and Metagenomics Reveal the Drought Tolerance of the Soil Microbiome and Its Promotion of Plant Growth

No, Jee Hyun; Nishu, Susmita Das; Hong, Jin-Kyung; Lyou, Eun Sun; Kim, Min Sung; Wee, Gui Nam; Lee, Tae Kwon

doi:10.1128/msystems.01249-21

Cited by 8 publications

(5 citation statements)

References 68 publications

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“…The metabolic activity of the strain under drought (−1.25 MPa) was investigated via Raman spectroscopy using heavy water (D 2 O). D 2 O labeling and Raman measurement of strain DR397 was performed according to previously described methods using 40% D 2 O water ( 36 ).…”

Section: Methodsmentioning

confidence: 99%

Transcriptional Response and Plant Growth Promoting Activity of Pseudomonas fluorescens DR397 under Drought Stress Conditions

Nishu

Lee

2022

Microbiol Spectr

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Section: Methodsmentioning

confidence: 99%

Transcriptional Response and Plant Growth Promoting Activity of Pseudomonas fluorescens DR397 under Drought Stress Conditions

Nishu

Lee

2022

Microbiol Spectr

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“…Here, a deeper understanding of the plant growth-promoting functions associated with drought stress-related taxa holds promise for future advancements. This knowledge could play a pivotal role in enhancing plant adaptation to drought stress, strengthening the plant immune system against yield losses, and reducing susceptibility to pathogens [ 83 ].…”

Section: Discussionmentioning

confidence: 99%

Interpretable machine learning decodes soil microbiome’s response to drought stress

Hagen,

Dass,

Westhues

et al. 2024

Environmental Microbiome

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Background Extreme weather events induced by climate change, particularly droughts, have detrimental consequences for crop yields and food security. Concurrently, these conditions provoke substantial changes in the soil bacterial microbiota and affect plant health. Early recognition of soil affected by drought enables farmers to implement appropriate agricultural management practices. In this context, interpretable machine learning holds immense potential for drought stress classification of soil based on marker taxa. Results This study demonstrates that the 16S rRNA-based metagenomic approach of Differential Abundance Analysis methods and machine learning-based Shapley Additive Explanation values provide similar information. They exhibit their potential as complementary approaches for identifying marker taxa and investigating their enrichment or depletion under drought stress in grass lineages. Additionally, the Random Forest Classifier trained on a diverse range of relative abundance data from the soil bacterial micobiome of various plant species achieves a high accuracy of 92.3 % at the genus rank for drought stress prediction. It demonstrates its generalization capacity for the lineages tested. Conclusions In the detection of drought stress in soil bacterial microbiota, this study emphasizes the potential of an optimized and generalized location-based ML classifier. By identifying marker taxa, this approach holds promising implications for microbe-assisted plant breeding programs and contributes to the development of sustainable agriculture practices. These findings are crucial for preserving global food security in the face of climate change.

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Section: Discussionmentioning

confidence: 99%

Interpretable Machine Learning Decodes Soil Microbiome’s Response to Drought Stress

Hagen,

Dass,

Westhues

et al. 2023

Preprint

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BackgroundExtreme weather events induced by climate change, particularly droughts, have detrimental consequences for crop yields and food security. Concurrently, these conditions provoke substantial changes in the soil metagenome. The application of interpretable Machine Learning with SHAP values to this metagenomic data and the comparison with conventional Differential Abundance Analysis methods holds immense potential for identifying marker taxa and enhancing the capacity to predict drought stress.ResultsThis study demonstrates that the metagenomics approach of Differential Abundance Analysis methods, and Machine Learning-based Shapley Additive Explanation values provide similar information and exhibit their potential as complementary approaches for identifying marker taxa and investigating their enrichment or depletion under drought stress. Additionally, the Random Forest Classifier trained on a diverse range of relative abundance data from the soil metagenome of various plant species achieves a high accuracy of 92.3 % at the genus rank. It demonstrates its generalization capacity to a wide spectrum of drought stress levels of various grass lineages.ConclusionsIn the detection of drought stress in the soil metagenome, this study emphasizes the potential of an optimized and generalized location-based ML classifier. By identifying marker taxa, this approach holds promising implications for microbe-assisted plant breeding programs and contributes to the development of sustainable agriculture practices. These findings are crucial for preserving global food security in the face of climate change.

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Raman-Deuterium Isotope Probing and Metagenomics Reveal the Drought Tolerance of the Soil Microbiome and Its Promotion of Plant Growth

Abstract: Soil microbiome has played a critical role in the plant survival during drought. However, the drought tolerance of soil microbiome and its ability to promote plant growth under drought is still scarcely studied.

Cited by 8 publications

References 68 publications

Transcriptional Response and Plant Growth Promoting Activity of Pseudomonas fluorescens DR397 under Drought Stress Conditions

Transcriptional Response and Plant Growth Promoting Activity of Pseudomonas fluorescens DR397 under Drought Stress Conditions

Interpretable machine learning decodes soil microbiome’s response to drought stress

Interpretable Machine Learning Decodes Soil Microbiome’s Response to Drought Stress

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