Dataset Information

Molecular Mapping of Water-Stress Responsive Genomic Loci in Lettuce (Lactuca spp.) Using Kinetics Chlorophyll Fluorescence, Hyperspectral Imaging and Machine Learning.

ABSTRACT: Deep understanding of genetic architecture of water-stress tolerance is critical for efficient and optimal development of water-stress tolerant cultivars, which is the most economical and environmentally sound approach to maintain lettuce production with limited irrigation. Lettuce (Lactuca sativa L.) production in areas with limited precipitation relies heavily on the use of ground water for irrigation. Lettuce plants are highly susceptible to water-stress, which also affects their nutrient uptake efficiency. Water stressed plants show reduced growth, lower biomass, and early bolting and flowering resulting in bitter flavors. Traditional phenotyping methods to evaluate water-stress are labor intensive, time-consuming and prone to errors. High throughput phenotyping platforms using kinetic chlorophyll fluorescence and hyperspectral imaging can effectively attain physiological traits related to photosynthesis and secondary metabolites that can enhance breeding efficiency for water-stress tolerance. Kinetic chlorophyll fluorescence and hyperspectral imaging along with traditional horticultural traits identified genomic loci affected by water-stress. Supervised machine learning models were evaluated for their accuracy to distinguish water-stressed plants and to identify the most important water-stress related parameters in lettuce. Random Forest (RF) had classification accuracy of 89.7% using kinetic chlorophyll fluorescence parameters and Neural Network (NN) had classification accuracy of 89.8% using hyperspectral imaging derived vegetation indices. The top ten chlorophyll fluorescence parameters and vegetation indices selected by sequential forward selection by RF and NN were genetically mapped using a L. sativa × L. serriola interspecific recombinant inbred line (RIL) population. A total of 25 quantitative trait loci (QTL) segregating for water-stress related horticultural traits, 26 QTL for the chlorophyll fluorescence traits and 34 QTL for spectral vegetation indices (VI) were identified. The percent phenotypic variation (PV) explained by the horticultural QTL ranged from 6.41 to 19.5%, PV explained by chlorophyll fluorescence QTL ranged from 6.93 to 13.26% while the PV explained by the VI QTL ranged from 7.2 to 17.19%. Eight QTL clusters harboring co-localized QTL for horticultural traits, chlorophyll fluorescence parameters and VI were identified on six lettuce chromosomes. Molecular markers linked to the mapped QTL clusters can be targeted for marker-assisted selection to develop water-stress tolerant lettuce.

SUBMITTER: Kumar P

PROVIDER: S-EPMC7935093 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Molecular Mapping of Water-Stress Responsive Genomic Loci in Lettuce (<i>Lactuca</i> spp.) Using Kinetics Chlorophyll Fluorescence, Hyperspectral Imaging and Machine Learning.

Kumar Pawan P Eriksen Renee L RL Simko Ivan I Mou Beiquan B

Frontiers in genetics 20210218

Deep understanding of genetic architecture of water-stress tolerance is critical for efficient and optimal development of water-stress tolerant cultivars, which is the most economical and environmentally sound approach to maintain lettuce production with limited irrigation. Lettuce (<i>Lactuca sativa</i> L.) production in areas with limited precipitation relies heavily on the use of ground water for irrigation. Lettuce plants are highly susceptible to water-stress, which also affects their nutri ...[more]

PMID: 33679897

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Project description:Bolting is a key process in the growth and development of lettuce (Lactuca sativa L.). High temperature can induce earlier bolting which decreases in both quality and production of lettuce. However, knowledge underlying lettuce bolting is still lacking. To better understand the molecular basis of bolting, a comparative proteomics analysis was conducted on lettuce stems in the bolting period induced by high temperature (33 °C) compared with a control (20 °C) using iTRAQ-based proteomics, phenotypic measures, and biological verifications. High temperature induced lettuce bolting, while control temperature did not. Of the 6656 proteins identified, 758 proteins significantly altered their expression level induced by high-temperature relative to the control, of which 409 were up-regulated and 349 down-regulated. Proteins with abundance level change were mainly involved in photosynthesis, carbohydrate metabolism, stress response, hormone synthesis, and signal transduction. These differential proteins were mainly enriched in pathways associated with photosynthesis and tryptophan metabolism involving in auxin (IAA) biosynthesis. Among the differentially expressed proteins associated with photosynthesis and tryptophan metabolism were up-regulated. Moreover, in gibberellin (GA) biosynthesis pathway, 10 of main enzymes of P450 were up-regulated. Proteins related to SAUR and GRP, implicated in IAA and GA signal transduction were up-regulated, and the phosphorylation and ubiquitination related proteins regulating IAA and GA signal transduction were also induced. These findings indicate that a high temperature enhances the function of photosynthesis, IAA and GA synthesis and signal transduction to promote the process of bolting, which is in line with the physiology and transcription levels of IAA and GA metabolism. Our data provide a first comprehensive dataset for gaining novel understanding of the molecular basis underlying lettuce bolting induced by high temperature. It is potentially important for further functional analysis and genetic manipulation for molecular breeding to breed new cultivar of lettuce to restrain early bolting, which is vital for improving vegetable quality.

Dataset Information

Molecular Mapping of Water-Stress Responsive Genomic Loci in Lettuce (Lactuca spp.) Using Kinetics Chlorophyll Fluorescence, Hyperspectral Imaging and Machine Learning.

Publications

Molecular Mapping of Water-Stress Responsive Genomic Loci in Lettuce (<i>Lactuca</i> spp.) Using Kinetics Chlorophyll Fluorescence, Hyperspectral Imaging and Machine Learning.

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