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UNIVERSITI PUTRA MALAYSIA

MAHMOUD DANAEE

FP 2013 50

STATISTICAL APPROACHES TO OPTIMIZE TISSUE CULTURE CONDITIONS FOR SECONDARY METABOLITE PRODUCTION OF

Phyllanthus pulcher WALL. EX MULL. ARG.

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STATISTICAL APPROACHES TO OPTIMIZE TISSUE CULTURE

CONDITIONS FOR SECONDARY METABOLITE PRODUCTION OF


By

MAHMOUD DANAEE

Thesis Submitted to the School of Graduate Studies, Universiti Putra Malaysia,

in Fulfillment of the Requirement for the Degree of Doctor of Philosophy

July 2013

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DEDICATION DEDICATION

Dedicated to

All My Teachers

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ABSTRACT

Abstract of thesis presented to the Senate of Universiti Putra Malaysia in fulfillment

of the requirement for the degree of Doctor of Philosophy

STATISTICAL APPROACHES TO OPTIMIZE TISSUE CULTURE

CONDITIONS FOR SECONDARY METABOLITE PRODUCTION OF


By

MAHMOUD DANAEE

July 2013

Chairman: Associate Professor Mihdzar Abdul Kadir , PhD

Faculty : Agriculture

Medicinal plants are rich sources of bioactive compounds, which are being used in

pharmaceutical industries. To date, due to commercial exploitation there is an acute

shortage of population in their natural habitats. Domestication and cultivation is

often difficult due to agro-climatic constraints. Hence, the production of secondary

metabolites via the cultivation of plants through new technologies such as tissue

culture, including through advanced bioreactor is the way forward. In this study,

Phyllanthus pulcher an important medicinal plant was used to establish tissue culture

system through the use of statistical models for estimation of secondary metabolites

via manipulation of media, elicitors and plant growth regulators. Three selected

accessions out of 35 accessions maintained in the field AGRO-GENE BANK

Department of Crop Science (UPM) were screened for secondary metabolites

following which accession 11383 was selected and used for tissue culture

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experiments which consisted of establishment of sterilization protocol, callus

induction, media optimization, elicitors and PGRs effects. Sterilization protocol was

established using Clorox and Ethanol. The results revealed that the best method for

leaf explants was the application of 15% Clorox for 15 minutes followed by

immersion in 70% Ethanol for 1 min. To induce callus, the best result was observed

on the leaf explant at 30 mgL-1 of 2, 4-D. After inducing the callus, effects of four

types of media (Murashinge-Skoog, B5, DKW and WPM) at three different strengths

(half , full and 1.5 strength) and 3 harvesting times (15, 30 and 45 days) on callus

growth and secondary metabolites production were studied under both solid and

liquid cultures. The results of this study showed that for both solid and liquid

cultures, the highest secondary metabolites were produced in full strength WPM and

1.5 strength WPM respectively. The effects of carbon were investigated using three

different sources of carbon (fructose, glucose and sucrose) at three concentrations (1,

2, and 3 %). Results showed that sucrose at 3% had significantly higher secondary

metabolites content. To determine the effects of PGRs, different concentrations of

BAP was applied with two kinds of auxin (NAA and 2, 4-D). Secondary metabolites

content as well as callus growth were improved when 2 mgL-1

BAP and 2 mgL-1

NAA were applied on the callus for 30 days. The effects of Methyl jasmonate

(MeJA) and Salicylic acid (SA) as two abiotic elicitors were evaluated for growth

and secondary metabolite production of callus culture in P. pulcher. The results

revealed that high concentration of MeJA (> 10 mM) inhibits the callus growth. The

results further revealed that 1 mM of MeJA resulted in the highest yield for total

flavonoid and phenolic contents. Applying SA also improved the secondary

metabolite content while in higher concentrations (> 50 mgL-1

) all samples died. In

the final section of this research based on findings of previous experiments and

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understanding of effective nutritional factors, PGRs and elicitors a Central

Composite Design (CCD) was conducted. The experiment included 54 runs with

different combinations of N, Ca, K, P, sucrose and SA. The data of this experiment

were analyzed using Response Surface Methodology (RSM) and Artificial Neural

Network (ANN) the result showed that ANN models are more flexible and adaptable

for prediction of secondary metabolite production in plant cell culture. Results

indicated that flavonoid and phenolic production in ANN model prediction were

higher than RSM prediction 4% and 19.6% respectively, while factors such as N, Ca,

K and SA had a lower concentration in ANN than RSM estimation.

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ABSTRAK

Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai

memenuhi keperluan untuk ijazah Doktor Falsafah

PENDEKATAN SECARA STATISTIK BAGI PENGOPTIMUMAN

KEADAAN KULTUR TISU UNTUK PENGHASILAN METABOLIT

SEKUNDER Phyllanthus pulcher WALL. EX MULL. ARG.

Oleh

MAHMOUD DANAEE

Julai 2013

Pengerusi: Prof. Madya Mihdzar Abdul Kadir , PhD

Fakulti : Pertanian

Tumbuhan perubatan adalah sumber yang kaya dengan sebatian bioaktif, yang

sedang digunakan dalam industri farmaseutikal. Setakat ini, disebabkan eksploitasi

komersial terdapat kekurangan populasi di habitat semulajadi mereka. Domestikasi

dan penanaman adalah sukar kerana kekangan agro-iklim. Oleh itu, pengeluaran

metabolit sekunder dengan penanaman tumbuhan melalui teknologi baru seperti tisu

kultur, termasuk melalui bioreaktor maju adalah cara ke hadapan. Dalam kajian ini,

Phyllanthus pulcher sebuah tumbuhan perubatan yang penting telah digunakan untuk

mendirikan sistem kultur tisu melalui penggunaan model statistik untuk anggaran

metabolit sekunder melalui manipulasi media, pengelisit dan pengatur pertumbuhan

tumbuhan. Tiga aksesi terpilih daripada 35 aksesi yang dikekalkan dalam ladang

AGRO-GENE BANK Jabatan Sains Tanaman (UPM) telah disaring untuk metabolit

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sekunder dimana aksesi 11383 telah dipilih dan digunakan untuk eksperimen kultur

tisu yang terdiri daripada penubuhan protokol pensterilan, induksi kalus,

pengoptimuman media, pengelisit dan kesan PGRs. Protokol pensterilan telah

dibentuk menggunakan Clorox dan etanol. Keputusan menunjukkan bahawa kaedah

terbaik untuk eksplan daun adalah penggunaan 15% Clorox selama 15 minit diikuti

dengan rendaman dalam etanol 70% untuk 1 min. Untuk mendorong kalus, hasil

yang terbaik telah diperhatikan pada eksplan daun pada 30 mgL-1

2, 4- D. Selepas

menginduksi kalus, kesan empat jenis media (Murashinge-Skoog, B5, DKW dan

WPM) pada tiga kekuatan yang berbeza (separuh, penuh dan 1.5 kekuatan) dan 3

masa penuaian (15, 30 dan 45 hari) terhadap pertumbuhan kalus dan pengeluaran

metabolit sekunder telah dikaji di kedua-dua kultur pepejal dan cecair. Hasil kajian

ini menunjukkan bahawa bagi kedua-dua kultur pepejal dan cecair, metabolit

sekunder yang paling tinggi dihasilkan masing-masing dalam kekuatan WPM penuh

dan kekuatan 1.5 WPM. Kesan karbon telah disiasat menggunakan tiga sumber yang

berbeza karbon (fruktosa, glukosa dan sukrosa) pada tiga kepekatan (1, 2, dan 3%).

Keputusan menunjukkan bahawa sukrosa pada 3% mempunyai kandungan metabolit

sekunder yang ketara tingginya. Untuk menentukan kesan bahan penggalak

tumbesaran, kepekatan BAP yang berbega telah digunakan dengan dua jenis auksin

(NAA dan 2, 4- D). Kandungan metabolit sekunder serta pertumbuhan kalus telah

diperbaiki apabila 2mgL-1

BAP dan 2 mg -1

NAA telah digunakan pada kalus selama

30 hari. Kesan Methyl jasmonat ( MeJa ) dan asid salisilik (SA) sebagai dua

pengelisit abiotik telah dinilai untuk pertumbuhan dan pengeluaran metabolit

sekunder kultur kalus P. Pulcher. Keputusan menunjukkan bahawa kepekatan MeJa

yang tinggi (> 10 mM ) menghalang pertumbuhan kalus. Keputusan juga

mendedahkan bahawa 1 mM MeJa menyebabkan hasil tertinggi bagi jumlah

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kandungan flavonoid dan fenolik. Penggunaan SA juga menambahbaik kandungan

metabolit sekunder manakala pada kepekatan yang tinggi (> 50 mgL-1

) semua

sampel mati. Di bahagian akhir kajian ini berdasarkan penemuan eksperimen

sebelumnya dan pemahaman faktor-faktor pemakanan yang berkesan, penggalak

tumbesaran dan pengelisit Rekaan Pusat Komposit (CCD) telah dijalankan. 54

eksperimen dijalankan dengan kombinasi N, Ca, K, P, sukrosa dan SA yang berbeza.

Data eksperimen ini dianalisis dengan menggunakan Kaedah Permukaan Sambutan

(RSM) dan Rangkaian Neural Tiruan (ANN). Keputusan menunjukkan bahawa

model ANN lebih fleksibel dan cepat menyesuaikan diri untuk ramalan pengeluaran

metabolit sekunder dalam kultur sel tumbuhan. Keputusan menunjukkan bahawa

flavonoid dan pengeluaran fenolik dalam ramalan model ANN adalah 4% dan 19.6%

lebih tinggi daripada ramalan RSM, manakala faktor-faktor seperti N, Ca, K dan SA

mempunyai kepekatan yang lebih rendah dalam ANN daripada anggaran RSM.

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ACKNOWLEDGMENTS

First of all, praise is to “Allah “the cherisher, and the sustainers of the world for

giving me strength, health and determination to complete this thesis.

My sincere and deepest gratitude goes to Associate Professor Dr. Mihdzar Abdul

Kadir, the chairman of my supervisory committee, for his kindness, guidance,

encouragement, patience, support and continuous follow-up during the course of this

study.

My appreciation and gratitude is also extended to members of my supervisory

committee Associate Professor Dr. Rosfarizan Mohamad and Assoc. Prof. Dr.

Uma Rani Sinniah for their advice, effective comments and support.

I owe much gratitude to Professor Dr. Mehdi Nassiri Mahallati for being one of the

greatest mentors one could ever hope to have throughout my graduate and

undergraduate careers. His encouragement always succeeded in helping me to reach

the next milestone.

My gratitude is also due to all staff of the Agrotechnology Lab, Department of

Agriculture Technology, Universiti Putra Malaysia for their cooperation.

Finally, I would like to thank my beloved wife, son, mother and brother for all their

love, patience and support to finalize the study.

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APPROVAL

I certify that a Thesis Examination Committee has met on 23rd

July 2013 to conduct

the final examination of Mahmoud Danaee on his thesis entitled “STATISTICAL

APPROACHES TO OPTIMIZE TISSUE CULTURE CONDITIONS FOR

SECONDARY METABOLITE PRODUCTION OF Phyllanthus pulcher WALL.

EX MULL. ARG.” in accordance with the Universities and University Colleges Act

1971 and the Constitution of the Universiti Putra Malaysia [P.U. (A) 106] 15 March

1998. The Committee recommends that the student be awarded the relevant degree

of Doctor of Philosophy.

Members of the Thesis Examination Committee were as follows:

Assoc. Prof. Dr. Maheran Abdul Aziz

Department of Agriculture Technology

Faculty of Agriculture

Universiti Putra Malaysia

(Chairman)

Prof. Dr. Mahmud Tengku Muda Mohamed

Department of Crop Science



(Internal Examiner)

Assoc. Prof. Dr. Faridah Qamaruzzaman

Department of Biology

Faculty of Science


(Internal Examiner)

Prof. Dr. Ashock Kumar Srivastana

Department of Biochemical Engg.& Biotechnology

Indian Institute of Technology Delhi

Hauz Khas

India

(External Examiner)

SEOW HENG FONG, Ph.D. Professor and Deputy Dean

School of Graduate Studies


Date:

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This thesis submitted to the Senate of Universiti Putra Malaysia has been accepted as

fulfillment for the requirement for the degree of Doctor of Philosophy. The members

of the Supervisory committee were as follows:

Mihdzar Abdul Kadir

Associate Professor



(Chairman)

Rosfarizan Mohamad, PhD

Associate Professor Faculty of Biotechnology and Biomolecular Sciences


(Member)

Uma Rani Sinniah, Ph.D

Associate Professor


Universiti Putra Malaysia (Member) Mohd Said B Saad, Ph.D

Associate Professor

SIME DARBY

(Member)

BUJANG BIN KIM HUAT, Ph.D Professor and Dean

School of Graduate Studies


Date:

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DECLARATION

I hereby declare that the thesis is based on my original work except for quotations

and citations, which have been duly acknowledged. I also declare that it has not been

previously or concurrently submitted for any other degree at Universiti Putra

Malaysia or other institutions.

MAHMOUD DANAEE

Date: 23 July 2013

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TABLE OF CONTENTS

Page

DEDICATION ii

ABSTRACT iii ABSTRAK vi

ACKNOWLEDGMENTS ix APPROVAL x

DECLARATION xii LIST OF FIGURES xvii

LIST OF TABLES xx LIST OF ABBREVIATIONS xxii

CHAPTER

1 GENERAL INTRODUCTION 1

1.1 Background of the study 1

1.2 Objectives 3

2 LITERATURE REVIEW 5

2.1 Medicinal plants 5

2.2 Secondary metabolites 6

2.2.1 Importance of secondary metabolites 8

2.2.2 Classification of secondary metabolite 9

2.2.3 Phenolic compounds 10

2.2.4 Major metabolic pathways 11

2.2.4.1 Phenolic acids 12

2.2.4.2 Flavonoids 14

2.3 Distribution and importance of Phyllanthus genus 16

2.3.1 Medicinal properties of Phyllanthus spp. 17

2.3.2 Phyllanthus pulcher 20

2.3.3 Botanical description 21

2.3.4 Medicinal properties 22

2.3.5 Tissue culture of Phyllanthus spp. 23

2.4 Biotechnology for production of secondary metabolite 25

2.4.1 Callus and cell suspension cultures 25

2.4.2 Organ cultures 32

2.4.2.1 Shoot cultures 32

2.4.2.2 Root cultures 32

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2.5 Factor affecting secondary metabolite production 35

2.5.1 Medium nutrients 36

2.5.2 Elicitors 37

2.5.2.1 Classification of elicitors 38

2.5.2.2 Methyl jasmonate elicitation 41

2.5.2.3 Salicylic acid elicitation 42

2.6 Plant growth regulators 43

2.7 Statistical methods in optimization process 44

2.7.1 Response surface methodology 45

2.7.2 Artificial neural network 48

2.7.3 RSM and ANN application in biological researches 53

3 SCREENING OF ACCESSIONS FOR SECONDARY METABOLITES

AND ESTABLISHMENT OF TISSUE CULTURE PROTOCOLS FOR

CALLUS PRODUCTION IN Phyllanthus pulcher 56

3.1 Introduction 56

3.2 Materials and Methods 57

3.2.1 Plant materials for screening of accessions 57

3.2.2 Sample preparation 57

3.2.3 Secondary metabolite measurement 58

3.2.4 Plant material for sterilization experiments 59

3.2.5 Explant sterilization and media preparation 60

3.2.6 Explant preparation for callus induction 61

3.2.7 Experimental design and data analysis 62

3.3 Results and discussion 62

3.3.1 Screening experiment 62

3.3.2 Sterilization experiments 64

3.3.3 Callus induction experiments 66

3.4 Conclusion 69

4 EFFECTS OF NUTRITIONAL FACTORS ON SECONDARY

METABOLITE PRODUCTION IN Phyllanthus pulcher 70

4.1 Introduction 70


4.2.1 Plant materials 71

4.2.2 Media preparation 71

4.2.3 Measuring of secondary metabolite 73

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4.3.1 Effect of media on callus growth (Solid culture) 75

4.3.2 Effect of media on secondary metabolites (solid culture) 79

4.3.3 Effect of media on callus growth (liquid culture) 83

4.3.4 Effect of media on secondary metabolites (liquid culture) 86

4.3.5 Effect of carbon on callus growth 91

4.4 Conclusion 96

5 EFFECTS OF ELICITATION ON SECONDARY METABOLITE

PRODUCTION IN Phyllanthus pulcher 98

5.1 Introduction 98

5.2 Materials and methods 99






5.3.1 Effect of SA on callus growth and secondary metabolites 101

5.3.2 Effect of MeJA on callus growth and secondary metabolites 106

5.4 Conclusion 111

6 EFFECTS OF PLANT GROWTH REGULATORS ON SECONDARY

METABOLITE PRODUCTION IN Phyllanthus pulcher 112

6.1 Introduction 112

6.2 Materials and methods 113






6.3.1 Effect of 2, 4-D and BAP on callus growth and secondary

metabolites 115

6.3.2 Effect of NAA and BAP on callus growth and secondary

metabolites 119

6.4 Conclusion 124

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7 OPTIMIZATION OF CALLUS CULTURE CONDITIONS FOR

SECONDARY METABOLITES CONTENT IN Phyllanthus pulcher

USING RSM AND ANN 126

7.1 Introduction 126



7.2.2 Designing of experiment (RSM) 127



7.2.5 Artificial neural network (ANN) 131


7.3.1 RSM modeling 133

7.3.1.1 Determination of Optimum Conditions using RSM 140

7.3.2 ANN modeling 141

7.3.2.1 Selecting the Best Network 142

7.3.3 Comparison of RSM and ANN 149

7.3.4 Model Validation 151

7.4 Conclusion 156

8 GENERAL CONCLUSION AND RECOMMENDATIONS FOR

FUTURE RESEARCH 157

8.1 General conclusion 157

8.2 Recommendation for future research 158

REFERENCES 160

APPENDICES 173 BIODATA OF STUDENT 193

LIST OF PUBLICATIONS 194

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LIST OF FIGURES

Figure Page

‎1-1 Research Framework 4

‎2-1 Common biosynthetic pathway to secondary metabolite 12

‎2-2 Structures and names of the most common and simple phenolic acids 14

‎2-3 General structure of flavonoids 15

‎2-4 Three main flavonoids 15

‎2-6 Morphology of Phyllanthus pulcher 21

‎2-6 Guidelines for the production of secondary metabolite from plant cell 27

‎2-7 Structures of Methyl jasmonate and Jasmonic acid 41

‎2-8 Structure of salicylic acid 42

‎2-9 Defense signaling pathways 42

‎ 2-10 Central composite design with 3 factors 47

‎2-11 Structure of three types of central composite design 48

‎2-12 Basic comparison between a biological neuron and an artificial neuron 49

‎ 2-13 Three layered feed-forward network. 50

‎3-1 Mean comparison of total flavonoid content among accessions. 63

‎ 3-2 Mean comparison of total phenolic contenet among accessions. 63

‎3-3 Propagation of selected accession 60

‎3-4 Percentage of contamination and survival of Internode explants 65

‎3-5 Percentage of contamination and survival of leaf explants 66

‎3-6 The primary induced callus 67

‎3-7 Callus induction of using different concentrations of 2,4-D 68

‎ 3-8 Callus fresh weight under different concentrations of 2, 4-D 68

‎4-1 Solid culture(A), Liquid culture(B), Shaking of liquid culture(C) 72

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‎4-2 Means of callus fresh weight under different treatments (solid culture) 76

‎4-3 Means of callus dry weight under different (solid culture) 77

‎4-4 Mean of callus fresh weight at different harvest time (solid culture) 77

‎4-5 Harvested callus after 30 days (Solid culture) 78

‎4-6 Mean of callus growth rate at different media (solid culture) 78

‎4-7 Total flavonoid yield under different treatments (solid culture) 81

‎4-8 Total phenolic yield under different treatments (solid culture) 82

‎4-9 Means of callus fresh weight under different treatments (liquid culture) 84

‎4-10 Means of callus dry weight under different treatments (liquid culture) 84

‎4-11 Harvested callus after 45 days (liquid culture) 85

‎4-12 Mean of callus fresh weight for all treatments in different time (Liquid

culture) 85

‎4-13 Total flavonoid yield under different treatments (liquid culture) 88

‎4-14 Total phenolic yield under different treatments (Liquid culture) 89

‎4-15 Means of callus fresh weight in different carbon sources and concentration 92

‎4-16 Means of callus dry weight in different carbon sources and concentrations 92

‎4-17 Total flavonoid yield in different carbon sources and concentration 94

‎4-18 Total phenolic yield in different carbon sources and concentration 94

‎5-1 Means of callus fresh weight in different SA concentrations 101

‎5-2 Total flavonoid yield in different SA concentrations 103

‎5-3 Total phenolic yield in different carbon sources and concentrations 104

‎6-1 Means of callus fresh weight in different concentrations of 2,4-D and BAP 115

‎6-2 Means of callus dry weight in different concentrations of 2, 4-D and BAP 116

‎6-3 Total flavonoid yield in different 2, 4-D and BAP concentrations 118

‎6-4 Total phenolic yield in different 2, 4-D and BAP concentrations 118

‎6-5 Means of callus fresh weight in different concentrations of NAA and BAP 119

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‎6-6 Means of callus dry weight in different concentrations of NAA and BAP 120

‎6-7 Total flavonoid yield in different NAA and BAP concentrations 121

‎6-8 Total phenolic yield in different NAA and BAP concentrations 122

‎7-1 Effect of phosphor and pottasium concentrations (mgL-1

) 138

‎7-2 Effect of nitrogen and salicylic acid concentration (mgL-1

) 138

‎7-3 Effect of sucrose and salicylic acid concentration (mgL-1

) 139

‎7-4 Effect of phosphorus and SA concentration (mgL-1

) 139

‎7-5 Effect of sucrose and salicylic acid concentration (mgL-1

) 140

‎7-6 Effect of phosphorus and potasium concentration (mgL-1

) 140

‎7-7 Accuracy of the networks with different hidden neurons 143

‎7-8 A multilayer perceptron (MLP) network 144

‎7-9 The scatter plots of ANN predicted and actual yield for QP algorithm 145

‎7-10 The scatter plots of ANN predicted and actual yield for IBP algorithm 146

‎7-11 The scatter plots of ANN predicted and actual yield for BBP algorithm 147

‎7-12 Importance of factors based on ANN model 148

‎7-13 The scatter plot of RSM predicted values versus actual values 150

‎7-14 The scatter plot of ANN IBP 6-7-2 predicted values versus actual values 150

‎7-15 The plot for RSM and ANN predicted yields versus actual yield 152

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LIST OF TABLES

Table Page

‎2-1 Secondary metabolites classification 9

‎2-2 Important Classes of Phenolic Compounds in Plants) 11

‎2-3 Phyllanthus subgenera, 17

‎2-4 Pharmacological effect of isolated compounds of some species of

Phyllanthus 18

‎2-5 Phyllanthus pulcher classification(USDA National Plant Data) 20

‎2-6 Tissue culture studies in Phyllanthus spp 24

‎2-7 Examples of plant cell culture methodologies for the

production of metabolites 29

‎2-8 Secondary metabolites produced of high levels by plant cell cultures 31

‎2-9 Secondary metabolite production from hairy root cultures 34

‎2-10 Factors influencing secondary metabolite production in plant cells 35

‎2-11 Biotic elicitors and production for secondary metabolites 39

‎2-12 Abiotic elicitors and production for secondary metabolites 40

‎2-13 Examples of RSM and ANN application in biological researches 55

‎4-1 Effect of type of media, strength and harvesting time (Solid culture) 80

4-2 Correlation among secondary metabolite in solid culture 82

‎4-3 Effect of type of media, strength and harvesting time (liquid culture) 87

‎4-4 Correlation among secondary metabolite content (liquid culture) 89

‎4-5 Effect of carbon sources and concentration 93

‎4-6 Correlation among secondary metabolite content 95

‎5-1 Effect of SA concentration on TFC, TPC and AA of callus culture 103

‎5-2 Effect of MeJA concentration on TFC,TPC and AA of callus culture 108

‎5-3 Comparison of secondary metabolite production between SA and MeJA 109

‎6-1 Effects of 2, 4-D and BAP on TFC, TPC and AA of callus culture 117

‎6-2 Effects of NAA and BAP on TFC, TPC and AA of callus culture 121

‎6-3 Comparison of secondary metabolite between BAP +NAA and

BAP + 2, 4-D 122

‎7-1 Experimental range and levels of the independent 128

‎7-2 Central composite design consisting of 54 experiments 129

‎7-3 Sequential model sum of squares for flavonoid yield 133

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‎7-4 Sequential model sum of squares for phenolic yield 133

‎7-5 Lack of fit tests for flavonoid yield 134

‎7-6 Lack of fit tests for phenolic yield 134

‎7-7 Regression equation of response surface reduced Model 135

‎7-8 Model coefficient estimated by regression for flavonoid yield 136

‎7-9 Model coefficient estimated by regression for phenolic yield 137

‎7-10 Result of optimization of nutritional factors for both flavonoids

and phenolic 141

‎7-11 Comparing among best networks 144

‎7-12 Result of optimization of nutritional factor for both flavonoids

and phenolic 148

‎7-13 Comparison of predictive capacity of RSM and ANN for two responses 149

‎7-14 Actual and predicted flavonoid and phenolic yield for validation 151

‎7-15 Validation of RSM and ANN for two responses 152

‎7-16 Comparing of optimized condition between ANN and RSM 153

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LIST OF ABBREVIATIONS

2,4-D 2,4-Dichlorophenoxy acetic acid

AA Antioxidant activity

ANCOVA Analysis of covariance

ANN Artificial neural network

ANOVA Analysis of variance

B5 Gamborg (1968) medium

BA or BAP N6-benzyladenine

CA Calcium

CCD Central composite design

CGR Callus growth rate

CRD Completely randomized design

CRD Completely randomized design

DCM Dichloromethane

DKW Driver and Kuniyuki Walnut (1984)medium

DMRT Duncan’s Multiple Range Test

DW Dry weight

FW Fresh weight

GLM General linear model

K Potassium

MeOH Methanol

MJ Methyl jasmonate

MS Murashige and Skoog (1962) medium

N Nitrogen

NAA 1-naphthalene acetic acid

NAA 1-Naphthaleneacetic acid

P Phosphorus

PGR Plant growth regulators

RCBD Randomized complete block design

RSM Response surface methodology

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SA Salicylic acid

TFC Total flavonoid content

TFC Total flavonoid content

TPC Total phenolic content

TPC Total phenolic content

v/v Volume by volume

w/v Weight by volume

WPM McCown and Lioyd (1981) medium

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CHAPTER 1

1 GENERAL INTRODUCTION

1.1 Background of the study

During last decades, an increasing attention was paid to the medicinal plants as a

natural source for pharmaceutical compounds, which can be used to treat many

diseases and illness, and therefore these are high market value. Only 5000 of 250000

to 300000 plant species have been identified for their medicinal characteristics.

Unfortunately, many of these species were almost extinct because of over collections

from their natural habitats for marketing purposes. Domestic cultivation of medicinal

plants compared to wild conditions led to a low amount of secondary metabolites

production and it is difficult to increase secondary metabolites content through

farming practices.

Today at the beginning of third millennium, biotechnology has become an important

applied science, which has shown an incredible effect on different aspects of human

life especially in health and wealth. Biotechnology has provided the possibility of

cell and organ cultures for many plant species including medicinal plants. The

production of secondary metabolites in plant cell suspension cultures has been

developed for various medicinal plants. In recent years, a new capable outlook for

metabolic engineering has opened to improve secondary metabolites production

through plant cell culture. Large-scale plant cell culture is essential for the industrial

production of phytochemicals.

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There are major advantages of a cell culture system over the conventional cultivation

of whole plants. Plant’s secondary metabolites can be produced under controlled

conditions independent of climatic factors. Cell culture systems are clean and free of

microbes and insects. Plants cells also could easily be multiplied to yield their

specific metabolites therefore automated control of cell growth and rational

regulation of metabolites processes can be applied. Secondary metabolite is

extractable from plant cell culture systems. In order to obtain high yields of

phytochemicals in plant cell culture systems, investigations have been concentrated

to optimize the cultural conditions, selecting high-producing strains, employing

precursor feeding, transformation methods and immobilization techniques.

Traditional methods for optimization are “one-factor-at-a time” techniques.

Unfortunately, this approach frequently fails to identify the variables that give rise to

the optimum response because the effects of factor interactions are not taken into

account in such procedures (Deepak et al., 2008). Application of factorial designs

and RSM is a common method in biotechnology to optimize the media components

and culture conditions. Response surface methodology (RSM) is an analytical tool to

determine the optimum conditions for a multi variable system and has been applied

for optimizing media for tissue culture. The level of nutritions, hormones, sucrose

etc. plays a crucial role in the growth and development of plants in vitro. In plant

tissue culture, in which the response variables are mostly numerical data, the

development of RSM in a generalized linear model (GLM) setup is of interest from

both a theoretical as well as an application perspective (Chakra borty et al., 2010). In

last two decades, artificial neural network has applied as one of the most efficient

method for empirical modeling and optimization in fermentation media optimization.

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Advanced statistical methods such as Response surface methodology (RSM) and

Artificial neural network (ANN) provide an alternative methodology for optimizing a

particular process by considering the interactions among the factors and give an

estimate of combined effect of these factors on a response. Phyllanthus pulcher as a

medicinal plant was used in this researches. The plants of the genus Phyllanthus

(Euphorbiaceae) are widely distributed in most tropical and subtropical countries,

and have long been extensively used in folk medicine in India and most other

countries for thousands of years in the treatment of a broad spectrum of diseases,

such as disturbances of the kidney and urinary bladder, intestinal infections, diabetes,

and the hepatitis B virus (Calixto et al. 1998; Unander et al. 1995).

1.2 Objectives

General Objective:

To optimize tissue culture conditions for secondary metabolites production in

P. pulcher

Specific Objectives:

To establish tissue culture protocol for callus production of P. pulcher.

To study the effect of nutritional factors on production of secondary

metabolites in P. pulcher.

To determine the effects of elicitors and plant regulators on production of

secondary metabolites in P. pulcher.

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To apply and compare advanced statistical models (RSM, ANN) for

optimizing tissue culture conditions for secondary metabolites production in

P. pulcher

Figure 1-1 shows the research framework carried out in line with the research

objectives.

Figure 1-1 Research Framework

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