Quantitative characteristics of pubescence in wheat (Triticum aestivum L.) are associated with photosynthetic parameters under conditions of normal and limited water supply

Пшеничникова, Т. А.; Дорошков, А. В.; Osipova, S. V.; Permyakov, A. V.; Permyakova, M. D.; Ефимов, В. М.; Afonnikov, D. A.

doi:10.1007/s00425-018-3049-9

Cited by 31 publications

(32 citation statements)

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“…Previously, photosynthetic indicators were studied in these two lines under natural light and contrasting water supply. The lines were found to be contrast in terms of gas exchange (Pshenichnikova et al, 2019). However, no clear answer was obtained in respect of the pubescence influence on the parameters of chlorophyll fluorescence, which describe the physiological state of the plants photosynthetic apparatus (Goltsev et al, 2016).…”

Section: Introductionmentioning

confidence: 91%

“…In Oryza sativa L. in trogression of a chromosome segment from the wild species Oryza nivara increased leaf pubescence, reduced transpira tion rate and increased water use efficiency due to increased stability of the boundary air layer (Hamaoka et al, 2017). The only experiment in T. aestivum L. showed that the stomatal conductivity and the photosynthetic rate in substituted and near-isogenic lines with genetically different leaf pubescence were inversely proportional to the density and trichomes length (Pshenichnikova et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Physiological responses to water deficiency in bread wheat (Triticum aestivum L.) lines with genetically different leaf pubescence

et al. 2020

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Studying the relationship between leaf pubescence and drought resistance is important for assessing Triticum aestivum L. genetic resources. The aim of the work was to assess resistance of common wheat genotypes with different composition and allelic state of genes that determine the leaf pubescence phenotype. We compared the drought resistance wheat variety Saratovskaya 29 (S29) with densely pubescent leaves, carrying the dominant alleles of the Hl1 and Hl3 genes, and two near isogenic lines, i: S29 hl1, hl3 and i: S29 Hl2aesp, with the introgression of the additional pubescence gene from diploid species Aegilops speltoides. Under controlled conditions of the climatic chamber, the photosynthetic pigments content, the activity of ascorbate-glutathione cycle enzymes and also the parameters of chlorophyll fluorescence used to assess the physiological state of the plants photosynthetic apparatus were studied in the leaves of S29 and the lines. Tolerance was evaluated using the comprehensive index D, calculated on the basis of the studied physiological characteristics. The recessive state of pubescence genes, as well as the introduction of the additional Hl2aesp gene, led to a 6-fold decrease in D. Under the water deficit influence, the fluorescence parameters profile changed in the lines, and the viability index decreased compared with S29. Under drought, the activity of ascorbate peroxidase, glutathione reductase and dehydroascorbate reductase in the line i: S29 hl1, hl3 decreased 1.9, 3.3 and 2.3 times, in the line i: S29 Hl2aesp it decreased 1.8, 3.6 and 1.8 times respectively, compared with S29. In a hydroponic greenhouse, line productivity was studied. Compared with S29, the thousand grains mass in the line i: S29 hl1, hl3 under water deficit was reduced. The productivity of the line i: S29 Hl2aesp was significantly reduced regardless of water supply conditions in comparison with S29. Presumably, the revealed effects are associated with violations of cross-regulatory interactions between the proteins of the trichome formation network and transcription factors that regulate plant growth and stress response.

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Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Physiological responses to water deficiency in bread wheat (Triticum aestivum L.) lines with genetically different leaf pubescence

et al. 2020

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“…RuBP was estimated in response to apparent mesophyll conductance (AMC) as follows [3]: (1). And WUE was also calculated as follows [10,17]: (2). Then the corresponding F 4 plants were harvested and subsequently air-dried to evaluated ear weight (EW), grain weight per ear (GW), and 100-kernel weight (KW).…”

Section: Field Experiments and Trait Evaluationmentioning

confidence: 99%

Genetic dissection of photosynthetic performances in maize under drought-stressed and well-watered environments

Zhao

Zhang

2020

Preprint

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BackgroundMaintaining photosynthetic capacity is a critical function that allows maize (Zea mays L.) to adapt to drought stress. The elucidation of genetic controls of photosynthetic performances, and tightly linked molecular markers under water stress are thus of great importance in marker-assisted selection (MAS) breeding. Meanwhile, little is known regarding their genetic controls under drought stress. Two F4 populations were developed to identify quantitative trait loci (QTLs) and dissect the genetic variation underlying six photosynthetic-related traits, namely, net photosynthetic rate (Pn), stomatal conductance (Gs), intercellular CO2 concentration (Ci), transpiration rate (Tr), ribulose 1,5-biphosphate carboxylase activity (RuBP), and water use efficiency (WUE) under drought-stressed and well-watered environments.ResultsFor two populations, we detected 54 QTLs under drought-stressed and well-watered environments by single-environment mapping with composite interval mapping (CIM), approximately 81.8~100 % QTLs displayed non-additive effects, and 43 of the 54 QTLs were identified under drought-stressed environment. We also dissected 54 QTLs via joint analysis of all environments with mixed-linear-model-based composite interval mapping (MCIM), 24 QTLs involved in QTL × environment interactions (QEIs), approximately 87.5 % QEIs were identified under drought-stressed environments, as well as 14 pair epistasis exhibited dominance-by-additive/dominance (DA/DD) effects under constracting environments. We further identified 8 constitutive QTLs (cQTLs) across two populations by CIM/MCIM under multiple environments. Remarkably, bin 1.07_1.10 (cQTL2), bin 6.05 (cQTL5), bin 7.02_7.04 (cQTL6), bin 8.03 (cQTL7), and bin 10.03 (cQTL8) exhibited 5 pleiotropic cQTLs that were consistent with phenotypic correlations among all photosynthetic-related traits. Additionally, 17 candidate genes were validated in above cQTLs.ConclusionsPhotosynthetic performances in maize were predominantly controlled by non-additive and QEIs effects, where more QEIs effects occurred in drought stress. 8 cQTLs affecting six photosynthetic-related traits could be useful for genetic improvement of these traits via QTL pyramiding, corresponding 5 QTLs clusters indicated tight linkage or pleiotropy in the inheritance of these traits, and 17 candidate genes involved in leaf morphology and development, photosynthesis, and stress reponse coincided with above corresponding cQTLs.

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“…RuBP was estimated in response to apparent mesophyll conductance (AMC) as follows [3]: RuBP = Pn/Ci (1). And WUE was also calculated as follows [10,17]: WUE = Pn/Tr (2). Then the corresponding F4 plants were harvested and subsequently air-dried to evaluated ear weight (EW), grain weight per ear (GW), and 100-kernel weight (KW).…”

Section: Field Experiments and Trait Evaluationmentioning

confidence: 99%

“…Using identified molecular markers and quantitative trait loci (QTLs) for photosynthetic-related traits under different watering treatments in marker-assisted selection (MAS) breeding is a promising way to maximize the productivity of maize grown in droght land. Until now, genetic studies on wheat (Triticum aestivum L.) [10], rice (Oryza sativa L.) [11][12][13], sunflower (Helianthus annuus L.) [14], rapeseed (Brassica napus L.) [15], cotton (Gossypium spp) [16][17] etc. for photosynthetic-related traits have been extensively conducted using QTL mapping during drought, high temperature, salt and alkali stresses.…”

mentioning

confidence: 99%

Genetic dissection of photosynthetic performances in maize under drought-stressed and well-watered environments

Zhao

Lü

Bai

et al. 2019

Preprint

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Background: Maintaining photosynthetic capacities is a critical function that allows maize ( Zea mays L.) to adapt to drought stress. The elucidation of genetic controls of photosynthetic performances, and tightly linked molecular markers under water stress are thus of great importance in marker-assisted selection (MAS) breeding. Meanwhile, little is known regarding their genetic controls under drought stress. Two F 4 populations were developed to identify quantitative trait loci (QTLs) and dissect the genetic variation underlying six photosynthetic-related traits, namely, net photosynthetic rate (Pn), stomatal conductance (Gs), intercellular CO 2 concentration (Ci), transpiration rate (Tr), ribulose 1,5-biphosphate carboxylase activity (RuBP), and water use efficiency (WUE) under drought-stressed and well-watered environments. Results: For two populations, we detected 54 QTLs under drought-stressed and well-watered environments by single-environment mapping with composite interval mapping (CIM), approximately 81.8~100 % QTLs displayed non-additive effects, and 43 of the 54 QTLs were identified under drought-stressed environment. We also dissected 54 QTLs via joint analysis of all environments with mixed-linear-model-based composite interval mapping (MCIM), 24 QTLs involved in QTL × environment interactions (QEIs), approximately 87.5 % QEIs were identified under drought-stressed environments, as well as 14 pair epistasis exhibited dominance-by-additive/dominance (DA/DD) effects under constracting environments. We further identified 8 constitutive QTLs (cQTLs) across two populations by CIM/MCIM under multiple environments. Remarkably, bin 1.07_1.10 (cQTL2), bin 6.05 (cQTL5), bin 7.02_7.04 (cQTL6), bin 8.03 (cQTL7), and bin 10.03 (cQTL8) exhibited 5 pleiotropic cQTLs that were consistent with phenotypic correlations among all photosynthetic-related traits. Additionally, 17 candidate genes were validated in above cQTLs. Conclusions: Photosynthetic performances in maize were predominantly controlled by non-additive and QEIs effects, where more QEIs effects occurred in drought stress. 8 cQTLs affecting six photosynthetic-related traits could be useful for genetic improvement of these traits via QTL pyramiding, corresponding 5 QTLs clusters indicated tight linkage or pleiotropy in the inheritance of these traits, and 17 candidate genes involved in leaf morphology and development, photosynthesis, and stress reponse coincided with above corresponding cQTLs.

show abstract

Quantitative characteristics of pubescence in wheat (Triticum aestivum L.) are associated with photosynthetic parameters under conditions of normal and limited water supply

Cited by 31 publications

References 19 publications

Physiological responses to water deficiency in bread wheat (Triticum aestivum L.) lines with genetically different leaf pubescence

Physiological responses to water deficiency in bread wheat (Triticum aestivum L.) lines with genetically different leaf pubescence

Genetic dissection of photosynthetic performances in maize under drought-stressed and well-watered environments

Genetic dissection of photosynthetic performances in maize under drought-stressed and well-watered environments

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