Contents of Macroelements and Growth of Sweet Cherry Rootstock in vitro

Ružić, Ð.; Šarić, M.; Cerović, R.; Ćulafić, Lj.

doi:10.1023/b:biop.0000023897.84367.41

Cited by 14 publications

(8 citation statements)

References 4 publications

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“…They may induce D type (D 3 ) cycles and so the progress of cell cycle from G 1 to S and may G 2 to M transfer is achieved by CDC2 gene expression induction by H 1 kinase histone and de-phosphorylation induction by Cdc25. Our adverse obtained results can be due to the fact that shooting is under influence of many factors such as genotype, culture medium ( Molassiotis et al, 2003 ), hormones, carbohydrates ( Ruzic et al, 2003 ; Nowak et al, 2004 ), different kinds of hormones ( Pruski, 2007 ; Ruzic and Vujovic, 2008 ; Sedlak and Paprstein, 2008 ), different kinds of agar, explant type and different light periods ( Magyar-Tábori et al, 2010 ). Researches have shown that high concentrations of hormonal combinations can decrease LS, quality and rate of shooting and increase callus development as low concentrations auxin can induce cell division but can act as an inhibitor of axillary bud growth in higher concentrations ( Bagheri et al, 2009 ).…”

Section: Discussionmentioning

confidence: 97%

Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA)

et al. 2017

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The efficiency of a hybrid systems method which combined artificial neural networks (ANNs) as a modeling tool and genetic algorithms (GAs) as an optimizing method for input variables used in ANN modeling was assessed. Hence, as a new technique, it was applied for the prediction and optimization of the plant hormones concentrations and combinations for in vitro proliferation of Garnem (G × N15) rootstock as a case study. Optimizing hormones combination was surveyed by modeling the effects of various concentrations of cytokinin–auxin, i.e., BAP, KIN, TDZ, IBA, and NAA combinations (inputs) on four growth parameters (outputs), i.e., micro-shoots number per explant, length of micro-shoots, developed callus weight (CW) and the quality index (QI) of plantlets. Calculation of statistical values such as R2 (coefficient of determination) related to the accuracy of ANN-GA models showed a considerably higher prediction accuracy for ANN models, i.e., micro-shoots number: R2 = 0.81, length of micro-shoots: R2 = 0.87, CW: R2 = 0.88, QI: R2 = 0.87. According to the results, among the input variables, BAP (19.3), KIN (9.64), and IBA (2.63) showed the highest values of variable sensitivity ratio for proliferation rate. The GA showed that media containing 1.02 mg/l BAP in combination with 0.098 mg/l IBA could lead to the optimal proliferation rate (10.53) for G × N15 rootstock. Another objective of the present study was to compare the performance of predicted and optimized cytokinin–auxin combination with the best optimized obtained concentrations of our other experiments. Considering three growth parameters (length of micro-shoots, micro-shoots number, and proliferation rate), the last treatment was found to be superior to the rest of treatments for G × N15 rootstock in vitro multiplication. Very little difference between the ANN predicted and experimental data confirmed high capability of ANN-GA method in predicting new optimized protocols for plant in vitro propagation.

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Section: Discussionmentioning

confidence: 97%

Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA)

et al. 2017

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“…Successful in vitro clonal propagation methods are reported in many rootstocks, including plum (Morini et al 1990;Fortuna et al 1996;Nacheva et al 2002;Vujović et al 2007), cherry (Ružić, Cerović 1987;Muna et al 1999;Ružić et al 2003;Sedlák et al 2008) and pear rootstocks (Yeo, Reed 1995;De Paoli et al 2002;Ružić et al 2008). While most of the studies were focused on the influence of nutrient media, including mineral composition, carbohydrates content and type/ concentration of plant growth regulators, the influence of repeated subculturing on shoot multiplicaIn vitro shoot multiplication as influenced by repeated subculturing of shoots of contemporary fruit rootstocks T. Vujović 1 tion and growth received less attention in literature (Grant, Hammat 1999).…”

mentioning

confidence: 99%

In vitro shoot multiplication as influenced by repeated subculturing of shoots of contemporary fruit rootstocks

Vujović¹,

Ružić²,

Cerović³

2012

Hortic. Sci.

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Vujović T., Ružić Dj., Cerović R., 2012. In vitro shoot multiplication as influenced by repeated subculturing of shoots of contemporary fruit rootstocks. Hort. Sci. (Prague), 39: 101-107.In vitro shoots of vegetative rootstocks for cherry (Gisela 5 and Gisela 6), plum (Fereley Jaspi) and pear (Pyrodwarf ) were repeatedly subcultured for 10 subcultures on Murashige and Skoog medium of unchanged hormonal composition. Shoot formation capacity decreased over repeated subculturing in all genotypes. The first significant decrease in multiplication index was observed after first subculture in Gisela 6 and Fereley Jaspi, while in Gisela 5 the decline occurred after second subculture, and remained at that level. As for Gisela 6 and Fereley Jaspi, multiplication index was mainly stable from second to forth subculture, whereupon the second decline in shoot formation was observed. Although Pyrodwarf showed very low multiplication capacity, shoot multiplication slightly increased over the first three subcultures and then declined. This irreversible decline could be due to residual effects of hormones. However, no visible morphological variations or aberrations of shoots were found in successive subcultures in any genotype. Quality of shoots in terms of shoot length varied during subculturing, but the highest quality was observed in later subcultures (from fifth subculture onwards). After subculturing, several media were evaluated for induction of rhizogenesis in order to achieve high rooting rates in tested rootstocks. The highest rooting ability (100%) among genotypes was observed in Fereley Jaspi, followed by Pyrodwarf and Gisela 6 (the best rooting percentage being 90% in both) and Gisela 5 (70%). Rooted shoots were successfully acclimatized under the mist system in greenhouse.

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“…The success of in vitro tissue culture is strongly dependent on the chemical composition of the culture medium (Ružić et al 2004). Macronutrient salts are indispensable for the growth of higher plants in vitro, namely N, P, K, Ca, Mg and S. From these, nitrogen is the most important component of the basal medium (Chawala 2002).…”

Section: ⎯⎯⎯⎯mentioning

confidence: 99%

A new medium formulation for in vitro rooting of carob tree based on leaf macronutrients concentrations

Gonçalves¹,

Correia²,

Martins-Loução³

et al. 2005

Biologia plant.

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Experiments were performed to optimize the macronutrients concentrations for in vitro rooting of Ceratonia siliqua micropropagated shoots. Several dilutions of Murashige and Skoog (MS) medium were tested: full-strength MS, halfstrength MS (½MS), and ½MS + full N. The frequency of in vitro rooting was enhanced when the ½MS was used (50 % rooted shoots). Mature leaves from 20 -30 year-old carob trees and from 2 year-old micropropagated plants were collected and the concentrations of macronutrients (N, P, K, Ca, Mg) assessed. Based on the mineral composition of the leaves a new medium was formulated and compared with the previous ones showing an increment of the rooting frequency to 80 %. Moreover, shoots rooted in the new medium did not show symptoms of apical necrosis that occurred in the other tested media.

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Contents of Macroelements and Growth of Sweet Cherry Rootstock in vitro

Cited by 14 publications

References 4 publications

Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA)

Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA)

In vitro shoot multiplication as influenced by repeated subculturing of shoots of contemporary fruit rootstocks

A new medium formulation for in vitro rooting of carob tree based on leaf macronutrients concentrations

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