Time-Series Chlorophyll Fluorescence Imaging Reveals Dynamic Photosynthetic Fingerprints of sos Mutants to Drought Stress

Resistance to drought stress is one of the most favorable traits in breeding programs yet drought stress is one of the most poorly addressed biological processes for both phenomics and genetics. In this study, we investigated the potential of using a time-series chlorophyll fluorescence (ChlF) analy...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2019-06, Vol.19 (12), p.2649
Main Authors: Sun, Dawei, Zhu, Yueming, Xu, Haixia, He, Yong, Cen, Haiyan
Format: Article
Language:English
Subjects:
Abiotic stress
Agricultural production
Arabidopsis thaliana
Automation
Biological activity
Chlorophyll
chlorophyll fluorescence imaging
Discriminant analysis
Drought
drought stress
Fingerprints
Fluorescence
Function analysis
Genes
Genomes
Genomics
Homeostasis
Kinases
Laboratories
Oxidative stress
Photosynthesis
Physiology
Proteins
Salt
salt overly sensitive (SOS) pathway
Signal transduction
Support vector machines
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Summary:Resistance to drought stress is one of the most favorable traits in breeding programs yet drought stress is one of the most poorly addressed biological processes for both phenomics and genetics. In this study, we investigated the potential of using a time-series chlorophyll fluorescence (ChlF) analysis to dissect the ChlF fingerprints of salt overly sensitive (SOS) mutants under drought stress. Principle component analysis (PCA) was used to identify a shifting pattern of different genotypes including mutants and wild type (WT) Col-0. A time-series deep-learning algorithm, sparse auto encoders (SAEs) neural network, was applied to extract time-series ChlF features which were used in four classification models including linear discriminant analysis (LDA), k-nearest neighbor classifier (KNN), Gaussian naive Bayes (NB) and support vector machine (SVM). The results showed that the discrimination accuracy of mutants SOS1-1, SOS2-3, and wild type Col-0 reached 95% with LDA classification model. Sequential forward selection (SFS) algorithm was used to obtain ChlF fingerprints of the shifting pattern, which could address the response of mutants and Col-0 to drought stress over time. Parameters including , and , etc. were significantly different between mutants and WT. This research proved the potential of ChlF imaging for gene function analysis and the study of drought stress using ChlF in a time-series manner.
ISSN:1424-8220
1424-8220
DOI:10.3390/s19122649