Heat stress elicits remodeling in the anther lipidome of peanut

Lwe, Zolian S. Zoong; Welti, Ruth; Anco, Daniel J.; Naveed, Salman; Rustgi, Sachin; Narayanan, Sruthi

doi:10.1038/s41598-020-78695-3

Cited by 29 publications

(31 citation statements)

References 59 publications

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“…3 ), which is a cumulative effect of lipid remodeling. Reducing lipid unsaturation levels in cellular membranes enables plants to maintain optimal membrane fluidity in response to increases in growth temperature [ 27 , 29 , 30 , 32 , 33 , 48 ]. Although a common response in plant lipidomes under high-temperature stress is reductions in unsaturation levels, the magnitude of those reductions is greater for the heat-tolerant genotypes than the heat-susceptible genotypes [ 27 , 29 , 30 , 32 , 33 , 48 ].…”

Section: Discussionmentioning

confidence: 99%

“…The conversion of 16:2 to 16:3 in plastids is also catalyzed by FAD7/8. Decreasing lipid unsaturation levels by lowering the amount of 18:3 and/or 16:3 fatty acids through reducing the expression of FAD3 , FAD7 , and/or FAD8 genes is likely a heat-acclimation mechanism in plants [ 30 , 32 , 49 , 50 ].…”

Section: Discussionmentioning

confidence: 99%

“…Fatty acid desaturase 2 catalyzes the conversion of 18:1 fatty acids to 18:2 in the endoplasmic reticulum (ER). It has been reported that the expression of FAD2 gene increased as an adaptation mechanism to heat stress in multiple species, including peanut ( Arachis hypogaea L.) [ 32 ], soybean ( Glycine max L. Merr.)…”

Section: Discussionmentioning

confidence: 99%

“…Previous research on other species has demonstrated that alterations in lipid metabolism significantly affect a plant’s ability to acclimate to high temperature stress [ 26 – 32 ]. Lipids and proteins are the major constituents of biological membranes.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, recent reports in other species demonstrate that heat tolerant and susceptible genotypes differ in the heat-induced changes in their lipidome. These changes have the potential as biomarkers for selecting heat-tolerant genotypes [ 29 , 30 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

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Alterations in the leaf lipidome of Brassica carinata under high-temperature stress

et al. 2021

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Background Brassica carinata (A) Braun has recently gained increased attention across the world as a sustainable biofuel crop. B. carinata is grown as a summer crop in many regions where high temperature is a significant stress during the growing season. However, little research has been conducted to understand the mechanisms through which this crop responds to high temperatures. Understanding traits that improve the high-temperature adaption of this crop is essential for developing heat-tolerant varieties. This study investigated lipid remodeling in B. carinata in response to high-temperature stress. A commercial cultivar, Avanza 641, was grown under sunlit-controlled environmental conditions in Soil-Plant-Atmosphere-Research (SPAR) chambers under optimal temperature (OT; 23/15°C) conditions. At eight days after sowing, plants were exposed to one of the three temperature treatments [OT, high-temperature treatment-1 (HT-1; 33/25°C), and high-temperature treatment-2 (HT-2; 38/30°C)]. The temperature treatment period lasted until the final harvest at 84 days after sowing. Leaf samples were collected at 74 days after sowing to profile lipids using electrospray-ionization triple quadrupole mass spectrometry. Results Temperature treatment significantly affected the growth and development of Avanza 641. Both high-temperature treatments caused alterations in the leaf lipidome. The alterations were primarily manifested in terms of decreases in unsaturation levels of membrane lipids, which was a cumulative effect of lipid remodeling. The decline in unsaturation index was driven by (a) decreases in lipids that contain the highly unsaturated linolenic (18:3) acid and (b) increases in lipids containing less unsaturated fatty acids such as oleic (18:1) and linoleic (18:2) acids and/or saturated fatty acids such as palmitic (16:0) acid. A third mechanism that likely contributed to lowering unsaturation levels, particularly for chloroplast membrane lipids, is a shift toward lipids made by the eukaryotic pathway and the channeling of eukaryotic pathway-derived glycerolipids that are composed of less unsaturated fatty acids into chloroplasts. Conclusions The lipid alterations appear to be acclimation mechanisms to maintain optimal membrane fluidity under high-temperature conditions. The lipid-related mechanisms contributing to heat stress response as identified in this study could be utilized to develop biomarkers for heat tolerance and ultimately heat-tolerant varieties.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Alterations in the leaf lipidome of Brassica carinata under high-temperature stress

et al. 2021

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Data‐driven sparse partial least squares

Lorenzo

Cloarec²,

Thiébaut

et al. 2021

Statistical Analysis

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In the supervised high dimensional settings with a large number of variables and a low number of individuals, variable selection allows a simpler interpretation and more reliable predictions. That subspace selection is often managed with supervised tools when the real question is motivated by variable prediction. We propose a Partial Least Square (PLS) based method, called data-driven sparse PLS (ddsPLS), allowing variable selection both in the covariate and the response parts using a single hyper-parameter per component. The subspace estimation is also performed by tuning a number of underlying parameters. The ddsPLS method is compared to existing methods such as classical PLS and two well established sparse PLS methods through numerical simulations. The observed results are promising both in terms of variable selection and prediction performance. This methodology is based on new prediction quality descriptors associated with the classical R 2 and Q 2 and uses bootstrap sampling to tune parameters and select an optimal regression model.

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