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Title of Manuscript: Morphogenetic Potential "in vitro" for Stevia rebaudiana (Bertoni) Grown on Mediawith Variable CompositionsAuthor(s): GABRIELA ZBUGHIN, TIBERIU-MIHAI STURZU, CONSTANTIN TOMA

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Title:

Morphogenetic Potential "in vitro" for Stevia rebaudiana (Bertoni)

Grown on Media with Variable Compositions

Running Title:

Morphogenetic Potential "in vitro" for Stevia rebaudiana

Title page

Morphogenetic Potential "in vitro" for Stevia rebaudiana (Bertoni) Grown

on Media with Variable Compositions

Gabriela Zbughin, Technological High School of Coţuşca, Department of Natural

Sciences, Botoşani County,

Tiberiu-Mihai Sturzu, “Petru Poni” Technological High School of Jassy,

Department of Industrial Chemistry, Natural Resources and Environmental

Protection,

Constantin Toma, “A. I. Cuza” University of Jassy, Faculty of Biology,

Department of Vegetal Biology,

Romania

2

Abstract: Stevia rebaudiana (Bertoni) is a plant originally from Paraguay,

which, in temperate climate produces seeds without germination capacity, the

only way being the vegetative propagation.

We studied the influence of growth regulators concentrations acid β-

indolylacetic acid and benzyl adenine added to Murashige and Skoog medium

(1962) on the processes of morphogenesis "in vitro" for this plant and

determined the composition of the mixture of growth regulators which maximize

the values of bio-measured characteristics. Culture media tested were determined

according to a central compound routable program of second degree. It was, in

Romania, the first application of such a complex program in Biotechnology.

              After processing the experimental results, there were obtained

mathematical models - in coded and real variables - that allow the optimization

of the processes of morphogenesis throughout the analyzed domain of growth

regulators concentrations. The mathematical models may be used to calculate the

value of bio-measured characteristics in any point of analyzed domain, because

Student and Fischer tests were performed and - after eliminating insignificant

coefficients – we found that the models are suitable for a probability of 95%.

The characteristics of alive may be calculated with complex equations.

              Using an original computer program, there were obtained 3D

representations that indicate the existence of optimum within the analyzed

domain for three of the five bio-measured characteristics.

Culture media compositions and nature of the hormonal balance in these

media led the regeneration of vitro-plants capable of acclimatization and life

"ex vitro" in the greenhouses of Botanical Garden from Jassy, Romania.

Keywords: 3D representation, axillaries buds, central compound routable program

of second degree, leaves, roots, secondary cops, stems, vitro-plants.3

Introduction

Stevia rebaudiana (Bertoni) is a perennial shrub from Asteraceae family, proceeding

from the north-east of Paraguay. Its leaves contain sweet substances from

glycoside group, the most important being the stevyoside, a natural sweetening

substance that is frequently used in Japan and Korea, with a power of sweetening

one hundred greater than sugar.

In temperate climate areas, stevia does not produce seeds. If the seeds are

formed they are either missed of germination capacity or the germination is too

weak [1]. For obtaining the material that is to be planted, which is necessary

for the foundation of a stevia culture, a fit method is "in vitro" micro-

propagation technique. Our goal was the study of the -idolylacetic acid (IAA)

and benzyl-adenine (BA) growing regulators’ influence to some bio-measured

characteristics of "in vitro" obtained plants and the determination of the

growing regulators mixture’s composition which maximize the bio-measured

characteristics.

Materials and methods

As an initial material we used vitro-plants of Stevia rebaudiana (Bert.) that

were obtained in colony of meristems at Biological Research Institute from Cluj-

Napoca. The multiplication of stevia for getting the biological material used

for the experiments was made on Murashige and Skoog medium (MS, 1962). For

realizing the experimental program we dimensioned uninodal seedlings, excluding

the apical portion of tulips, seedlings that were given by the same group.

Crop mediums that were tested were MS (1962) with an addition of IAA and BA

in various concentrations in accordance with a central compound routable program

of second degree (CCRP2). The experimental matrix is presented in Table 1, the

experimental results’ column being the average of 10 experimental

4

determinations. The sterilization of mediums was made at a temperature of 18 –

19° C, a light intensity of 3,000 lx, fluorescent white rays, for a period of 14

hours of light and 14 hours of dark. After a period of "in vitro" evolution we

made some bio-measures.

Results and their interpretation

Having as a base the experimental results from Table 1, we got the

coefficients for a regressive equation, whose type is [2]:

yi = b0 + b1 x1 + b2 x2 + b12 x1 x2 + b11 x1 2 + b22 x2 2 (1)

x1 and x2 being independent codified variables, and yi being regressive estimated

values for the bio-measured characteristics. In accordance with these

coefficients one can get the regressive equation:

yi = a0 + a1 z1 + a2 z2 + a12 z1 z2 + a11 z1 2 + a22 z2 2 (2)

z1 and z2 being IAA and BA concentrations, thus we realized the mathematical

modeling in real variable, using the relationships:

The mathematical models of (1) or (2) type, that were obtained out of

experimental results (see Table 2 and Table 3) can be used for the calculating

the bio-measured characteristics in any point of extremis experiment realizing

interval, because there were realized Student and Fischer tests [3] (see Table 2

and Table 3) and – after non-significant coefficients’ elimination – one could

5

(3)

(4)

(5)

see that the models are adequate for an confidence level of 5% (a probability of

95%).

Table 1- Experimental matrix for CCP2

No.of

exp.

Ran-

domisati-on

INDEPENDENTVARIABLES

DEPENDENT BIOMEASURED VARIABLES (average values)

x1

z1,

IAAconcen-

tration mg/L

x2

z2 ,

BAconc.mg/L

y1,

number of

leaves

y2,

height of

stems, cm

y3,

number ofaxillarie

sbuds

y4,

number ofsecondary

cops

y5,

number of

basal

roots

1. 013 -1 0.30 -1 0.30 28.9 5.25 2.1 0.7 1.6

2. 003 1 0.90 -1 0.30 20.0 4.57 0.2 0.4 1.3

3. 008 -1 0.30 1 0.90 31.8 4.60 3.6 2.1 0.8

4. 004 1 0.90 1 0.90 39.0 3.50 2.7 3.0 2.1

5. 009 -1.414

0.18 0 0.60 29.5 3.85 1.0 0.9 1.4

6. 005 +1.414

1.02 0 0.60 30.2 4.60 2.9 1.7 1.6

7. 007 0 0.60 -1.414

0.18 24.3 7.10 4.1 0.3 2.3

8. 010 0 0.60 +1.414

1.02 35.5 3.50 6.1 2.4 0.9

9. 002 0 0.60 0 0.60 35.8 6.90 3.7 2.6 2.4

10. 012 0 0.60 0 0.60 33.4 5.70 1.6 2.9 3.8

11. 001 0 0.60 0 0.60 34.6 6.20 2.3 3.0 3.1

12. 006 0 0.60 0 0.60 35.6 6.50 2.7 2.8 2.9

13. 011 0 0.60 0 0.60 34.9 6.60 2.8 2.7 3.3

Table 2 – Regressive equations coefficients in codified

variables and Student’s tests

tc = Student calculating criterion, ttab 0,05 (4) = 2,132, S – significant , N – non

significant

y1 y2 y3 y4 y5

6

Coeffi-

cient

Value

tcResult

of test

Value

tc Resultof test

Value

tcResult

of test

Value

tcResult

of test

Value

tcResult of test

b0 34.74

89.23

S 6.38

31.35

S 2.6 7.64

S 2.80

39.6

S 3.1 13.46

S

b1 -0.10

0.32

N -0.08

0.56

N -0.01

0.05

N 0.21

3.87

S 0.16

0.88

N

b2 4.42

14.4

S -0.85

5.29

S 0.85

3.15

S 0.87

15.58

S -0.24

1.36

N

b12 4.45

10.2

S -0.10

0.46

N 0.25

0.65

N 0.30

3.79

S 0.40

1.55

N

b11 -2.39

7.25

S -1.14

6.65

S -0.68

2.34

S -0.69

11.57

S -0.83

4.23

S

b22 -2.49

7.55

S -0.61

3.54

S 0.89

3.08

S -0.67

11.15

S -0.77

3.97

S

Table 3 – Regressive equations coefficients in real variables

Coeff

i-

cient

y1 y2 y3 y4 y5

a0 24.34598 0.80651 2.81031 -3.62615 -1.52681

a1 1.93349 15.71172 7.34083 7.97257 8.86976

a2 18.32783 6.00463 -10.77283 9.82205 6.84335

a12 49.44444 -1.16667 +2.77778 3.33333 4.4444

a11 -26.61483 -12.75963 -7.54563 -7.70927 -9.1682

a22 -27.7262 -6.7856 +9.95966 -7.4314 -8.61248

Table 4 – Multiple correlation coefficients and Fischer’s tests

y1 y2 y3 y4 y5

7

0.

98

1.87<6

.59

0.

91

3.13<6

.39

0.

78

3.28<6

.39

0.

98

5.39<6

.94

0.

92

0.433<6

.26

Multiple correlation coefficients that are shown in Table 4 prove that

there are very good pieces of correlation between the dependent variable yi and

the two independent variables, that means the bio-measured characteristics can

be correlated with the two growing regulators’ concentrations being influenced

by them.

Considering the regressive equations, there were obtained the tri-

dimensional (3D) representations that are shown in 1-5 figures. Analyzing these

3D representations one can get the following conclusions:

the average number of leaves (Fig. 1) grows with the growing of the two

regulators concentrations, but the maximum value is out of the analyzed

domain, for z1* = 2.03 mg/L IAA and z2

* = 2.15 mg/L BA; for the extremal

experiment realizing domain is preferable to choose a mixture of 1 mg/L

IAA and 1 mg/L BA, point that is situated on the gradient direction, that

confirms the previous pieces of information [4]; analyzing the calculated

Student criteria (see Table 2) one can infer an influence of 55.6% of BA

concentration, of 18.4% for IAA concentration and 26% for the interaction

of the two growing regulators, that confirms the known data published in

specialized literature regarding the cytokinines’ effect in morphogenesis

processes and their interaction with the auxynes [5];

the average height of stems (Fig. 2) has a maximum in extremis experiment

realizing domain, having a value of 6.68 cm, for a mixtures of growing

8

regulators of 0.6 mg/L IAA and 0.39 mg/L BA; analogous one can infer an

influence of 57% for BA concentration and of 43% for IAA concentration,

that is in accordance with the known data, published in specialized

literature [6, 7];

the average number of axillaries buds (Fig. 3) hasn’t any maximum in

extremis experiment realizing domain, but there is a “saddle” area having

as middle the point of (0.6; 0.457) co-ordinates; one can establish that

– inside the analyzed domain – the hyperbolas converges to a point that

corresponds to a 0.6 mg/L IAA and 1 mg/L BA mixture; the influences that

can be inferred out of the Student criteria are 27% for IAA and 73% for BA

concentrations, that are confirmed by the previous remarks;

the average number of secondary cops (Fig. 4) is maximum (3.15) for a

mixture of growing regulators of 0.695 mg/L IAA and 0.815 mg/L BA; BA

concentration has an influence of 58.2%, IAA concentration has an

influence of 33.6% and the interaction of the two regulators – an

influence of 8.2%;

the average number of basal roots (Fig. 5) is maximum – 3.1 – for a 0.62

mg/L IAA and 0.56 mg/L BA concentrations, which corresponds to the basic

level; IAA concentration influences in a per cent of 51.58% and BA

concentration has an influence of 48.42%.

9

Fig. 1. 3D representation number

of leaves – IAA concentration – BA

concentration

Fig. 2. 3D representation height

of stems – IAA concentration – BA

concentration

Fig. 3. 3D representation number of

axillaries buds – IAA concentration –

BA concentration

Fig. 4. 3D representation number of

secondary cops – IAA concentration –

BA concentration

Fig. 5. 3D representation number

of basal roots – IAA

10

concentration – BA concentration

Analyzing the ensemble of the five 3D representations, one can see that

three of the five bio-measured proprieties (the average height of the stevia

vitro-plants, the average number of auxiliary buds and the average number of

basal roots) have a maximum, for a concentration of about 0.6 mg/L IAA in the

growing regulators mixture, that corresponds to the basic level. This

concentration corresponds to the optimum value of axillaries buds for the

analyzed domain and leads to very good values for the average number of leaves.

Owing to these causes, the concentration z1*=0.6 mg/L IAA can be considered as

optimum for the ensemble of the five dependent variables taken into account in

the described experiment.

For establishing the optimal value of BA concentration in growing regulators

mixture, there were used the regressive equations for codified variable, that

were previous presented, being resulted on the basis of experimental data and

being statistical verified, and calculating the dependent variables’ values for

divers values of BA concentration; the BA concentration was mentioned at the

optimal value. The results of calculus are given in Table 5.

11

These data permits the obtaining of some polynomial regressive equations

[8] of second degree, having a single independent variable, whose form is:

yi = a0 + a1 z2 + a2 z22

(7)

For determining the regressive equations’ coefficients of (7) form there

was used a Turbo-Pascal computer program, having been obtained the values from

Table 6.

Table 5 – The dependent variables’ values function of BA concentration in the mixture of growing regulators, for IAA concentration z1 = 0,6 mg/L

No.crt.

z2 ,BA

concen-tration,mg/L

y1,Averagenumber ofleaves

y2,Averageheight ofvitroplants

Y3,Averagenumber ofaxillaries

buds

y4,Averagenumber ofsecondary copse

y5,Averagenumber ofbasalroots

1. 0.18 23.50 6.36 3.20 1.23 1.502. 0.30 27.82 6.62 2.66 1.26 2.313. 0.60 34.74 6.38 2.62 2.80 3.104. 0.90 36.66 4.92 4.37 3.00 2.325. 1.02 36.00 4.31 5.62 2.69 1.51

Table 6 – Polynomial regressive equations’ coefficients yi = f (z2 ), for z1 = 0.6 mg/L

Coeffi-

cient

y1 = f (z2) y2 = f (z2) y3 = f (z2) y4 = f (z2) y5 = f (z2)

a0 15.66 5.79 4.55 - 1.69 - 16a1 48.86 4.39 -9.34 12.06 10.92a2 - 28.36 5.81 10.18 - 7.62 - 9.90

12

On the basis of the regressive equations’ coefficients from Table 6, having

been used the mentioned program, we obtained the graphics from figures 6 and 7,

which were taken from the computer’s screen.

Analyzing the graphics from Fig. 6 and 7 and using the polynomial

regressive equations, it results that, at a concentration of IAA in the

growing regulators mixture of 0.6 mg/L, one can get the best results for all the

proprieties whether the BA concentration in the growing regulators mixture is

varied in the interval of 0.6 and 0.8 mg/L.

13

Fig. 6 – The variation of average number of

leaves function of

BA concentration, for IAA concentration of 0.6

mg/L

Fig. 7 – The variation of some average

proprieties

function of BA concentration,

for IAA concentration of 0.6 mg/L

If one works with a mixture of 0.6 mg/L IAA and 0.6 mg/L BA, one gets the

best values for the average height of plants (6.3 cm) and the average number of

basal roots (3.1), and good values for the average number of leaves (34.7),

average number of axillaries buds (3.6) and average number of secondary cops

(2.8).

If one works with a mixture of 0.6 mg/L AIA and 0.8 mg/L BA, one gets the

best values for the average number of leaves (35.6), average number of

secondary cops (3.1), the average number of axillaries buds (3.6) and good

results for the average height of stems (5.6) and the average number of basal

roots (2.8).

If one compares these results with the control sample (MS-1962, without

adding a mixture of regulators), one establishes:

- the growth of the leaves number from 20.6 – for the control sample – to 35.6

if one uses an addition of growing regulators with 0.6 mg/L IAA and 0.8 mg/L

BA;

- the growth in the average height for the stems of stevia from 5.35 cm – for

the control sample to 6.3 cm – for an addition of growing regulators of 0.6

mg/L IAA and 0.6 mg/L BA;

- the growth of average number of axillaries buds from 1.6 – for the control

sample to 3.6 – for an addition of growing regulators of 0.6 mg/L IAA and 0.8

mg/L BA;

- the growth of average number of secondary cops from 1.2 – for the control

sample to 3.1 – for an addition of growing regulators of 0.6 mg/L IAA and 0.8

mg/L BA;

- a constant maintaining of the average number of basal roots either for the

control sample (3.0) or for an addition of 0.6 mg/L IAA and 0.6 mg/L BA, fact

14

that confirms the previous known data, that the striking root can be obtain

without any addition of hormones.

Conclusions

After the processing of the experimental average results with a computer

one can establish the following:

there were obtained mathematical models – both in codified variable and in

real variable – which permit the estimations of the bio-measured

characteristics in all over the extremis experiment realizing domain (0.18 –

1.02 mg/L);

there were obtained graphics which contains 3D representations that show the

existence of an optimal point in the analyzed domain for four out of the five

bio-measured characteristics (the height of the stems, average number of

secondary cops and the average number of basal roots);

there was studied the influence of the growing regulators’ concentration upon

the bio-measured characteristics on basis of significance level of regressive

equations’ coefficients;

there was analyzed the five graphics’ ensemble and it was established that

the value of 0.6 mg/L IAA in the growing regulators’ mixture, added to MS

(1962) is the best for all the five dependent variables;

on basis of some polynomial regresses with a single independent variable and

of the graphical constructions, there was established that – at the optimal

concentration of IAA of 0.6 mg/L – the best values of the bio-measured values

are obtained whether the BA concentration in growing regulators’ mixture has

0.6 mg/L value (for the average number of basal roots and the average height

of stems) or 0.8 mg/L (for the average number of leaves, average number of

axillaries buds and the average number of secondary cops);

15

culture media compositions and nature of the hormonal balance in these media

led regeneration of vitro-plants capable of acclimatization and life "ex

vitro" in the greenhouses of Botanical Garden from Jassy, Romania.

List of abbreviations

MS (1962) - Murashige and Skoog medium (1962);

IAA - -idolylacetic acid;

BA – benzyl adenine;

CCRP2 - central compound routable program of second degree.

Acknowledgement

The cost of materials for experiments was supported by National Education

Ministry of Romania.

References

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[2] Taloi, D.; Florian, E.; Bratu, C.; Berceanu, E. The optimization of some

metallurgic processes; DPE: Bucharest, 2003.

[3] Marinoiu, V.; Stratula, C.; Marinescu, C. Numerical methods applied in

chemical engineering; Technical Publish House: Bucharest, 2006.

[4] Cachiţă, C. D. „In vitro” methods for culture plants. Theoretical and

practical basis; Ceres Publish House: Bucharest, 2007.

[5] Constantinovici, D.; Răşcănescu, M.; Cachiţă, D. In: Some aspects about "in

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