1

Mining microbial diversity of rhizosphere of

Crocus sativus by metagenomic approach

THESIS

SUBMITTED TO THE UNIVERSITY OF JAMMU

FOR THE AWARD OF DEGREE OF

DOCTOR OF PHILOSOPHY

IN

MICROBIOLOGY

By

SHEETAL AMBARDAR

Under the Supervision of

DR. JYOTI VAKHLU

SCHOOL OF BIOTECHNOLOGY

UNIVERSITY OF JAMMU

JAMMU-180006

2014

2

SCHOOL OF BIOTECHNOLOGY, UNIVERSITY OF JAMMU,

JAMMU-180006

CERTIFICATE

This is to certify that:

1. the thesis entitled “Mining microbial diversity of rhizosphere of Crocus

sativus by metagenomic approach” embodies the work done by Ms. Sheetal

Ambardar under my Supervision for the period required under statues,

2. the candidate has put in the attendance in the School of Biotechnology for the

period required,

3. the thesis being submitted for the degree of Doctor of Philosophy in

Microbiology by Ms. Sheetal Ambardar has not been submitted for any

other degree and is worthy of consideration for the award of Ph.D degree of

University of Jammu,

4. the conduct of research Scholar remained good during the period of research,

and

5. the candidate has fulfilled the statutory condition as laid down in Ph.D statutes.

(Dr. Jyoti Vakhlu) (Prof. M. K. Dhar)

Supervisor Director

3

ACKNOWLEDGEMENTS

First of all, I want to thank the Almighty God with whose blessings my Ph.D work

got completed.

I extend my heartiest gratitude to my supervisor, Dr. Jyoti Vakhlu, Associate

Professor, Department of Biotechnology, University of Jammu, for being a

tremendous mentor for me. I would like to thank her for encouraging my research

and for allowing me to grow as a research scientist. Her advice on both research

as well as on my career have been invaluable.

I owe my gratitude and reverence to Prof. Manoj K. Dhar, Director, School of

Biotechnology, for his support apart from facilitating the department with well-

equipped laboratories and library.

I would like to thank all the faculty members of the department, Dr. B.K. Bajaj, Dr.

Sanjana Kaul, Dr. Madhulika Bhagat, Dr. Ritu Mahajan and Dr. Nisha Kapoor for

their guidance, encouragement and motivation.

My acknowledgments are due for Prof. Michel Aragno, University of Neuchâtel,

Switzerland for his scientific guidance. I am also thankful to Prof. F.A. Nehvi, and

Ms. Salwee Yasmin, SKUAST-K, J&K-India Mr. Farooq Ahmad Joo and Mr. C.L.

Bhat, State Agriculture Department, J&K, India and Saffron growing farmers for

their help in sample collection and information about Saffron cultivation.

I am also thankful to Prof. Gunasekaran (Madurai Kamraj University) and Prof

Rup Lal (Delhi University) for imparting me training on cloning dependent

approach under MKU-NRCBS visiting program andbioinformatics analyses of

sequence.

I am thankful to Council of scientific and industrial research (CSIR) for providing

the fellowship that has supported the research work.

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I would like to thank all the research scholars Dr Avneet Kaur, Ms. Puja Gupta,

Ms. Simi Grewal, Mr. Puneet Gupta, Ms. Rikita Gupta, Ms. Sakshi Sharma, Ms.

Surbhi Jasrotia, Ms. Deepika Trakroo and Ms. Sonal Mahajan for their help and

support all through the work.

I also would like to convey my sincere thanks to all non-teaching staff members for

their cooperation and timely help.

This work would not have been possible without the kind encouragement, support

and assistance of my family. They are and will always remain part of my hard

work as well as success.

Sheetal Ambardar

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ABBREVIATIONS

Amp Ampicillin

bp Base pair

CFU Colony Forming Unit

cm Centimeter

DNA Deoxyribose nucleic acid

EDTA Ethylene diamine tetra acetate

E.coli Escherichia coli

gm Gram

IPTG Isopropyl-β-D-thiogalactoside

IAA Indole 3- Acetic Acid

Kb Kilobase

LB Luria Bertani

M Molar

mM Millimolar

ml Millilitre

NaCl Sodium chloride

ng Nano-gram

PCR Polymerase Chain Reaction

PGPB Plant Growth Promoting Bacteria

SDS Sodium dodecyl sulphate

TAE Tris, glacial acid, EDTA

U Units

X-gal 5-bromo-4-chloro3indolyl-β-D-galactoside

μg Microgram

μl Microlitre

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PUBLISHED RESEARCH PAPERS

Research Paper:

Ambardar S and Vakhlu J (2013) Plant growth promoting bacteria from Crocus

sativus rhizosphere. World Journal of microbiology and biotechnology. 29

(12): 2271-2279

Book Chapter:

Vakhlu J, Ambardar S and Johri B.N (2012). Metagenomics – a relief road to

novel microbial genes and genomes. In Editor/s T. Satyanarayana, Bhavdish

Narain Johri, Anil Prakash. Microorganisms in Sustainable Agriculture and

Biotechnology, 263-294 Springer.

Research paper in Press:

Ambardar S, Kour R and Vakhlu J (2014) Rhizobacteria from Crocus sativus

grown in Kashmir, India ISHS Acta horticulturae. IV International Symposium on

Saffron Biology and Technology. (In press).

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LISTS OF CONTENTS

S.No. CONTENT PAGE

NUMBER

1. INTRODUCTION 1-4

2. REVIEW OF LITERATURE 5-29

2.1 Importance of Crocus sativus 5

2.2 Classification 6

2.3 Life cycle of Saffron 6

2.4 Saffron cultivation and production 9

2.4.1. International 9

2.4.2. National 11

2.5. Factor affecting Saffron production 11

2.6. Plant–Microbe assocaitions 12

2.6.1. Cultivation dependent approach 15

2.6.1.1. Plant –microbe association in Saffron 19

2.6.2. Cultivation independent (metagenomics) approach 20

2.6.2.1. Cloning dependent approach 21

2.6.2.1.1. Limitation of cloning dependent approach 26

2.6.2.2. Cloning independent approach 28

3. MATERIALS AND METHODS 30-50

3.1. Sample collection and physiochemical analysis of soil 30

3.2. Cultivation dependent approach 30

3.2.1. Comparison of cultivable bacterial load 30

3.2.2. Isolation and characterisation of bacteria 31

3.2.2.1. Polyphasic characterization 31

3.2.2.2. Bacterial identification by 16SrRNA 33

amplification

3.2.3. Screening of bacteria for PGP traits 36

3.2.3.1. In vitro analysis of PGPR properties 36

3.2.3.2. In vivo analysis of PGPR properties 37

3.3. Cultivable independent (Metagenomics-Cloning dependent) 37

Approach

3.3.1. Metagenomic DNA isolation 38

3.3.2. Purification of metagenomic DNA 38

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3.3.3. PCR amplification of 16S rRNA gene 39

3.3.4. Ligation 39

3.3.5. Preparation of competent cells 40

3.3.6. Transformation of competent cells 41

3.3.7. Colony PCR 42

3.3.8. Amplified ribosomal DNA restriction analysis (ARDRA) 42

3.3.9. Plasmid isolation 42

3.3.10. Sequence analysis 43

3.4. Cloning independent approach 48

3.4.1. PCR amplification of ITS gene 48

3.4.2. Pyrosequencing 49

3.4.3. Bioinformatics analysis of sequences obtained 49

3.5. GenBank Accession numbers 50

4. RESULTS AND DISCUSSION 51-102

OVERVIEW 51

4.1. Soil analysis 53

4.2. Culture dependent approach 53

4.2.1. Bacterial load 53

4.2.2. Rhizosphere, cormosphere and bulk soil bacteria 58

4.2.3. Screening of bacteria for PGP traits 64

4.2.3.1. In vitro analysis of PGPR properties 64

4.2.3.2. In vivo analysis of PGPR properties 65

4.3. Cultivation independent (Metagenomics) approach 69

4.3.1. Relative bacterial diversity between different niches 77

during flowering and dormant stage

4.4. Comparative account of bacteria catalogued by cultivation 85

dependent and cultivation independent approach

4.5. Cloning independent (Metagenomics) approach 92

4.5.1. Relative fungal diversity in different niches during 93

flowering and dormant stage

5. SUMMARY & CONCLUSION 103-104

6. BIBLIOGRAPHY 105-141

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CHAPTER-1

INTRODUCTION

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INTRODUCTION

The stigma of Saffron, Crocus sativus L. is world's most expensive spice, selling

for over $2000/kg, and is popularly known as golden condiment (Sharaf-Eldin et

al., 2008). It is reported that, approximately 75,000 Crocus blossoms or 225,000

stigmas are required to make one pound of the spice (Melnyk et al., 2010;

Ferna´ndez et al., 2011). Saffron cultivation in the world extends through 0 to 90°E

longitude (Spain to Kashmir) and 30 - 45 °N latitude (Persia to England). In India,

Saffron cultivation is limited to the state of Jammu & Kashmir, which constitutes

the northern most extremity of India and is situated between 32º 17‟ and 36º 58‟

north latitude and 37 º 26‟ and 80 º 30‟ east longitude (Wani et al., 2011). The total

area under Saffron cultivation in Kashmir is 3675 hectares and total production is

91.88 quintals with an average yield of 2.5 kg per hectare

(http://www.greaterkashmir.com/news/2012/Dec/13/saffron-production-3.asp).

Saffron is used mainly as flavouring and colouring agents in food and it also

promotes satiety (Gout et al., 2010). Saffron and its various components have

been reported to have various medicinal properties like antidepression,

antinociceptive, anti-inflammatory, anticonvulsant, antigastric, antiparkinsonian,

antigenotoxic, mutagenic or antimutagenic, tumoricidal hypolipaemic and

tumoricidal effects (Sharaf-Eldin et al., 2008; Wani et al., 2010; Melnyk et al.,

2010; Boskabady et al., 2010) and may also be useful in preventing atherosclerosis

(Souret et al., 1999). The main chemical constituents of Saffron are crocin,

crocetin, picrocrocin, and safranal (Abdullaev 2002). Crocin, a C44 glycoside [di

(β-D gentiobiosyl) ester of crocetin] is reported to be most promising agent in

cancer therapy (Souret et al., 1999; Abdullaev 2002; Chryssanthi et al., 2011).

Crocus sativus L. (Saffron) is a corm-bearing, perennial herb that originated from

a wild precursor Crocus cartwrightianus and belongs to family Iridaceae and

subfamily Crocoideae (Goldblatt et al. 2006; Ferna´ndez et al., 2011). Saffron is an

autumn blooming plant, whose activity slows down in spring in contrast to most

flowering plants (Nehvi and Yasmeen, 2010). It is a sterile triploid (3n= 24) and

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propagates by underground vegetative organs called corms. Saffron has an

interesting life cycle of two years which is characterized by three distinct stages,

dormant (July-Aug), flowering (Oct- Nov) and vegetative (Jan-May). Corms, when

sown are dormant for two months and flower during mid Oct- Nov followed by

initiation of grass like leaves which leads to next year dormancy (Yasmin and

Nehvi 2010, Yasmin and Nehvi 2013).

Microbes especially bacteria associated with plants are known to affect the plant

positively as well as negatively. The plant growth promoting bacteria are example

of positive influence rendering bacteria and bacterial pathogens are example of

negatively influencing bacteria. Bacterial associations and their beneficial effect

has been reported from various parts of the plant like roots (Berendson et al., 2012,

Hartmann et al., 2009), leaves (Furnkranz et al., 2008, Kim et al., 2012) and stem

(Ikeda et al., 2010, Bell et al., 1995) but so far no report has been available on

corm from any of the corm bearing plant. PGPR competitively colonize plant roots

and promote plant growth either indirectly or directly (Kloepper et al., 1980).

Indirect promotion of plant growth occurs when PGPR lessen or prevent the

deleterious effects of one or more phytopathogenic organisms; while direct

promotion of plant growth by PGPR involves either providing plants with a

compound synthesized by the bacteria or facilitating the uptake of certain nutrients

from the environment (Leveau 2007).

In last two decades, by development of cultivation independent bacterial

cataloguing techniques, called metagenomics, eGenomics or community genomics,

it has been realized that to get a complete bacterial diversity/community analysis

of any niche, the cultivation based isolation methods need to be complemented by

cultivation independent technique (Amman et al., 1995; Riesenfeld et al., 2004;

Handelsman et al., 1998, 2004). Metagenomics is a powerful tool for assessing the

phylogenetic diversity of complex microbial assemblages present in environmental

samples such as soil, sediment, or water (Simon and Daniel 2009) that allows the

discovery of interactions between microorganisms and environment and assign the

ecosystem functions to microbial communities (Lopez-Garcia and Moreira 2008;

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Sjöling and Cowan 2008). The application of metagenomics is mining of

metagenomes for phylogenetic genes and genes encoding novel biocatalysts and

drugs. Large-scale sequencing of metagenomic DNA permits the identification of

the most frequently represented functional genes and metabolic pathways that are

relevant in a given ecosystem (Simon and Daniel 2009). Cultivation independent

method can be further based on cloning dependent and cloning independent. Both

methods have advantages and limitation of their own. There are reports of

construction of novel genomes of yet to be cultivated microbes with the help of

metagenomics (Woyke et al., 2006, 2009).

Metagenomic techniques have been used to study the diversity of microbes

associated with various plants like rice (Arjun and Kumarapillai 2011), Cactus

(Garrido et al., 2012), Deschampsia antarctica and Colobanthus quitensis

(Teixeira et al., 2010). In oil seed rape and maize complete bacterial diversity was

only revealed by cultivation independent techniques as compared to routine

cultivation based techniques (Kaiser et al., 2001; Pereira et al., 2011).

The production of Saffron and the area under cultivation is decreasing yearly in

Pampore Kashmir. State agriculture department has initiated programmes to start

its cultivation in other similar areas, in the State. Although PGPB and

metagenomic bacterial diversity has been investigated in many plants but there

were no such reports in case of Saffron. Our lab has initiated the work on the

bacterial diversity on the underground parts of Saffron and we have reported

PGPR isolated from Saffron rhizosphere by cultivation dependent techniques

(Ambardar and Vakhlu 2013). Though microbes associated with roots of many

plants have been investigated but to our surprise no such study has been conducted

on the underground parts of plants such as corm in Gladiolus and Colchicum, tuber

in sweet potatos, bulbs in onion and garlic. By analogy to rhizosphere and

phyllosphere, a term cormosphere has been coined in reference to the microbes

associated with the corm of Saffron. The final goal of the study initiated in our lab

is to develop bacterial consortia for enhanced production of Saffron and

cataloguing the complete microbial diversity of the microflora associated with

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Saffron. The idea behind cataloguing complete diversity is to make bacterial

consortia for growth promotion more effective by studying community dynamics,

as in field microflora will grow as wild community in contrast to control laboratory

conditions.

In the present study we are reporting the diversity of the microflora associated with

the roots and corms of Saffron by cultivation independent and cultivation

dependent technique. This is the first report on the microflora associated with

Saffron and indeed the first report on the microflora associated with corm of any

plant.

The objectives set forth for present work are: -

1. Analysis of physio-chemical characteristics of soil.

2. Isolation and purification of cultivable microflora from rhizosphere and

cormosphere of saffron at the time of sowing and harvesting. Microscopic

and molecular analysis of purified microflora.

3. Standardization of direct DNA isolation from soil and amplification of

phylogenetically relevant genes from isolated DNA.

4. Cloning and sequencing of amplicons to catalogue microbial community

inhabiting the rhizosphere of Saffron plants from different fields and

geographical locations.

5. To draw correlation between types of microbial consortia in rhizosphere

and production of Saffron.

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CHAPTER-2

REVIEW OF LITERATURE

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REVIEW OF LITERATURE

Crocus sativus is a perennial plant that is commonly known as “Saffron” in

English, “Kesar” in Hindi and “Kong” in Kashmiri. The word Saffron is derived

from French term Safran, and the Latin word safranum. It is also related to the

Italian Zafferano and Spanish Azafran and Arabic zafaran (Wani et al., 2011;

Husaini et al., 2013).

2.1. Importance:

Saffron is popularly known as golden condiment as it is the world's most expensive

spice selling for over $ 2000/kg (Sharaf-Eldin et al., 2008; Melnyk et al., 2010).

Saffron contains novel or poorly characterized bioactive molecules that are used as

a spice for flavouring and colouring food and medicinal supplement over

thousands of years (Ferna´ndez et al., 2011). The main chemical constituents of

Saffron are crocin, crocetin, picrocrocin, and safranal. Crocin, a C44 glycoside [di

(β-D gentiobiosyl) esterofcrocetin] is the most promising for use in cancer therapy

(Souret et al., 1999; Abdullaev 2002). Crocetin also seems to have a hypolipaemic

effect and may be useful in preventing atherosclerosis (Souret et al., 1999). Saffron

and its components are also reported to have medicinal properties like

antidepression effect, antinociceptive and anti-inflammatory effects,

anticonvulsant effect, antigastric effects, antiparkinsonian effect, antigenotoxic

effect and mutagenic or antimutagenic effects (Sharaf-Eldin et al., 2008; Wani et

al., 2010; Gout et al., 2010; Melnyk et al., 2010; Boskabady et al., 2010;

Chryssanthi et al., 2011). In addition, it is also having effect on learning behavior,

on ocular blood flow and retinal function, on coronary artery disease, on blood

pressure which has been discussed in detail by Wani and coworker (2010).

Imenshahidi and coworkers (2010) has studied the effects of Saffron stigma

aqueous extract and two active constituents, crocin and safranalon blood pressure

of normotensive and desoxycorticosterone acetate-induced hypertensive rats and

found that the aqueous extract of Saffron stigma has hypotensive properties too.

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2.2. Classification: - Saffron (Crocus sativus L.) belongs to family Iridaceae and

subfamily Crocoideae (Table 1). Crocoideae is the largest of the four subfamilies

currently recognized in Iridaceae (Goldblatt et al. 2006). Origin of Crocus sativusis

traced to its‟ wild precursor Crocus cartwrightianus. The genus consists of 88

small, corm-bearing, perennial species (Ferna´ndez et al., 2011). Crocus consists of

9 species, Crocus cartwrightianus and its derivatives, C. sativus, C. moabiticus,

C.oreocreticus, C. pallasii, C. thomasii, C. badriaticus, C. asumaniae and

C.mathewii (Nehvi and Shafiq, 2008). The different species of Crocus L genus are

reported to be distributed in Central and Southern Europe, North Africa, and from

Southwest Asia to Western China (Mathew 1982; Petersen et al., 2008). The

majority of species are restricted to Turkey and the Balkan Peninsula. Greece alone

is homeland of ca. 40% of the world‟s wild Crocus diversity (Tsoktouridis et al.

2009) while several countries have also representatives of some Crocus species

including Italy, Spain and Hungary (Ferna´ndez et al., 2011)

2.3. Life cycle of Saffron

Crocus sativus L. (Saffron) blooms in autumn when other plants are preparing to

protect themselves against the rigors of winters and contrary to others, its activity

slows down during spring (Nehvi and Yasmeen, 2010). Saffron is a bulbous

perennial herbaceous plant attaining a height of 25 to 40 cm. Saffron plant consists

of three main parts as corm, foliar structure (leaves) and floral organs (flower).

Saffron is a sterile triploid (2n = 24) and does not set viable seeds. Saffron

infertility is mainly related to the male gametophyte, thus, the plant does not

propagate by seeds but the underground portion called as corms similar to other

plants like potato, sweet potato, onion and rhizome (Yasmin and Nehvi 2013).

Saffron has two years plant cycle with three different stages as dormant, flowering

and vegetative and starts in the month of July of the first year. During dormancy,

corms lack roots, shoots and flowers that start appearing in flowering and

vegetative stages (Fig 1).

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Table 1: Taxonomical classification of Crocus sativus

Kingdom Plantae

Divison Magnoliophyta

Class Liliopsida

Order Asparagales

Family Iridaceae

Sub-Family Crocoideae

Genus Crocus

Species sativus

Fig1: Life cycle of Crocus sativus 1. Vegetative stage (Mar- Apr): when leaves and

roots are formed on daughter corms, 2. Dormant stage (Jul-Aug): when there are

no roots and shoots, 3. Flowering stage (Oct-Nov): when flowers bloom and roots

are well developed

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Corms are internally made up of starch-containing parenchyma cells and during

dormancy (July-Aug), there is decrease in starch concentration in the corms. Starch

is converted into sucrose and other suitable soluble sugars which go to tissues

where buds are being differentiated and developed (Medina 2003; Nehvi and

Yasmeen 2010). Corms that are covered by tunic are dormant during summer,

sprout in autumn. Activation for flowering stage begins from September when the

day temperature reaches about 25°C, with night temperature of about 15°C. Corms

begin to sprout producing 1 to 7 lilac to mauve colored flowers along with roots

(Botella et al., 2002). Roots (Adventitious roots) emerges radically from internodes

of corms and are thin white in color, numerous and variable in length (Fig 1).

The Saffron flower has an underground ovary, a style 5 to 9 cm long dividing at

the top into three red trumpet like stigmas (2 to 3 cm long) which when dried, form

the commercial spice Saffron. The stigma is attached to a style along with three

yellow stamens, which has little of the active components and is only included

with the lower grades of Saffron. Flowering starts in the second fortnight of

October and lasts up to first week of November. Flowers emerge in three to four

flushes with massive flowers in second flush (Yasmin and Nehvi 2013).

Vegetative phase starts immediately in November after flowering is over, with

young leaves emerging from the corms (Fig 1). Each corm produces five to eleven

green leaves or monophylls, 1.5 and 2.5 mm wide found per sprout and are called

bristles and can measure up to 50 cm (Dhar and Mir 1997; Lucceno 1999). Corm

consists of nodes and internodes bearing the apical, sub apical and auxillary buds

that differentiate into daughter corms. The photosynthetic activity of the leaves

during this stage contributes to the development of daughter corms besides

contribution from the mother corm which wrinkles to leave space for new corms.

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The apical bud takes a year to acquire its maximum size and become a corm.

During this stage, another type of roots known as “contractile roots” emerges at the

base of the daughter corms which are much thicker than the adventitious roots and

help in development of daughter corm (Yasmin and Nehvi 2013). After vegetative

stage, corms again enter a second year dormant stage from May and at the

beginning of November, commencement of degradation of mother corm is visible,

which looks quite wrinkled and flat (Medina 2003; Nehvi and Yasmeen 2010).

After completion of life cycle, plouging of old corms and planting of new corms

are done during dormant stage.

2.4. Saffron cultivation and production

2.4.1. International

Saffron believed to have originated from Greece, Asia Minor and Persia (Iran),

spreading eastwards to Kashmir and China (Fig 2). Its cultivation in the world

extends through 0 to 90 °E longitude (Spain to Kashmir) and 30 - 45 °N latitude

(Persia (Iran) to England). Today Saffron is cultivated from Eastern Mediterranean

(Spain) to India (Kashmir) and is unknown in wild state (Mathew, 1999). Iran is

responsible for more than 90% of the world‟s Saffron production, followed by

Greece, Morocco and Kashmir (Husaaini et al., 2010).

The world-production of Saffron has been estimated be to approximately 300 tons

per year (Kumar et al., 2008). The amount of Saffron production in Iran was 230

tons which constitutes 93.7% of the world Saffron production in 2005. Greece with

5.7 tons and Morocco and Kashmir with 2.3 tons come respectively in second and

third positions (Ghorbani 2008). In Iran, 50,000 hectares are under Saffron

cultivation in comparison of 2667 ha in Kashmir, India (Kafi et al., 2006;

Ghorbani 2008; Husaaini et al., 2013). The production of Saffron is therefore

among the major incomes for the country and 97% of the Saffron production in

Iran is situated in the Khorasan province (Mollafilabi 2003; Agayev et al., 2007;

Ghorbani 2008).

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Fig 2: Cultivation of Saffron worldwide

(Source: http://www.crocusbank.org/database/saffronmap.htm)

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2.4.2. National

In India most of the Saffron production is limited to the state of Jammu &

Kashmir. The State of Jammu & Kashmir (Kashmir) is situated between 32º 17‟

and 36º 58‟ north latitude and 37 º 26‟ and 80 º 30‟ east longitude and falls in the

north-western range of the Himalayas. Saffron cultivation in Kashmir started in the

reign of king Laltaditya during 550 A.D. (Wani et al., 2011). In Kashmir, Saffron

is grown on uplands (Kerewas) which are lacustrine deposits located at an altitude

of 1585 to 1677 m above the sea level, under temparate climatic conditions (Kanth

et al., 2008). The soil is calcareous in nature, heavy textured with silty clay loam

(Husaini et al., 2010).In Kashmir Saffron is chiefly grown in the Pulwama District

(73%) (Pampore, Balhuma, Wayun, Munpur, Mueej, Konibal, Dus, Zundhur,

Letpur, Sombar, Baras, Ladu and Khrew), Badgam District (Chadura, Nagam,

Lasjan, Ompora and Kralpura), Anantnag District (Zeripur, Srechan, Kaimouh,

Samthan and Buch), Srinagar District (Zewan; Zawreh and Ganderbal) and Doda

District (Kishtwar) (Kafi and Showket 2007). Kashmiri Saffron, having very high

reputation but low in production, is generally unavailable outside the India. In

Kashmir the average yield of Saffron amounts to 1.5-3.0 kg/hectare (McGimpsey,

1993). The total area under Saffron cultivation in Kashmir has decreased from

5707 ha in 1997 to 2667 ha in 2009 with decrease in production from 15.95 tons in

1997 to 5.61 tonnes in 2009 (Husaini et al., 2013). Thus, a decrease of 114% in

area and 184% in production was observed in just a short span of 12 years

(Ghorbani 2008; Husaini et al., 2013) and is due to unfortunate political situation

in the region as well (Aytekin and Acikgoz 2008).

2.5. Factor affecting Saffron production

Various factors which effect the production of Saffron are corm quality,

environment, temperature, irrigation, use of fertilizers, soil chemistry and microbes

present in soil. Saffron grows well in Mediterranean environment having cold

winters, warm dry summers and autumn-winter-spring rainfall and can survive

frost as cold as -100C (Willard 2001). Reports have shown that the temperature

22

also help in controlling growth and flowering of Crocus species by affecting the

enzyme activity in plant metabolism (Keyhani et al., 2002). Saffron production

does not require much water but it has been reported that first irrigation is very

important for flower emergence and length of flowering period of Saffron

(Temperini et al., 2008). R.K. Giri and his group (2008) from Central Institute of

Temperate Horticulture Srinagar (Jammu and Kashmir) had reported the influence

of weather and climate on the growth and production of Saffron. They have shown

that the optimum amount of annual rainfall and snowfall (about 80-100 cm.) is

essential for good production of Saffron but excess rainfall lowers its production

and a decrease in relative humidity below 65 % reduces the production of Saffron.

Moreover, the optimum range of temperature for Saffron production lies between

20 °C to 22 °C in Kashmir valley.

Reports have showed that application of organic fertilizers compared with

chemical fertilizers caused one week earlier flower emergence (Koocheki, 2003)

and increase in the dry weight of the Saffron flower. Along with all these factors

the chemical criteria of soil such as organic content, available phosphorus, mineral

nitrogen, exchangeable potassium and C/N ratio positively effect the Saffron

production. It has been reported that 16-18% of Saffron yield depends upon soil

variables and 1-10% is related to water availability (Jahan and Jahani, 2007).

Another factor which affects the yield of Saffron is use of fertilizers and manure,

of which animal manure holds superiority to chemical fertilizers. Animal manure

is reported to enhance the physical criteria of the soil which includes better

aeration, better water holding capacity and improvement of nutrient exchange

between the soil (Koocheki, 2003; Mollafilabi, 2003).

2.6. Plant-microbe associations

The soil microbial community acts as a reservoir of microbes that directly

influences the structure and composition of the above ground plant community,

promotes plant growth, increases stress tolerance and mediates local patterns of

nutrient cycling (Leveau 2007; Hartman et al 2009; Hinsinger et al., 2009; Hayat et

23

al., 2010; Saharan and Nehra 2011; Bakker et al., 2013; Coats et al., 2013).

Interactions between plants and microbes are an integral part of our ecosystem.

Plant–microbe interactions have been utilized to improve plant growth for the

production of food, fibre, biofuels and key metabolites. The Plant–microbial

interactions can be classified into three basic groups: (i) negative (pathogenic)

interactions; (ii) positive interactions in which either both partners derive benefits

from close association (symbiosis), both partners derive benefits from loose

association or only one partner derives benefits without harming the other

(associative); and (iii) neutral interactions (none is benefitted or harmed) (Singh et

al., 2004). The more common interactions are commensalism or mutualism, where

either one or both partners benefit from the relationship respectively. The

mutualistic interaction can be beneficial in directly providing nutrients to the plant

(biofertilizer) or increasing the availability of compounds such as iron or

phosphate (Wu et al., 2009).

Plant microbe interactions occur at rhizosphere (roots), phyllosphere (aerial plant

part), and endosphere (internal transport system) (Sekar 2010). Plant microbes

interaction including rhizosphere interactions (Singh et al., 2004a; Hayat et al

2010; Bakker et al., 2013), plant-microbe interactions at molecular level (Sorenson

et al., 2009; Knief et al., 2012), endophyte applications (Ryan et al., 2008; Gottel

et al., 2011), rhizosphere bacteria responses to transgenic plants (Fillion, 2008) and

phyllosphere interactions (Raaijmakers et al. 2002, Morris and Kinkel, 2002;

Muller and Rupple 2014) are well reviewed.

The phyllosphere, the microbial habitat found on the surface of leaves, may be one

of the largest microbial habitats on earth, with terrestrial leaf surface area

estimated to exceed 108 km2 globally (Morris and Kinkel, 2002). Phyllosphere

bacteria are also important in that they likely represent an important source of

bacteria in the atmosphere (Lighthart, 1997) and they may play key roles in

nutrient cycling by fixing nitrogen (Muller and Rupple 2014). Phyllosphere

bacteria isolated from an apple orchard showed the antagonistic activity against

24

Venturia inaequalis, the causal agent of apple scab in vitro (Kucheryav et al.,

1999). Bacteria have been also isolated from the stem and root of sugarcane

capable of nitrogen fixing and phytohormone production that helped in the growth

of micropropagated sugarcane plantlets (Mirza et al, 2001). Yim and coworkers

(2010) isolated pink-pigmented facultative methylotrophic bacteria from rice stem

and leaf that were involved in plant growth promotion. Phyllosphere bacteria

isolated from rice were able to produce ACC deaminase and regulated ethylene

regulation (Chinnadurai et al., 2009). Castro and co-worker (2011) isolated 97

strains of endophytic bacteria from the root, stem, leaf tissues and rhizoplane of

Cytisus striatus that showed plant growth promoting traits and could be

particularly useful for exploiting the phytoremediation potential of C.striatus.

Pseudomonas spp. and Bacillus spp. mediate crop protection by exerting multiple

mechanisms of inhibitory activity such as the production of extracellular enzymes,

competition, induced systemic resistance (ISR) and antibiosis (Raaijmakers et al.,

2002; Ryu et al., 2003). Bacillus spp has been reported to colonize the

phyllosphere and act as a biocontrol agent in cherry trees (Kim et al., 2012).

Much of interaction between plants and soil microorganisms occurs in the

rhizosphere or initiated in the rhizosphere. Direct interactions between plants and

rhizosphere-dwelling microorganisms occur at, or near, the surface of the root.

Upon introduction and establishment, invasive plants modify the soil microbial

communities and soil biochemistry affecting bioremediation efforts and future

plant communities (Coats et al., 2013). Rhizosphere was first described by Lorenz

Hiltner in 1904 and represents the most dynamic habitat on Earth. The rhizosphere

zone is distinguished from bulk soil as it is under the influence of root exudates

(Hinsinger et al., 2009). Root exudation includes the secretion of ions, free oxygen

and water, enzymes, mucilage and a diverse array of carbon-containing primary

and secondary metabolites (Uren 2000; Bais et al., 2006; Haichar et al., 2008). The

exudation of small molecular weight compounds by the roots results in an

enhancement of microbial biomass and activity in rhizosphere as compared to the

bulk soil. This rhizosphere effect is caused by the fact that a substantial amount of

25

the carbon fixed by the plant, 5–21%, is secreted, mainly as root exudates (Barret

et al., 2011). In the rhizosphere, diverse and complex interaction occurs between

plant roots, soil microbiota and the soil which have been evolved due to mutual

benefits between plants as well as microbes. The plant partner provides substrate

and energy flow into the rhizosphere and in return gets nutrients and minerals,

essential for its development and growth from microbes (Hartmann et al., 2009).

Various bacteria have been reported from the rhizosphere of wheat (Prashant et al,

2009, Abbasi et al, 2011), maize (Djuric et al, 2011), sugarcane (Ashraf et al,

2011), apple (Mehta et al., 2010), Rice (Himadri et al, 2013) and medicinal plants

(Vasudha et al, 2013). However till date no work has been reported that reveal

interaction of microbes with corms or rhizosphere of Saffron or any other plant

that bear corms.

Rhizosphere soil is replete with a variety of microorganisms such as rhizobacteria,

pathogenic soil-borne fungi, and arbuscular mycorrhizal fungi. The microbes

associated with the rhizosphere can be studied by two different approaches

Cultivation dependent approach

Cultivation independent – metagenomics approach

2.6.1 Cultivation dependent approach:

This approach includes the isolation and characterisation of microbes from the

environment niche like rhizosphere. Microbes residing in the rhizosphere can be

beneficial or detrimental for the plant and therefore can influence crop yields

significantly. For example, some specific bacterial populations, called plant

growth-promoting rhizobacteria (PGPR), competitively colonize plant roots and

can promote plant growth and/or reduce the incidence of soil-borne diseases

(Kloepper et al., 1980). PGPR are also termed as plant health promoting

rhizobacteria (PHPR) or nodule promoting rhizobacteria (NPR) and are associated

with the rhizosphere which is an important soil ecological environment for plant-

microbe interactions. Interaction of plant growth promoting rhizobacteria (PGPR)

with host plants is an intricate and interdependent relationship involving not only

26

the two partners but other biotic and abiotic factors of the rhizosphere region

(Dutta and Podile 2010). Rhizosphere microorganisms may also depend on other

members of the community to provide nutrient sources as one bacterium may

convert a plant exudates into a form that can be used by another. When multiple

bacterial species co-exist they do not colonize in distinct areas as pure cultures but

as complex communities known as biofilms and this is thought to be the case also

for rhizosphere bacteria living on plant roots. The population dynamics of the

rhizosphere microorganisms can change as the root structure and patterns of root

exudation alter during development and as environmental conditions such as water

availability and temperature alter (Sekar 2010). Various plant growth-promoting

rhizobacteria (PGPR) reported are Pseudomonas, Azospirillum, Azotobacter,

Klebsiella, Enterobacter, Alcaligenes, Arthrobacter,Burkholderia, Bacillus and

Serratia and are known to suppress plant diseases; help in induction of systemic

resistance, solubilisation of phosphate and production of siderophores and

phytohormones (Kloepper et al., 1989; Okon et al.,1994; Glick 1995; Joseph et al.,

2007).

PGPR vary in their degree of intimacy with the plant, from intracellular, i.e.

existing inside root cells, to extracellular, i.e. freeliving in the rhizosphere (Leveau

2007).According to their relationship with the plants, PGPR can be divided into

two groups: symbiotic bacteria and free-living rhizobacteria (Khan 2005). PGPR

can also be divided into two groups according to their residing sites: iPGPR (i.e.,

symbiotic bacteria), which live inside the plant cells, produce nodules, and are

localized inside the specialized structures; and ePGPR (i.e., free-living

rhizobacteria), which live outside the plant cells and do not produce nodules, but

still prompt plant growth (Gray and Smith 2005) (Hayat et al., 2010). The best-

known iPGPR are Rhizobia, which produce nodules in leguminous plants. Species

of Rhizobium (Rhizobium, Mesorhizobium, Bradyrhizobium, Azorhizobium,

Allorhizobium and Sinorhizobium) have been successfully used worldwide to

permit an effective establishment of the nitrogen-fixing symbiosis with leguminous

crop plants (Bottomley and Maggard 1990). On the other hand, non-symbiotic

27

nitrogen fixing bacteria such as Azotobacter, Azospirillum, Bacillus, and Klebsiella

sp. are also used to inoculate a large area of arable land in the world with the aim

of enhancing plant productivity (Lynch 1983; Hayat et al., 2010).

PGPR can affect plant growth either indirectly or directly; indirect promotion of

plant growth occurs when PGPR lessen or prevent the deleterious effects of one or

more phytopathogenic organisms; while direct promotion of plant growth by

PGPR involves either providing plants with a compound synthesized by the

bacterium or facilitating the uptake of certain nutrients from the environment

(Leveau 2007).

General mechanisms of plant growth promotion by PGPR include:

1. Associative nitrogen fixation (Kennedy et al. 1997, 2004),

2. Production of Phytohormoneslike auxin, i.e indole acetic acid (IAA)

(Sachdev et al., 2009), abscisic acid (ABA) (Dangar and Basu 1987;

Dobbelaere et al. 2003), gibberellic acid (GA) and cytokinins (Dey et al.

2004),

3. Production of 1-aminocyclopropane-1-carboxylate (ACC) deaminase to

reduce the level of ethylene in the root of developing plants thereby

increasing the root length and growth (Li et al. 2000),

4. Antagonism against phytophatogenic bacteria by producing siderophores,

ß-1, 3-glucanase, chitinases, antibiotic, fluorescent pigment and cyanide

(Cattelan et al. 1999; Pal et al. 2001; Glick and Pasternak 2003),

5. Induction of pathogen resistance in the plant (Hayat et al., 2010),

6. Solubilization and mineralization of nutrients, particularly mineral

phosphates (de Freitas et al., 1997; Richardson 2001; Banerjee and Yasmin

2002),

7. Promotion of mycorrhizal functioning, decreasing (organic or heavy metal)

pollutant toxicity, enhanced resistance to drought (Alvarez et al., 1996),

salinity, waterlogging (Saleem et al., 2007) and oxidative stress (Stajner et

al., 1995) and

28

8. Production of water-soluble B group vitamins niacin, pantothenic acid,

thiamine, riboflavine and biotin etc (Martinez- Toledo et al., 1996; Sierra et

al., 1999; Sekar 2010; Hayat et al., 2010)

In the present study the bacteria isolated from the rhizosphere and cormosphere of

Saffron were screened for three PGP traits as Phosphate solubilization,

Siderophore production and IAA production.

Phosphate solubilization: - Phosphorus (P) is major essential macronutrients

for biological growth and development. The ability of some microorganisms to

convert insoluble phosphorus (P) to an accessible form, like orthophosphate, is an

important trait for increasing plant yields (Rodriguez et al., 2006; Chen et al.,

2006). The most efficient PSM belong to genera Bacillus, Rhizobium and

Pseudomonas and within rhizobia, two species nodulating chickpea,

Mesorhizobium ciceri and Mesorhizobium mediterraneum, are known as good

phosphate solubilizers (Rivas et al., 2006). Igual and coworker (2001) have

reported the use of phosphate solubilising bacteria as inoculants increases the P

uptake by plants (Igual et al., 2001). Bacterial strains Azotobacter vinelandii and

Bacillus cereus when tested in vitro are found to solubilise phosphate and thus can

help in the growth of plants (Husen 2003).

Siderophore production: - Siderophores (Greek: "iron carrier") are small,

high-affinity ironchelating compounds secreted by many bacteria (Neilands 1995;

Prashant et al., 2009). Microbes release siderophores to scavenge iron from these

mineral phases by formation of soluble Fe3+ complexes that can be taken up by

active transport mechanisms.

IAA production: - Plant hormones are chemical messengers that affect a

plant's ability to respond to its environment. Hormones are organic compounds that

are effective at very low concentration; they are usually synthesized in one part of

the plant and are transported to another location. They interact with specific target

tissues to cause physiological responses, such as growth or fruit ripening. Botanists

29

recognize five major groups of hormones: auxins, gibberellins, ethylene,

cytokinins, and abscisic acid. IAA (indole-3-acetic acid) is the member of the

group of phytohormones and is generally considered the most important native

Auxin (Ashrafuzzaman et al., 2009). It functions as an important signal molecule

in the regulation of plant development including organogenesis, tropic responses,

cellular responses such as cell expansion, division, and differentiation, and gene

regulation (Ryu and Patten 2008). Diverse bacterial species possess the ability to

produce the auxin phytohormone IAA.

The potential for auxin biosynthesis by rhizobacteria can be used as a tool for the

screening of effective PGPR strains (Khalid et al., 2004). The effects of auxins on

plant seedlings are concentrations dependent, i.e. low concentration may stimulate

growth while high concentrations may be inhibitory (Arshad and Frankenberger

1991). The strains which produce the highest amount of auxins i.e. indole acetic

acid (IAA) and indoleacetamide (IAM) in non-sterilized soil, causes maximum

increase in growth and yield of the wheat crop (Khalid et al., 2004). Even the

strains, which produce low amounts of IAA, release it continuously, thus

improving plant growth.

2.6.1.1. Plant –microbe association in Saffron

Till date, there are no reports on native microbes of Saffron. However some

researchers have studied the application of microbes with established plant growth

promotion properties on production of Saffron isolated from other plants. In Spain,

a recent study has shown that the application of B.subtilis FZB24 spore solution to

Saffron corms significantly increased leaf length, flower per corm, total stigma

biomass and decreased the time required for corms to sprout. Moreover, there was

significant increase in the quantity of picrocrocin, crocetin and safranal compounds

when the plants were soil drenched with B.subtilis FZB24 spore solution 14 weeks

after the sowing (Mahmoud et al., 2008). Aytekin and Acikgoz (2008) has reported

that the production of Saffron can be increased by treatment of Saffron corms with

30

a synthetic hormone (polystimulin A6 and K) and microorganism based material

like biohumus and effective microorganism. Biohumus is a natural organic

fertilizer by processing organic waste through earthworm and effective

microorganism comprises of mixed culture of beneficial microorganism including

photosynthetic and lactic acid bacteria, yeast, actinomycetes and fermenting fungi.

The best results were seen in case of biohumus and effective microganism

application to corms.

The extracts of Saffron were also studied for the plant growth regulator effects on

wheat plant. Extract treatment resulted in decreasing the rate of shoots and roots

growth but increasing numbers of secondary roots, width of shoots and roots thus

act as a growth inhibitor for wheat. The extracts of Saffron are rhizogenesis

stimulator in wheat indicating that Wheat isn‟t suitable for intercrop culture with

Saffron (Hashemloian et al., 2006). In another study, the pigments of Saffron were

screened for antibacterial activity against Salmonella. It was found that the

pigments Safranal, Crocin and their chemical relatives showed antibacterial

activity against Salmonella and thus can significantly reduce the risk of food

contamination with Salmonella by this spice (Pintado et al., 2011).

2.6.2. Cultivation independent (metagenomics) approach

An often-cited estimate is that as much as 99% or more of microbial life remains

unculturable, and therefore cannot be studied and understood in a way that

microbial ecologists have become accustomed to over the past century. Cultivation

independent approaches (Metagenomics) arose in reaction to the observation that

the majority of microorganisms on Earth resist life in captivity, i.e. they cannot be

grown in broth or on plates in the laboratory. In the area of microbial ecology, the

term „metagenomics‟ is now synonymous with the culture-independent application

of genomics techniques to the study of microbial communities in their natural

environments (Chen and Pachter 2005). The phrase „metagenome of the soil‟ was

first used by Handelsman and coworkers (1998) to describe the collective genomes

of soil microflora. Metagenomics exploits the fact that while some microorganisms

31

are cultivable and others are not, all of them (i.e., 100%) are life-forms based on

DNA as a carrier of genetic information. The metagenomic toolbox allows

accessing, storing, and analyzing this DNA and thus can provide an otherwise

hard-to-attain insight into the biology and evolution of environmental

microorganisms, independent of their culturable status (Leveau 2007).

In metagenomics, three major and oftentimes overlapping directions can be

recognized. The first is aimed at linking phylogeny to function through

phylogenetic anchoring (Amann, 1995, Riesenfeld et al., 2004), which involves the

screening of large-insert environmental libraries for clones that carry

phylogenetically informative genes and analyzing their flanking DNA for genes

that reveal possible environmental functions of the DNA‟s owner. A second trend

includes the exploitation of metagenome for the discovery of enzymes with novel,

industrial and possibly exploitable properties. Metagenomics provides industry

with an unprecedented chance to bring biomolecules into industrial application

(Lorenz and Eck, 2005). The third and most recent trend in metagenomics is the

mass sequencing of environmental samples which offers a more global or systems-

biology view of the community under study and has led to more complete

assessments of genetic diversity and to first insights into the interactivities that

occur in microbial communities (Leveau, 2007). Metagenomics can unravel the

microbial communities associated by underground parts of plants in two different

ways cloning dependent and cloning independent approach.

2.6.2.1. Cloning dependent approach:

Microbial communities associated with the rhizosphere of plants can also be

studied using cloning dependent approach which includes the cloning of

phylogenetic gene markers in suitable vector and its analysis. PCR will inevitably

uncover phylogenetic genes from the metagenome using universal primers. Indeed,

there is ample evidence that the microbial diversity as measured by phylogenetic

markers such as ribosomal RNA genes can differ dramatically between bulk and

rhizosphere soil (Sanguin et al., 2006). The rhizosphere can be viewed as an

32

environment that incomparison to the bulk soil is enriched in particular types of

microorganisms, including PGPR.

For analysis of prokaryotic diversity, the phylogenetically relevant genes (16S

rRNA gene ~1500 bp) are usually amplified using Taq polymerase that

preferentially adds Adenine residue at 3´ end of the product. Such PCR products

can be cloned into linearised vectors that have complementary 5´ Thymine

overhangs. TA cloning is an easier and quicker subcloning technique that does not

use restriction enzymes and relies on the ability of adenine and thymine

(complementary base pairing) on different DNA fragments to hybridize and ligate

in the presence of ligase. The ligated products are transformed in E.coli and further

identified by blue/white screening.The blue/white screening is based on a genetic

engineering of the lac operon in the E.coli laboratory strain serving as the host cell.

The vector encodes α subunit of lacZ protein with an internal multiple cloning site

(MCS), while the chromosome of the host strain encodes the remaining Ω subunit

to form a functional β- galactosidase enzyme. X-gal (analogue of lactose) is used

for screening purpose and is broken down, by the enzyme, into a product which is

naturally oxidized to form a blue colored pigment. In the clone having the insert,

the colonies are white because of insertional inactivation of the gene coding for α

peptide. Even though blue/white screening can be used to determine if inserts are

present, colony PCR can be used to determine insert size and/or orientation in the

vector.

Colony PCR is designed to quickly screen for plasmid inserts directly from E. coli

colonies while reserving some of the bacteria for further growth and plasmid

preparation. Alternately, the presence of an insert and its size can be determined by

growing each colony in broth, the plasmid purified by a boiling or alkaline

preparation protocol, digestion of the plasmid with restriction enzyme(s) that

excises the insert, followed by separation by agarose gel electrophoresis (Wheeler,

2009). Further analysis for diversity in the sequence of 16S rRNA gene requires

the use of ARDRA (Amplified Ribosomal DNA Restriction Analysis) which leads

33

to species specific banding pattern which can be used for identification of clones

that are different from others. Amplified Ribosomal DNA Restriction Analysis

(ARDRA) is the extension of the technique of RFLP (Restriction Fragment Length

Polymorphism) to the gene encoding the small (16S) ribosomal subunit of bacteria.

This technique was originally developed by Vanechoutte and coworkers

(1993) who used the method to characterize Mycobacterium species and has been

used by others researchers for similar characterization of other bacterial species.

The technique involves an enzymatic amplification using primers directed at the

conserved regions at the ends of the 16S rRNA gene, followed by digestion using

tetra cutter Restriction Enzymes. The pattern obtained is said to be representative

of the species analyzed. RFLP banding patterns can be used to screen clones (Pace,

1997) or used to measure bacterial community structure (Massol-Deya et al.,

1995). Patterns obtained from three or more restriction enzymes can be used to

phylogenetically characterize cultured isolates and 16SrRNA genes obtained

through cloning from community DNA. Bacterial diversity in rhizosphereof rice

(Arjun and Kumarapillai 2011) and Romanda sp. (Dokic et al., 2010) was also

studied by screening of the 16S rDNA library by ARDRA. Liu and coworkers

(1997) had also PCR amplified rDNA and digested with a 4-base pair cutting

restriction enzyme to detect different fragment lengths on agarose or non-

denaturing polyacrylamide gel electrophoresis to study the community analysis

(Liu et al., 1997; Tiedje et al., 1999). Once the different clones have been

recognized; the 16S rRNA gene can be sequenced from the different clones to

generate a huge amount of data which can be analyzed with the help of a vast array

of bioinformatics tools available now-a-days. Currently, phylogenetic analysis of

rRNA sequence alignments is routinely performed to generate a backbone for

microbial classification and identification. In general such analyses attempt to

reconstruct the evolutionary history of the marker molecules. Given the limited

information content of the rRNA markers and the noise in the huge contemporary

datasets, the ultimate goal of finding exact phylogenies might never be attained.

Nevertheless, the ongoing progress in software development and optimization as

34

well as rapid achievements concerning computer hardware power, increasingly

allow multiple comparative analyses of comprehensive data sets. Consequently,

phylogenetic conclusions can be inferred on a more solid basis than in past (Kunin

et al., 2008).

Metagenomics has contributed a lot to study the microbial communities of the

rhizosphere (in particular PGPR) like the discovery of novel plant growth

promoting genes and gene products (Leveau 2007). Production of the plant

hormone indole 3-acetic acid (IAA) by metagenomic library clones can be

measured using high-pressure liquid chromatography or colorimetric assays, while

cytokinins and their metabolites are detectable in supernatants by e.g.

immunoaffinity chromatography. Genes for nitrogen fixation are retrievable with

the use of nitrogen-free media (Hashidoko et al., 2002; Ding et al., 2005; Tejera et

al., 2005). Screening rhizosphere DNA for PGPR-related genes by PCR or

Southern hybridization has several advantages as sequence-based approaches in

metagenomics (PCR and Southern hybridizations) will uncover only those genes

that match the specificity of the primers or probes that were used to find them

(Leveau 2007).

An analysis of the rhizosphere by comparative metagenomics holds the promise to

reveal several important questions regarding the unculturable fraction of the

rhizosphere community. A comparison of metagenomic DNA isolated directly

from rhizospherecan be compared to DNA isolated from all the colonies forming

on solid media after plating from that same rhizosphere (i.e. the culturable

fraction) which are expected to be different (Sliwinski and Goodman 2004). EGT

fingerprinting by shotgun sequencing (Tringe and Rubin 2005) or suppressive

subtractive hybridization (Galbraith et al., 2004) of bulk and rhizosphere soil

compartments could reveal differences in the type of gene adaptations that each

compartment selects for. It is expected that genes with PGPR-like functions would

be enriched in the rhizosphere library. Furthermore, by comparison of the

functions enriched for in a library from rhizosphere soil versus one from bulk soil,

35

the degree of the selection in each of the compartments for particular microbial

activities, specifically those with PGPR relevance, can be estimated (Leveau

2007). Similarly, comparison of the genomic diversity of disease-suppressive and

nonsuppressive soils could expose genetic factors that contribute to or are

predictive of the suppressiveness towards e.g. pathogenic microorganisms or

nematodes (Weller et al., 2002; Leveau 2007).

One of the important contribution of metagenomics to the study of rhizosphere is

characterization of (not-yet-) culturable PGPRs. There is ample evidence that the

microbial diversity can differ dramatically between bulk and rhizosphere soil as

measured by phylogenetic markers such as ribosomal RNA genes (Kielak et al.,

2009; Teixera et al., 2010). There are several examples of existence of not-yet

culturable PGPR and their contribution to plant health, e.g. Pasteuria penetrans, a

not-yet-culturable bacterium parasitic to plant-pathogenic nematodes (Fould et al.,

2001), the nitrogen fixing activity by viable-but-not-culturable Azoarcus grass

endophytes (Hurek et al., 2002), and the obligate biotrophism of arbuscular

mycorrhizal (AM) fungi (Leveau 2007).

Erkel and co-workers (2006) has described the use of metagenomic sequencing to

reconstruct the 3.18-Mbp genome of rice cluster I (RC-I) Archaea with origin in

the rice rhizosphere. DNA for the shotgun library was isolated from a

methanogenic enrichment culture using rice paddy soil as an inoculum. While RC-

I Archaea do not necessarily qualify as PGPR, the study shows that shotgun

sequencing in combination with enrichment strategy allows the metagenomic

analysis of rhizobacteria with a particular function of interest. PCR targeting the

16S rDNA has been used extensively and helps in identification of prokaryotes as

well as the prediction of phylogenetic relationships. 18S rDNA and internal

transcribed spacer (ITS) regions are increasingly used to study fungal

communities. However, the available databases for fungi are not as extensive as

for prokaryotes (Prosser, 2002). Molecular-based methods for ecological studies

initially relied on cloning of target genes isolated from environmental samples.

36

Although sequencing has become routine, sequencing thousands of clones is

cumbersome, therefore, many other techniques have been developed to assess

microbial community diversity in which DNA is extracted from the environmental

sample, purified and amplification of target DNA (16S, 18S or ITS) using

universal or specific primers is done and the resulting products are separated in

different ways (Kirk et al., 2004).

2.6.2.1.1. Limitation of cloning dependent approach:

Molecular techniques (metagenomics) have been used to overcome the limitations

of culture-based methods; however, they are not without their own limitations as

described below.

a. Lysis bias: Bacteria exist in or on the surface of soil aggregates; therefore, the

ability to separate these cells from soil components is vital for studying

biodiversity (Trevors, 1998a). If the method of cell extraction used is too

gentle, Gram-negative, but not Gram-positive bacterial cells would be lysed. If

the method is too harsh, both Gram-negative and Gram positive cells may be

lysed but their DNA may become sheared (Wintzingerode et al., 1997). The

variation in the ability to break open cells or fungal structures can lead to

biases in molecular-based diversity studies. Lysis efficiency of cells or fungal

structures varies between and within microbial groups (Prosser, 2002).

b. Extraction bias: The method of DNA or RNA extraction used can also bias

diversity studies. Harsh extraction methods, such as bead beating, can shear the

nucleic acids, leading to problems in subsequent PCR detection. Different

methods of nucleic acid extractions will result in different yields of product

(Wintzingerode et al., 1997).

c. Contaminants: With environmental samples, it is necessary to remove

inhibitory substances such as humic acids, which can be coextracted and

interfere with subsequent PCR analysis. Subsequent purification steps can lead

to loss of DNA or RNA, again potentially biasing molecular diversity analysis

(Wintzingerode et al., 1997).

37

d. PCR amplification Bias: Differential amplification of target genes can also

bias PCR-based diversity studies. Typically, 16S rRNA, 18S rRNA or ITS

regions are targeted by primers for diversity studies because these

genes/fragments are present in all organisms, they have well defined regions

for taxonomic classification that are not subject to horizontal transfer and have

sequence databases available to researchers. Wintzingerode and coworker

(1997) discussed some issues surrounding differential PCR amplification

including different affinities of primers to templates, different copy numbers of

target genes, hybridization efficiency and primer specificity. In addition,

sequences with lower G+C content are thought to separate more efficiently in

the denaturing step of PCR and, therefore, could be preferentially amplified.

e. Species identification bias: Another problem associated with measuring

microbial diversity in soil is the problem of defining microbial species (Torsvik

et al., 1998; Trevors, 1998b). There is no official definition of a bacterial

species (Brose et al., 2003). Moreover, Hey (2001) listed over 24 definitions of

species, all of which were different. The traditional species definition was

based on higher plants and animals and does not readily apply to prokaryotes

or asexual organisms (Godfray, 2002). The genetic plasticity of bacteria,

allowing DNA transfer through plasmids, bacteriophages and transposons,

complicates the concept of bacterial species.

f. Cloning Bias: Cloning bias is a tendency of certain regions of the genome to

be cloned less often than others during sequencing, and thus less likely to be

sequenced. This results in a lower expected read coverage in these regions,

which magnifies the gap-producing effects of random chance. To get enough

data to sequence these regions, a higher overall coverage is needed. The most

prominent cause of cloning bias is AT-richness, which affects cloning rates in

the Sanger sequencing process.

38

2.6.2.2. Cloning independent approach:

To date, characterization has been limited to cloning-based sequencing and

conventional culture-based studies, which generally underestimate community

diversity as a result of inherent biases in their methodologies as mentioned in the

limitation section elsewhere. Pyrosequencing, a cloning- and culture-independent

sequencing approach, eliminates these elements of bias from the analysis and

enables extensive sequencing of microbial populations (Tedersoo et al., 2010).

Total metagenome sequencing by next generation sequencing is a better alternative

to PCR and cloning as it delivers fast, inexpensive and accurate genome

information without any bias inherent to PCR and cloning (Metzker 2009, Quail et

al., 2012)

Gottel and co-worker (2011) has reported the community profile (bacterial and

fungal) of the root endophytic and rhizospheric habitats of Populus deltoidsby 454

pyrosequencingusing separate primers targeting the V4 region for bacterial 16S

rRNA and the D1/D2 region for fungal 28S rRNA genes. Both fungal and bacterial

rhizosphere samples were highly clustered compared to the more variable

endophyte samples in a UniFrac principal coordinates analysis Rhizosphere

bacteria were dominated by Acidobacteria (31%) and Alphaproteobacteria (30%),

whereas most endophytes were from the Gammaproteobacteria (54%) as well as

Alphaproteobacteria (23%). Endophytic bacterial richness was also highly variable

and 10-fold lower than in rhizosphere samples originating from the same roots.

Fungal rhizosphere and endophyte samples had approximately equal amounts of

the Pezizomycotina (40%), while the Agaricomycotina were more abundant in the

rhizosphere (34%) than endosphere (17%) (Gottel et al., 2011).

DNA-based pyrosequencing was used to characterize the bacterial communities of

potato rhizosphere and bulk soil. The rhizospheres of six cultivars at three growth

stages (young, flowering and senescence) were sequenced, in addition to

corresponding bulk soils. Around 350,000 sequences were obtained (5,700 to

38,000 per sample) and grouping of the sequences showed that members of the

39

Actinobacteria, Alphaproteobacteria, next to as-yet-unclassified bacteria,

dominated. Beta-, Gamma- and DeltaproteobacteriaandAcidobacteria were the

other groups that were consistently found at lower abundance. Principal

components analyses revealed that rhizosphere samples were significantly

different from corresponding bulk soil in each growth stage. Across all potato

cultivars, the young plant stages revealed cultivar-dependent bacterial community

structures, which disappeared in the flowering and senescence stages. Furthermore,

Pseudomonas, Beta-, Alpha- and Deltaproteobacteria flourished under different

ecological conditions than the Acidobacteria (Inceoglu et al., 2011)

The bacterial and fungal community diversity in the rhizosphere of

Berberisthunbergii DC (Japanese barberry) was characterized by tag-encoded FLX

amplicon 454 pyrosequencing (TEFAP) and the effects of soil type, soil chemistry

and surrounding plant cover was studied on the soil microbial community

structure. Result suggested that a high degree of spatial variation in the rhizosphere

microbial communities with apparent effects of soil chemistry, location and

canopy cover on the microbial community structure. Acidobacteria,

Actinobacteria, Proteobacteria and Verrucomicrobia were the dominant bacterial

phyla, whereas Ascomycota and Basidiomycota phyla comprised the most

abundant fungal communities. Bulk soil chemistry had more effect on the bacterial

community structure than the fungal community. An effect of geographic location

was apparent in the rhizosphere microbial communities, yet it was less significant

than the effect of surrounding plant cover (Coats et al., 2013).

40

CHAPTER-3

MATERIALS & METHODS

41

MATERIALS AND METHODS

3.1. Sample collection and physiochemical analysis of soil:

Saffron fields of Wuyan village (74°58′0″E, 34°1′30″N, 5173ft) of Pulwama

district were selected for composite sampling (Fig 3, Courtesy: State Agriculture

Department, J&K, India). Soil samples were collected from the bulk, cormosphere

and rhizosphere of Saffron during the flowering period (October-November, 2010)

and dormant stage (Jul-Aug 2011) of the life cycle of Saffron. The bulk soil was

collected by vigorously shaking the roots. The soil which remains adhere to the

roots was taken as rhizosphere soil whereas the corms sheath was taken as

cormosphere. During dormant stage, rhizosphere sample could not be studied

because corms lack roots in this stage, thus only bulk and cormosphere samples

were analysed. A total of five samples were analysed for microbial diversity and

the soil sampling was done as per the protocol of Luster and co-workers (2009).

The samples were collected in triplicate and pooled together. Samples were

transported to the laboratory at 4°C (in ice) and stored at -200C till processed

further for physicochemical and community DNA extraction. Standard protocol of

Hamza and coworker (2008) was used for the analysis of pH, electrical

conductivity, organic Carbon, Calcium, Magnesium, bulk density, available

Nitrogen, Phosphorus and Potassium of the collected soil samples.

3.2. Cultivation dependent approach:

3.2.1. Comparison of cultivable bacterial load:

Bacterial load associated with bulk soil, rhizosphere and cormosphere during two

growth stages was calculated by dilution plate technique (Stotzky et al., 1966;

Luster et al., 2009). For dilution of bulk soil, 1gm of soil was added to 10 ml

normal saline and serially diluted whereas in case of rhizosphere and cormosphere,

1 gm of roots and corm sheath was used respectively. The samples were diluted

upto10-3

and 10-4

dilution level and spread on LB agar plates and incubated at 280

42

C for 24 hrs. Colony forming units (CFU)/gm soil/sample was calculated for each

of the sample using a given formula (Joshi and Bhat 2011).

CFU/gm = Number of colonies X dilution factor/ volume spread

The bacterial load associated with bulk soil, rhizosphere and cormosphere was

compared vis a vis niche and growth stages and their significance was confirmed

statistically by F test. The bacterial load was compared between bulk soil,

rhizosphere and cormosphere during flowering and dormant stage separately. To

compare the bacterial load between two growth stages, cormosphere and bulk soil

samples during both growth stages were compared respectively. Rhizosphere

samples could not be compared as the plants bear full grown roots during

flowering stage but roots are completely absent during dormant stage.

3.2.2. Isolation and characterisation of bacteria:

Isolation of cultivable bacteria from bulk soil, rhizosphere and cormosphere during

both the stages was done by conventional agar plate method using soil dilution

method (Stotzky et al., 1996) following the protocol given by Luster and

coworkers (2009). Fifty bacterial isolates were randomly selected from each

sample and purified by streak plate method.

3.2.2.1. Polyphasic characterization:

250 bacteria were characterized on the basis of colony morphology, microscopy,

biochemical characterization and 16S rRNA gene analysis. Microscopy of

bacterial isolates was done using Gram‟s staining kit (Sigma). Isolates were

screened for biochemical property using biochemical test strips (Himedia) and

manually by Bergy‟s manual of systemic bacteriology (Sneath et al., 1986).

Biochemical test strips consists of Gram positive and Gram negative test strip each

including 12 different tests.

43

Fig 3: Sampling site Wuyan, district Pulwama, Kashmir, J&K

44

Different tests for Gram negative bacteria are Citrate, Lysine, Ornithine utilization,

Urease detection, Phenylalanine deamination, Nitrate reduction, H2S Production

and Carbohydrate Utilization including Glucose, Adonitol, Lactose, Arabinose,

Sorbitol whereas Gram negative biochemical tests strips consists of Malonate,

VogesProskauer, Citrate utilization, ONPG, Nitrate reduction, Catalase, Arginine,

Carbohydrate Utilization of Glucose, Sucrose, mannitol, Arabinose and Trehalose.

The characterized bacteria were preserved on LB agar slants at 4 0C and also in

50% glycerol at -80 0C.

3.2.2.2. Bacterial identification by 16S rRNA amplification:

Bacterial cultures were further identified on molecular level by analysis of the

phylogenetic relevant gene like 16S rRNA gene sequence. Analysis of 16S rRNA

gene sequence includes isolation and purification of genomic DNA, amplification

of 16S rRNA gene, sequencing of amplicon and sequence analysis.

a. Genomic DNA isolation: Genomic DNA was isolated from all the bacteria

using the GES protocol (Pitcher et al., 1989) and Genomic DNA isolation kit

(Qiagene) according to manufacturer‟s protocol.

Protocol:

3ml of broth cultures were harvested at the end of the exponential growth phase by

centrifugation at l000 g for 15 min and a small (rice grain-sized) cell pellet was

obtained.

The cells of Gram-positive species were resuspended in 100 µl of fresh lysozyme

in TE buffer and the suspensions were incubated at 37°C for 30 min whereas

Gram-negative species were resuspended in 100 µl of TE buffer without enzymic

treatment.

Cells were lysed with 0.5 ml of GES reagent and cell suspensions were vortexed

briefly and checked for lysis (5-10 min).

45

The lysates were cooled on ice and 0.25 ml of cold ammonium acetate (7.5 mol/l)

was added with mixing gently and held on ice for further 10 min

0.5 ml of chloroform and isoamylalcohol mixture (24:1) was added, the phases

were mixed thoroughly and centrifuged (12000 g) for 10 min.

Supernatant fluid was transferred to fresh tube, mixed with 0.54 volumes of cold 2-

propanol, inverted for 1 min to precipitate DNA

The fibrous DNA precipitate was deposited by centrifugation at 6500g for 20 s,

pellet was washed in 70% ethanol and air dried; and dissolved in 50µl of milliQ

Reagent preparation:

1. GES: Guanidium thiocyanate (5 mol/l),

EDTA (100mmol/l),

Sarkosyl (0.5% v/v)

Guanidium thiocyanate (60 g), 0.5 mol/l EDTA at pH 8 (20 ml) and deionized

water (20 ml) were heated at 65°C with mixing until dissolved.After cooling, 5ml

of 10% v/v sarkosyl were added; the solution was made up to 100 ml with

deionized water and stored at room temperature.

2. Ammonium acetate (7.5 mol/l)

3. Lysozyme(50 mg/ml)

4. Chloroform and isoamyl alcohol mixture (24:1 v/v)

5. TE buffer: TrisHcl (10mM), EDTA (1mM)

b. Purification of genomic DNA: Genomic DNA was purified by gel elution,

Phenolation and purification through column or the combination of two methods.

Gel elution: DNA was eluted from 0.7 % Low melting agarose gel by elution

kit (Macherey – Nagel, Nucleospin Extract II kit)using the manufacturer‟s

protocol as under

1. 100mg of the gel slice was mixed with 200µl of binding buffer (NT) and

was incubated at 50°C for 5-10 minutes.

46

2. The contents were transferred to a column, centrifuged at 11000g for 1

minute and flow through was discarded.

3. 700µl of wash buffer (NT3) was added to column and centrifuged as above

followed by dry spin at 11000g for 2 minutes.

4. Column was kept in a fresh eppendorf tube (collection tube) and 20µl

milliQ was added to the column (incubated at room temperature for 10

minutes) and centrifuged as above to collect the DNA.

Phenolation

1. Crude DNA was diluted with milliQ water and an equal volume of

P:C:I (Phenol:chloroform: isoamylalcohol; 25:24:1) was added and

centrifuged at 13000g for 10 minutes.

2. The upper aqueous layer was carefully collected in a fresh eppendorf

and again an equal volume of C: I (24:1) was added followed by

centrifugation.

3. The upper aqueous layer was carefully collected in a fresh eppendorf

and DNA was precipitated by adding 5M NaCl (1/10 volume) and

ethanol (double the volume) and incubated at -20°C for 2 h.

4. The precipitated DNA was centrifuged at 13000g for 30 minutes and

the pellet was washed with 70% ethanol, air dried and suspended in

milliQ

Purification through column

1. 200µl of Isopropanol was added per 100µl of crude DNA and the contents

were transferred to a column

2. Centrifugation was done at 11000g for 1 minute and flowthrough was

discarded

3. 700µl of 70% ethanol was added and centrifuged as above

4. Dry spin was done for 2 minutes and flowthrough was discarded

5. Column was kept in a fresh eppendorf tube (collection tube) and 20µl

milliQ was added to the column (incubated at room temperature for 10

minutes) and centrifuged as above to collect the DNA

47

c. PCR amplification of 16S rRNA gene from bacterial culture:

Partial 16S rRNA gene region, flanking V1-V3 region was amplified (~500bp)

using universal primers Bac8f (5‟-AGAGTTTGATCCTGGCTCAG-3‟) and

Univ529 (5‟-ACCGCGGCKGCTGGC-3‟). The PCR was performed following the

protocol standardized by Fierer and coworkers (2007) with modifications and

amplification was carried out in 10µl reaction volume with 2 U recombinant Taq

DNA polymerase, 10-50 ng of genomic DNA, 0.2mM of dNTPs mix, 1.5mM

MgCl2, 10 pM of each primer and the appropriate buffer supplied by the

manufacturer (Fermentas). The PCR program was denaturation at 950C for 5

minutes followed by 30 cycles of denaturation at 950C for 60 seconds, annealing at

540C for 30 seconds followed by extension at 72

0C for 90 seconds and final

extension at 720C for 10 min.

d. Sequencing and phylogenetic analyses:

16S rDNA amplicons were custom sequenced at CIF, UDSC, New Delhi and

SciGenom Labs Private Ltd., Cochin, Kerala, India. The resulting nucleotide

sequences were assigned bacterial taxonomic affiliations based on the closest

match to sequences available at the NCBI database (http://www.ncbi.nlm.nih.gov/)

using the EzTaxon version 2.1 (www.eztaxon.org).

3.2.3. Screening of bacteria for PGP traits:

3.2.3.1. In vitro analysis of PGPR properties: Rhizobacteria were analyzed in

vitro for plant growth promoting properties as:

a. Phosphate-solubilisation test: This test was detected by formation of

transparent halos around bacterial colonies on the Pikovskaya agar after 72 hour

incubation, at 25°C (Sharma et al., 2011).

b. Siderophore production test: This test was detected by the formation of orange

halos on CAS (chrome azurol S agar) agar plates after 48 hour incubation at 25°C,

as described by Alexander (1991).

c. Indole acetic acid production: IAA production was quantitatively done

according the protocol of Sachdev and coworkers (2009) and

48

spectrophotometrically analyzed at 580nm. The bacteria showing PGP traits were

subjected to pot trials.

3.2.3.2. In vivo analysis of PGPR properties by Pot Assay:

PGPR formulation: Inoculum was prepared by growing each of the selected

bacteria with PGP traits in LB broth individually at 28 ± 10C with 180rpm for 48

hrs. The different bacteria with PGP traits required for consortium were checked

for co-inhibition by Kirby beur plate assay (Kirby et al., 1966). The inoculum

containing 107–10

8 CFU/ ml of each isolate was prepared by mixing them in equal

proportions. Subsequently the consortia were mixed with the sterile talc, Calcium

carbonate (autoclaved twice at 1210C for 15 min) in 1:3 ratios (1 consortium: 3

talc) and driedat 35 -370C for 4 days. Finally 1% CMC was mixed to the

consortium powder and CFU was calculated by serial dilution method. The PGPR

consortium was kept at room temperature prior to seed inoculation. The corms

selectedfor the experiment were of uniform size and shape. Corms were inoculated

by mixing with PGPR formulation talc at10% w/v. Control consisted of the corms

treated with talc having nutrient broth and CMC without the isolates Treated corms

were dried under shade for 6–8 h. The soil collected from Saffron fields was air-

dried, sieved (2- mm/10-mesh) and filled in the twenty pots. Twenty inoculated

and uninoculated corms were sown in soil filled pots maintaining one corm per

pot. The pots were arranged randomly with twenty repeats (ten each of treatment

and control) at ambient light and 200C temperature and were irrigated time to time.

The plants were harvested after 5 months and results analyzed. The data collected

were statistically analyzed using a completely randomized design in the pot trials.

One way ANOVA test was used to test if results were statically significant. All the

statistical tests were performed at p<0.1 (Gupta et al., 2011).

3.3. Cultivation independent (Metagenomics- Cloning dependent) approach

Bacterial diversity associated with bulk soil, cormosphere and rhizosphere was

studied by cultivation independent approachusing cloning dependent method

which included construction and analysis of 16S rRNA gene metagenomic library

49

of all the five samples.16S rRNA gene metagenomic library was constructed by

cloning of 16S rRNA gene, amplified from the metagenome of five samples.

3.3.1 Metagenomic DNA isolation: Metagenomic DNA isolation from all the

samples was carried out using the Pang‟s protocol (Pang et al., 2008) which is a

chemical-enzymatic lysis method.

Procedure:

Soil (20 g) was suspended in 50 ml of DNA extraction buffer along with 1

ml of Lysozyme (10 mg/ml) and incubated at 37ºC for 1 h.

Sample was further incubated at 65 ºC with SDS (2 ml, 20%, w/v) and

proteinase K (15 μl, 20 mg/ml) for 2 h and then centrifuged at 6,000 rpm

for 10 min to remove soil residue.

Supernatant was transferred into a clean tube, and then precipitated by

using half-volume of PEG (30%, w/v) and incubated at room temperature

for another 2 h.

DNA was pelleted and resuspended in milliQ.

Reagents required:

1. Extraction buffer

TrisCl (pH-8) 100mM

Na-EDTA(pH-8) 100mM

NaCl 1.5M

2. Lysozyme (10mg/ml)

3. SDS (20%)

4. Proteinase K (20mg/ml)

5. Polyethylene glycol (30%)

3.3.2. Purification of metagenomic DNA: Crude mDNA was further purified

using three different methods as phenolation, Gel elution and column purification

in order to remove the inhibitory substances such as humic acids which interfere

with PCR amplification. These three procedures have been mentioned during

50

purification of genomic DNA and were used in combination or as such to obtain

the desired purity of metagenomic DNA.

3.3.3. PCR amplification of 16S rRNA gene (1.5 Kb) from the metagenome

16S rRNA gene was amplified from the metagenome of all the samples using the

universal 16S rRNA gene primers 8F (5'- AGA GTT TGA TCC TGG CTC AG-3')

and 1522R (5'-AAG GAG GTG ATC CAN CCR CA-3') (Hong et al., 2009). The

PCR was performed following the protocol standardized by Hong and coworkers

(2009) and the amplification of full 16S rRNA gene was carried out in 10µl

reaction volume with 2 U Taq DNA polymerase, 10-50ng of metagenomic DNA,

0.2mM of dNTPs mix, 1.5mM MgCl2, 10 pM of each primer and the appropriate

buffer supplied by the manufacturer (Fermentas). The PCR program was

denaturation at 950C for 5 minutes followed by 30 cycles of denaturation at 95

0C

for 60 seconds, annealing at 550C for 30 seconds followed by extension at 72

0C for

90 seconds and final extension at 720C for 10 min.

Preparation of insert: For insert preparation, maxi prep PCR was done i.e. a

single reaction of 10µl volume was repeated 20 times to get desired concentration.

The PCR products (1.5Kb) were pooled and gel eluted from 1% low melting

agarose gel by PCR purification kit (Qiagene) according to manufacturer‟s

protocol.

3.3.4. Ligation of the insert into T-vector:

The insert was ligated to T-vector as the PCR products amplified by Taq

polymerase has 3‟A overhang which will bind complementary to T overhang of

vector. 16S rRNA gene (150 ng) was ligated to pTZ57R/T vector (110 ng) using

T4 DNA ligase (10 U) with the vector: insert molar ratio of 2:5 in the appropriate

buffer supplied by the manufacturer (Fermentas) and kept for incubation at 16ºC

for 16h. The ligation reaction was deactivated by incubating at 65ºC for 5 mins.

The required concentration of the insert was calculated by applying following

formula:

51

Conc. of insert (ng) = ((conc. of vector x size of insert) / size of vector) x molar

conc. ratio

3.3.5. Preparation of competent cells (Cohen et al., 1972)

a. Procedure :

1. Seed culture was prepared by inoculating a single colony of DH5α strain

into a test tube containing LB broth and incubated overnight at 37ºC under

shaking

2. 1% seed culture was inoculated to LB broth (50ml) and incubated for 2-3 h

at 37ºC (shaking) to attain the early log phase (108 cells/ml).

3. The culture was kept on ice for 20 minutes after incubation and then

dispensed in ice cold centrifuge cups aseptically inside a laminar air flow

4. Pellet was obtained by centrifugation at 4000g for 10 minutes at 4ºC and

the supernatant was discarded

5. The cell pellet was dissolved in 20 ml of 100mM CaCl2 (ice cold) and was

kept on ice for 30 minutes and centrifuged at 4000g for 5 minutes at 4ºC

and the supernatant was discarded

6. The pellet was dissolved in transformation buffer and dispensed into ice

cold eppendorfs (100µl in each) and stored at -80ºC

7. One of the vials was used to check competency of the cells by transforming

with pUC19 (20 ng/µl)

b. Material required:

1. E.coli DH5α strain

2. LB broth

1. Yeast Extract - 0.5%,

2. Tryptone - 1%,

3. NaCl - 0.5%

3. CaCl2 (100mM)

4. Transformation buffer

1. CaCl2(100mM)

2. Glycerol (15%)

52

5. pUC 19 (20 ng/µl)

3.3.6. Transformation of competent cells with ligation mixture

a. Procedure

100 µl of competent cells were transformed with 10 µl ligation mix as follows:

10 µl of ligation mix was transferred aseptically to 100 µl of competent

cells and kept on ice for 20 minutes

Heat shock was given at 42°C for 90 seconds and the aliquots were

immediately transferred to ice, kept for 2 minutes

1ml SOC media was added to the aliquots and incubated at 37°C for 1h

under shaking condition

100 µl of this transformed culture was spread on LB+ Amp+ X-gal+ IPTG

(LAXI) plate and kept for overnight incubation

For positive control 100 µl of competent cells were transformed with 1µl of

pUC 19 (20 ng/µl) while for negative control competent cells were as such

spread on LAXI plate.

b. Material required:

1. DH5α competent cells

2. Ligation mixture

3. pUC 19 (control)

4. SOC

Bacto-tryptone (2% w/v),

Yeast extract (0.5% w/v),

NaCl (10mM),

KCl (2.5mM),

MgCl2(10mM),

MgSO4(20mM),

Glucose (20mM).

53

3.3.7. Colony PCR

Colony PCR was performed for verification of 16S rDNA insert in the positive

(white) clones (Pace, 1997) using M13 primers as M13 forward primer 5´ GTA

AAA CGA CGG CCA GT 3´and M13 reverse primer: 5´ CAG GAA ACA GCT

ATG AC 3´. The colony was picked with sterile toothpick aseptically and

suspended in milliQ and kept at 95°C for 5 minutes. The suspension was

centrifuged to obtain pellet while the plasmid that remains suspended in the

supernatant was used as the template DNA for amplification. Amplification was

carried out in 10µl reaction volume with 2 U recombinant Taq DNA polymerase,

10-50ng of plasmid DNA, 0.2mM of dNTPs mix, 1.5mM MgCl2, 100pM of each

primer and the appropriate buffer supplied by the manufacturer (Fermentas). The

PCR program was denaturation at 950C for 5 minutes followed by 30 cycles of

denaturation at 950C for 60 seconds, annealing at 54

0C for 30 seconds followed by

extension at 720C for 90 seconds and final extension at 72

0C for 10 min.

3.3.8. Amplified ribosomal DNA restriction analysis (ARDRA)

ARDRA was done to remove the redundancy i.e. repetition of clones having same

16S rRNA gene (Vanechoutte et al., 1993). The PCR-amplified products (500 ng)

of positive recombinants were digested with by a tetra cutter restriction enzyme

Alu I (5U) having the (Restriction site: 5'...A G^C T...3'3'...T C^G A...5') in the

appropriate buffer supplied by the manufacturer (Fermentas).The restricted

fragments were gel electrophoresed in 2% agarose gels at 80 V and stained with

ethidium bromide. Restriction by Alu 1 enzyme resulted in different restriction

bands per clone and 50 different clones were selected on the basis of different

banding pattern from each metagenomic library (Total 250) for sequencing so that

single clone is representing 40-50 clones.

3.3.9. Plasmid isolation

Plasmid was isolated from the 250 clones which were found to be different after

ARDRA analysis using QIAprep spin miniprep kit (QIAGEN). Plasmids from

54

these clones were sent to SciGenom Labs Private Ltd., Cochin, Kerala, INDIA for

16S rRNA gene sequencing.

3.3.10. Sequence analysis:

Approximately 1477 nucleotides of each ARDRA representative library clones

were sequenced using the forward and reverse M13 primers (SciGenom Labs

Private Ltd, Cochin, Kerala). The screened representatives i.e. the different 16S

rDNA sequences obtained from sequencing results was analyzed using

bioinformatic tools (Table 2). The sequences obtained were edited for various

quality (Q-value 20, minimum length=1450) using CLC sequence viewer,

sequence analyser, pairwise alignment and bioedit software (Hall 1999). The

various bioinformatics tools are as follows:

a. Aling two sequences: 16S rRNA gene sequence of ~900 bp was generated

using M13 forward and reverse primer and the overlapping region between the two

was found by BLASTn-Aling two sequences software which is pairwise sequence

alignment software

(http://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastSearch&BLAST_SPE

C=blast2seq&LINK_LOC=align2seq). This overlapping region was edited using

various softwares like BioEdit and CLC sequence viewer. BLAST uses Pairwise

sequence alignment methods to find the best-matching pairwise (local) or global

alignments of two query sequences. Pairwise alignments can only be used between

two sequences at a time, but they are efficient to calculate and are often used for

methods that do not require extreme precision (such as searching a database for

sequences with high similarity to a query).

b. VecScreen software: The full 16S rRNA gene sequences were screened for

vector contamination using VecScreen software. VecScreen is online software that

quickly finds segments of a nucleic acid sequence of vector

origin (http://www.ncbi.nlm.nih.gov/tools/vecscreen/about/). It helps researchers

to identify and remove any segments of vector origin before analyzing or

submitting sequences, thus help in preventing the erroneous analysis of the

55

sequence. These erroneous sequences can delay the release of the sequence in a

public database and leads to wastage of time and effort in analysis of contaminated

sequence and also pollute the public databases.

c. DECIPHER: The resulting sequences were further examined for chimera by

the DECIPHER online chimera analysis program. DECIPHER is a new method for

finding 16S rRNA chimeric sequences which is based upon detecting short

fragments that are uncommon in the phylogenetic group

(http://decipher.cee.wisc.edu/FindChimeras.html; Wright et al., 2012). Chimeric

sequences are the sequences that are composed of two or more distinct sequences

concatenated into a single one due to errors in PCR amplification and sequencing.

When chimeras are comprised of sequences from different lineages, they could be

misinterpreted as representing novel lines of descent. The presence of chimeras in

a database artificially increases measurements of diversity (Ashelford et al., 2005;

Quince et al., 2009).

d. Taxonomic affiliations:

The resulting non -chimeric sequences were assigned bacterial taxonomic

affiliations using various search tools like BLAST, RDP and EZtaxon.

BLASTn:

Basic Local Alignment Search Tool (BLAST) is an algorithm for comparing

primary biological sequence information, such as the nucleotides of DNA

sequences or the amino acid sequences of different proteins

(http://blast.ncbi.nlm.nih.gov). Nucleotide-BLAST (BLASTn) is a program for

DNA query that enables a researcher to compare the query sequence with the

sequences available at the NCBI nucleotide database

(http://www.ncbi.nlm.nih.gov). BLAST can be used for several purposes including

identifying species, locating domains, establishing phylogeny, DNA mapping, and

comparison.When working with a DNA sequence from an unknown species,

BLAST can be used to correctly identify a species and/or find homologous species

(Janda and Abbott 2007).

56

Table 2: Bioinformatics tools used for sequence analysis

S.No Bioinfomatics

softwares

Source

1 VecScreen software http://www.ncbi.nlm.nih.gov/tools/vecscreen/about/

2 DECIPHER http://decipher.cee.wisc.edu/FindChimeras.html

3 BLASTn http://blast.ncbi.nlm.nih.gov

4 BLAST- Aling two

sequences

http://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastSe

arch&BLAST_SPEC=blast2seq&LINK_LOC=align2seq

EZtaxon www.eztaxon.org

5 CLC sequence

viewer

http://www.clcbio.com/products/clc-sequence-viewer/

6 Bioedit software http://www.mbio.ncsu.edu/bioedit/bioedit.html

7 RDP CLASSIFIER http://rdp.cme.msu.edu

8 RDP rarefaction https://pyro.cme.msu.edu/rarefaction/form.spr

9 RDP LIBCOMP http://rdp.cme.msu.edu/comparison/comp.jsp

10 NCBI-BankIt http://www.ncbi.nlm.nih.gov/WebSub/?tool=genbank

11 EstimateS software http://viceroy.eeb.uconn.edu/estimates/

12 ClustalX 2.1 http://www.clustal.org/clustal2/

13 Phylip 3.69 , http://evolution.genetics.washington.edu/phylip/getme.html

14 MEGA 5.05 http://www.megasoftware.net

15 ITOL http://itol.embl.de/

16 UNIFRAC http://bmf2.colorado.edu/unifrac/

57

EzTaxon:

EzTaxon tool specifically analyse the 16S rRNA gene sequences and classify upto

species level by comparing with the database of type strains of prokaryotes (Chun

et al., 2007). The search is based on two search engines, namely, BLASTn and

megaBLAST that provides a similarity-based search, multiple sequence alignment

and various phylogenetic analyses. The EzTaxon server can be used for automated

and reliable identification of prokaryotic isolates only (Chun et al., 2007).

Ribosomal Database Project II (RDP):

Ribosomal Database Project II is a search tool for comparing 16S/18S/28S rRNA

ribosomal sequences with the available ribosomal database.

RDP-Classifier:16S rRNA sequences can be assigned to taxonomical hierarchy

using rRNA classifier of the Ribosomal Database Project (RDP

II)(http://rdp.cme.msu.edu/) where the sequence can be classified upto genus level

by comparing with the sequences available in ribosomal databases (Cole et al.,

2014).

e. Rarefaction analysis: The 16S rRNA gene sequences from all the metagenomic

libraries were used to generate the rarefaction curves using the rarefaction tool of

pyrosequencing pipeline of Ribosomal Database Project-II

(http://rdp.cme.msu.edu). The 16S rRNA gene sequences were clustered into

(Operational Taxonomic Unit) OTUs with a cut off value of >97% sequence

similarity using its Alinger and Complete linkage clustering software and the

rarefaction curves were plotted between the number of OTUs and the number of

sequences analyzed.

f. Library Compare:

16S rRNA gene sequences of the two metagenomic libraries were compared using

Library Compare software of RDP II which classify the sequence upto genera level

and compares the bacterial composition of two samples.

58

g. UNIFRAC:

The comparison of bacterial diversity amongst the five samples was done using

UniFrac program (http://bmf2.colorado.edu/unifrac/). UniFrac tests were

performed using 100 permutations and P-test and unifrac significances were

calculated to test whether bacterial communities of each pair of samples were

significantly different. Principal coordinate analysis (PCoA) was used to represent

the bacterial diversity in form of clusters in quadrants (Hamady et al., 2010).

h. EstimateS:

Bacterial diversity associated with bulk soil, cormosphere and rhizosphere during

two growth stages was estimated using EstimatesS software. EstimateS software is

designed to assess and compare the diversity and composition of species

assemblages based on sampling data. EstimateS compute a variety of biodiversity

statistics, including rarefaction, estimators of species richness, diversity indices,

Hill numbers, and similarity measures. In the present study, bacterial diversity was

estimated by calculating relative alpha diversity between two niches by the

Shannon and Simpson‟s diversity indices, phylotype richness by the abundance

based coverage estimate (ACE) and the bias corrected by Chao1 values (Shannon

and Weaver, 1949; Chao and Bunge, 2002).

i. Phylogenetic Tree:

Phylip: Phylogenetic and molecular evolutionary analysis of 16S rRNA gene

sequences from bulk soil, cormosphere and rhizosphere of Saffron was conducted

by constructing neighbour-joining tree using algorhithm and software package of

Phylip 3.69 (Tuimala 2004). PHYLIP (the PHYLogeny Inference Package) is a

package of programs for inferring phylogenies (evolutionary trees) and includes

various methods like parsimony, distance matrix, and likelihood methods,

including bootstrapping and consensus trees (Felsenstein 2011). 16S rRNA gene

sequences along with the standard reference sequence were aligned using ClustalX

software (version 2.1) which is a multiple sequence alignment tool (Thompson et

al., 1997).Standard reference sequence, having close sequence similarity

59

(>97%),were obtained from the National Center for Biotechnology Information

(NCBI) Taxonomy Homepage. The phylogenetic trees were constructed using the

neighbour-joining method and 1,000 bootstrap replications were assessed to

support internal branches (Hillis and Bull 1993). Tree was viewed using ITOL

(http://itol.embl.de/) and edited in MEGA (Version 5.05) software (Tamura et al.,

2011).

MEGA: MEGA (Molecular Evolutionary Genetics Analysis version 5.05) is an

integrated tool for conducting automatic and manual sequence alignment, inferring

phylogenetic trees, mining web-based databases, estimating rates of molecular

evolution, inferring ancestral sequences, and testing evolutionary hypotheses

(Kumar et al., 2008; Garrido et al., 2012).

3.4. Cloning independent approach:

The fungal diversity associated with the rhizosphere, cormosphere and the bulk

soil during two growth stages was studied by cloning independent approach which

included pyrosequencing and analysis of ITS (Inter transcribed spacer region) gene

sequences amplified from metagenome of five samples.

3.4.1. PCR amplification of ITS gene: -

Inter transcribed spacer region (ITS1-ITS4) was be amplified using Universal

primer ITS1F (5‟-TCCGTAGGTGAACCTGCGG-3‟) and ITS4R (5‟-

TCCTCCGCTTATTGATATGC-3‟) (White et al., 1990) from the metagenomic

DNA already isolated from bulk soil, rhizosphere and cormosphere during two

stages. The PCR mixture contained 1-10 ng of DNA extracted from bulk soil,

cormosphere and rhizosphere of Saffron, 10 pM of universal primers, 1X PCR

buffer (Fermentas), 2.5mM MgCl2, 2.5U of Taq DNA polymerase (Fermentas),

0.2mM each deoxynucleoside triphosphate (Fermentas) and sterile filtered MilliQ

water to a final volume of 50 µl. Negative controls comprised of same assay

without the template. PCR amplification was performed in a DNA thermocycler

(Eppendorf, India) following the amplification program of, initial denaturation at

60

94°C for 5 min, 30 cycles of 94°C for 30 sec, annealing at 55°C for 30 sec, and

extension at 72°C for 1min 30 sec, and a final extension of 10 min at 72°C. The

amplicons of approximately 550 bp were analyzed by electrophoresis on 1%

agarose gel and a 100 bp DNA ladder (Fermentas) was taken as the molecular size

standard. The amplicon (~550 bp) was gel purified using a gel elution kit (Qiagen)

and sent for pyrosequencing.

3.4.2. Pyrosequencing:-

Amplified ITS region from metagenomic DNA (~500bp) of bulk soil, rhizosphere

and cormosphere during both the stages was attached to 5 unique MIDs that was

help in sample identification when five samples was be analyzed in parallel on one

454 picotiter plate. Each MID included a unique barcode sequence and an

oligonucleotide sequence which is complementary to the adapter attached on beads

of emulsion PCR. These unique tagged DNA fragments were further ligated to

common adaptors and were subjected to emulsion PCR that resulted in an array of

millions of spatially immobilized PCR colonies which is further sequenced by

pyrosequencing.

3.4.3. Bioinformatics analysis of sequences obtained:

The sequences obtained by prosequencing of ITS gene from bulk soil, rhizosphere

and cormosphere during two growth stages was analysed using various

bioinformatics softwares. All of the raw sequence data was be sorted based on

sample-specific barcode tags and primer and tag sequences were trimmed from

sorted sequences and the ambiguous and short sequences with a length less than

500 nucleotides was removed. The resulting sequences were analysed using

CDHIT program, Ribosomal Database Project (RDP-II) and pyrosequencing

pipeline by Mothur (Li et al., 2006; Schloss et al., 2009; Cole et al., 2014).

Clustering was performed at 97% similarity using CD-Hit, a representative

sequence from each cluster was selected based on abundance to generate OTU‟s.

CD-HIT is a very widely used program for clustering and comparing protein or

61

nucleotide sequences. CD-HIT is very fast that can efficiently cluster a huge DNA

database with millions of sequences. CD-HIT helps to significantly reduce the

computational and manual efforts in many sequence analysis tasks and aids in

understanding the data structure (Li et al., 2006). The OTU‟s obtained from each

cluster were then classified with Mothur software. Mother is software package that

can be used to trim, screen, and align sequences; calculate distances; assign

sequences to operational taxonomic units; and describe the α and β diversity, thus

analyzing community sequence data characterized by pyrosequencing (Schloss et

al., 2009). The ITS gene sequence (~550 bp) was analysed for their reference

counterparts in databases using Mothur pyrosequencing pipeline (software version

1.28.0) and Ribosomal Database Project (RDP-II) against the UNITE fungal

database using the wang method of classification. Rarefaction curves were

generated along with calculation of diversity indices using pyrosequencing

pipeline of Ribosomal Database Project II (Cole et al., 2014).

3.5. GenBank Accession numbers:

The sequences obtained in this study were submitted to nucleotide databases by

NCBI-BankIt and available at the GenBank under accessions numbers JN084065-

74, JX233807, KF595049-75, JF836006, KF550436-43, JQ751317, JQ713596-98,

JX260425, JX279932-JX279941, JX289937-JX289942, JX294738-JX294750,

JX852636-JX852677, JX945529- JX945568, JX962747- JX962749, KC138682-

KC138694, KC283045-KC283065 and KJ619977-KJ619986

62

CHAPTER-4

RESULTS & DISCUSSION

63

OVERVIEW OF THE WORK DONE

64

RESULTS AND DISCUSSION

Crocus sativus, commonly known as “Saffron”, is world‟s most expensive spice

and globally, India ranks third in its production (Ghorbani 2008; Kamalipour and

Akhondzadeh 2011). In India, Saffron grows only in Jammu and Kashmir. It is

chiefly grown in the Pulwama district of Kashmir (Pampore, Balhuma, Wuyan,

Konibal, Dus, Letpur, Ladu and Khrew) that contributes about 73 % of the total

production. This is followed by Badgam district (Chadura, Nagam, Lasjan,

Ompora and Kralpura), Anantnag district (Zeripur, Srechan, Kaimouh, Samthan

and Buch), Srinagar district (Zewan; Zawreh and Ganderbal) in Kashmir province

and Kishtwar district in Jammu Province (Kafi and Showket 2007). There are

number of factors that affect the production of Saffron, such as climate,

temperature, irrigation, use of fertilizers and the chemistry of soil. Microbes are

known to influence chemistry and formation of soil in addition to their direct

influence on plants growth in both positive and negative way. Plant-bacterial

associations especially, bacterial interactions in rhizosphere, phyllosphere and

stem are well documented (Davis and Brlansky, 1991;Wilson et al., 1999; Mercier

and Lindow, 2000;Andrews and Harris, 2000; Yang et al., 2000; Lindow et al,

2003; Kuiper et al., 2004; Jong Yim et al., 2010; Yutthammo et al., 2010; Redford

et al., 2010; Castro et al., 2011; Soka et al., 2012, Bulgarelli et al., 2013; Mwajita

et al., 2013; Bakker et al., 2013; Meena and Baljeet, 2013; Vivian et al., 2013

Jackson et al., 2013; Neumann et al., 2014). However Saffron rhizosphere and

cormosphere is a naïve niche that has not been explored so far. Corms are borne by

plants across plant families such as Alismataceae, Araceae, Asparagaceae,

Asteraceae, Iridaceae, Colchicaceae, Cyperaceae and Musaceae but so far no

microbial associations with cormosphere have been investigated in any corm

bearing plant. Cormosphere is a term coined in present study, in analogy with

rhizosphere and phyllosphere. Saffron fields of Wuyan (74°58′0″E, 34°1′30″N,

5173 ft) from Pulwama district were selected for the present study (Fig 3). Present

study deals with cataloguing and characterization of microbes associated with the

65

rhizosphere, cormosphere and bulk soil of Saffron for the first time during two

growth stages (dormant and flowering) by cultivation dependent and independent,

metagenomic approach.

The hypothesis being tested in the present study is:-

Profile (abundance and composition) of microbes associated with root and

corm of Saffron vary in two niches, at two different growth stages.

4.1. Soil analysis:

Saffron likes light friable soil with high nutrient content and thrives best in deep,

well drained clay-calcareous soil that has loose texture and permits easy root

penetration (Husaini et al., 2010). The bulk soil of Wuyan field was analyzed for

various physiochemical properties and the results have been tabulated in table 3.

The rhizosphere soil could not be analyzed as it required substantial number of

plants which could not be procured. Husaini and coworkers (2010) have reported,

soil of Saffron fields in Kashmir to be slightly alkaline with pH ranging from 6.3

to 8.3, electric conductivity between 0.09- 0.30 dsm-1

and have 0.35% organic

carbon, 4.61% calcium carbonate. The physio-chemical properties of Saffron soil

in present study were similar to earlier reports, with higher organic carbon content.

4.2. Culture dependent approach

4.2.1. Bacterial load:

The bacterial load associated with bulk soil, cormosphere and rhizosphere of

Saffron was calculated during two growth stages and was maximum in Saffron

cormosphere followed by rhizosphere and bulk soil (Fig 4, Table 4). Although,

higher density of bacteria near roots in comparison to bulk soil, has been reported

in many plants (Nannipieri et al., 2007; Joshi et al., 2011; Timmusk et al., 2011;

Berendsen et al., 2012; Rout et al., 2013; Gaiero et al., 2013) but there are no

reports on bacterial association with cormosphere of any plant. It is established

that rhizodeposition influences root- microbe interaction in most of the plants

(Soderberg et al., 2004; Johansen and Olsson 2005; Hinsinger et al., 2009;

Berendsen et al., 2012; Rout et al., 2013; Gaiero et al., 2013; Bakker et al., 2013).

66

The rhizosphere is reported to stimulate microbial growth, as it is relatively rich in

organic substrates and may contain up to 1011

bacteria/gm of root in comparison to

107-8

/gm in soil (Egamberdieva 2008; Berendsen et al., 2012). Root exudates

consisting of sugars, amino acids, organic acids, aromatics, polysaccharides,

proteins and various other secondary metabolites results in much larger numbers of

microbes in the rhizosphere, than in the nearby bulk soil (Brencic and Winans,

2005; Buee et al., 2009; Weinert et al., 2011; Timmusk et al., 2011). The microbial

density is reported to be higher in potato rhizosphere and in wild barley where the

bacterial load was ~200 folds in rhizosphere than bulk soil (Weinert et al., 2011;

Timmusk et al. 2011).

In present study, the bacterial load was ~45 fold in rhizosphere and ~56 fold in

cormosphere in comparison to bulk soil and ~1.2 fold in cormosphere in

comparison to rhizosphere, during flowering stage. During dormant stage when the

roots are absent (Fig 1), bacterial load was also ~ 24 fold in cormosphere than bulk

soil (Table 5). Difference in bacterial load of rhizosphere and cormosphere with

bulk soil during both the stages was significantly higher (p<0.05), whereas the

difference in bacterial load between cormosphere and rhizosphere was

insignificant (p=0.5) as estimated by F test. Better nutrient availability in

rhizosphere is reported to be a reason for high bacterial load on/near roots and

same could be true for Saffron cormosphere, as corm acts as a store for nutrients in

addition to the organ for vegetative propagation. Corms are reported to store

monosaccharides like lyxose, xylose, ribose, glucose, mannose, galactose,

rhamnose, cellobiose, maltose, lactose and, fructose (Ma et al., 2012), phenolic

compounds (Esmaeili et al., 2011), peroxidases (Rahmani et al., 2012) and some

metals (Esmaeili et al., 2013).

In phyllosphere simple sugars such as glucose, fructose and sucrose are the

dominant carbon sources and these are heavily populated by microbes (Davis and

Brlansky, 1991; Wilson et al., 1999; Mercier and Lindow, 2000; Soka et al., 2012;

67

Mwajita et al., 2013). Andrews and Harris (2000) has reported that bacteria are the

most numerous colonists of leaves, often being found in numbers averaging 106 to

107 cells/cm

2 (upto 10

8 cells/g) of leaf. The availability of carbon-containing

nutrients on leaves is a major determinant of epiphytic microbial colonization

(Wilson and Lindow, 1994, Andrews and Harris. 2000, Lindow and Brandl, 2003;

Yutthammo et al., 2010, Castro et al., 2011; Knief et al., 2012; Jackson et al.,

2013).

On comparing bacterial load during the two growth stages in annual life cycle of

the herb, the bacterial load in cormosphere was more during dormant stage

(Rootless stage during July-August) than flowering stage (October-November)

(Fig 4, Table 5). Rhizosphere sample between two stages could not be compared

since dormant stage lacks roots. The concentration of starch decreases in corm

during dormancy, as it is converted into sucrose and other soluble sugars and these

sugars transfer to tissues where buds are differentiated and developed (Medina,

2003; Nehvi and Yasmeen, 2010). The high bacterial load during dormant stage

may be in response to the changing biochemical profile of the corm and

availability of simple sugars. It has been reported that the abundance and

composition of bacteria in soil and rhizosphere associated with strawberry, potato

and oilseed rape are influenced by many factors, including the host plant, the stage

of plant growth, cropping practices and changing season (Smalla et al., 2001;

Houlden et al., 2008; Joshi et al., 2011). Seasonal variation in the bacterial load has

been studied in wheat rhizosphere as well, wherein the bacterial load increases

from 1st day (sowing) and was maximum at 90

th day followed by decrease in their

number by 120th

day due to root decay (Joshi et al., 2011).

68

Table 3: Physiochemical analysis of pooled soil sample from Saffron fields

Sample pH Electrical

conductivity

Organic

Carbon

Ava. N Av. P Av.K Ca Mg Bulk

Density

units ds/m % Kg/ha Kg/ha Kg/ha ppm ppm gm/cc

Wuyan 7.35 0.13 1.36 306 26 504 3000 552 1.198

Fig 4: Bacterial load (CFU/gm of soil) in bulk soil, cormosphere and rhizosphere

of Saffron indicating highest load in cormosphere

69

Table 4: Bacterial load (CFU/gm of soil) in bulk soil, cormosphere and

rhizosphere of Saffron indicating highest load in cormosphere

S.No. Soil type Flowering stage Dormant stage

Samples CFU/gm of soil CFU/gm of soil

1 Bulk soil 1.4 X 106 8.5x10

6

2 rhizosphere 6.4 X 107 -

3 Cormosphere 7.9.x107 2x10

8

Table 5: Comparison of bacterial load between bulk soil, rhizosphere and

cormosphere during two stages

1 Flowering stage Bulk vs rhizosphere ~45 fold more in

rhizosphere

2 Bulk vs cormosphere ~56 fold more in

cormosphere

3 Rhizosphere vs

cormosphere

~1.2 fold more in

cormosphere

4 Dormant stage Bulk vs cormosphere ~ 24 fold more in

cormosphere

6 Flowering vs

Dormant

Cormosphere vs

cormosphere

~2.6 fold more in

dormant

Bulk vs bulk ~6 fold more in

dormant

70

4.2.2. Rhizosphere, cormosphere and bulk soil bacteria:

Randomly selected 50 bacterial isolates from pooled sample collected from bulk

soil, rhizosphere and cormosphere, during two growth stages (total 250 bacteria)

were characterized further on the basis of microscopic and biochemical analysis

followed by 16S ribotyping with gene sequence analysis of hypervariable region

(V1-V3 region) (Fig 5, 6, Table 6). 250 bacterial isolates were identified as 51

different bacterial types, out of which 6 bacteria were isolated from rhizosphere,

10 from bulk soil and 14 from cormosphere during flowering stage whereas, 8

were isolated from bulk soil and 13 from cormosphere during dormant stage (Fig

7). In comparison to the bulk soil the bacterial load was quite high in rhizosphere

and cormosphere but number of different bacterial types isolated from these two

niches was low, this result substantiates the fact that specific root/corm–microbe

interaction occurs in Saffron.

During flowering stage, Gram negative bacteria were predominant in the

rhizosphere and cormosphere whereas Gram positive bacteria were dominant in

the cormosphere during dormant stage (Fig 5). Gram negative bacteria are reported

to be dominant in rhizosphere in comparison to bulk soils (Soderberg et al., 2004;

Johansen and Olsson 2005; Buee et al., 2009; Bakker et al., 2013), which is true

for Saffron as well. Domination of Gram negative microbes in the plant

rhizosphere is due to their stimulation by rhizodeposition, which otherwise inhibit

Gram-positive bacteria (Soderberg et al., 2004; Johansen and Olsson 2005;

Nannapieri et al., 2007; Hartman et al., 2008; Buee et al., 2009).The dominance of

Gram positive bacteria in cormosphere during dominant stage is matter of further

investigation.

Cultivable bacterial profile varies in bulk soil, cormosphere and rhizosphere and

has been represented in Fig 7. During flowering stage, Saffron rhizosphere was

predominantly colonized by Pseudomonas genus whereas bulk soil was dominated

71

by Bacillus during both the stages (Fig 7). Pseudomonas spp. are well known root

colonizers and are able to proliferate by using plant-secreted amino acids such as

proline, lysine, phenylalanine, glutamate etc and exhibit positive chemotaxis

towards plant exudates (Molina et al., 2000; Vílchez et al., 2000; Espinosa-Urgel

and Ramos, 2001; Espinosa-Urgel et al., 2002; Herrera and Ramos, 2007; García-

Salamanca et al., 2013). Pseudomonas sp. is also reported to be PGPR in plants

like canola (Farina et al., 2012), paddy (Noori and Saud 2012), maize (Neal et al.,

2012), soyabean (Wahyudi et al., 2011), tomato (Amkraz et al., 2010), coffee

(Muleta et al., 2009) and tea (Tilak et al., 2005). Bulk soil is reported to be mostly

inhabited by the Bacillus, Brevibacteria and Arthrobacter as these bacteria can

survive in oligotropic i.e. low nutrient environment (Janssen et al., 2002; Buee et

al., 2009; Gottel et al., 2011). Cormosphere bacteria showed difference in

dominance pattern during the two growth stages as it was dominated by

Stenotrophomonas during flowering stage and by Bacillus during dormant stage

(Fig 7). The occurrence and distribution of bacteria in the soil and rhizosphere are

reported to be influenced by many factors, including the stage of plant growth, host

plant, and cropping practices (such as tillage and crop rotation) (Smalla et al.,

2001; Houlden et al., 2008). Microbial communities have been reported to show

seasonal variation in case of pea, wheat and sweet potato (Dunfield and Germida,

2003; Houlden et al., 2008). The variation in the cormosphere may be attributed to

change in the nutrient content of the corms in addition to the seasonal variations

during two growth stages.

During flowering stage, not a single bacterial species was common to bulk soil,

rhizosphere and cormosphere. However, Bacillus aryabhattai was common to bulk

and rhizosphere soils whereas Brevibacterium frigoritolerans between bulk and

cormosphere. Cormosphere and rhizosphere did not share any common bacterial

species during flowering stage.

72

Fig 5: Microscopic analysis of bacterial isolates from the different niches during

two growth stages indicating dominance of Gram negative bacteria in rhizosphere

and cormosphere.

A B

Lane 1-26: Genomic DNA from bacteria Lane 1: 100 bp Marker

Lane 2-4: 16S rRNA gene

amplicon

Fig 6: Genomic DNA isolation (A) and 16S rRNA gene (~500bp) amplification (B)

from bacterial isolates

73

Table 6: Biochemical characterization and 16S rRNA gene sequence identification of bacterial

isolates of Saffron isolated during flowering (WBF= bulk soil; WRF= rhizosphere; WCF=

cormosphere) and dormant stage (WBD= bulk soil; WCD= Cormosphere)

Sta

ges

Gram

positive

bacteria Mal

on

ate

VP

Cit

rate

ON

PG

Nit

rate

Red

uct

ion

Cat

alas

e

Arg

inin

e

Su

cro

se

Man

nit

ol

Glu

cose

Ara

bin

ose

Tre

hal

ose

Identification

based on

biochemical

tests

Identification based

on 16S rRNA gene

sequencees Seq

uen

ce

sim

ilar

ity

(%

)

Flo

wer

ing

Sta

ge

WBF1 + + - - + + - - - + + + Bacillus sp. Arthrobacter sp. 95%

WBF2 - + - - - + - - - + - + B.coagulans B.methylotrophicus 98%

WBF3 - + - - + + - + + + + + Bacillus sp. B.aryabhattai 100%

WBF4A - + - - + + - - + + - + Bacillus sp. B.aryabhattai 98%

WBF4B - + - - + + - - - + + + Bacillus sp. B.aryabhattai 100%

WBF5A - - - - - + - + - + - - B.coagulans Brevibacterium

halotolerans

100%

WBF5B - - - - - + - + - + - - B.coagulans Brevibacterium

halotolerans

100%

WBF6 - - - - + + - - - + - + Bacillus sp. Brevibacterium

frigoritolerans

100%

WBF8 - - - - - + - + - + - - B.coagulans Brevibacterium

halotolerans

99%

WRF5 - + - - + + - + + + + + Bacillus sp. B.aryabhattai 99%

WCF2 - + + - + + - + - + - + B.cereus B.cereus 99%

WCF6 + - + + + + - + + + + + B.megaterium B. megaterium 100%

Do

rman

t st

age

WBD1 - - - - - + - - - + - + B.coagulans B.pumilus 99%

WBD4 - - - - - + - + - + + + B.coagulans B.simplex 99%

WBD 5B - - - - + + - - - + - + B.cereus B.thuringiensis 99%

WBD7 - + - - - + - + + + + + B.pumilus B.safensis 99%

WBD8 + - - - + + - - - + - - Bacillus sp. B.anthracis 100%

WBD10A - - - - + + - - - + - - Bacillus sp. B.thuringiensis 99%

WBD10B - + + - + + - + - + - + B.cereus B.cereus 99%

WBD11B - + - - + + - + + + + + B.subtilis B.subtilis 99%

WCD1 - + - - - + - + + + - - B.coagulans B.methylotrophicus 100%

WCD2 - - - - + + - + - + - + B.coagulans B.thuringiensis 99%

WCD4 - - - - + + - + - + - + B.coagulans B. mycoides 100%

WCD5 + + + + + + + + + + + + B.atrophaeus B.megaterium 100%

WCD7A - - - + - + - + + + - + B.coagulans B.aryabhattai 100%

WCD11 + + + + + + - + + + + + Bacillus sp. B. megaterium 99%

WCD15 + + + + + + - + + + + + Bacillus sp. B. megaterium 99%

74

Gram

negative

bacteria Cit

rate

Ly

sin

e

Orn

ith

ine

Ure

ase

TD

A

Nit

rate

Red

uct

ion

H2S

P

rod

uct

ion

Glu

cose

Ad

on

ito

l

Lac

tose

Ara

bin

ose

So

rbit

ol

Identification

based on

biochemical

tests

Identification based

on 16S rRNA gene

sequencees Seq

uen

ce s

imil

arit

y

(%)

Flo

wer

ing

sta

ge WBF7 + - - - - + - + - - + + Pseudomonas Pseudomonas

parafulva

98%

WRF1 + - - - - + - + - + - + Pseudomonas Acinetobacter

calcoaceticus

99%

WRF2 + - - - - + - + + - - - Pseudomonas Pseudomonas

tremae

99%

WRF3 + - - - - + - + - + + - Pseudomonas Pseudomonas

kilonensis

99%

WRF4 + - - - - + - + - - - - Pseudomonas Chryseobacterium

elymi

99%

WRF6 + - - - - + - + + - - - Pseudomonas Pseudomonas

koreensis

99%

WCF1b + - + - - + - + - - - - Enterobacter Enterobacter

cloacae

100%

WCF3 + - - - - + - + - - + - Enterobacter Enterobacter

hormaechei

99%

WCF4 + + + - - + - + - - - - Enterobacter Enterobacter

cloacae

99%

WCF5 - + - - - + - - - - - - Serratia Stenotrophomonas

maltophilia

99%

WCF7 - - - - - - - + - + + - Aeromonas Sphingobacterium

multivorum

99%

WCF8 - + + - - + - - - - - - Serratia Stenotrophomonas

maltophilia

99%

WCF12 - + + - - + - - - - - - Serratia Stenotrophomonas

maltophilia

99%

WCF20 + - + - - + - + - - - - Enterobacter Enterobacter

cloacae

99%

WCF33 - + + - - + - - - - - - Serratia Stenotrophomonas

maltophilia

99%

WCF41 - + + - - + - - - - + - Serratia Stenotrophomonas

maltophilia

99%

WCF42 - + + - - + - - - - - - Serratia Serratia plymuthica 99%

Do

rman

t st

age WCD3 + - + - - + - + + + + + Enterobacter Enterobacter

ludwigii

100%

WCD13 + - + - - + - + + + + + Enterobacter Enterococcus

faecium

100%

WCD14 + - - - - + - + + - + + Enterobacter Enterobacter

ludwigii

99%

WCD16 + - + - - + - + - - + + Enterobacter Enterobacter

cloacae

99%

WCD19 + - - - - + - + - + + + Enterobacter Enterobacter

hormaechei

99%

75

Fig 7: Cultivable bacteria from bulk soil, cormosphere and rhizosphere of Saffron during

two growth stages with identification of specific bacteria from different niche during two

growth stages

76

In dormant stage, B.thuringiensis was the only common bacterial species between

cormosphere and bulk soil. The bacterial composition of cormosphere also varied

during two growth stages but some bacteria were present during both the growth

stages like E. cloacae, E. hormaechei and B.megaterium.

4.2.3. Screening of bacteria for PGP traits in vitro and in vivo:

4.2.3.1. In vitro analysis of PGPR properties

Bacterial isolates from rhizosphere and cormosphere during two stages were

screened for plant growth promotion properties like indole acetic acid production

(IAA), phosphate solubilization and siderophore production (Fig 8) and selected

bacteria were tested in pots subsequently to assess their in vivo efficacy (Fig 9).

The results of in vitro PGP traits of all the bacteria have been tabulated in Table 7.

More phosphate solubilisers were isolated from rhizosphere as compared to

cormosphere (Fig 9) and the reason could be that phosphate uptake is through roots

and corm is not the organ for absorbance. During flowering stage, 6 rhizosphere

and 14 cormosphere bacteria produced IAA in a range of 3.69-28.5 µg/ml and 3–

70 µg/ml respectively with Pseudomonas koreensis WRF6 producing maximum

IAA in rhizosphere and Enterobacter cloacae WCF4 in cormosphere (Table 7).

Interestingly, during dormant stage, 13 cormosphere bacteria produced IAA with

Enterobacter cloacae WCD16 producing maximum 348.6 µg/ml. IAA is reported

to influence the root initiation and development, cell division and tissue

differentiation, seed and tuber germination; and resistance to stressful conditions

(Tsakelova et al., 2006). The presence of better IAA producing bacteria during

dormant stage as compared to flowering stage can be attributed to the fact that IAA

producing bacteria may be helping the corm to resist the stressful condition during

this stage. In addition, dormant stage is followed by flowering stage, which is

characterized by the well-developed roots, and IAA producing bacteria can

contribute in tuber germination and root initiation by influencing the cell division

of corm thus helping to proceed into next stage. IAA production is also reported

from bacterial colonizers of the phyllosphere in rice (Mwajita et al., 2013). Aziz

77

and coworkers (2012) has reported that the inoculation of B. cereus strain

producing IAA significantly increased adventitious root number and length, shoot

height and fresh weight of shallot bulbs as compared to the uninoculated controls.

4.2.3.2. In vivo analysis of PGPR properties by Pot Assay:

Six rhizobacteria namely Pseudomonas tremae, Pseudomonas kilonensis,

Pseudomonas korensis, Acinetobacteria calcoaceticus, Chryseobacteria elymi and

Bacillus aryabhattai showing in-vitro PGP traits were subjected to in- vivo

testing, in pot assay (Fig 9). These bacteria were used in consortia, as none of

bacterial isolates showed antagonistic activity against each other. Inoculation of

corms with bacterial consortia (1012

CFU/gm) affected their growth positively as

compared to control (Table 8). Bacterial consortia increased average number of

roots and shoots but the effect on shoot length and root length was statistically

insignificant. In addition incidence of corm rot disease was less (40%) as

compared to control (60%). Significant increase in cormslets/ daughter corms

production was observed as compared to controls. The mother corms were

shrunken thus giving rise to cormlets in test whereas the control corms remained

unaffected (Table 8). The results of Pot assay were in concordance with the

findings of Sharaf-Eldin and coworkers (2008) wherein the authors have observed

positive effect of commercially available PGPR Bacillus subtilis FZB24 strain on

the Saffron. In another study, Aytekin and coworkers (2008) have reported the

positive effect of commercially available synthetic growth hormone, biohumus and

Effective Microorganisms™ (EM) on the corm numbers and dry and wet stigma

weights of Saffron. Synthetic hormone consists of Polystimulins A6 and K and two

different microorganism based materials consists of biohumus/vermicompost and

“Effective Microorganisms™” (EM). In both the above mentioned reports effect of

commercially available Bacillus subtilis or hormones and Effective microorganism

was observed on the growth and production of Saffron but bacteria isolated in the

present study are native to Saffron. The synthetic application of bacteria to any

plant possesses the risk of inoculum colonization and sustainability, which is not

the case if PGPR used are indigenous to the plant.

78

Fig 8: Bacterial isolates showing phosphate solubilisation (A), siderophore

production (B) and indole acetic acid production (C) indicated by clearing zone in

phosphate solubilisation and siderophore production and pink colour in IAA

production.

A

B

C

79

Table 7: Qualitative and quantitative plant growth promotion properties of bacterial isolates

from rhizosphere and cormosphere during two stages

S.

No

Identified Rhizobacteria Phosphate

solubilization

Siderophore

production

IAA

production

(µg/ml)

1 Acinetobacteriacalcoaceticus WRF1

RH

IZO

SP

HE

RE

- - 18.50 ±0.5

2 Pseudomonas tremae WRF2 + +++ +++ 3.80± 0.30

3 Pseudomonas kilonensis WRF3 + + +++ 3.69±0.50

4 Chryseobacteria elymi WRF4 - - 12.18±0.6

5 Bacillus aryabhattai WRF5 - ++ 14.00±0.2

6 Pseudomonas koreensis WRF6 - _ 28.5±0.33

7 Enterobacter cloacaeWCF1B

CO

RM

OS

PH

ER

E F

LO

WE

RIN

G

- - 22±0.6

8 B.cereusWCF2 - + 5±0.22

9 Enterobacter hormaecheiWCF3 - + 3±0.12

10 Enterobacter cloacaeWCF4 - - 70±0.25

11 Stenotrophomonas maltophiliaWCF5 - - 30±0.5

12 B. megateriumWCF6 - ++ 47±0.44

13 Sphingobacterium multivorumWCF7 - + 46±0.64

14 Stenotrophomonas maltophiliaWCF8 - ++ 30±0.6

15 Stenotrophomonas maltophiliaWCF12 - - 45±0.55

16 Enterobacter cloacaeWCF20 - - 40±0.35

17 Stenotrophomonas maltophiliaWCF33 - - 27.5±0.2

18 Brevibacterium frigoritoleransWCF34 - + 32±0.46

19 Stenotrophomonas maltophiliaWCF41 - ++ 42±0.56

20 Serratia plymuthicaWCF42 + - 19±0.5

21 B.methylotrophicus WCD1

CO

RM

OS

PH

ER

E D

OR

MA

NT

- - 1.55±0.21

22 B.thuringiensis WCD2 - + 5.42±0.4

23 Enterobacter ludwigii WCD3 - - 52.8±0.6

24 B. mycoides WCD4 - + 7.75±0.3

25 B.megaterium WCD5 - + 6.19±0.2

26 B.aryabhattai WCD7A - + 3.87±0.15

27 Enterobacter cloacae WCD7B2 - - 125±0.6

28 B.megaterium WCD11 - + 245.5±0.5

29 Enterococcus faecium WCD13 - + 160±0.53

30 Enterobacter ludwigii WCD14 ++ - 190.6±0.4

31 Enterobacter cloacae WCD16 - - 348.6±0.3

32 Enterobacter hormaechei WCD19 +++ - 302.1±0.5

80

Fig 9: Plant growth promotion profile of bacterial isolates from rhizosphere and cormosphere

of Saffron indicating rhizobacteria (P.tremae WRF2 & P.kilonensis WRF3) showing maximum

PGP properties

Table 8: In-vivo effect of rhizobacterial consortia on the growth of Saffron corms as compared

to control in pot trials

Growth Parameters Test Control

Av. No. of roots 3.6±1.82 1.2±0.81

Av. Length root 1.15±0.54 0.1±0.06

Av no shoot 5.6±0.49 4.3±0.53

Av shoot length 6.59±1.59 9.95±3.90

cormlets 3.9±1.11 0.5±0.4

Av weight 3.14±0.19 3.16±0.17

Disease 4/10 6/10

Shape of corms shrinked No effect

81

4.3. Cultivation independent (Metagenomics) approach

As only 1% of bacteria and 1-5% of fungus can be cultivated using cultivation

dependent approach, the complete microbial diversity associated with Saffron

could not be studied by cultivation dependent approach alone. Cultivation

independent (metagenomics) approaches can be used to study the rest of 99% of

microbial diversity by analyzing the DNA sequences directly isolated from the

environment, bypassing the cultivation based techniques. In the present study,

bacterial diversity was studied by cloning dependent approach and fungal diversity

was studied using cloning independent approach to draw a complete picture.

Bacterial diversity associated with bulk soil, cormosphere and rhizosphere of

Saffron during two stages was studied by the construction and analysis of 16S

rRNA gene metagenomic library of all the five samples. Total metagenomic DNA

was extracted using various protocol (Zhou et al., 1996, Wechter et al., 2003,

Brady et al., 2007, and Pang et al., 2008) but good quality 16S rDNA was

successfully amplified from DNA isolated using Pang‟s protocol (2008) (Fig 10).

The 1500 bp 16S rRNA gene was amplified by PCR using 16S rRNA gene

universal primers (Fig 11). 1.5 Kb amplicon generated was ligated to pTZ57R/T

vector (Fermentas) in 2:5 molar ratio. On an average, 10,000 clones were obtained

from each of the five libraries of bulk soil, rhizosphere and cormosphere during

flowering stage and bulk soil and cormosphere during dormant stage. Clones were

selected randomly for insert confirmation and 1.5 Kb (16S rDNA) insert was

verified by colony PCR (Fig 12). ARDRA was performed to remove the

redundancy (repetition of same clone) in which the PCR-amplified products of

positive recombinants were digested with the restriction enzymes Alu1

(Fermentas) (Fig 13, 14). The restricted fragments were analysed by MultiNA,

Microchip electrophoretic system (Schimadzu, Japan) and a phylogenetic tree was

constructed based on the different banding pattern obtained by ARDRA using the

viewer softwares of MultiNA. One clone each was selected from the different

clads of phylogenetic tree so that one clone represents about 40-50 clones.

82

Randomly 50 clones (having representation in triplicate libraries of each sample)

were selected from each metagenomic library and the total 250 clones, thus

selected, were considered as the representative of ~12500 clones from all the 5

metagenomic libraries.

The plasmids were isolated and sent for sequencing full length 16S rRNA gene

(~1.5 kb) by Sanger‟s method at SciGenom sequencing service, Kerela India. The

resulting nucleotide sequences were assigned bacterial taxonomic affiliations

based on the closest match to sequences available at the NCBI database

(http://www.ncbi.nlm.nih.gov/) and the total bacteria catalogued by culture

independent approach are represented in Fig 15 and phylogenetic tree constructed

in Fig 16, during both the growth stages.

Rarefaction curves generated by RDP rarefaction tool, using a 97% identity cut

off, indicated that the bulk soil was having more bacterial diversity as compared to

rhizosphere and cormosphere during both the stages as maximum OTUs were

obtained in these niches (Fig 17). The observation that the bacterial diversity is

higher in bulk soil as compared to rhizosphere and cormosphere was further

complemented by the diversity indices like ACE Mean, ICE Mean, Chao 1 Mean,

Chao 2 Mean, Shannon Mean, Simpson Mean (Table 9). Rarefaction curves in all

the five samples did not approach the plateau level indicating less representation of

bacterial diversity. To catalogue whole of the diversity repetitive sampling needs

to be done, pooled metagenomic DNA using different isolation protocols needs to

analyzed and analysis should be done by cloning & PCR independent direct

sequencing on next generation sequencing platforms.

83

Lane 1: 1Kb Marker

Lane 2-6: Metagenomic DNA (Lane2: WBF; 3: WBD; 4: WRF; 5 WCF;

6: WCD)

Fig 10: Metagenomic DNA isolation from different samples

(Flowering stage: WBF= Bulk soil; WCF= Cormosphere; WRF= rhizosphere; dormant stage:

WBD= Bulk soil; WCD =Cormosphere)

Lane1: 1Kb Marker

Lane 2-6: 16S rRNA full gene amplicon (Lane2: WBF; 3: WRF; 4: WCF; 5

WBD; 6: WCD)

Fig 11: PCR amplification of full 16S rRNA gene (~1.5 Kb) from metagenome of different

samples

(Flowering stage: WBF= Bulk soil; WCF= Cormosphere; WRF= rhizosphere; dormant

stage: WBD= Bulk soil; WCD =Cormosphere)

84

Lane M: 1Kb marker

Lane 1-28: Colony PCR products

Fig 12: Colony PCR of the positive clones of 16S rRNA gene metagenomic library

for insert confirmation

Lane 1: 100 bp marker

Lane 2-13: Colony PCR products

Fig 13: ARDRA pattern of different clones by Alu1 restriction enzyme for selection

of different clones

85

Fig 14: ARDRA pattern of different clones (A) and

phylogenetic tree (B) constructed based on

different pattern using MultiNA, Microchip

electrophoretic system

A

B

86

Fig 15: Bacteria catalogued from 16S rRNA gene metagenomic library of bulk soil,

cormosphere and rhizosphere of Saffron during two growth stages where each niche was

inhabited by different set of bacteria as represented by different colours.

87

B

C

D E

Fig 16: Phylogenetic tree of bulk soil, cormosphere and rhizosphere 16S rDNA

sequences during two growth stages indicating the dominance of Proteobacteria in

rhizosphere and cormosphere during both stages and bulk soil dominated by

Acidobacteria during flowering stage and Proteobacteria during dormant stage.

88

Fig 17: Rarefaction curves for bacterial OTUs clustering at 97% rRNA sequence similarity

depicting higher bacterial diversity in bulk soil during dormant stage. Curves represent

sequences for bulk (WBF), cormosphere (WCF) and rhizosphere (WRF) during flowering

and bulk (WBD) and cormosphere (WCD) during dormant stage of Saffron

Table 9: Diversity indices of bulk soil, rhizosphere and cormosphere during two growth

stages indicating bacterial diversity and richness was maximum in bulk soil during dormant

stage.

Stages FLOWERING STAGE DORMANT STAGE

Diversity

indices

Bulk soil Cormosphere Rhizosphere Bulk soil Cormosphere

ACE Mean 20.5 12 11 73 18.5

ICE Mean 13 6 8 18 16

Chao 1 Mean 20.5 12 11 73 18.5

Chao 2 Mean 13 6 8 18 16

Shannon

Mean

2.21 1.02 1.72 2.51 2.44

Simpson

Mean

8.14 2.34 5.05 11.45 10.84

4.3.1. Relative bacterial diversity between different niches during flowering

and dormant stage:

89

Bacteria phyla namely Acidobacteria, Actinobacteria, Bacteroidetes, Firmicutes,

Proteobacteria, Gemmatimonadetes and Planctomycetes along with some

unclassified phylum were catalogued by cultivation independent technique.

Significant (P >0.001) differences were observed in bacterial diversity assembled

across cormosphere rhizosphere and bulk soil i.e. Proteobacteria phyla was

dominant in rhizosphere (84%) and cormosphere during both the stages

(Flowering=92%, dormant=58%) whereas bulk soil was dominated by

Acidobacteria during flowering (56%) and Proteobacteria (54%) during dormant

stage (Fig 18). During the two growth stages, Proteobacteria was dominant in the

cormosphere but during dormant stage, in addition to Proteobacteria (58%),

Bacteroidetes (30%) and Acidobacteria (4%) were also present (Fig 18).

Rhizosphere sample could not be compared since corms lack roots during dormant

stage. Predominance of Proteobacteria in nutrient rich rhizosphere is due to the

presence of root exudates whereas bulk soil being nutrient poor was dominated by

Acidobacteria. The ratio of Proteobacteria to Acidobacteria indicates the trophic

level of soils with nutrient-rich soils favouring Proteobacteria and nutrient-poor

soils favouring Acidobacteria (Gottel et al., 2011). Dominance of Proteobacteria

has been attributed to the nutrient-rich conditions of the rhizosphere in case of

Trifolium repens and Lolium perenne, (Marilley et al., 1999), maize (Sanguin et

al., 2006.), soybean (Xu et al., 2009), rice (Arjun and Kumarapillai 2011) and

grasslands (Singh et al., 2007). Proteobacteria make available inorganic nutrients

to the plants favouring plant growth (García-Salamanca et al., 2013) and are

reported to be active in disease suppression as well (Berendsen et al., 2012). The

total dominance of Proteobacteria in cormosphere reiterates the status of corm as

nutrient rich source for bacterial growth. Since no data is available on the bacteria

associated with corm present in any plant, thus the comparison was done with that

of rhizosphere.

A total of 55 bacteria genera were catalogued from all the three niches during both

the stages combined together. Out of 55 bacterial genera, 27 were catalogued from

flowering stage (Bulk soil=13, rhizosphere= 8, cormosphere=6) and 28 from

90

dormant stage (Bulk soil=16, cormosphere=13) (Fig 19, 20). Each niche was

dominated by different bacterial genera like Pseudomonas in rhizosphere, Pantoea

and Chryseobacterium in cormosphere during flowering and dormant stage

respectively, whereas uncultivable Acidobacteria, GP4 and Arthrobacter in bulk

soil during flowering and dormant stage respectively. Acidobacteria GP4 and

Arthrobacter are known to dominate the bulk soil due to its nutrient poor

conditions (Gottel et al., 2011). The dominance of Pseudomonads in rhizosphere is

in accordance with reports in literature and also complements our study on Saffron

rhizosphere using cultivation dependent approach (Ambardar and Vakhlu 2013).

Pseudomonads are chemically attracted to the root exudates and are selected over

all other microbes due to their PGP properties (Saharan et al., 2011). In

cormosphere, although, Proteobacteria were dominant during two growth stages

but during dormant stage, Chryseobacteria (Bacterial genera of Bacteriodetes)

were found dominating in cormosphere thereby indicating that different genera

dominated different growth stages. Pantoea and Chryseobacterium are reported to

be beneficial PGPR in plant rhizosphere (Mishra et al., 2011; Cho et al., 2010) but

their dominance in cormosphere needs further investigation. Plant growth stage is

one of the factor influencing the occurrence and distribution of bacteria in the soil

(Smalla et al., 2001; Houlden et al., 2008). In rhizosphere, it is well known that

production and diffusion of root exudates varies at different plant development

stages favouring the development of specific microorganisms more adapted to that

stage, as reported in rhizosphere of pea, wheat and sweet potato (Smalla et al.,

2001; Dunfield and Germida, 2003; Cavaglieri et al., 2009; Houlden et al., 2008).

Similarly, variation in cormosphere bacterial community during two growth stage

can be attributed to variation in nutrient content present in cormosphere and

change in environmental conditions due to different growth stage.

91

Fig 18: Comparison of relative abundance of bacterial phyla in bulk soil,

rhizosphere and cormosphere during flowering and dormant stage

92

Fig 19: Comparison of various bacterial genera inhabiting bulk soil, rhizosphere and cormosphere

during flowering (A) and dormant stage (B).

Fig 20: Comparison of bacterial genera in cormosphere during two growth stages in cormosphere

A B

93

During flowering stage 13 bacterial genera were specific to bulk soil, 6 to

cormosphere and 8 to the rhizosphere whereas during dormant stage, 11 bacteria

were specific to bulk soil and 12 to cormosphere (Fig 19, 20), thereby indicating

niche-specific association. This result was further complemented by phylogenetic

tree constructed for different niches at a particular stage and also from a particular

niche during two growth stages. In both cases, the bacterial sequences from each

niche as well as different growth stages were clustered into separate clad (Fig 21,

22). Inceoglu and co-worker (2011) have reported that the bacterial diversity of

rhizosphere samples were significantly different from bulk soil in potato using

direct cloning independent high through put sequencing at different growth stages.

Phylogenetic composition of all the samples in Saffron was significantly different

as evident by the UniFrac significance and P-test significance values for the

bacterial communities (P <0.05 for all pairwise comparisons). This difference is

also evident by principle coordinate analysis of Unifrac Analysis program where

rhizosphere, cormosphere and bulk soil bacterial communities formed clusters in

different quadrants (Fig 23, 24).

Although specific bacteria were catalogued from each niche, some overlaps of

bacteria were also observed between the different niches. During flowering stage,

out of 27 genera catalogued none of the genus was common in all the three niches

despite of the small distance (<~10 mm) between the three soil types (Fig 19) and

similar results were observed by cultivation based analysis mentioned in the

beginning. There were some bacteria which were common in at least two soil

types, like P.frederiksbergensis and Acidobacteria GP6 common in bulk soil and

rhizosphere; Pantoea vagans, Pa.agglomerans, Pa.eucrina and Enterobacter

ludwigii in rhizosphere and cormosphere; and Staphylococcus epidermidis in

cormosphere and bulk soil (Fig 19). On contrary, out of 28 genera catalogued

during dormant stage, Stenotrophomonas maltophilia and Rhizobium lusitanum

were common in the bulk soil and cormosphere (Fig 19) thereby indicating that

even the common bacteria between bulk soil and cormosphere were also different

during two growth stages. During two growth stages, 19 bacterial genera were

94

catalogued in cormosphere, out of which, only two bacterial species were common

namely Stenotrophomonas maltophilia and Enterobacter ludwigii which acts as

reservoir in corm during its annual cycle (Fig 20). This clearly shows that the bulk

soil, cormosphere and rhizosphere bacterial communities were different during two

growth stages with little overlaps.

Pseudomonas frederiksbergensis seems to be specific to Saffron rhizosphere, as it

has not been reported from any other plant rhizosphere but from soil at coal

gasification site (Andersen et al., 2000). Acidobacteria, known to be more

abundant in environments with low C availability, prefer bulk soil to nutrient-rich

rhizosphere (George et al., 2011). The C value of bulk soil (1.36%) of Saffron also

allows the growth of Acidobacteria. Pantoea agglomerans and P.vagans have

been reported as PGPR and have been isolated from the rhizosphere of maize and

chickpea (Mishra et al., 2011) and Eucalyptus leaves (Brady et al., 2009)

respectively. Pantoea agglomerans was producing IAA and solubilizing tri-

calcium phosphate whereas Pantoea vagans is a biocontrol agent, indicating that

these species may be performing similar functions in the cormosphere too. Sturz

and co-worker (2000) screened the endophytes isolated from the peel and inner

layers of potato for biocontrol agents and Pantoea agglomerans was found from

the internal layers of potato which was in contrast to Saffron, where it was found

from outer surface. To our knowledge, Pantoea eucrina has not been reported

from the rhizosphere or any other part of any plant. S.maltophilia has also been

reported from the rhizosphere of oilseed rape and sugar beet (Berg et al., 1996;

Dunne et al., 1997) showing various PGP traits and Enterobacter ludwigii from the

rhizosphere of Lolium perenne (Shoebitz et al., 2009). Stenotrophomonas

maltophilia and Enterobacter ludwigii showing PGP traits have been isolated from

cormosphere in the present study by culture dependent approach. Rhizobium

lusitanum and Staphylococcus epidermidis were not isolated by culture dependent

approach but are reported to nodulate the roots of Phaseolus vulgaris (Valverde et

al., 2006) and from soil and human skin (Kloos et al., 1975) respectively but not

from root, corm or underground tuber of any plant.

95

A B

Fig 21: Phylogenetic tree comparing the bacterial sequences of bulk soil (Red),

rhizosphere (Blue) and cormosphere (Green) during flowering (A) and dormant

stage (B) where bacterial sequences from each niche clustered separately depicted

by different colours .

A B

Fig 22: Phylogenetic tree comparing the bacterial sequences of cormosphere (A)

and bulk soil (B) during flowering (Red) and dormant (Black) stage where

bacterial sequences from each stage clustered separately depicted by different

colours .

96

Fig 23: Principal coordinates analysis of bacterial community of cormosphere (Flowering

COR, Dormant C), rhizosphere (RHI) and Bulk (Flowering BUL, Dormant B) and Outgroup

(OUT/O) during flowering (A) and dormant (B) stages where bacterial community of each

niche clustered in different quadrants hence are diverse.

A B

Fig 24: Principal coordinates analysis of bacterial community during flowering (F) and

dormant stage (F) in A) cormosphere and B) bulk soil where bacterial community of each

stage clustered in different quadrants .

A B

97

Bacterial communities in the rhizosphere are assumed to be a subset of the total

bacterial community present in bulk soils (Buee et al., 2009). In Saffron, less

number of bacteria were common between two niches as bulk soil and rhizosphere

shared 7% of total bacteria (2 bacteria); rhizosphere and cormosphere shared 15%

of total bacteria (4 bacteria) whereas bulk soil and cormosphere shared 4% of total

bacteria (1 bacterium) during flowering stage and 7% of total bacteria (2 bacteria)

during dormant stage. As Saffron reproduces by formation of daughter corms,

which arise from the mother corm, it would not be out of place to hypothesize that

the bacteria may be transferred from mother corm to daughter corm rather than

from bulk soil.

4.4. Comparative account of bacteria catalogued by cultivation dependent

and cultivation independent approach:

Out of the total seven phyla isolated using the two approaches Acidobacterium,

Gemmatimonadetes and Planctomycetes were not isolated by cultivation

dependent methods. Dominance of Proteobacteria in rhizosphere and cormosphere

was revealed by both the approaches. 39 genera were catalogued from bulk soil,

rhizosphere and cormosphere using cultivation independent approach whereas only

11 genera were isolated using cultivation dependent approach during both the

growth stages. Out of 39 bacterial genera, only 8 genera were isolated using

cultivation dependent approach on single media (Fig 25). Since full-length 16S

rRNA gene was analysed using metagenomic approach, the sequences were

catalogued upto species level. A total of 30 species were catalogued from all the

samples using cultivation independent approach, out of which four species namely,

Serratia plymuthica, Stenotrophomonas maltophilia, Enterobacter ludwigii,

Pseudomonas koreensis were also isolated using cultivable approach. Though

cultivation independent approach gave better idea about the bacterial types present

in comparison to cultivation dependent approach but bacteria with potential

application in Saffron fields were isolated by cultivation dependent approach. The

bacteria catalogued from different niche of Saffron during two growth stage by

cultivation independent and dependent approach have been also reported from

98

other plant rhizosphere that has been tabulated in Table 10, 11. Thirteen novel

rhizobacterial species were identified from Saffron rhizosphere, which have not

been reported from any plant rhizosphere using cultivation dependent or

independent techniques namely P.koreensis, P.kilonensis P.frederiksbergensis,

P.baetica, P.mohnii, P.reinekei, Pa.eucrina, Pa.conspicua, E.asburiae, E.kobei,

B.niacin, B. acidiceler and B.soli. Out of the various species of Pseudomonas

catalogued from Saffron rhizosphere by metagenomic analysis, only P.koreensis

and P.kilonensis have been isolated using cultivation dependent approach

(Ambardar and Vakhlu 2013). P.koreensis and P.kilonensis has been isolated from

agricultural land (Sikorski et al., 2001; Kwon et al., 2003) but has not been

reported from any plant rhizosphere. P.koreensis has been reported to produce bio

surfactant effective against Pythium ultimum and Phytophthora infestans (Hultberg

et al., 2010).

99

Fig 25: Total bacterial genera catalogued from cormosphere, rhizosphere and

bulk soil by cultivation dependent and independent approach.

100

Table 10: Comparison of bacteria isolated from cormosphere (Flowering WCF, Dormant WCD),

rhizosphere (WRF) and Bulk (Flowering WBF, Dormant WBD) of Saffron using cultivation

dependent approach with other reported plant rhizosphere bacteria and soil bacteria

Saffron bacteria Literature

survey

Plant Growth Promoting

Properties

References

P.tremae WRF Coffee

seedlings

Phosphate solubilisation, HCN &

siderophores production, and

antagonize fungal pathogens

Muleta et al.,

2009

P.kilonensis Agricultural

soils

Not studied Sikorski et al.,

2001;

P.koreensis Agricultural

soils

Not studied Kwon et al.,

2003

Acinetobacteria

calcoaceticus

Wheat

rhizosphere

Phosphate solubilization,

siderophore and IAA production

Sarode et al.,

2009

Chryseobacteria

elymi

Wild rye

rhizosphere

Indole acetic acid production Cho et al.,

2010

B.aryabhattai WBF/

WRF

Erigeron

canadensis

rhizosphere

Not studied Lee & Song

2012

P.parafulva WBF Strawberry

phyllosphere

Antagonistic activity against

pathogenic fungus Botrytis cinerea

in vitro.

Nkpwatt et al.,

2006

B.methylotrophicus Tomato

rhizosphere

Indole-3-acetic acid and

siderophores production

Almoneafy et

al., 2012

B.halotolerans Prosopis

strombulifera

rhizosphere

IAA production, nitrogen fixation,

proteolytic activity and biocontrol

against Alternaria sp.

Sgroy et al.,

2009

B.frigoritolerans Arid soils of

Morocco

Not studied Delaporte and

Sasson 1967

Arthrobacter Bulk soil of

Potato

PGPR Joseph et al.,

2007

B.frigoritolerans WBF/

WCF

Arid soils of

Morocco

Not studied Delaporte and

Sasson 1967

E.cloacae WCF Rice

rhizosphere

Solubilizing phosphate, IAA

production

Shankar et al.,

2011

E.hormaechei Wheat

rhizosphere

Auxin, HCN and lipase production Egamberdieva

2008

S.maltophilia Oilseed rape

rhizosphere

Siderophore, chitinase and protease

production & anti-fungal activity

against Pythium ultimum

Berg et al.,

1996

S.plymuthica Melon roots Chitinolytic and proteolytic

activities, siderophore production

Kamensky et

al., 2003

B.cereus Soil Samples - Stetten et al.,

1999

B. megaterium Chickpea

rhizosphere

Phosphate solubilization Elkoca et al,

2008

101

S.multivorum Wheat

rhizosphere

Degrading chitosan Yang et al.,

2010

B.anthracis WBD

Soil samples -- Vahedi et al.,

2009)

B.pumilus Soybean

rhizosphere

- Wahyudi et

al., 2011

B.simplex Peanut

rhizosphere

- Yan Y et al.,

2011

B.safensis Desert soil

samples

- Raja & Omine

2011

B.subtilis Greenhouse

soils

- Chung et al.,

2008

B. cereus Soil Samples - Stetten et al.,

1999

B.thuringiensis WBD/

WCD

Canola

rhizosphere Not studied Freitas et al.,

1997

B.methylotrophicus WCD Tomato and

potato

rhizosphere

Indole-3-acetic acid and

siderophores production

Almoneafy et

al., 2012

B.mycoides Sugar beet

leaves

Reduced the severity of disease

Cercospora leaf spot of sugar beet

Bargabus et

al., 2004

B. megaterium Chickpea

rhizosphere

Phosphate solubilization Elkoca et al.,

2008

E. ludwigii Lolium

perenne

rhizosphere

Nitrogenase and IAA production,

phosphate solubilisation and

antagonized Fusarium solani

Shoebitz et al.,

2009

E. cloacae Rice

rhizosphere

IAA production phosphate

solubilisation

Shankar et al.,

2011

E. hormaechei Wheat

rhizosphere

Auxin, HCN and lipase production Egamberdieva

2008

Enterococcus

faecium

Mango and

mulberry

rhizosphere

IAA and siderophores production Chen et al.,

2005

102

Table 11: Comparison of bacteria isolated from cormosphere (Flowering WCF, Dormant WCD),

rhizosphere (WRF) and Bulk (Flowering WBF, Dormant WBD) of Saffron using cultivation

independent approach with other reported plant rhizosphere and soil samples

Bacteria catalogued

by metagenomics

Source Literature survey References

Pseudomonas

chlororaphis

WRF

Green Pepper rhizosphere Shen et al., 2012

P. thivervalensis Arabidopsis thaliana

rhizosphere

Achouak et al., 2000

P. moraviensis Wheat rhizosphere Yadav et al., 2013

P. brassicacearum Brassica napus rhizosphere Ivanova et al., 2009

Serratia plymuthica Melon roots Kamensky et al., 2003

Serratia ficaria Oilseed rape rhizosphere Kalbe et al.,1996

Bacillus drentensis Cactus rhizosphere Garrido et al., 2012

E. asburiae Mustard rhizosphere Munees et al 2010

Bacillus acidiceler Forensic samples Peak et al., 2007

P.reinekei Sediment of the River Elbe,

Germany

Camara et al., 2007

P.koreensis Agricultural soils Kwon et al. 2003

P.mohnii Sediment of the River Elbe,

Germany

Camara et al., 2007

P.baetica Fish pathogen Lopez et al., 2011

Bacillus soli Soil from hay fields Heyrman et al., 2004

Bacillus niacini Acidic soil Yadav et al., 2011

Enterobacter kobei Clinical samples Kosako et al., 1996

Pseudomonas

frederiksbergensis

WRF/

WBF

Coalgasification site Andersen et al., 2000

Pantoea eucrina WRF/

WCF

Human samples Brady et al., 2010

Pa.conspicua Human samples Brady et al., 2010

Pseudomonas

monteilii WBF Wheat rhizosphere Yadav et al., 2013

P. plecoglossicida Wheat rhizosphere Yadav et al., 2013

Shigella boydii

WCF

Diarrhoeal stools from

patients

Ansaruzzaman et al.,

2005

S. flexneri Human Pathogens Zhou et al., 2010

S. sonnei Human Pathogens Holt et al., 2012

Escherichia fergusonii Crudeoil-contaminated

sediments

Pasumarthi et al., 2013

E.coli Sugarcane stem Suliman et al., 2007

Stenotrophomonas

maltophilia

Sugarcane stem Suliman et al., 2007

Pantoea brenneri Human urethra and sputum Brady et al., 2010

103

Arthrobacter oxydans WBD

Soil samples Vahedi et al., 2009

A.globiformis Cactus bulk soil/ rhizosphere Garrido et al., 2012

A.pascens Soil sample Rozwadowski 1991

Acinetobacter

radioresistens

Soil and cotton Nishimura 1988

Bradyrhizobium

oligotrophicum

Aquatic legume plant roots Okubo et al., 2013

Burkholderia cepacia Wheat rhizosphere Yadav et al., 2013

Phyllobacterium

myrsinacearum

Bulk soil of cotton Hallman et al., 1999

Ralstonia

mannitolilytica

Clinical samples Daxboeck et al., 2005

Stenotrophomonas

rhizophila

Oilseed rape rhizosphere Wolf et al., 2002

Stenotrophomonas

maltophilia WBD/

WCD

Oilseed rape rhizosphere Berg et al., 1996

Rhizobium lusitanum Phaseolus vulgaris roots Valverde et al., 2006

Burkholderia

fungorum

WCD

Environmental sample Coenye et al., 2001

Chryseobacterium

proteolyticum

Bulk soil of rice field Yamaguchi 2000

Chitinophaga

ginsengisegetis

Soil of a ginseng field in

South Korea Lee et al., 2007

C.arvensicola Wetlands of Northern Russia Pankratov et al., 2006

C. pinesis Soil samples Rio et al., 2010

Enterobacter cloacae Rice rhizosphere Shankar et al., 2011

E. aerogenes Mangroves rhizosphere Vazquez et al., 2000

E. ludwigii Lolium perenne rhizosphere Shoebitz et al., 2008

Luteibacter

rhizovicinus

Barley rhizosphere Jaohenson et al., 2005

Ochrobactrum

haematophilum

Cucumber rhizosphere Zhou et al., 2011

104

4.5. Cloning independent (Metagenomics) approach

Fungal diversity assessment (2013) was initiated after bacterial diversity analysis

(2010), and by this time pyrosequencing technology was readily available in India

at reasonable prices (2013). Culture independent approaches based on cloning-

based methods generally underestimate microbial diversity as it suffer from

inherent cloning bias in the methodology and also the representation of microbial

diversity is restricted to the total number of clones analyzed. Next generation

sequencing, a culture-independent sequencing approach that is also cloning

independent, eliminates bias resulting from cloning and PCR from the analysis and

enables extensive sequencing of microbial populations resulting in better

representation of microbial diversity (Tedersoo et al., 2010).

The fungal diversity associated with the rhizosphere, cormosphere and the bulk

soil during two growth stages was studied by cloning independent approach which

included pyrosequencing and analysis of ITS (Inter transcribed spacer region) gene

sequences. ITS gene (~550 bp) was amplified from the metagenome of all the five

samples using universal primer and the purified amplicons were sent for

pyrosequencing at Roche Diagnostics India Pvt. Ltd., New Delhi (Fig 26).

Pyrosequencing was preceded by library preparation that included ligation of the

ITS gene from each niche to 5 unique MIDs (Molecular Identifier tags) and

emulsion PCR. Each MID included a unique barcode sequence unique to each

sample and an oligonucleotide sequence which is complementary to the adapter

attached on beads of emulsion PCR. These tagged DNA fragments were further

subjected to emulsion PCR that resulted in an array of millions of spatially

immobilized PCR colonies which is further sequenced by pyrosequencing.

Sequencing was performed with 454, GS Junior (454 Life Sciences, Brandford,

USA). A total of 89,527 reads (47,078,862 bp) were obtained, with a median read

length of 517bp. The raw sequence data was then de-multiplexed/sorted based on

sample-specific barcode tags which were subsequently trimmed from sorted

sequences and analysed by CDHIT program, Ribosomal Database Project (RDP-

105

II) and pyrosequencing pipeline by Mothur (Cole et al., 2014; Li et al., 2006;

Schloss et al., 2009). The number of reads obtained per sample varied from 5000-

31000 (Table 12). Rarefaction curves in all the samples approached the plateau

level indicating fungal diversity has been represented well and will not increase on

repetitive sampling. Rarefaction curves (97% identity) in all the five samples

indicated that the diversity was more in rhizosphere as more OTUs were

catalogued as compared to cormosphere and bulk soil, which was in contrast to the

bacterial diversity where the bulk soil was more diverse (Fig 27). The fact that the

fungal diversity is more in rhizosphere as compared to bulk soil and cormosphere

was further complemented by the diversity indices like Chao 1 Mean and Shannon

Mean which were more in case of rhizosphere comparatively (Table 13).

4.5.1. Relative fungal diversity in different niches during flowering and

dormant stage:

Six fungal phyla namely Ascomycota, Basidiomycota, Zygomycota,

Glomeromycota Chytridiomycota and Fungi phylum Incertae sedis along with

unclassified phylum were catalogued from all the niches during flowering and

dormant stage (Fig 28). Fungal diversity associated with cormosphere and

rhizosphere was different than bulk soil. During both dormant and flowering stage

Zygomycota were dominant in rhizosphere and cormosphere whereas Ascomycota

were dominant in bulk soil (Fig 28). The relative abundance of Zygomycota was

maximum in cormosphere (Flowering=54.3%; Dormant=95.4%) followed by

rhizosphere (Flowering=43%) in comparison to bulk soil (Flowering =7.8%;

Dormant=19.4%). During the two growth stages, Zygomycota was dominating the

cormosphere but during flowering stage, in addition to Zygomycota (50%),

Ascomycota (33%) and Basodiomycota (5%) were also present (Fig 28).

106

Lane1: 100 bp Marker

Lane 2-6: ITS gene amplicon (Lane2: WCF; 3: WRF; 4: WBF; 5 WBD; 6:

WCD)

Fig 26: PCR amplification of ITS gene (~550bp) from metagenome of different

samples

(Flowering stage: WBF= Bulk soil; WCF= Cormosphere; WRF= rhizosphere;

dormant stage: WBD= Bulk soil; WCD =Cormosphere)

Table 12: Total number of Fungal ITS sequences obtained per sample by pyrosequencing

and its clustering at 97% similarity

Sample Raw reads Filtered Reads OTU 97%

WBF 5824 5,478 319

WRF 31,865 29,851 2103

WCF 18,986 18,031 969

WCD 20,884 19,624 259

WBD 11,618 10,336 706

107

Fig 27: Rarefaction curves for fungal OTUs clustering at 97% rRNA sequence similarity

from the five niches representing more diversity in rhizosphere as compared to bulk soil.

Curves represent sequences for Bulk (WBF), Cormosphere (WCF) and rhizosphere (WRF)

during flowering and bulk (WBD) and cormosphere (WCD) during dormant stage of

Saffron

Table 13: Diversity indices indicating fungal diversity and richness in five different niches

during two growth stage representing maximum diversity in rhizosphere as compared to

bulk soil.

Stages FLOWERING STAGE DORMANT STAGE

Diversity

indices

Bulk Cormosphere Rhizosphere Bulk Cormosphere

Chao 1 Mean 2,945.33 11,268.77 18,438.88 5,782.78 4,966.01

Shannon Mean 5.20093 6.32379 7.14 5.70209 4.2225

108

Fig 28: Comparison of relative abundance of fungal phyla in bulk soil, rhizosphere

and cormosphere during flowering and dormant stage indicating the dominance of

Zygomycota in rhizosphere and cormosphere and Ascomycota in bulk soil during

both stages

109

Zygomycetes and Hyphomycetes are reported to establish, most readily in the

rhizosphere because they metabolize simple sugars (Sylvia, 2005) which is true for

Saffron rhizosphere also. Zygomycota are reported from the rhizosphere of wheat,

Japanese barberry, Pisum sativum, Acacia auriculiformis and Bambusa balcooa

(Smit et al., 1999; Xu et al., 2012; Coats et al., 2013; Das et al., 2013). Abundance

of Zygomycota in cormosphere can be attributed to presence of simple sugars like

lyxose, xylose, ribose, glucose, mannose, galactose, rhamnose, cellobiose, maltose,

lactose and, fructose in corm (Ma et al., 2012). Bulk soil, on the contrary, was

dominated by Ascomycota (Flowering=69.6%; Dormant=67.6%) (Fig 28) with its

abundance decreasing in rhizosphere (38.4%) and cormosphere

(Flowering=39.1%; Dormant=3.5%). Ascomycota, dominant in bulk soil of Saffron

has been reported to be dominant in the bulk soil of wheat (Panton et al., 2014) and

rhizosphere of grass (Mouhamadou et al., 2013).

A total of 148 fungal genera, 99 during flowering stage and 49 during dormant

stage were catalogued from all the niches (Fig 29). During flowering stage, 19

fungal genera were catalogued from bulk soil, 35 from cormosphere and 45 from

rhizosphere whereas during dormant stage, 44 fungal genera were catalogued from

bulk soil and 5 from cormosphere with each niche being dominated by different

fungal genera. Rhizosphere was dominated by Mucor, bulk soil was dominated by

Pseudogymnoascus during flowering and Rhizopus during dormant stage, whereas

cormosphere was dominated by Cryptococcus during flowering stage and Rhizopus

during dormant stage. Bulk soil, being dominated by Ascomycota, in both growth

stages was dominated by Rhizopus (Fungal genera of Zygomycota) during dormant

stage whereas cormosphere, being dominated by Zygomycota during both stages,

was dominated by Cryptococcus (Fungal genera of Basidiomycota) thereby

indicating that different genera were dominant in two growth stages irrespective of

common phylum. Variation in cormosphere fungal diversity can be attributed to

changing biochemical profile of the corm along with environmental changes which

are true for rhizosphere as well (Cavaglieri et al., 2009; Houlden et al., 2008;

110

Coats et al., 2013). Mucor, Rhizopus, Cryptococcus and Pseudogymnoascus has

been reported from various plant rhizosphere like red pepper, maize, grass and oak

respectively (Gomes et al., 2003; Kwasana et al., 2004; Ajokpaniovo et al., 2011;

Shivanna and Vasanthakumari 2011).

During flowering stage, 32 fungal genera were specific to rhizosphere, 22 to

cormosphere and 6 to bulk soil whereas during dormant stage, 40 fungal genera

were specific to bulk soil and only one to cormosphere thereby indicating niche-

specific association. The fungal species catalogued from bulk soil, rhizosphere and

cormosphere during two growth stages have been also reported from various plant

rhizospheres that have been tabulated in Table 14. During flowering stage, out of

99 fungal genera, 13 were common in the three niches whereas during dormant

stage, out of 49, only 4 genera were common (Fig 29). During two growth stages,

out of 40 fungal genera catalogued in cormosphere, only one genus was common

with the bulk soil (Fig 30).

On analyzing the genera upto species level, during flowering stage only 5 fungal

species namely Mortierella elongata, Rhizopus arrhizus, Cryptococcus fuscescens,

Cryptococcus macerans and Cryptococcus magnus were common between

rhizosphere, cormosphere and bulk soil and during dormant stage, only Rhizopus

arrhizus was common species in bulk soil and cormosphere. Even during two

growth stages, Rhizopus arrhizus was common fungal species in cormosphere

which act as reservoir during the life cycle. Rhizopus arrhizus was common

between bulk soil and cormosphere and also between cormosphere during two

growth stage indicating thereby it may have transferred from bulk soil and may be

plant growth promoting fungi (PGPF). Mortierella elongate, Rhizopus arrhizus,

Cryptococcus fuscescens, Cryptococcus macerans and Cryptococcus magnus have

been reported from the rhizosphere of pea, grass, poplar, sugarcane and maize

respectively (Azeredo et al., 1998; Gomes et al., 2003; Stefani et al., 2009;

Savanna et al., 2011; Xu et al., 2012) but their isolation from corm has not been

reported. Presence of these fungal species in bulk soil, rhizosphere and

111

cormosphere indicate that these species may be migrating from one niches to

others. In Saffron, during flowering stage, 5 % of total fungi were common in three

niches and during dormant stage whereas only 2% of total fungi were common in

bulk soil and cormosphere. During two growth stages in cormosphere, only 2.5%

of total fungi were common indicating thereby percentage of common fungi were

less as compared to percentage of common bacteria in Saffron. The cormosphere

fungi also, as hypothesized in case of the bacteria, may not be migrating from bulk

soil but from the mother corms to daughter corms.

The results of the present study in comparison to reports in the literature suggest

that the hypothesis, “Profile (abundance and composition) of microbes associated

with root and corm of Saffron vary in two niches, at two different growth stages”,

seems to be true so far.

112

Fig 29: Comparison between fungal genera catalogued from bulk soil, rhizosphere and

cormosphere during two stage indicating specific fungi inhabits different niche

Fig 30: Comparison between fungal genera catalogued from cormosphere during two growth

stage indicating specific fungi inhabits during different growth stage

A B

113

Table 14: Comparison of fungi catalogued from cormosphere (Flowering WCF, Dormant

WCD), rhizosphere (WRF) and Bulk (Flowering WBF, Dormant WBD) of Saffron using

cultivation independent approach with other reported plant rhizosphere and soil samples

Fungus catalogued by

metagenomics

Source Literature survey References

Epicoccum nigrum

WRF

Sugarcane endophyte Fa´varo et al., 2012

Laetisaria arvalis Biocontrol in sugarbeet Allen et al., 1985

Lectera longa Soil Cannon et al., 2012

Actinomucor elegans Soil Kurakov et al., 2008

Mucor ardhlaengiktus Garden soil Mehrotra and Mehrotra 1978

Waitea circinata Agriculture soil Leiner et al., 1995

Thelebolus globosus Lakes of Antartica Hoog et al., 2005

Ilyonectria robusta Pathogen to grape vine Cabral et al., 2012

Clitopilus hobsonii Grape vine disease pathogen Fischer et al., 2003

Schizophyllum commune Fungal pathogen Chowdary et al., 2013

Leptosphaerulina chartarum Contaminated broth Wu et al., 2013

Rhodotorula ferulica Polluted river water Sampio and Uden 1991

Candida smithsonii Beetel associated Suh and Backwell 2004

Mortierella elongata WRF/

WBF/

WCF

Pea rhizosphere Xu et al., 2012

Rhizopus arrhizus Grass rhizosphere Shivanna et al., 2011

Cryptococcus macerans Sugarcane leaf Azeredo et al., 1998

Cryptococcus magnus Maize rhizosphere Gomes et al., 2003

Cryptococcus fuscescens Poplar rhizosphere Stefani et al., 2009

Tetracladium maxilliforme WRF/

WBF

Grassland soil Klaubauf et al., 2010

Embellisia chlamydospora WRF/

WCF

Natural vegetatation root

endophyte

Macia-Vicente et al., 2008

Fusarium cf equiseti Tomato rhizosphere Jamiołkowska et al., 2005

Paecilomyces marquandii Soil Jamali et al., 2012

Preussia flanaganii Soil Zhang 2012

Mucor circinelloides Soil Kurakov et al., 2008

Cryptococcus laurentii Agathosma betulina

rhizosphere

Cloete et al., 2009

Cryptococcus oeirensis From Bark beetel Giordano et al., 2012

Cryptococcus paraflavus Steppe plants Golubev et al., 2004

Rhodotorula glutinis Sugerbeet rhizosphere El-Tarabily 2004

Bulleromyces albus Panax ginseng Rhizosphere Hong et al., 2002

Rhodotorula vanillica Aquatic habitat Sampaio 1995

Aspergillus penicillioides WBF Rice grain Makun et al., 2011

Oidiodendron truncatum Psychrophillic fungus Li et al., 2012

Cryptococcus victoriae Deschampsia Antarctica

rhizosphere

Vaz et al., 2011

Wallemia sebi Hyper saline environment Padamsee et al., 2012

Aspergillus niveus Sunflower rhizosphere Souza-Motta et al., 2003

114

Aspergillus pseudodeflectus WCF Sargassum fusiform Parasite Ogava et al., 2004

Aspergillus terreus Desert soil Awaad et al., 2012

Aspergillus wentii Marine algae Li et al., 2014

Candida parapsilosis Human pathogen Trofa et al., 2008

Penicillium canescens Cucumber rhizosphere Girlanda et al., 2001

Holtermanniella festucosa Soil Wuczkowski et al., 2011

Hannaella zeae Ferns phyloplane Albu 2001

Rhodotorula cycloclastica Soil Nguyen et al., 2004

Rhodotorula mucilaginosa Skin lession Alvarez-Perez et al., 2010

Rhodotorula taiwanensis Plants in Taiwan Huang et al., 2012

Sakaguchia dacryoidea Sea Water Gadanho et al., 2003

Rhizopus arrhizus WCD

/WCF

Grass rhizosphere Shivanna et al., 2011

Rhizopus arrhizus WBD/

WCD

Grass rhizosphere Shivanna et al., 2011

Aspergillus penicillioides WBD

/WBF

Rice grain Makun et al., 2011

Mortierella elongata Pea rhizosphere Xu et al., 2012

Cryptococcus fuscescens Poplar rhizosphere Stefani et al., 2009

Rhizopus arrhizus Grass rhizosphere Shivanna et al., 2011

Embellisia chlamydospora

WBD

Natural vegetatation root

endophyte

Macia-Vicente et al., 2008

Paecilomyces marquandii Soil Jamali et al., 2012

Aspergillus terreus Desert soil Awaad et al., 2012

Aspergillus pseudodeflectus Sargassum fusiform Parasite Ogava et al., 2004

Aspergillus granulosus Human pathogen Sutton et al., 2009

Exophiala equina Human pathogen Hoog et al., 2011

Saksenaea oblongispor Human pathogen Alverez et al., 2010

Podospora anserina Dung Espagne et al., 2008

Nectria mariannaeae - Samuels and Seifert 1991

Fusarium cf equiseti_ Tomato rhizosphere Jamiołkowska et al., 2005

Candida hawaiiana Morning glory flower Lachance et al., 2003

Penicillium pimiteouiense Maize rhizosphere Oliveira et al., 2009

Leucoagaricus barssii Soil Vellinga et al., 2011

Glomus caledonium Maize rhizosphere Huang et al., 2006

Cryptococcus laurentii

Agathosma betulina

rhizosphere

Cloete et al., 2009

Rhodotorula mucilaginosa Skin lession Alvarez-Perez et al., 2010

Rhodotorula ferulica Polluted river water Sampio and Uden 1991

Syncephalastrum racemosum Soil Huang et al., 2014

Debaryomyces occidentalis Soil Boby et al., 2008

Ilyonectria robusta Pathogen to grape vine Cabral et al., 2012

Lecanicillium psalliotae Meloidogyne incognita Eggs Jun-Zheng et al., 2012

Cordyceps pseudomilitaris Insect pathogen Jaturapat et al., 2001

Clonostachys rosea Grass rhizosphere Shivanna and

Vasanthakumari 2011

115

CHAPTER-5

SUMMARY &CONCLUSION

116

SUMMARY & CONCLUSION

The work was initiated to test the hypothesis that the profile of microbes associated

with root and corm of Saffron vary in two niches at two different growth stages. In

the present study, the microbes associated with rhizosphere, cormosphere and bulk

soil of Saffron were catalogued during two growth (dormant and flowering) stages

by cultivation dependent and independent approach.

Bacterial diversity associated with the rhizosphere, cormosphere and the bulk soil

during two growth stages was studied by cultivation dependent and independent

(cloning dependent) approach whereas the fungal diversity was studied by

cultivation independent (cloning independent) approach. A total of seven bacteria

and six fungal phyla were catalogued along with some unclassified phylum from

the different niche during two stages. Microbial diversity of each niche was

significantly (P >0.001) different from other during two growth stages.

Cataloguing of different microbes associated with each niche during two growth

stages of Saffron indicated that plant microbe interactions select specific microbes

that are beneficial for that particular organ of the plant. Specific bacterial

association with roots and corm was also evident by the increase in bacterial load

in rhizosphere and cormosphere as compared to bulk soil in cultivation dependent

approach. Also, beneficial effect of bacteria on the Saffron was also confirmed by

for plant growth promotion traits exhibited by Saffron rhizobacteria in vitro and in

vivo. Saffron rhizosphere was inhabited by Proteobacteria and Zygomycota phyla

that were in concordance with literature.

Pyrosequencing, a cloning independent sequencing approach, was used to study

the fungal diversity as cloning dependent methods generally underestimate

microbial diversity. This was true for Saffron also as less number of bacterial

genera (55) were catalogued as compared to fungal genera (148), despite the fact

that the bacterial diversity is reported to be more than fungal diversity in soil and

rhizosphere. Next generation sequencing (Pyrosequencing) eliminates bias

resulting from cloning and enables extensive sequencing of microbial populations

117

resulting in better representation of microbial diversity. In Saffron, Rarefaction

curves, in case of fungal diversity, approached the plateau level in all the samples

(expect rhizosphere) indicating that fungal diversity has been represented well and

will not increase on repetitive sampling which was in contrast to bacterial diversity

by cloning dependent approach. Fungal diversity (2013) assessment was initiated

after bacterial diversity analysis (2010), and by this time pyrosequencing

technology was readily available in India at reasonable prices (2013).

Though cultivation independent approach gave better idea about the bacterial types

present in comparison to cultivation dependent approach but bacterial with

potential application in Saffron fields were isolated by cultivation dependent

approach. Eleven novel rhizofungal species and thirteen novel rhizobacterial

species were identified from Saffron rhizosphere, which have not been reported

from any plant rhizosphere using cultivation dependent or independent techniques.

Fungal species specific to Saffron rhizosphere are Actinomucor elegans, Clitopilus

hobsonii, Candida smithsonii, Ilyonectria robusta, Lectera longa,

Leptosphaerulina chartarum, Mucor ardhlaengiktus, Rhodotorula ferulica,

Schizophyllum commune,Thelebolus globosus and Waitea circinata whereas the

specific bacterial species are P.koreensis, P.kilonensis P.frederiksbergensis,

P.baetica, P.mohnii, P.reinekei, Pa.eucrina, Pa.conspicua, E.asburiae, E.kobei,

B.niacin, B. acidiceler and B.soli. Out of the various bacterial species of

Pseudomonas catalogued from Saffron rhizosphere by metagenomic analysis,

P.koreensis and P.kilonensis have been isolated using cultivation dependent

approach in our study.

This is a first report on microbial association in rhizosphere and cormosphere of

Saffron wherein the microbes present in Saffron are specific to their niche and the

growth stage. Rhizosphere of various plants has been reported earlier but

cormosphere has not been reported from any plant though corms are present in

many plants.

118

CHAPTER-6

BIBLIOGRAPHY

119

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http://www.greaterkashmir.com/news/2012/Dec/13/saffron-production-3.asp

Source: http://www.crocusbank.org/database/saffronmap.htm

www.eztaxon.org

http://www.ncbi.nlm.nih.gov/

http://www.ncbi.nlm.nih.gov/tools/vecscreen/about/

http://decipher.cee.wisc.edu/FindChimeras.html

http://blast.ncbi.nlm.nih.gov

http://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastSearch&BLAST_SPEC

=blast2seq&LINK_LOC=align2seq

155

www.eztaxon.org

http://www.clcbio.com/products/clc-sequence-viewer/

http://www.mbio.ncsu.edu/bioedit/bioedit.html

http://rdp.cme.msu.edu

https://pyro.cme.msu.edu/rarefaction/form.spr

http://rdp.cme.msu.edu/comparison/comp.jsp

http://www.ncbi.nlm.nih.gov/WebSub/?tool=genbank

http://viceroy.eeb.uconn.edu/estimates/

http://www.clustal.org/clustal2/

http://evolution.genetics.washington.edu/phylip/getme.html

http://www.megasoftware.net

http://bmf2.colorado.edu/unifrac/

http://itol.embl.de/

1 23

World Journal of Microbiology andBiotechnology ISSN 0959-3993Volume 29Number 12 World J Microbiol Biotechnol (2013)29:2271-2279DOI 10.1007/s11274-013-1393-2

Plant growth promoting bacteria fromCrocus sativus rhizosphere

Sheetal Ambardar & Jyoti Vakhlu

1 23

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ORIGINAL PAPER

Plant growth promoting bacteria from Crocus sativus rhizosphere

Sheetal Ambardar • Jyoti Vakhlu

Received: 25 September 2012 / Accepted: 31 May 2013 / Published online: 9 June 2013

� Springer Science+Business Media Dordrecht 2013

Abstract Present study deals with the isolation of rhi-

zobacteria and selection of plant growth promoting bacteria

from Crocus sativus (Saffron) rhizosphere during its

flowering period (October–November). Bacterial load was

compared between rhizosphere and bulk soil by counting

CFU/gm of roots and soil respectively, and was found to be

*40 times more in rhizosphere. In total 100 bacterial

isolates were selected randomly from rhizosphere and bulk

soil (50 each) and screened for in-vitro and in vivo plant

growth promoting properties. The randomly isolated bac-

teria were identified by microscopy, biochemical tests and

sequence homology of V1–V3 region of 16S rRNA gene.

Polyphasic identification categorized Saffron rhizobacteria

and bulk soil bacteria into sixteen different bacterial spe-

cies with Bacillus aryabhattai (WRF5-rhizosphere; WBF3,

WBF4A and WBF4B-bulk soil) common to both rhizo-

sphere as well as bulk soil. Pseudomonas sp. in rhizosphere

and Bacillus and Brevibacterium sp. in the bulk soil were

the predominant genera respectively. The isolated rhizo-

bacteria were screened for plant growth promotion activity

like phosphate solubilization, siderophore and indole acetic

acid production. 50 % produced siderophore and 33 %

were able to solubilize phosphate whereas all the rhizo-

bacterial isolates produced indole acetic acid. The six

potential PGPR showing in vitro activities were used in pot

trial to check their efficacy in vivo. These bacteria con-

sortia demonstrated in vivo PGP activity and can be used as

PGPR in Saffron as biofertilizers.This is the first report on

the isolation of rhizobacteria from the Saffron rhizosphere,

screening for plant growth promoting bacteria and their

effect on the growth of Saffron plant.

Keywords Rhizosphere � Saffron � Bulk soil �Pseudomonas � PGPR

Introduction

Rhizosphere, first described by Hiltner (1904), represents

the most dynamic habitat on the earth (Hinsinger et al.

2009). The rhizosphere zone is different from bulk soil, as

it is under the influence of root exudates. Sloughing off of

root cells, root death and the exudation of carbon com-

pounds select a specific rhizosphere community (Hartmann

et al. 2009). Hence, a rather small subset of the whole soil

bacterial diversity, majority of which are Gram negative,

finally colonize roots successfully (Soderberg et al. 2004;

Johansen and Olsson 2005). In the rhizosphere, diverse and

complex interaction occurs between plant roots, soil mic-

robiota and the soil which has evolved due to mutual

benefits, between plants and microbes. The plant partner

provides substrate and energy flow into the rhizosphere and

in return gets nutrients and minerals, essential for its

development and growth (Hartmann et al. 2009). Nanni-

pieri et al. (2007) have concluded in their review that, the

number of microorganisms is higher in rhizosphere than

bulk soil as assessed by the ‘‘Most Probable Number

analysis’’. Rhizosphere has been the focus of agricultural

research for many years, due to its importance in crop

productivity, soil health and sustainable agriculture

(Li et al. 2007; Ryan et al. 2009; Ordookhani et al. 2011).

Rhizosphere of various plants like rice, cucumber, apple

and soyabean has been extensively studied (Johansen and

Olsson 2005; Ashrafuzzaman et al. 2009; Joshi and Bhatt

S. Ambardar � J. Vakhlu (&)

School of Biotechnology, University of Jammu,

Jammu 180 006, India

e-mail: jyotimetagenomic@gmail.com; jyotivakhlu@gmail.com

123

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DOI 10.1007/s11274-013-1393-2

Author's personal copy

2011; Mahaffee and Kloepper 1997; Mehta et al. 2010;

Wahyudi et al. 2011).

Plant growth-promoting rhizobacteria (PGPR) exert plant

growth promotion and/or biocontrol effects and are found in

the rhizosphere, root surface as well as inside the root tissues.

These PGP rhizobacteria can improve the extent or quality of

plant growth directly by increasing nutrient cycling such as,

biological nitrogen fixation (Ahmad et al. 2008), siderophore

production, solubilization of phosphorus, synthesis of phy-

tohormones or indirectly by synthesis of biocontrol com-

pounds to inhibit phytopathogens (Lucy et al. 2004;

Cummings 2009).The plant growth promoting bacteria iso-

lated so far, mainly belongs to two divisions namely Firmi-

cutes and Proteobacteria. The use of PGPR is steadily

increasing in agriculture as nutrient supplements to soil and

as biocontrol agents. They offer an alternative to chemical

fertilizers, antibiotics, herbicides and pesticides (Tilak et al.

2005; Ordookhani et al. 2011).

Crocus sativus, commonly known as Saffron, is an

autumn-flowering perennial plant and is a sterile triploid

with chromosome number 3n = 24. Being sterile, repro-

duces vegetatively by underground, bulb-like, starch-stor-

ing organs known as corms and has unique corm–root

cycle. Saffron is economically important, as it is world’s

highest priced medicinal, aromatic plant and is referred as

the ‘Golden Condiment’. Iran, Spain and India (J&K State)

are the major Saffron producing countries in the world. In

India Saffron is grown in Pulwama district in Kashmir and

Kishtwar district in Jammu division so far (Yasmin and

Nehvi 2013), though comparable climatic conditions are

found in adjoining states. Cultivation of Saffron only in

specific belts in world, it’s economic importance and corm-

root cycle makes it an interesting candidate for studying

it’s rhizosphere. This is first report on the plant growth

promoting bacteria from Saffron or indeed any genera of

family Iridiaceae to which it belongs.

Materials and methods

Soil sampling

Soil samples were collected from the bulk and rhizosphere of

Saffron during the flowering period (October–November

2010). Saffron fields of Wuyan village (74�580000E, 34�103000N,

5,173 ft) of Pulwama district were selected for composite

sampling (Courtesy: State Agriculture Department, J&K,

India). The soil sampling was done as per the protocol of Luster

et al. (2009). Standard protocol of Hamza et al. (2008) was used

for the analysis of pH, electrical conductivity, organic Carbon,

Calcium, Magnesium, bulk density, available Nitrogen,

Phosphorus and Potassium of the collected soil samples. The

soil shed by vigorously shaking of the roots was taken as bulk

soil and the soil that remained adhere to the roots was taken as

rhizosphere soil. The soil samples were stored at -20 �C for

further use.

Bacterial isolation and identification

Isolation of cultivable bacteria from the bulk soil was done

by conventional agar plate method (Stotzky et al. 1966)

and rhizobacteria were isolated from roots by protocol

developed by Luster et al. (2009).The comparison of bac-

terial load was done by dilution plate technique by com-

paring the CFU/gm (Stotzky et al. 1966; Joshi and Bhatt

2011). Bacterial isolates were randomly selected and

purified by streak plate method which were further stored

on LB agar slants at 4 �C. Selected bacterial isolates were

identified by microscopy using Gram’s staining kit (Sigma)

followed by biochemical characterization by Biochemical

test Kits (Himedia). Purified bacterial cultures were pre-

served in 50 % glycerol at -80 �C.

Identification of bacterial isolates by 16S rRNA

amplification

Genomic DNA was isolated using the protocol given by

Pitcher et al. (1989). Partial 16S rRNA region, flanking

V1–V3 region was amplified (*500 bp) using universal

primers Bac8f (50-AGAGTTTGATCCTGGCTCAG-30)and Univ529 (50-ACCGCGGCKGCTGGC-30). The PCR

was performed following the protocol standardized by Fi-

erer et al. (2007) with modifications instead of 0.5 lM,

100 pM primer were used and instead of 25 cycles, 30

cycles PCR were run. The template DNA concentration for

PCR reaction was 50 ng and the PCR program as dena-

turation at 95 �C for 5 min followed by 30 cycles of

denaturation at 95 �C for 60 s, annealing at 54 �C for 30 s

followed by extension at 72 �C for 90 s and final extension

at 72 �C for 10 min.

Sequencing and phylogenetic analyses

16S rDNA amplicons were custom sequenced at CIF, UDSC,

New Delhi, India. The resulting nucleotide sequences were

assigned bacterial taxonomic affiliations based on the closest

match to sequences available at the NCBI database

(http://www.ncbi.nlm.nih.gov/) using the EzTaxon version

2.1 (www.eztaxon.org). Sequences of bacteria obtained were

deposited in the GenBank nucleotide sequence database

under accession no JN084065.1–JN084074.1, JQ713596–

JQ713598, JQ751317, JF836006.1 and JX233807. The 16S

rRNA gene sequences were aligned using multiple sequence

alignment tool ClustalX 2.1 version. Phylogenetic and

molecular evolutionary analysis was conducted using Phylip

2272 World J Microbiol Biotechnol (2013) 29:2271–2279

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3.69 (http://evolution.genetics.washington.edu/phylip.html)

and MEGA 5.05 software version (Tamura et al. 2011). The

phylogenetic tree was constructed by neighbor-joining

method using distance matrix from alignment.

Screening of bacteria for PGP traits

Rhizobacteria were analyzed in vitro for plant growth

promoting properties such as phosphate solubilization,

siderophore and indole acetic acid production. Phosphate-

solubilisation was detected by formation of transparent

halos around bacterial colonies on the Pikovskaya agar

after 72 h incubation, at 25 �C (Sharma et al. 2011). Sid-

erophore production was detected by the formation of

orange halos on CAS (chrome azurol S agar) agar plates

after 48 h incubation at 25 �C, as described by Alexander

and Zuberer (1991). Indole acetic acid production esti-

mated according to the protocol given by Sachdev et al.

(2009).

Pot trials

PGPR formulation

Inoculum was prepared by growing each of the selected

bacteria with PGP traits in LB broth individually at

28 ± 1 �C with 180 rpm for 48 h. The different bacteria

with PGP traits required for consortium were checked for

co-inhibition by Kirby beur plate assay (Kirby et al. 1966).

The inoculum containing 107–108 CFU/ml of each isolate

was prepared by mixing them in equal proportions. Sub-

sequently the consortia were mixed with the sterile talc,

Calcium carbonate (autoclaved twice at 121 �C for 15 min)

in 1:3 ratios (1 consortium: 3 talc) and dried at 35–37 �C

for 4 days. Finally 1 % CMC was mixed to the consortium

powder and CFU was calculated by serial dilution method.

The PGPR consortium was kept at room temperature prior

to seed inoculation. The corms selected for the experiment

were of uniform size and shape. Corms were inoculated by

mixing with PGPR formulation talc at 10 % w/v. Control

consisted of the corms treated with talc having nutrient

broth and CMC without the isolates Treated corms were

dried under shade for 6–8 h. The soil collected from Saf-

fron fields was air-dried, sieved (2-mm/10-mesh) and filled

in the twenty pots. Twenty inoculated and uninoculated

corms were sown in soil filled pots maintaining one corm

per pot. The pots were arranged randomly with twenty

repeats (ten each of treatment and control) at ambient light

and 20 �C temperature and were irrigated time to time. The

plants were harvested after 5 months and results analyzed.

The data collected were statistically analyzed using a

completely randomized design in the pot trials. One way

ANOVA test was used to test if results were statically

significant. All the statistical tests were performed at

P \ 0.1 (Gupta et al. 2011).

Results

Saffron, C. sativus likes light friable soil that has high

nutrient content. It thrives best in deep, well drained clay-

calcareous soil that has loose texture and permits easy root

penetration. The physiochemical analysis of soil from

Saffron fields revealed, that it is neutral in pH (7.35) with

306 kg/ha available Nitrogen, 26 kg/ha Phosphorus, 504 kg/

ha Potassium, 3,000 ppm Calcium, 552 ppm Magnesium,

1.36 % organic Carbon, bulk density of 1.198 gm/cc and

0.13 ds/m electric conductivity.

Bacterial isolates from bulk soil and rhizosphere

Bacterial load in the bulk soil and rhizosphere was 1.4 9 106

CFU/gm and 6.4 9 107 CFU/gm respectively, about 40 fold

more in rhizosphere than in bulk soil. A total of 100 bacteria

were randomly selected (50 each) from the rhizosphere and

bulk soil composite samples of Saffron during the flowering

period, as roots are fully grown during this period. The

bacterial strains isolated from Saffron rhizosphere belonged

to 3 phyla namely Bacteroidetes, Firmicutes and Proteo-

bacteria of 4 different genera namely, Acinetobacter, Bacil-

lus, Chryseobacterium and Pseudomonas. Bulk soil bacteria

isolates belonged to 3 phyla namely Actinobacteria, Firmi-

cutes and Proteobacteria of 4 different genera namely, Art-

hobacter, Bacillus, Brevibacterium and Pseudomonads

(Tables 1, 2). To ascertain their taxonomic positions, gene

sequence analysis of hypervariable region (V1–V3 region) of

16S rRNA was done in addition to microscopy and bio-

chemical characterization. Rhizosphere bacterial isolates

were identified into six different bacterial species namely

Acetinobacteria calcoaceticus WRF1, Pseudomonas tremae

WRF2, Pseudomonas kilonensis WRF3, Chryseobacterium

elymi WRF4, Bacillus aryabhattai WRF5 and Pseudomonas

koreensis WRF6. Bulk soil isolates were identified as ten

different species namely Arthrobacter sp WBF1, Bacillus

methylotrophicus WBF2, B. aryabhattai WBF3, B. aryab-

hattai WBF4A, B. aryabhattai WBF4B, Brevibacterium

halotolerans WBF5A, B. halotolerans WBF5B, Brevibacte-

rium frigoritolerans WBF6, B. halotolerans WBF8 and

Pseudomonas parafulva WBF7. Percentage sequence simi-

larity of 16S rRNA genes of these bacteria and GenBank

accession numbers are given in Table 2. Phylogenetic tree

based on 16S rRNA gene sequence (V1–V3 region) cluster

the Saffron rhizobacteria and bulk soil bacteria into separate

clads, except for B. aryabhattai WRF5 from rhizosphere and

P. parafulva WBF7 from Bulk soil (Fig. 1).

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Bacteria with PGP traits

Bacterial isolates were screened for plant growth promotion

properties like indole acetic acid production (IAA), phos-

phate solubilization and siderophore production. 100 %

bacterial isolates produced indole acetic acid, 50 % of them

produced siderophore and 33 % were able to solubilise

phosphate. P. tremae WRF2 and P. kilonensis WRF3 pro-

duced indole acetic acid, siderophore and solubilized

phosphate whereas P. koreensis WRF6 showed maximum

production of the indole acetic acid (28.5 lg/ml) as com-

pared to other isolated Saffron Rhizobacteria (Table 3).

Acinetobacteria calcoaceticus WRF1 and C. elymi WRF4 were

able to produce only indole acetic acid and B. aryabhattai

WRF5 was also able to produce siderophore in addition to

IAA (Table 3).

Pot trials

The bacterial isolates showing in vitro PGP traits were

subjected to in vivo screening in pot assay. None of the six

bacterial isolates selected, showed antagonistic activity

against each other. Thus could be used as consortia (1012

CFU/gm) for bacterial formulation. Seed inoculation with

bacterial formulation affected the growth of corms posi-

tively as compared to control (Table 4). Bacterial consortia

increased average number of roots and shoots but the effect

on shoot length and root length was insignificant statisti-

cally. In addition incidence of corm rot disease occurrence

was less (40 %) as compared to uninoculated control

(60 %). Significant increase in cormslets/daughter corms

production was observed as compared to uninoculated

controls (Table 4). The mother corms were shrunken thus

giving rise to cormlets in test whereas the control corms

remained unaffected.

Discussion

Bacterial load

Fresh roots emerge in October–November at the end of

dormant period in corm and grow throughout the flowering

season. In the present study, *40 fold increase in bacterial

load was observed in rhizosphere as compared to bulk soil.

Higher density of bacteria near roots has been reported in

other plants as well (Nannipieri et al. 2007; Joshi and Bhatt

2011; Timmusk et al. 2011). In wild barley *200 folds

increased bacterial load is reported in rhizosphere

(0.4 9 106 CFU/gm) than bulk soil (0.2 9 104 CFU/gm)

(Timmusk et al. 2011). Joshi and Bhatt (2011) have

reported in wheat rhizosphere that the bacterial load

increases till 90th day followed by decrease in their numberTa

ble

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Table 2 Bacterial isolates from bulk and rhizosphere soil on the basis of 16S rRNA gene sequence

S. no. Isolates Closest NCBI match/closest type strain % identity Accession number

Bulk soil

1 WBF1 Arthrobacterglobiformis DSM 20124(T) 95.026 JN084065.1

2 WBF2 B. methylotrophicus CBMB205(T) 98.561 JN084066.1

3 WBF3 B. aryabhattai B8W22(T) 100.000 JX233807

4 WBF4A B. aryabhattai B8W22(T) 98.569 JN084067.1

5 WBF4B B. aryabhattai B8W22(T) 100.000 JN084068.1

6 WBF5A Brevibacteriumhalotolerans LMG 21660(T) 100.000 JF836006.1

7 WBF5B Brevibacteriumhalotolerans LMG 21660(T) 100.000 JN084069.1

8 WBF6 Brevibacteriumfrigoritolerans DSM 8801(T) 100.000 JN084070.1

9 WBF7 P. parafulva AJ 2129(T) 98.475 JQ751317

10 WBF8 Brevibacteriumhalotolerans LMG 21660(T) 99.670 JN084071.1

Rhizosphere

11 WRF1 Acinetobactercalcoaceticus DSM 30006(T) 99.745 JN084072

12 WRF2 P. tremae CFBP 6111(T) 99.777 JQ713597

13 WRF3 P. kilonensis 520-20(T) 99.782 JN084073.1

14 WRF4 Chryseobacteriumelymi RHA3-1(T) 99.125 JQ713598

15 WRF5 B. aryabhattai B8W22(T) 99.390 JN084074.1

16 WRF6 P. koreensis Ps 9-14(T) 99.363 JQ713596

Fig. 1 Phylogenetic tree of

bacterial isolates from bulk soil

and rhizosphere of Saffron

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by 120th day, with root decay. It is established that rhi-

zodeposition influences root-microbe interaction in most of

the plants which seems to be true for Saffron as well

(Soderberg et al. 2004; Johansen and Olsson 2005; Hin-

singer et al. 2009).

Bacterial isolates from bulk soil and rhizosphere

To take random samples, 50 bacteria each, were picked

from the rhizosphere and bulk soil isolates and all the 100

bacteria were identified by polyphasic method mentioned in

the ‘‘Materials and methods’’.The rhizobacteria were cata-

logued into four different bacterial genera of six different

species, whereas the bulk soil bacterial isolates were

grouped into four different genera of ten different bacterial

species (Tables 1, 2). Though, sequencing of the entire

1,500-bp sequence is usually required, when describing a

new species. However, for most of the bacterial isolates the

initial 500-bp sequence (V1–V3 region) provides adequate

differentiation for identification, as it has substantial

sequence difference between different strains (Clarridge

2004). In total 6 rhizobacteria showing PGP traits were

isolated from rhizosphere of Saffron using single culturing

media (LB Agar). Isolation of 32, 15, 13 and 10 rhizobac-

teria have been reported from wheat, sweet potato, apple

and rice respectively using similar culturing technique

(Mahaffee and Kloepper 1997; Sarode et al. 2009; Yasmin

et al. 2009; Ashrafuzzaman et al. 2009). Few types of

bacterial isolates retrieved, despite high bacterial load on

the same media in the Saffron rhizosphere further sub-

stantiates the belief that specific root–microbe interaction

occur in Saffron, though it needs to be substantiated with

more experiments.

Comparing rhizosphere and bulk soil bacterial isolates at

phylum level revealed that the Firmicutes and Proteobac-

teria are present in both bulk and rhizosphere soils but

Bacteroidetes were present only in rhizosphere whereas

Actinobacteria were present in bulk soil only. Phylogenetic

tree based on 16S rRNA gene sequence (V1–V3 region)

clusters the Saffron rhizobacteria and bulk soil bacteria into

separate clads, except for B. aryabhattai WRF5 from rhi-

zosphere and P. parafulva WBF7 from Bulk soil (Fig. 1)

clearly indicating difference in the microbial types present.

However, B. aryabhattai WRF5 from rhizosphere clusters

with other Bacillus strains from bulk soil and P. parafulva

WBF7 from bulk soil clusters with those from rhizosphere

indicating thereby some evolutionary relationship. Phylo-

genetic clad of Rhizosphere comprises of subclads of

various genera like Pseudomonas, Acetinobacteria and

Chryseobacterium whereas bulk soil clad comprised of

Bacillus, Brevibacteria and Arthrobacter.

The rhizosphere is colonized predominantly by Gram

negative microbial community; they are reported to be

stimulated by rhizodeposition whereas Gram-positive

bacteria are reported to be inhibited (Soderberg et al. 2004;

Johansen and Olsson 2005). Similar results were found in

Saffron rhizosphere as Pseudomonas genera (consisting of

P. tremae, P. kilonensis and P. koreensis), A. calcoaceticus

and C. elymi are the Gram negative bacteria, whereas Gram

positive bacteria was represented only by single species of

B. aryabhattai. However, more Gram positive bacteria

were found in bulk soil dominated by different species of

Bacillus and Brevibacterium. Saffron though has specific

combination of rhizobacteria but it seems to follow the

distribution pattern of Gram -ve bacteria near roots and

Gram ?ve in the bulk as in cucumber and pea plants

(Mahaffee and Kloepper 1997; Soderberg et al. 2004).

Table 4 Effect of inoculation with rhizobacteria consortia on the

growth of corms of Saffron in pot trials

Growth parameters Test Control

Av. no. of roots 3.6 ± 1.82 1.2 ± 0.81

Av. length root 1.15 ± 0.54 0.1 ± 0.06

Av no shoot 5.6 ± 0.49 4.3 ± 0.53

Av shoot length 6.59 ± 1.59 9.95 ± 3.90

Cormlets 3.9 ± 1.11 0.5 ± 0.4

Av weight 3.14 ± 0.19 3.16 ± 0.17

Disease 4/10 6/10

Shape of corms Shrinked No effect

Table 3 PGPR properties of

rhizobacteriaS.

no.

Identified Rhizobacteria Phosphate solubilization

(SI)

Siderophore

production

IAA production (lg/

ml)

Positive results (%) 33.0 % 50.0 % 100 %

1 A. calcoaceticus WRF1 - - 18.50

2 P. tremae WRF2 ? (6) ? 3.80

3 P. kilonensis WRF3 ? (1.2) ? 3.69

4 Chryseobacteria elymi

WRF4

- - 12.18

5 B. aryabhattai WRF5 - ? 14.00

6 P. koreensis WRF6 - - 28.5 (max)

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Saffron rhizobacteria

All the rhizobacteria demonstrated at least one plant growth

property with 50 % of isolates producing siderophore, 33 %

solubilising phosphate and all of them produced indole

acetic acid. Out of 133 isolates from wheat rhizosphere,

29.32 % had ability to produce siderophore, 22.56 % solu-

bilised phosphate and 12.03 % produced indole acetic acid

(Joshi and Bhatt 2011). Majority of rhizobacteria are

reported from the three subdivisions of Proteobacteria phyla,

a-proteobacteria, b-proteobacteria and c-proteobacteria

(Ahmad et al. 2008). Saffron rhizosphere is also dominated

by c-proteobacteria characterized by Pseudomonas and

Acinetobacteria genera. Pseudomonas sp. is known to be

dominant in rhizosphere of various plants (Tilak et al. 2005;

Khakipour et al. 2008) so is true for Saffron rhizosphere as

three out of six bacterial strains showing PGP trait were

identified as Pseudomonads (P. tremae WRF2, P. koreensis

WRF6 and P. kilonensis WRF3). P. koreensis WRF6 and

P. kilonensis WRF3 isolated from Saffron rhizosphere have

neither been reported from any rhizosphere and nor have

their PGP properties assayed, but have been isolated from

the agricultural soils (Sikorski et al. 2001; Kwon et al.

2003). P. koreensis WRF6 shows maximum production of

the indole acetic acid (28.5 lg/ml) (Table 3) and is com-

parable to the other common growth promoting Pseudo-

monads e.g. P. putida (24.08 mg/l) and P. fluorescens

(31.6 mg/l) (Khakipour et al. 2008). Not many reports are

available on P. tremae with PGP traits except one isolated

from healthy wild coffee seedlings. It was able to mobilize

mineral phosphate, produce HCN and siderophores, and

effectively antagonize deleterious coffee fungal pathogens

(Muleta et al. 2009).

Chryseobacterium elymi WRF4 and A. calcoaceticus

WRF1 isolated from Saffron rhizosphere showed only

Indole acetic acid production (Table 3). C. elymi RHA3-1

reported from the rhizosphere of wild rye produced indole

acetic acid (Cho et al., 2010). A. calcoaceticus SCW1

isolated from wheat rhizosphere had various PGP traits like

phosphate solubilization, siderophore and indole acetic

acid production (Sarode et al. 2009) and A. calcoaceticus

P23 isolated from duckweed rhizosphere has phosphate

solubilizing property (Yamaga et al. 2010). B. aryabhattai,

isolated from halophytic plants rhizosphere was able to fix

nitrogen but could not produce IAA (Siddikee et al. 2010)

which was in contrast to our observation. B. aryabhattai is

the common Gram positive bacteria between Saffron rhi-

zosphere and bulk soil (rhizosphere: B. aryabhattai WRF5

and bulk soil; B.aryabhattai WBF3, WBF4A and WB4B).

16S rRNA gene sequences of all the strains were compared

and were found to be 98 % similar. The biochemical tests

like catalase, oxidase, nitrate reduction, carbohydrate fer-

mentation were similar for these strains but they varied in

solubilisation of phosphate. All the three bulk soil strains

solubilized phosphate, but surprisingly the strain isolated

from rhizosphere did not. Moreover, three bulk soil

B.aryabhattai strains differed in their colony morphology

and microscopy too. The results indicated that B. aryab-

hattai WRF5 isolated from rhizosphere is specific to Saf-

fron rhizosphere and has not migrated from the bulk soil.

Pot trials

The bacterial formulation of the bacterial isolates showing

PGP traits was prepared and subjected to Pot trails by

following the method developed by Gupta and coworker

(2011). Bacterial formulation prepared significantly pro-

moted growth of Saffron as is evident by the statistical tool

ANOVA in all the traits tested except for shoot length and

root length. In general, inoculation resulted in increased

shoot and root number, increased production of daughter

cormlets and decreased occurrence of the corm rot disease

incidence in pots. This is in concordance with the findings

of Sharaf-eldin et al. (2008) where the authors have

observed positive effect of commercially available PGPR

Bacillus subtilis FZB24 strain on the Saffron. In another

study, Aytekin and Acikgoz (2008) have reported the effect

of commercially available synthetic growth hormone,

biohumus and Effective MicroorganismsTM (EM) on the

production of Saffron. Synthetic hormone consists of

Polystimulins A6 and K and two different microorganism

based materials consists of biohumus/vermicompost and

Effective MicroorganismsTM (EM). Saffron corms were

treated in four different ways—hormone alone, biohumus

alone, EM alone and EM ? biohumus to determine whe-

ther these treatments have any statistically meaningful

effects on corm numbers and dry and wet stigma weights. It

has been shown that EM ? biohumus were the most

effective choice for improved Saffron cultivation.

In the both the mentioned reports effect of commercially

available Bacillus subtilis or hormones and Effective

microorganism was observed on the growth and production

of Saffron but bacteria mentioned have not been isolated

from Saffron rhizosphere (Sharaf-eldin et al. 2008; Aytekin

and Acikgoz 2008). The synthetic application of bacteria to

any plant possesses the risk of inoculum colonization and

sustainability which is not the case if PGPR used are indig-

enous to plant. We therefore propose that use of the bacterial

formulation prepared in present study for Saffron growth and

production will be a good alternative to chemical treatments.

Conclusion

Many factors are responsible for restricted cultivation of C.

sativus in specific belts of particular geographical regions.

World J Microbiol Biotechnol (2013) 29:2271–2279 2277

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Soil is one of the important factors and so are the

microbes residing in the soil. Present communication for

the first time, reports the cultivable PGPR present in rhi-

zosphere of Saffron (grown in Pulwama, J&K, India), their

phylogenetic and biochemical characterization along with

potential plant growth promoting functions. 40 fold

enhancement of bacterial load in rhizosphere in compar-

ison to bulk soil establish specific root–microbe interac-

tion. The three species of Pseudomonas isolated from the

Saffron rhizosphere are specific to Saffron rhizosphere, as

P. koreensis WRF6 and P. kilonensis WRF3 have not

been reported from any other rhizosphere and P. tremae

have been isolated only from coffee seedlings. Pseudo-

monas pudita and P. flouresence, the common PGPR of

most of the plants, are absent in Saffron rhizosphere

though A. calcoaceticus was present. Bacterial formula-

tion of all the six isolates showing PGPR, showed the

positive effect on the growth of Saffron.

Acknowledgments Authors are grateful to Prof. Michel Aragno,

Honorary professor University of Neuchatel, Switzerland for his sci-

entific advice. We are thankful to Mr Farooq Ahmad and Mr C.L. Bhat

and, State agriculture Department J&K, India, for their help in sample

collection and information about Saffron cultivation. SA is thankful to

CSIR-UGC for Fellowship. We are also thankful to Department of

Biotechnology for financial support under DBT funded project.

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263T. Satyanarayana et al. (eds.), Microorganisms in Sustainable Agriculture and Biotechnology, DOI 10.1007/978-94-007-2214-9_14, © Springer Science+Business Media B.V. 2012

Abstract A huge quantum of the genetic and metabolic diversity of the biosphere is locked up within the microbial communities present in and on earth, as majority of earth’s biomass comprises microbes. Ironically we cannot access more than 1% of this bioresource through routine culturing techniques as we have underestimated the power of microbes to resist culturing. This 1% of the microbial diversity accessed so far contributes to more than 80% of the industrial biotechnology at present and what miracles rest of the 99% can perform is anybody’s guess. Metagenomics (also known as e-genomics, community genomics and environmental genomics) is clutch of molecular biology techniques used to hunt the culture resisting, yet- to- be cultured microbes. Metagenomics is a fruit born of the wedlock between modern molecular techniques and the idea, that microbial diversity can be analyzed by direct DNA isolation from environmental samples (metagenomic DNA), it’s cloning, screening and sequencing. It would not be farfetched to conclude that after invention of micro-scope and culturing techniques, metagenomics is third big revolution in microbiol-ogy. Metagenomic revolution would not have been possible without the availability of low cost DNA sequencing services, bioinformatics tools and high through put screening techniques. This chapter will deal with the techniques used in metagenom-ics and achievements made so far along with future applications and challenges.

Keywords Metagenomics • Biodiscovery • Biodiversity • Functional screening • Sequence based screening • Bioprospecting

J. Vakhlu (*) • S. Ambardar School of Biotechnology , University of Jammu , Jammu 180006 , India e-mail: jyotivakhlu@gmail.com

B. N. Johri Department of Biotechnology and Bioinformatics , Barkatullah University , Bhopal 462026 , India e-mail: bhavdishnjohri@rediffmail.com

Chapter 14 Metagenomics: A Relief Road to Novel Microbial Genes and Genomes

Jyoti Vakhlu , Sheetal Ambardar , and B. N. Johri

264 J. Vakhlu et al.

14.1 Introduction

The next big discovery in the fi eld of microbiology would be to fi nd the place where microbes are not able to survive as it has been amply demonstrated that microbes are present at all the impossible places, hot springs to Antarctic soil, geysers to glacier ice, deep sea vents to clouds and from gut of the insect to human gut. During last 10 years microbiologists have been able to convince researcher with the help of a budding technique called metagenomics that so far we have not been able to cultivate more than 1% of total microbes present on and in earth. It would not have been pos-sible to establish their presence by routine microscopic and cultivation based tech-niques though the fi rst indication, that we are not able to characterize majority of the microbes, was given by combination of microscopic and culturing techniques, “The great plate count anomaly”. “The great plate count anomaly” is the discrepancy between sizes of population estimated by dilution plating and by microscopy (Handelsman 2004 ) . If we compare direct microscopic cell count after 4-6-diamid-ino-2-phenylindole (DAPI) staining of bacteria with the number of the microbial colonies growing on nutrient agar, it appears that in natural samples less than one cell in thousand produces a colony. Amann and his coworkers have reported that 0.001–0.01% of microorganisms in sea water, 0.25% in fresh water, 0.25% in sediments and only 3% of soil microorganisms are found to be cultivable (Amann 1995 ) . Cataloguing the microbes on the basis of DNA and not on cellular identifi cation generated a great debate among the scientifi c community and fi nally in 1998 Jo Handelsman coined the term “Metagenomics” for culture-independent study of microbes in any environ-ment. Metagenomics can be used interchangeably with e-genomics and community genomics, etc. Metagenomics is drawing attention not only in microbiology but also in other branches of science like chemical sciences, earth science, agriculture, envi-ronment remediation, human health, biodefense and microbial forensics.

14.2 Achievements and Application

Microbiologist have focused on the isolation, purifi cation and study of single spe-cies in laboratory but lately ample evidences suggest that most of the life supporting activities are carried out by complex microbial community-integrated, intricate and balanced. Our understanding of microbial community has lagged behind and meta-genomics can bridge the gap. Metagenomics in our view is the third big revolution in microbiology after discovery of microscope and culturing techniques. It is not only going to infl uence microbiology but all life forms, as microbes are present and perform vital functions in all life forms and habitats.

Metagenomics is a genomic technique born out of wedlock between a new idea of direct isolation of DNA from any environmental sample and various sophisti-cated molecular biology techniques. It includes massively parallel sequencing with-out cloning, high throughput assay system targeting metabolic pathways. It also

26514 Metagenomics: A Relief Road to Novel Microbial...

includes artifi cial transcriptional regulators activating reporter genes to indicate enzymatic substrate conversion and cDNA cloning from extracted mRNA to directly clone actively expressed genes from a microbial consortium. Thus, metagenomics not only provides us new insights into microbial (taxonomic/genetic diversity) life but also access to genes (metabolic diversity) producing novel biomolecules.

14.2.1 Biodiversity

The total number of prokaryotic cells on earth has been estimated to be 4–6 × 10 30 comprising of 10 6 to 10 8 separate species (Simon and Daniel 2009 ) . More than 95% of the microbial wealth is uncharacterized and therefore is a treasure of unexplored genetic and metabolic diversity. The metagenome consists primarily of the yet to be cultivated bacterial genome and a minor fraction of the culturable bacterial genome as in most microbial habitats, unculturable bacteria dominate the total microbial community. Microbial diversity is explored usually by two approaches, i.e. by sequencing and analysis of phylogenetic anchors or reconstruction of whole genomes, after shotgun sequence (Fig. 14.1 ).

14.2.1.1 Assessment of Microbial Diversity by 16S/18S rRNA and Its Region

Conventional methods to establish the presence and identity of microbes are by colony characteristics, microscopy, biochemical tests and 16S ribotyping, but in metagenomic analysis the DNA is directly isolated and analyzed. Hence the identi-fi cation in the latter is based only on 16S ribosome, ITS region sequence analysis and is cultivation independent. Microbial diversity is explored usually by several approaches like metagenomic library construction, mass sequencing and amplifi ca-tion of 16S/18S/ITS region from metagenome as shown (Fig. 14.2 ) but the most common so far is analysis of conserved ribosomal RNA (rDNA) gene sequences (Woese 1987 ) . The metagenomics-based bacterial diversity analysis largely depends on the sequence analysis of small subunit ribosomal RNA (16S rRNA) genes which are amplifi ed using broad range (universal) PCR primers based on the sequences that are well-conserved among prokaryotes. The 16S rRNA genes amplifi ed from

Metagenomic approaches for Microbial diversity

Reconstruction of microbialgenomes

Assessment of microbial diversityby 16S/18S rRNA and ITS region

Fig. 14.1 Two different ways to explore uncultivable microbial diversity

266 J. Vakhlu et al.

metagenome are used to make libraries of clones, where each clone represents a 16S rRNA gene from a prokaryotic species. Individual clones are sequenced and the 16S rRNA gene sequence similarity analysis is used to identify the species level phyloge-netic types or phylotypes (Chung et al. 2008 ; Rajendhran and Gunasekaran 2009 ) .

In recent years signifi cant progress is being made in the fi eld of metagenomics due to development of high throughput next generation sequencing technologies (Simon and Daniel 2009 ) . Large amount of sequencing data has been generated using such techniques and analysis of this data has changed our view of microbial world. Extensive sequencing of ribosomal gene has resulted in generation of several large reference databases such as ribosomal data base project (RDP) II and Greengenes or Silva (Simon and Daniel 2009 ) . These comprehensive databases allow classifi cation of environmental 16S rRNA gene sequences. The sequences which do not have their counterpart in databank or show limited similarity percent-age with known sequences are referred to as novel sequences.

As an example, microbial diversity of four extreme environments, explored by different culture independent (metagenomics) approaches is summarized in Table 14.1 .

The advent of metagenomics has greatly accelerated our understanding about the evolution of various domains of prokaryotes and eukaryotes and provides a much broader ability to access diversity within different phyla, such as the following:

Bacteria

Twelve major divisions (phyla) have been identifi ed in the Bacterial domain on the basis of rRNA sequence data by Woese in 1987. The analyzed bacteria represent

Metagenomic DNA isolation

16SrRNA amplification

Sanger’s sequencing

Pyrosequencing

Sanger’s sequencing

Ribosomal Data Base

Reconstruction ofwhole genome Establishment of microbial phylogeny

Cloning16SrRNA amplification

Mass sequencing(Cloning dependent/ independent)

Metagenomic library

Fig. 14.2 Metagenomic approach for evaluation of microbial diversity

26714 Metagenomics: A Relief Road to Novel Microbial...

almost all major cultured groups of Bacteria. In just over a decade, culture-independent surveys have identifi ed 40 well-resolved major bacterial divisions which show that there are about 30 major bacterial divisions with few or no cultured representatives (Xu 2006 ) .

Archaea

Archaea were thought to be only present in extreme habitats but culture-independent methods have identifi ed that archaea are also widespread in diverse nonextreme habitats such as garden and forest soils, water and sediments in marine and freshwa-ter lakes, as well as extreme habitats such as hot springs, saline lakes and deep-ocean thermal vents (Xu 2006 ) . The culture-independent methods have also revealed major new types of archaea. Phylogenetic analyses has suggested that domain

Table 14.1 Metagenomic approach to monitor microbial diversity of four extreme environments

S. No. Environmental samples Approach Diversity References

1 Acid mine drainage

Shotgun sequencing and Genome reconstruction

Leptospirillum group II (L. ferriphilum)

Tyson et al. ( 2004 )

Leptospirillum group III Sulfobacillus, Ferroplasma A-plasma and G-plasma

2 Sargasso Sea 16S r RNA analysis of Metagenomic DNA and pyrosequencing

Proteobacteria (subgroups Alpha , Beta and Gamma ), Firmicutes, cyanobacteria and species in the CFB (phyla Cytophaga, Flavobacterium and Bacteroides )

Venter et al . ( 2004 )

3 Yellowstone park

Linker amplifi ed shotgun library techniques

Thermophilic crenarchaeal viruses

Schoenfeld et al. (2008)

Acidianus rod-shaped virus (ARV), Sulfolobus

islandicus rod-shaped virus 1 (SIRV1) and SIRV2, and Sulfolobus islandicus fi lamentous virus (SIFV). Pyrobaculum spherical virus (PSV)

4 Antartica 16S r RNA analysis of Metagenomic DNA and pyrosequencing

Bifi dobacterium (phylum Actinobacteria ), Arcobacter (phylum Proteobacteria) and Faecalibacterium (phylum Firmicutes)

Teixeira et al. ( 2010 )

268 J. Vakhlu et al.

archaea contains at least 50 distinct phylogenetic groups with 33 from the current Euryarchaeota, 13 from Crenarchaeota, 1 each from Korarchaeota, Nanoarchaeota, and the ancient archaeal group (AAG). Among these 50 phylogenetic groups, only 13 have cultured representatives.

There has been signifi cant interest in applying metagenomics to learn more about the members of the archaea in soil and as planktonic organisms in seawater (Riesenfeld et al. 2004 ) . The discovery of 16S rRNA gene sequences that affi liate with the archaea in diverse terrestrial and marine environments on Earth has signifi -cantly altered the microbiologist’s image of archaea.

The study of the symbiotic community of an Axinella sp., a sea sponge, has been an important application of metagenomics to archaea. In a culture-independent 16S rRNA gene survey, 65% of the symbiotic community associated with the sponge was represented by a single archaeal 16S rRNA gene sequence which was named as Cenarchaeum symbiosum. The complete genome sequence of C. symbiosum will contribute to understanding its biology and symbiotic relationship with its sponge host (Riesenfeld et al. 2004 ) .

Virus

Viruses are known to be the most numerous biological entities on the planet and are known to infect microorganisms but little is known about their diversity in this envi-ronment. Diversity of virus has been explored more in aquatic environment as com-pared to the soil. Transmission electron microscopy has estimated the viral abundance in soil to be of the order, 1.5 × 10 8 g −1 (Kim et al. 2008 ) . Molecular analysis is essen-tial to investigate the diversity of virus because majority of viruses are diffi cult to cultivate due to lack of suitable host; less than one percent of microbial hosts have been cultivated. Moreover, a PCR based approach could not be employed in case of viruses as there are no universal conserved genes or markers for viruses like 16S rRNA gene for bacteria. These limitations are being circumvented by metagenomic analyses of uncultured viral communities and can provide insights into the composition and structure of environmental viral communities (Edwards and Rohwer 2005 ) .

The metagenomics of viruses began in 2002 with the publication of two uncul-tured marine viral communities (Breitbart et al. 2002 ) . In both cases, the small size of viral genomes approximately 50 kb on average was an advantage because less sequencing was required (Wommack et al. 1999 ; Steward et al. 2000 ) . In this approach, viruses are purifi ed and concentrated by sequential fi ltration and ultracen-trifugation as in most water samples, it is necessary to concentrate virions from several hundred litres to obtain enough DNA for cloning. The whole genome of virus is further extracted, amplifi ed, shotgun cloned and pyrosequenced (Edwards and Rohwer 2005 ; Angly et al. 2006 ) .

Linker amplifi ed shotgun library techniques has been reported to amplify viral DNA using linkers in PCR amplifi cation. This technique is limited to double stranded DNA viruses as the linkers can only ligate to double stranded DNA and not to single stranded DNA virus (Edwards and Rohwer 2005 ) . This limitation is

26914 Metagenomics: A Relief Road to Novel Microbial...

circumvented by multiple displacement amplifi cation (MDA) which has helped in acquiring the sequence of both double and single stranded DNA viruses (Angly et al. 2006 ) . MDA is based on whole genome amplifi cation method which uses Ǿ29 DNA polymerase and random hexamers to amplify the minute amount of DNA to ~80 m g (Dean et al. 2002 ) . Ǿ29 DNA polymerase amplifi es the DNA via rolling circle replication in which short DNA amplifi es more effi ciently than linear DNA (Kim et al. 2008 ) .

Viral classifi cation is based on characteristics of the virions and host range and not on sequence data as universal ribosomal DNA (rDNA) sequences cannot be used for deriving the phylogenetic and taxonomic relationships due to the lack of single sequence common in all viral genomes. The most common sequence-based approach is to use a single gene locus, such as a capsid or DNA polymerase gene, to characterize a specifi c viral group which could be done by designing PCR primers for these genes; the diversity of specifi c genes in the environment can be assessed by cloning and sequencing of DNA products amplifi ed directly from environmental samples. This single-locus approach has been used to show that there are many groups of uncultured viruses in the environment.

Recent culture independent studies have shown that both DNA-based and RNA-based viruses are common in terrestrial as well as freshwater and marine environ-ments (Edwards and Rohwer 2005 ) . For example, in an analysis of picorna-like viruses (a group of positive-sense single-stranded RNA viruses that are major patho-gens to plants and animals), Culley et al. ( 2003 ) identifi ed high, unexpected diver-sity in the sea. Indeed, all of the picorna-like sequences from marine samples were different from known picorna-like viruses in the databases. Of specifi c note is a virus isolated in this study that is lytic pathogen to a toxic-bloom-forming alga Heterosigma akashiwo . This data suggests that picorna-like viruses may be impor-tant contributors in the regulation of marine phytoplankton population dynamics.

Bacteriophages

Phages are the most abundant and diverse group of biological entities on the planet. Pedulla and coworkers ( 2003 ) have described ten new mycobacteriophage genomes using metagenomic approach. Over 50% of the open reading frames (ORFs) in the genomes are unrelated to anything in GenBank and only one of the new mycobac-teriophage is signifi cantly related to a previously sequenced phage. These fi ndings are surprising, because all of the new phages belong to the most thoroughly studied group of ds DNA phage, the Siphoviruses.

Eukaryotes

Direct amplifi cation and cloning of 16S rRNA genes from environmental samples has considerably expanded the information about prokaryotic diversity and sequencing of these environmental rRNA gene libraries has revealed new, often uncultivated,

270 J. Vakhlu et al.

groups of the Bacteria and Archaea which may be present in great abundance. In a similar way, molecular analysis of small subunit 18S rRNA genes has revealed a wide diversity among eukaryotes (Grant et al. 2006 ) .

Photosynthesis in marine environment is basically due to four major phytoplank-ton lineages of eukaryotes namely prymnesiophytes, alveolates, stramenopiles, and prasinophytes. Genomes of only two lineages (stramenopiles, and prasinophytes) have been analyzed by culture dependent approaches whereas “picoplanktonic” members of the prymnesiophyte lineage remain poorly characterized but, have long been inferred to be ecologically important. Eukaryote targeted metagenomic approach was used to analyze uncultured pico-prymnesiophytes sorted out by fl ow cytometry from subtropical North Atlantic waters (Florida Straits, the Sargasso Sea, the Pacifi c Ocean). Using 18S rRNA gene analysis, the evolutionary history and ecology of pico-prymnesiophyte was examined wherein a vast majority of sequences were from uncultured prymnesiophytes. This showed that picoprymnesiophytes belonged to broadly distributed uncultivated taxa. On an average, picoprymnesio-phytes form 25% of global picophytoplankton biomass, with differing contributions in fi ve biogeographical provinces spanning across tropical to subpolar systems (Cuvelier et al. 2010 ) .

14.2.1.2 Reconstruction of Microbial Genomes

A novel insight into community structure was provided by Tyson and coworkers ( 2004 ) by fi rst reconstructing the multiple genomes directly from a natural sample of acid mine drainage biofi lm by random shotgun sequencing. In the conventional shotgun sequencing approach, all shotgun fragments are derived from clones of the same genome but in this modest approach the shotgun fragments were derived from multiple genomes (Tyson et al. 2004 ) .

Acid mine drainage (AMD) is a worldwide environmental problem driven by microbial activity that leads to extremely acidic outfl ows from metal mines. The acid is produced by oxidation of sulfi de minerals that are exposed to air as a result of mining activity. In order to understand the mechanisms by which the microbes tolerate the extremely acidic environment and to evaluate how the tolerance mecha-nisms affect the geochemistry of the environment, reconstruction of microbial genomes was done so as to explore the distribution and diversity of metabolic path-ways involved in AMD (Allen and Banfi eld 2005 ; Tyson and Banfi eld 2005 ; Ram et al. 2005 ; Tyson et al. 2004 ) . Metagenomic DNA was extracted from biofi lm of AMD and sheared into small fragments to construct a small insert plasmid library (average insert size 3.2 kilobases (kb)) for random shotgun sequencing. A total of 76.2 million base pairs (bp) of DNA sequence were generated from 103,462 high-quality reads (averaging 737 bp per read) and the shotgun data set was assembled with JAZZ, a whole-genome shotgun assembler. This modest sequencing effort resulted in the construction of complete genome of

(a) Leptospirillum group II bacteria ( L . ferriphilum and L. ferrooxidans23 ) (b) Ferroplasma group II archaeon (uncultured/novel)

27114 Metagenomics: A Relief Road to Novel Microbial...

(c) Leptospirillum group III bacteria (uncultured) (d) Ferroplasma group I archaeon

Not only were the genomes reconstructed but their metabolism was also recon-structed and their role in the function of the community was also understood. Analysis of the gene complement for each organism revealed the pathways for car-bon and nitrogen fi xation and energy generation, and provided insights into survival strategies in an extreme environment (Tyson et al. 2004 ) .

14.2.2 Biodiscovery

14.2.2.1 Proteorhodopsin Function and Phylogeny

The most dramatic discovery from metagenomics to date, sequencing of a clone isolated from seawater that was initially identifi ed because it carried a bacterial 16S rRNA gene, revealed a sequence with high similarity to bacteriorhodopsin genes (Beja et al. 2000a ) . This provided the fi rst indication that rhodopsins are not limited to the archaea, as previously thought. Subsequent heterologous expression of the bacteriorhodopsin gene in E. coli produced a functional biochemical characteriza-tion of the protein, completing the full spectrum of studies that link phylogeny to function (Schloss and Handelsman 2003 ) .

Discovery of the rhodopsin-like photoreceptors in marine Bacteria exemplifi es the type of biological surprise that can be revealed through metagenomic analysis. Previously, rhodopsins had been found only in archaea, not in members of the domain Bacteria. Beja and coworkers ( 2000a ) sequenced a 130-kb fragment that contained the 16S rRNA operon of an uncultured g -Proteobacterium and discov-ered a bacteriorhodopsin, which indicated a novel taxon of marine phototroph (Riesenfeld et al. 2004 ) . Bacteria that harbour proteorhodopsin variants are wide-spread and also the class of proteorhodopsins previously observed, is a small subset of the total proteorhodopsin diversity which has been proved by the work of Venter and coworkers ( 2004 ) using a shotgun sequencing approach.

14.2.2.2 Gutless Worm

The inability to cultivate most host-associated microbe associations hampers our understanding of the intricate interactions. Comprehensive analysis of the symbi-otic microbial community in the eukaryotic host Olavius algarvensis was performed by metagenomic approach. Olavius algarvensis is a gutless oligochaete which belongs to an unusual group of marine worms having symbiotic association with a highly specifi c consortium of phylogenetically diverse bacteria. This symbiosis has lead to the complete reduction of the worm’s digestive and excretory systems. Two nearly complete and two partial genomes of the oligochaetes’ predominant symbionts were assembled using shotgun sequencing of a bacteria-enriched sample combined with nucleotide-signature based binning. Metabolic pathway reconstruction from

272 J. Vakhlu et al.

the sequenced genomes revealed that all the four symbionts are capable of autotrophic carbon fi xation and provides multiple sources of organic carbon to their host as two of them are sulfur-oxidizing and two sulfate-reducing bacteria (Giere and Erseus 2002 ) .

14.2.2.3 Human Microbiomics

Human microbiomics is an emerging discipline which deals with micro-organisms that live in and on humans and their impact and their role in human physiology and human health. The genomes of the microbes that live in and on humans make up the “human microbiome” and; these provide traits that humans do not have. Therefore, human beings are considered to be super organisms with trillions of associated microorganisms (Jones et al. 2008 ) . The human microbiota is expected to outnum-ber human cells at least by one order of magnitude and is composed of more than 1,000 different species level phylotypes. The total number of microbial genes may exceed the total number of human genes by two orders of magnitude (Rajendhran and Gunasekaran 2009 ; Qin et al. 2010 ; Lee et al. 2010 ) .

Microbes are reported to be living in plenty in various human body parts like skin, oral cavity, oesophagus, stomach, colon, vagina etc. and this microbial diver-sity has been revealed by the sequence analysis of 16S rDNA genes amplifi ed from the metagenomic samples from various human body parts. The core microbiome was shown to exist at a level of genes rather than at organismal level. It has opened way for new thoughts that we must try to study the link between host metabolism and gut microbiota genes rather than looking at species identifi cation (Rajendhran and Gunasekaran 2009 ) .

The human metagenome-based 16S rRNA gene analyses have also been used to detect uncultivated organisms that cause disease (Gao et al. 2007 ) . The fi rst novel pathogen to be identifi ed by sequence-based method was Rochalimaea henselae (redes-ignated as Bartonella henselae ), an organism responsible for bacillary angiomatosis (cat scratch disease) (Weng et al., 2006 ). Ehrlichia chaffeensis causing a febrile illness associated with tick bites and Tropheryma whipplei causing the Whipple’s disease are other examples of pathogens identifi ed using this approach (Gao et al. 2007 ) .

14.2.2.4 Microbes and Obesity

Microbes decide the health and predisposition to various non-infectious diseases of humans by the genes encoded as in case of obesity; a strong association between the microbiota and obesity was revealed by the metagenomic sequence data of faecal specimens from obese and lean individuals (Ley et al. 2005 ) . This is an interesting discovery possible with metagenomics which showed that certain individuals are predisposed to obesity as they have gut microbiota that has increased capacity to harvest energy from given diet as compared with the lean individuals.

27314 Metagenomics: A Relief Road to Novel Microbial...

Reports have shown that more than 90% of the colonic bacteria in humans and mice are Firmicutes and Bacteroidetes belonging to bacterial domain (Rajendhran and Gunasekaran 2009 ) and it was observed that the percentage of bacteroidetes reduces (50%) with a proportional increase in Firmicutes in obese mice as com-pared with lean mice.(Ley et al. 2005 ) .

This was further complemented by a transplantation experiment by Turnbaugh and coworkers ( 2006 ) that had colonized germ-free mice with an obese microbiota which resulted in a signifi cantly greater increase in total body fat than colonization with a lean microbiota.

14.2.3 Bioprospecting

The fact that most of the biocatalysts employed for biotechnological or industrial purposes are microbial in origin however so far we have not been able to tap more than 5% of the genetic and metabolic resources (MacNeil et al. 2001 ) . Therefore, the amount of metabolic diversity present in nature to harness novel genes encoding is vast which could be explored with the help of metagenomic techniques. Metagenome represents the genomes of uncultured microbes as a rich source for isolation of many novel genes. Several different laboratories have successfully isolated novel genes encoding different enzymes and secondary metabolites from microbial communities and their metagenomes without cultivation of the microbes (Beja et al. 2000a ; Henne et al. 1999 ; 2000 ; MacNeil et al. 2001 ; Rondon et al. 2000 ) . The microbial niches studied have been highly diverse and ranged from moderate environments, such as river soil (Henne et al. 1999 ) , to rather extreme environments, like the deep sea (Beja et al. 2000a ) . Already, the novel antibiotic, antibiotic resistance, vitamin and biore-mediating genes have been isolated with metagenomic approach (Gillespie et al. 2002 ; Handelsman 2004 ; Zeyaullah et al. 2009 ) .

The list of genes such as biocatalyst, antibiotics isolated by metagenomic technique is quite impressive and for easy comprehension has been presented in Table 14.2 .

14.3 Metagenomic-Techniques

14.3.1 Sampling Sites and Enrichment of Microbes

Metagenomics aims at studying yet to be cultivated organisms to understand the true diversity of microbes, their functions, cooperation and evolution, in environ-ments such as soil, water, ancient remains of animals, or the digestive system of animals and humans (Huson et al. 2009 ) . Various environments for metagenomic DNA extraction could be broadly classifi ed as biotic and abiotic habitats. Biotic habitat comprises of living sampling sites like animals, plants and insects. Animals are

274 J. Vakhlu et al.

Tabl

e 14

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tats

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27514 Metagenomics: A Relief Road to Novel Microbial... S.

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na

276 J. Vakhlu et al.

the reserviors of the microbes which can either be present in rumen of cattle or on the skin and in blood, gut, oral cavity and vaginal cavity of humans. Plants represent a unique kind of environmental niche and serves as complex habitats like rhizo-sphere, phylosphere and endosphere for colonization by different kinds of microbes. Rhizosphere represents the area surrounding the root directly infl uenced by microbes (Pandey and Palini 2007 ) whereas phylosphere is the surface of leaves colonized by benefi cial microbes (Kadivar and Stapleton 2003 ). Endophytes reside inside the plant tissue and the region colonized by them is referred to as endosphere (Backman and Sikora 2008 ) . In few plants, microbes are also found inhabiting particular sites such as nodules and leaf galls. In addition, microbes are also known to chew on the nitrogen-based fertilizers applied to the crops. Much of this fertilizer is not directly used by the plants but is instead consumed by the microbes and may be lost forever from the plant food cycle (Edwards 2009 ) . Different types of environment from where metagenome can be isolated are summarized in Fig. 14.3 . Metagenomic studies have been done in various habitats, including sea water (Venter et al. 2004 ) , ice cores (Bidle et al. 2007 ) , deep mine communities (Edwards et al. 2006 ) and also from the organisms, which harbour various symbionts, such as unknown and

Metagenomic samples

Abiotic habitat

Plants

Phylophytes

RumenSkin

Oral Cavity

Soil

Blood

StratosphereMarine Water

CloudsDeep Sea Vents

Air

Ocean Base

Surface

Biotic habitat

Animals Insect

Human Cattle Endophyte Rhizophytes

Gut

Gut

Vaginal Cavity Water

Indoor air

Fresh Water

Fig. 14.3 Habitats on which a metagenomic approaches has been applied

27714 Metagenomics: A Relief Road to Novel Microbial...

unculturable bacteria, protozoa or viruses. For example, the symbiont communities of honey bees (Cox-Foster et al. 2007 ) , the guts of mice (Turnbaugh et al. 2008 ) and humans (Booijink et al. 2007 ) , marine sponges (Schmitt et al. 2007 ) and plant-rhizobacteria (Kennedy et al. 2007 ) have revealed many new symbiont taxa (Weihong et al. 2009 ).

Water, soil and air comprises the abiotic habitat which are fl ooded with microbes and they are fundamental constituents of our soil, turning over the dead vegetable and animal matter and returning the nutrients therein back into the soil (Edwards 2009 ) . Different marine environments have been explored for total metagenomic analyses like ocean surface waters, mesopelagic waters, the deep sea, water col-umns and sea subfl oor sediments (Kennedy et al. 2010 ) . Microbes are tenacious, able to survive in seemingly inhospitable environments. Recently, bacteria have been found living inside rocks deep in the Earth; these remarkable creatures use uranium radiation instead of sunlight or heat as an energy source. Microbes have adapted to extremes of acidity or temperature as well; some can live in solid ice at or below 0°C, forming small channels with highly-concentrated solutes that prevent them from freezing and allow them to move around.

Air is another source of metagenome as airborne microbes are often attached to dust particles or water droplets from sneezes and coughs. On evaporation of water in aerosols, the microbes become droplet nuclei and clumps and thus can stay air-borne and drift with air fl ows (Tringe et al. 2008 ) . Low concentrations of airborne microbes often challenge their metagenomic extraction so such particles enriched with microorganisms can be collected by sampling large volumes of air through air handling units (AHU) in modern building ventilation systems.

14.3.1.1 Enrichment of Microbes

Enrichment of microorganisms with special traits and the construction of metage-nomic libraries by direct cloning of environmental DNA have great potential for identifying genes and gene products for biotechnological purposes. Enrichment can be of two types: one by enrichment in laboratory and other by in-situ enrichment before isolation. In laboratory, enrichment could be done by supplementing the environmental samples with some specifi c substrate or media for selective growth of desired microbes whereas in-situ enrichment approach involves the enrichment of environmental matrix at its own sampling site followed by DNA extraction. Both of these can be put in metagenomics as the fi rst case deals with cultivated metage-nome and later with cultivable and yet-to-be cultured microbes.

The fi rst approach comprises the construction of metagenomic libraries by direct extraction and cloning of metagenomic DNA from environmental samples which can be screened for the targeted genes. In one example, enrichment technology and metagenomics was combined for the identifi cation of a large number of diverse genes which confer the ability to oxidize short-chain polyols or reduce the corre-sponding carbonyl compounds. The resulting DNA libraries were screened for the presence of genes conferring a carbonyl compound-producing phenotype on

278 J. Vakhlu et al.

Escherichia coli ECL707 during incubation in the presence of short-chain (C2 to C4) polyols with adjacent hydroxyl groups (Knietsch et al. 2003 ) . It has been reported that the number of positive clones in a screen can be increased by with environmental samples which exhibit an enrichment of microorganisms containing the desired activity for library production (Daniel 2002 ) , though the total microbial diversity assessed will decrease in this approach.

In the second approach, selective medium can be employed for the culture enrich-ment by growing the target microorganisms. Out of all the inherent selection pressures which are based on nutritional, physical or chemical criteria, substrate utilization is most commonly employed. For example, culture enrichment on car-boxymethylcellulose resulted in four-fold enrichment of cellulase genes in a small insert expression library (Rees et al. 2003 ) .

Enrichment culture on agar plates was exploited for metagenomic isolation of genes encoding a variety of enzymes like agarases, amylolytic enzymes, cellulases, and lipases. The selected cosmid clones were sequenced in detail which resulted in identifi cation of the sequences of four conserved clusters encoding agarases, a cluster of two lipase genes, and many other enzymes with high biotechnological potential (Voget et al. 2003 ) . The poor quality of the isolated DNA hinders the construction of environmentally derived DNA libraries with large inserts. This has led to the isolation of DNA from the metagenome of a microbial community after precultiva-tion in the laboratory. Laboratory enrichment cultures are expected to have only limited biodiversity but this technique has proven to be highly effi cient for rapid isolation of large DNA fragments for cloning of operons and genes with great bio-technological value. Additionally, laboratory enrichment allows pre-selection of microbes that already carry the desired traits, resulting in high frequency of gene detection and isolation (Cowan et al. 2005 ) .

The limitation of this technique is that enrichment of particular trait leads to the loss of microbial diversity as in this approach the enzymes and the corresponding genes are recovered from the identifi ed organisms. In this manner, a large fraction (>99%) of the microbial diversity in an environment is lost due to diffi culties in enriching and isolating microorganisms in pure culture. While pre-enrichment increases the likelihood of fi nding genes that encode the target trait, this must be balanced against the concurrent decrease in genomic diversity. It is also important that the pre-enrichment step may limit the chances of fi nding an enzyme that dis-plays optimal activity outside the range of conditions chosen for the pre-enrichment (Elend et al. 2006 ) .

14.3.2 Isolation, Enrichment and Normalization of Metagenomic DNA

Cell recovery and direct lysis are the two principal strategies for the recovery of metagenomic DNA and its extraction is a compromise between the vigorous extrac-tion and the minimisation of DNA shearing along with less coextraction of inhibiting

27914 Metagenomics: A Relief Road to Novel Microbial...

organic acid contaminants. Mechanical bead beating has been shown to recover more diversity compared with chemical treatment (Cowan et al. 2005 ) . However, a major diffi culty associated with the methods employed is related to contamination of purifi ed DNA with polyphenolic compounds which are copurifi ed with the DNA. These compounds are diffi cult to remove, and it is well known that polyphenols also interfere with enzymatic modifi cations of isolated DNA.

14.3.2.1 Enrichment

The pre-enrichment of the sample provides an attractive means of enhancing the screening hit rate by applying one of several enrichment options ranging from whole-cell enrichment, to the selection and enrichment of target genes and genomes. For example, the eukaryotic cell population was effectively removed in the Sargasso Sea genome sequencing project by size-selective fi ltration (Cowan et al. 2005 ) .

Gene enrichment could be done using various enrichment strategies like Suppressive subtraction hybridisation (SSH) and Differential expression analysis (DEA) (Cowan et al. 2005 ) . SSH is a powerful technique for specifi c gene enrich-ment which identifi es genetic differences between microorganisms. In this approach, adaptors are ligated to the DNA populations and DNA fragments which are unique to each DNA sample are selected by subtractive hybridization and one can thus analyze genetic differences between two closely related bacteria. Recently, this technique has been used to identify differences between complex DNA samples from the rumen of two different animals (Galbraith 2004 ) .

DEA is a particularly effective enrichment tool which was successfully applied to identify bacterial genes upregulated in the absence of iron (Cowan et al. 2005 ) . This approach compares the expression profi le of metagenomic sample, pre- and post- exposure to a specifi c substrate or xenobiotic which identifi es the expression of genes up-regulated for the specifi c activity.

In viral metagenomic DNA analyses, the presence of free and cellular DNA hin-ders the DNA isolation. The viral DNA signal will be lost if the free DNA is not removed and the presence of any cellular contamination will overwhelm the viral signal as the average viral genome (~50 kb long) is about 50 times smaller than the average microbial genome (2.5 Mb). A combination of differential fi ltration with tangential fl ow fi lters (TFF), DNase treatment and density centrifugation in caesium chloride (CsCl) is used to separate the intact viral particles from the microorganisms and free DNA (Edwards and Rohwer 2005 ) .

14.3.2.2 Normalisation

An even representation of the population’s genomes within the sample is not found when total DNA is extracted directly from environmental samples as rare organisms will contribute a relatively low proportion and the genome population might be overshadowed by a limited number of dominant organisms. This could lead to a selective bias in downstream manipulations such as PCR.

280 J. Vakhlu et al.

The selective bias can be partially resolved by means of experimental normalisation (Short and Mathur 1999 ) which includes separation of genotypes by CsCl gradient centrifugation in the presence of an intercalating agent, such as bis-benzimide, for the buoyant density separation of genomes based on their % G and C content. Equal amounts of each band on the gradient are combined to represent a normalised metagenome.

Normalisation can also be achieved by denaturing fragmented genomic DNA, and re-annealing under stringent conditions (e.g. 68°C for 12–36 h). Abundant ssDNA will anneal more rapidly to generate double-stranded nucleic acids than rare DNA species. Single-stranded sequences are then separated from the double-stranded nucleic acids, resulting in an enrichment of rarer sequences within the environmental sample (Short and Mathur 1999 ) .

14.3.3 Analysis of Isolated Metagenomic DNA for Taxonomic and Metabolic Diversity

Metagenomics has been defi ned as function based or sequence based cultivation independent analysis of the collective microbial genomes, present in a given habitat (Simon et al. 2009 ; Riesenfeld et al. 2004 ) . Analysis of metagenomic bounty basi-cally depends upon two types of screening procedures: function-based and sequence-based approach. Function based screening is based on construction of metagenomic expression libraries which are further screened for target enzyme activities. After an active clone is identifi ed, the sequence of the clone is determined, the gene of inter-est and its respective products are further analyzed, and explored for their biotech-nological potential (Craig et al. 2010 ; Simon and Daniel 2009 ; Schmeisser et al. 2007 ; Streit and Schmitz 2004 ; Uchiyama and Miyazaki 2009 ) . The advantage of directly screening for enzymatic activities from metagenome libraries is that the sequences and enzyme activities are functionally guaranteed and researchers access previously unknown genes and their encoded enzymes (Fig. 14.4 ).

In sequence based approach, target genes are amplifi ed from metagenomic DNA using polymerase chain reaction with conserved sequences as primers and are cloned in the appropriate expression system (Simon and Daniel 2009 ; Streit and Schmitz 2004 ) . The sequence-based metagenomics approach relies on the prior knowledge of proteins possessing the activity of interest, and the screening is per-formed toward the genes that are predicted to encode proteins with specifi c func-tion. Prior knowledge regarding specifi c type of a protein or a functional pathway can be easily obtained due to the availability of vast gene databases, including meta-genomic databases that continue to grow exponentially (Kyrpides 2009 ) .

14.3.3.1 Sequence Based Screening of the Metagenomic Bounty

Sequence-driven analysis relies on the use of conserved DNA sequences to design hybridization probes or PCR primers to screen metagenomic libraries for clones

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that contain sequences of interest (Schloss and Handelsman 2003 ) . Target genes are identifi ed either by PCR-based or hybridization-based approaches employing prim-ers and probes derived from conserved regions of known genes and gene products (Daniel 2005 ; Handelsman 2004 ) . Thus, only genes harbouring regions with simi-larity to the sequences of the probes and primers can be recovered by this approach. The sequence-based metagenomics approach relies on the prior knowledge of proteins possessing the activity of interest, and the screening is performed toward the genes that are predicted to encode proteins (Chistoserdova 2010 ) . The sequence-based screening approach is limited to the identifi cation of new members of known gene families as dependent on known conserved sequences, and cannot uncover non-homologous enzymes. Although some members of the fi eld view this approach as too undirected to yield biological understanding, others stress that there is so little known about some divisions of Bacteria that any genomic sequence is helpful in guiding the design of experiments to reveal their biology (Schloss and Handelsman 2003 ) . The advantage of this screening strategy is the independence on gene expres-sion and production of foreign genes in the host library (Lorenz et al. 2002 ) . In addition, sequence-driven screening is not selective for full-length genes and func-tional gene products thus can disclose target genes, regardless of gene expression and protein folding in the host, and irrespective of the completeness of the target gene’s sequence.

Several novel functional enzymes have been recovered by employing sequence driven approaches such as chitinases, alcohol oxidoreductases, diol dehydratases, and enzymes conferring antibiotic resistance (Simon et al. 2009 ) . Two novel glycopeptide-encoding gene clusters were isolated from desert soil by a PCR-based screen which

Metagenomic DNA

Wholegenome sequencing

(WGS)Blast

Identificationof genes

PrimerFull generetrieving

SequenceBased

PCR based onConserved primers

Metagenomic Library Direct masssequencing

Cloning

Screening

Amplification&

CloningFunction

Based

Fig. 14.4 Metagenomic approach for discovery of genes

282 J. Vakhlu et al.

was important for the development of novel glycopeptides and novel glycopeptides analogs, which can serve as substitutes of currently used antibiotics such as vancomycin (Banik and Brady 2008).

Genes encoding polyketide synthases (PKSs) and peptide synthetases, which contribute to synthesis of complex antibiotics, are two hotly pursued examples. The PKSs are modular enzymes with repeating domains containing divergent regions which are fl anked by highly conserved regions. The conserved regions have pro-vided the basis for designing probes to identify PKS genes among metagenomic clones (Courtois et al. 2003 ) . Mixing and matching PKS domains from different sources has yielded new antibiotics, stimulating interest in the discovery of new PKS genes.

New approaches are directed toward identifying sequences that are unique to uncultured microorganisms or those specifi c to a particular environment. These methods involve:

1. Profi ling clones with microarrays that identify previously unknown genes in environmental samples,

2. Subtractive hybridization to eliminate all sequences that hybridize with another environment or subtractive hybridization to identify differentially expressed genes

3. Genomic sequence tags which will enhance the effi ciency of screening and aid in identifying minor components in communities and genes that defi ne community uniqueness. (Riesenfeld et al. 2004 )

Metagenomic diversity can also be accessed using sequence based screening by sequencing the phylogenetically relevant genes like 16S/18S rRNA and ITS region. So the function based screening cannot help us identifying these regions.

Direct Analysis by Sequencing

The recent development of next generation sequencing technologies, which do not require cloning or PCR amplifi cation, and can produce huge numbers of DNA reads at an affordable cost, has boosted the number and scope of metagenomic sequenc-ing projects.

The original Human Genome Project cost $3 billion (U.S.) and was completed in a little over 13 years. Currently, commercial sequencing of a human genome costs about $350,000 and takes about 6 months. Our understanding of the world around us, and particularly microbes that inhabit it has been revolutionized by cheaper, higher-throughput sequencing technique namely “Pyrosequencing” which has dramatically affected all aspects of sequence-based biology (Edwards 2009 ) .

Pyrosequencing is the most advanced technology of the next-generation sequencers which uses an enzymatic reaction to generate a pulse of light each time a particular letter is read from a DNA strand. In this technique, a single piece of DNA is attached to a spherical bead 28 m m in diameter which could easily fi t in 54 m m wells in glass plates thus allowing only single bead to fi t in a well and ensuring only one piece of

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DNA is read at a time. The current capacity of this machine, called a 454 FLX, is to sequence about 200 consecutive letters from as many as 500,000 pieces of DNA in just a few hours.

Analysis After cloning

Cloning by PCR

Cloning by PCR is one of the applications of metagenomics to gene discovery. Metagenomic library are constructed by isolating metagenomic DNA from the selective environment and amplifying the target gene using specifi c primers. The amplicons thus obtained are further ligated in appropriate vector and transformed into the suitable host to form the metagenomic library. The vectors can be selected depending on the size of fragments (amplicon)for cloning, plasmids (optimal range of DNA fragments 0.5–2 kb, upper limit, ~10 kb), bacteriophages (7–10 kb, ~20 kb), cosmids or fosmids (35–40 kb; ~45 kb) and bacterial artifi cial chromosomes (BAC, 80–120 kb, ~200 kb). The fi eld of metagenomics has benefi tted signifi cantly from the large insert capacity vectors like cosmid, BAC and YAC cloning systems as they are ideal for studying genome organizations of unculturable microorganisms in the environment (Xu 2006 ) . Thus, either single genes and primary gene products, or secondary metabolites could be targeted from the expression of complete operons depending on the choice of vector and host.

Sets of putative gene fragments have been amplifi ed from metagenomic DNA extracts using primer sets specifi c to bacterial lipase and nitrile hydratase a -subunit sequences. The amplicons are cloned and sequenced and are aligned with known databases. The nitrile hydratase genes show very high homology to each other and known full-length genes whereas the putative lipase gene amplicons show very low levels of homology with known sequences (Cowan et al. 2004 ) .

Inverse PCR (I-PCR), a method that can be used for cloning the upstream and downstream fl anking regions of known sequences for the amplifi cation of known gene families, has found little utility when applied to metagenomes. The reason could be the potential increased complexity and low copy numbers of target sequences in the metagenome which could be overcome by Pre-amplifi ed I-PCR or PAI-PCR (Kennedy et al. 2010 ) . In PAI-PCR, pre-amplifi cation of the template DNA is done, prior to I-PCR, in order to enrich the target DNA sequences by iso-thermal DNA amplifi cation, for example, by using phi29 DNA polymerase, on the basis of rolling-circle amplifi cation (RCA) (Rector et al. 2004 ) or multiple displace-ment amplifi cation (MDA) (Gonzalez et al. 2005 ) . The selective amplifi cation of a specifi c DNA region by the isothermal amplifi cation is generally diffi cult because the specifi city of the isothermal amplifi cation is not high, especially when the reac-tion temperature is low or the initial amount of target DNA is small. To reduce non-specifi c amplifi cation derived from miss-priming, primers containing locked nucleic acids (LNAs) may be useful. Locked nucleic acids are DNA analogues in which the furanose ring in the sugar-phosphate backbone is chemically locked. Locked nucleic

284 J. Vakhlu et al.

acids obey the Watson–Crick pairing rules, but have an increased specifi city and a high affi nity to complementary DNA (Yamada et al. 2008 ) . This pre-amplifi ed I-PCR (PAI-PCR) method increased the sensitivity of PCR almost 10,000 times compared with the standard IPCR in model experiments using Escherichia coli (Yamada et al. 2008 ) . This approach could also be useful for marine DNA-samples where the amount of extracted DNA is low (Kennedy et al. 2010 ) . Using PAI-PCR, DNA glycosyl hydrolase genes have been identifi ed from vermiform appendices of horses and termite guts. The fl anking sequences of the target genes were amplifi ed and cloned successfully using PAI-PCR, whereas standard I-PCR resulted in no amplifi cation (Yamada et al. 2008 ) .

Metagenomic PCR amplifi cation methods being used successfully to identify families of homologous genes, suffer from the limitation that the primary PCR gen-erates only partial gene sequences. Full-length sequences are subsequently obtained by hybridization screening of a complete metagenomics library or by genome walk-ing (Cowan et al. 2004 ) . Morimoto and Fujii ( 2009 ) conducted a PCR-DGGE tar-geting benA and tfdC, which encode the alpha subunits of benzoate 1, 2-dioxygenase and chlorocatechol 1,2-dioxygenase, respectively. The complete functional genes were recovered by metagenome walking (Morimoto and Fujii 2009 ) . In contrast to PCR-based techniques, the subtractive hybridization approach allows the recovery of multiple gene targets in a single reaction. For example, recovery of multicopper oxidases from metagenomic DNA was done by subtractive hybridization magnetic bead capture in which conserved regions of the target genes are amplifi ed from a metagenomic DNA sample by PCR using biotinylated degenerated primers. The resulting amplifi ed target gene fragments are immobilized on streptavidin-covered magnetic beads, which are then used as probes for capturing the full-length genes from metagenomic DNA by hybridization. (Meyer et al. 2007 ) In a few cases, microarray technology has been employed for sequence-driven screening of metag-enomic DNA and libraries. A recent example is the recovery of genes encoding blue light-sensitive proteins (Pathak et al. 2009 ) .

Culture-independent techniques such as 16S ribosomal RNA gene coding DNA (16S rDNA) analysis and metagenomic sequencing provide a less biased perspec-tive on environmental microbes because DNA is sampled directly from the environ-ment. AHU fi ltration strategy was employed for air sample collection and both 16S rDNA and metagenomic analyses were done to characterize the airborne biological diversity in an indoor urban environment (Tringe et al. 2008 ) .

Gene-specifi c PCR has two major drawbacks. First, the functionally similar genes resulting from convergent evolution are not likely to be detected by a single gene-family-specifi c set of PCR primers as the design of primers is dependent on existing sequence information and second, only a fragment of a gene will be ampli-fi ed by gene-specifi c PCR thus requires additional steps to access the full-length genes like hybridization in which amplicons can be labelled as probes to identify the putative full-length gene and genome walking (Cowan et al. 2005 ) .

Sequence-dependent bias could be decreased using methods requiring only one gene-specifi c primer as compared to standard twin-primer PCR amplifi cation pro-cedures for example, the use of immobilised oligonucleotides designed to target a

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specifi c gene fragment or consensus sequence by affi nity binding which is used for recovery of polyA RNA cDNA library construction (Cowan et al. 2005 ) .

Metagenomic Library

Construction of metagenomic library from soils or sediments is one of the various metagenomic strategies that are used for targeting genes having specifi c catalyst characteristics such as substrate range or temperature and pH optima. Soils or sedi-ments are known to harbour a high level of microbial diversity and wide diversity of biocatalysts (Schmeisser et al. 2007 ) .

The basic steps of metagenomic library construction include generation of suit-able sized DNA fragments, cloning of fragments into an appropriate vector and screening for the gene of interest and have been extensively and successfully used for over three decades. DNA fragmentation is a signifi cant problem when construct-ing metagenomic libraries as the vigorous extraction methods which are required for high yields of environmental DNA often results in excessive DNA shearing. This highly sheared DNA (e.g. 0.5–5 Kbp fragments) cannot be restricted to gener-ate ligatable sticky ends without signifi cant loss of the total gene complement so an alternative approach uses blunt-end or T–A ligation to clone randomly sheared metagenomic fragments (Wilkinson 2002 ) .

Random shotgun sequencing approaches involves direct cloning of metagenome without prior sequence knowledge for data generation. Venter et al. ( 2004 ) were the fi rst to apply whole genome shotgun sequencing to samples of the Sargasso Sea in order to characterize the microbial community and identify new genes and species. Whole metagenome shotgun sequencing approaches have been mostly employed for the cloning and sequencing of microbial DNA from marine environments. This involves the generation of small-insert DNA clone libraries, and their subsequent analysis using Sanger dideoxy sequencing which generates sequences that can then be used to query the known databases for function or phylogenetic relationship. This approach can give read lengths ranging from 600 to 900 bp in length, which can be extended through the entire fosmid clone. (Kennedy et al. 2010 )

Microarrays represent a powerful high-throughput system for analysis of genes. They are typically used to monitor differential gene expression, to quantify the envi-ronmental bacterial diversity and catalogue genes involved in key processes (Cowan et al. 2005 ) . Microarray technology could also be used for the pre-selection of genes in metagenomic libraries before shotgun sequencing, thereby reducing the sequenc-ing burden and reducing the proportion of sequences unassigned by database sequence similarity searches (Sebat 2003 ) .

Depending on the ability to clone large fragments of metagenomic DNA, various metagenomic libraries are constructed like cosmid, bacterial artifi cial chromosome (BAC) libraries, fosmid library and phage-display expression libraries (Cowan et al. 2005 ) . The large fragments of metagenomic DNA are able to target the entire func-tional operons with the possibility of recovering entire metabolic pathways. Cosmid and BAC libraries have been widely used for the construction of metagenomic libraries (Beja 2004 ; Daniel 2004 ) . Fosmid vectors provide an improved method for

286 J. Vakhlu et al.

cloning and stably maintaining cosmid-sized (35–45 Kbp) inserts in E. coli whereas phage-display expression libraries provide DNA sequences of isolates by affi nity selection of the surface-displayed expression product

Metatranscriptomic Library

Analysis of community transcripts isolated directly from the environment or from microcosms is referred to as metatranscriptomics (Chistoserdova 2010 ) . Metagenomic complementary DNA (cDNA) libraries have been constructed from mRNA that has been isolated from environmental samples. Sequencing and charac-terization of metatranscriptomes has been employed to identify expressed biological signatures in complex ecosystems (Simon et al., 2009 ).

Constructing libraries derived from environmental mRNA is more challenging than generation of metagenomic DNA libraries due to diffi culties associated with isolation of RNA, separation of mRNA from other RNA species, and instability of mRNA (Sjöling and Cowan 2008 ; Frias-Lopez et al. 2008 ) but at the same time is advantageous for gene discovery as it requires a much smaller sequence space compared to metagenomic DNA which focuses on the expressed subset of genes (Warnecke and Hess 2009 ) .

14.4 Functional Screening of the Metagenomic Bounty

Construction of metagenome expression libraries can be done by inserting fragmented metagenomic DNA into expression vectors which is further cloned in a suitable host system for examining gene expression. Function-driven screening of metagenomic libraries is not dependent on sequence information or sequence similarity to known genes. Thus, this is the only approach that bears the potential to discover new classes of genes that encode either known or new functions (Heath et al. 2009 ; Rees et al. 2003 ) . In addition, function-driven screening often requires the analysis of more clones than sequence-based screening for the recovery of a few positive clones (Daniel 2005 ) . The major advantage of a function-based screening approach is that only full-length genes and functional gene products are detected. For a clone to be functionally active i.e., to correctly express an active enzyme, it must contain the complete gene sequence or even a gene cluster (where the gene sequence depends on more than one genetic subunit). This requires selection of suitable vector systems and expression hosts.

14.4.1 Expression Vectors Systems

The expression vector varies with the range of target insert DNA or size of gene required for cloning. For small target genes, plasmids or Lambda expression vectors

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with inserts between 2 and 10 kilobase (kb) are used for constructing DNA fragment libraries which are further screened for enzyme expression. Larger target genes having size range between 20 and 40 kb require expression libraries in cosmids and fosmids and up to 100–200 kb in bacterial artifi cial chromosome vectors.

14.4.2 Expression Hosts

The incapability to discover functional gene products during function-based screens of metagenomic libraries might be a result of the inability of the host to express the foreign genes and to form active recombinant proteins. Many genes from environmental sam-ples may not be expressed effi ciently in heterologous hosts due to differences in codon usage, transcription and/or translation initiation signals, protein- folding elements, post-translational modifi cations, such as glycosylation, or toxicity of the active enzyme. Selection of suitable expression hosts or suitable vector systems containing appropriate transcription and translation-initiation sequences can reduce this limitation.

Although common, E. coli host strains have relaxed requirements for promoter recognition and translation initiation but it is not compatible with many environ-mental genes thus an expression host such as the E. coli Rosetta strains (Novagen, Madison, Wisconsin, USA) have been developed that contain the tRNA genes for rare amino acid codons or co-expression of the chaperone proteins, such as GroES, GroEL, and heat-shock proteins. In addition, host systems such as, the yeast Pichia pastoris , and bacterial hosts such as Pseudomonas putida , Streptomyces lividans , or Bacillus subtilis a re also suitable for heterologous gene expression (Li et al. 2009 ) .

Enzymatic functions of individual clones can be identifi ed by chemical dyes and insoluble or chromophore-containing derivatives of enzyme substrates which can be incorporated into the growth medium (Daniel 2005 ; Ferrer et al. 2009 ; Handelsman 2004 ) . Examples of this simple activity-based approach are the detection of recom-binant E. coli clones exhibiting protease activity on indicator agar containing skimmed milk as protease substrate (Lee et al. 2007 ; Waschkowitz et al. 2009 ) or the detection of lipolytic activity by employing indicator agar containing tributyrin or tricaprylin as enzyme substrates (Hårdeman and Sjöling 2007 ; Heath et al. 2009 ; Lee et al. 2006 ) . Clones with proteolytic or lipolytic activity are identifi ed by formation of clearing zone or halo on solidifi ed indicator medium.

14.4.3 Modifi cation of Function-Based Screening Substrate Induced Gene-Expression Screening (SIGEX)

Function-based methods are modifi ed specifi cally for exploring metagenome librar-ies as reported by Uchiyama and colleagues ( 2005 ) who have developed substrate induced gene-expression screening to rapidly identify clones. SIGEX is based on induced gene expression and can be induced by a target substrate and display

288 J. Vakhlu et al.

catabolic gene expression. Catabolic operons are often adjacent to cognate transcriptional regulators and promoters that are induced by the substrate. In this approach, an operon-trap expression vector is used which contains the gene for a promoter less green fl uorescent protein (gfp). This expression vector was employed for cloning of environmental DNA. If expression of a target gene is induced by the substrate, the gfp gene is co-expressed, and positive clones can rapidly be separated from other clones by fl uorescent activated cell sorting (Handelsman 2005 ; Uchiyama et al. 2005 ) .

14.4.4 Metabolite-Regulated Expression

A similar screening strategy termed metabolite-regulated expression has been employed in which metagenomic clones producing small molecules are identifi ed by a biosensor that detects small diffusible signal molecules, which induce quorum sensing (QS). When a threshold concentration of the signal molecule is exceeded, GFP is produced. Subsequently, positive fl uorescent clones are identifi ed by fl uo-rescence microscopy (Williamson et al. 2005 ) .

14.4.5 Heterologous Complementation of Host Strains or Mutants

Simon and coworkers ( 2009 ) have reported a new approach that uses specifi c host strains that require heterologous complementation by foreign genes for growth under selective conditions. Only recombinant clones harbouring the targeted gene and producing the corresponding gene product in an active form are able to grow. DNA polymerase-encoding genes were identifi ed from metagenomic libraries derived from glacier ice using this approach. An E. coli mutant was used as host for the metagenomic libraries which carried a cold-sensitive lethal mutation in the 5 ¢ -3 ¢ exonuclease domain of the DNA polymerase I. Only recombinant E. coli strains complemented by a gene conferring DNA polymerase-activity are able to grow at a growth temperature of 20°C. In this way, a high selectivity of the screen is achieved (Simon et al. 2009 ) .

14.5 Future Challenges

The four pillars that hold the science of metagenomics are, (i) direct Meta- nucleic acid isolation, (ii) high through put sequencing technologies (iii) Heterologous gene expression and (iv) bioinformatics tools to understand meta- data. All four are raw and need strengthening. After initial euphoria over characterization of microbial communities by direct meta genomic isolation, there are number of results that

28914 Metagenomics: A Relief Road to Novel Microbial...

clearly prove that no single isolation method is suitable for isolation of nucleic acid from all the cells in any environmental samples. Metagenomic isolation is basic to any Metagenome based goal hence isolation and purifi cation of the DNA directly from soil needs to be polished. In conventional genomics the need to look beyond E. coli for Heterologous gene expression has been felt and voiced long ago, but for functional expression based screening of metagenomic bounty need for alternate expression hosts is indispensible. The recent development of ultra-high throughput sequencing technologies produces huge numbers of DNA reads at an affordable cost which further requires the new ways of user-friendly and powerful tools for comparative analysis of metagenomic data (Huson et al. 2009 ) . The taxonomical content of metagenome is usually estimated by comparison against DNA and pro-tein sequence databases of known sequences. Metagenomic studies characterize the composition as well as diversity of uncultured microbial communities for which BLAST-based comparisons have typically been used. MEGAN (“MEtaGenome ANalyzer”) is a computer program that allows analysis of large metagenomic data-sets in which set of DNA reads (or contigs) is compared against databases of known sequences using BLAST or another comparison tool. However, these bioinformatics tool suffers from various limitations like sampling biases, high percentages of unknown sequences, and the use of arbitrary thresholds to fi nd signifi cant similarities which can decrease the accuracy and validity of estimates. GAAS (Genome relative Abundance and Average Size) is a complete software package that implements a novel methodology to control for sampling bias via length normalization, to adjust for multiple BLAST similarities by similarity weighting, and to select signifi cant similarities using relative alignment lengths. (Angly et al. 2009 ) GAAS provides improved estimates of community composition and average genome length for meta-genomes in both textual and graphical formats. A gap is constantly created due to difference in the rates of collecting sequence data using high throughput techniques and interpretation of these sequences. This gap is being bridged by another software tool namely CAMERA project. CAMERA stands for Community Cyberinfrastructure for Advanced Marine Microbial Ecology Research and Analysis project which helps in developing global methods for monitoring microbial communities in the ocean and their response to environmental changes. CAMERA’s database includes environ-mental metagenomic and genomic sequence data, associated environmental param-eters, precomputed search results, and software tools to support powerful cross-analysis of environmental samples. The main aim of CAMERA is to create a rich, distinctive data repository and bioinformatics tools resource that will address many of the unique challenges of metagenomics and enable researchers to unravel the biology of environmental microorganisms (Seshadri et al. 2007 ) .

14.6 Conclusions

Soil habitats contain the greatest microbial diversity of all the environments on earth and the power of metagenomics has changed the microbiologists approach to access microbial diversity to a larger extent than that has been viewed in the petridish

290 J. Vakhlu et al.

(Zeyaullah et al. 2009 ) . Metagenomics can provide the tools to balance the abun-dance of knowledge attained from culturing with an understanding of the uncul-tured majority of microbial life. (Handelsman 2004 ) Metagenomics has unraveled the diversity of many familiar habitats, including deep sea thermal vents; acidic hot springs, temperate, desert, and cold soils; Antarctic frozen lakes; and eukaryotic host organs – the human mouth and gut, termite guts, gutless microbe and plant rhizospheres and phyllospheres (Handelsman 2004 ) .

The potential for application of metagenomics to biotechnology seems endless as it has redefi ned the concept of a genome, and accelerated the rate of gene dis-covery (Handelsman 2004 ) . Soil microbial communities are almost unlimited resource of new genes encoding useful products, which could be explored by the power of metagenomics. Thus, metagenomic tools facilitate the recovery of a high amount of new enzymes, antibiotics and other molecules from small fraction of the soil metagenome (Daniel 2005 ; Simon and Daniel 2009 ) . It can be expected that the number of novel genes identifi ed through metagenome technologies will exceed the number of genes identifi ed through sequencing individual microbes (Streit and Schmitz 2004 ) .

Although considerable progress has been made in the characterization of micro-bial communities by effi cient high-throughput random sequencing which permit cloning-independent and low-cost sequencing analyses of metagenomes, a further improvement of sequencing technologies combined with a reduction in sequencing costs and development of appropriate bioinformatic tools for analysing the enormous amount of data produced is required (Daniel 2005 ; Simon and Daniel 2009 ) .

A great challenge of metagenomics is to defi ne the origin of a specifi c metage-nomic clone as it is important to defi ne the origin of the clones in terms of the utility of the genes and functional genomics of the uncultured microorganisms to really solve the enigma of culture problem of microorganisms (Lee 2005 ) .

Acknowledgement The authors would like to thank the Department of Biotechnology, Ministry of Science and technology for their support. Author SA is thankful to UGC-CSIR for fellowship.

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1

Rhizobacteria from Crocus sativus grown in Kashmir India

Sheetal Ambardar, RanjitKour and JyotiVakhlu

School of Biotechnology, University of Jammu, Jammu-6, India

Email: jyotivakhlu@gmail.com

ABSTRACT:

Rhizosphere bacteria are root associated bacteria and are since long shown to effect the growth

and yield of crop. Despite being world‟s costliest spice there are no reports available of

rhizosphere ecology of Saffron. This is the first report on Rhizobacteria isolated from saffron

rhizosphere and bulk soil from selected fields. The Rhizobacteria were identified on the basis of

microscopy, biochemical and molecular analysis and seems to be specific to soil type rather than

plant.

Key words:Saffron,Rhizosphere, Bulk Soil, 16S rDNA V1- V3

INTRODUCTION:

Rhizosphere, first described by Lorenz Hiltner in 1904, represents the most dynamic habitat on

Earth (Hinsingeret al.2009). The rhizosphere zone is distinguished from bulk soil as it is under

the influence of root exudates. In the rhizosphere, diverse and complex interaction occurs

between plant roots, soil microbiota and the soil, which have been evolved due to mutual

benefits between plants and microbes. The plant partner provides substrate and energy flow into

the rhizosphere and in return gets nutrients and minerals, essential for its development and

growth(Hartmann et al.2009). Rhizosphere has been the focus of agricultural research for many

years because of its importance in crop productivity, soil health and sustainable agriculture (Li et

al. 2007; Ryan et al. 2009; Ordookhani et al., 2011). It is well established that the number of

2

microorganisms is higher in rhizosphere than bulk soil and this has been assessed by plate counts

or the “Most Probable Number analysis”(Nannipieri et al. 2007). In the root system, sloughing

off of root cells, root death and the exudation of carbon compounds select a specific rhizosphere

community (Hartmann et al.2009). Hence a rather small subset of the whole soil microbial

diversity, majority of which are Gram negative bacteria, finally colonize roots

successfully(Soderberg et al. 2004; Johansen and Olsson 2005). Rhizosphere of various plants

like rice, tea, cucumber, apple and soyabean has been extensively studied (Mahaffee et al. 1997;

Johansen and Olsson 2005; Mazumdar et al., 2007; Ashrafuzzaman et al. 2009; Mehta et al.

2010; Joshi and Bhatt 2011; Wahyudi et al. 2011). Saffron is one of the world‟s highest priced

medicinal, aromatic plants and is referred as the „Golden Condiment‟.Iran, Spain & India (J&K

State) are the main Saffron producing countries in the world. In India Saffron is grown only in

Pulwama district in Kashmir and Kishtwar district in Jammu division (www.merinews.com)

though comparable climatic conditions are found in adjoining states. Crocus sativus, commonly

known as saffron, is an autumn-flowering perennial plant and is a sterile triploid with

chromosome number 3n=24. Being sterile, itreproduces vegetatively by underground bulb-like

starch-storing organs known as corms and has unique corm–root cycle. Cultivation of Saffron

only in specific belts in world, it‟s economic importance and corm-root cycle makes it an

interesting candidate for studying it‟s rhizosphere. Present study deals with the isolation of

bacteria from Saffron rhizosphere being cultivated in two different fields Wuyan and Khrew.

MATERIALS AND METHODS

Soil Sampling: Bulk and Rhizosphere soil samples were collected from Wuyan and Khrew

fields of Saffron during the flowering period (Oct-Nov) as roots are well developed during this

3

period. The Saffron fields of Wuyan village (74°58′0″E, 34°1′30″N, 5173ft) are more productive

in comparison to the Khrew fields (74°98′0″E, 34°02‟0″N, 5272ft). The yield of Saffron from

Wuyan fields was 2.6 kg/hectare whereas from Khrew fields it was 1.5 kg/hectare in 2010. The

soil sampling was done as per the protocol of Luster and coworkers (2009). The soils were

analyzed for pH, electrical conductivity, organic Carbon, Calcium, Magnesium, bulk density,

available Nitrogen, Phosphorus and Potassium according to protocols of Hamza and coworkers

(2008).The bulk soil was collected by vigorously shaking the roots and the soil that remains

adhere to the roots is considered as rhizosphere soil. The soil samples were stored at -200C.

Culture based studies: Isolation of cultivable bacteria from the bulk soil of Wuyan and Khrew

fields was done by following conventional agar plate method using soil dilution method (Stotzky

et al. 1996). Roots were the source of rhizobacteria and were isolated as per the protocol of

Luster and coworkers (2009). A comparison of bacterial load between the bulk and rhizosphere

microflora of two fields was done by dilution plate technique and comparing the CFU/gm

(Stotzky et al. 1996; Joshi and Bhat 2011). Isolates were randomly selected and purified by

streak plate method which were further stored on LB agar slants at 4 0C and preserved in 50%

glycerol at -80 0C. All the cultures were then screened microscopically using Gram‟s staining kit

(Sigma) followed by biochemical characterization (Sneath et al., 1986).

Culture identification by 16S rRNA amplification:16S rRNA sequence analysis was done for

identification of bacterial types. Genomic DNA was isolated from all the bacteria using the GES

protocol (Pitcher et al. 1989) and a partial 16S rRNA region flanking V1-V3 region was

amplified (~500bp) using universal primers Bac8f (5‟-AGAGTTTGATCCTGGCTCAG-3‟) and

Univ529 (5‟-ACCGCGGCKGCTGGC-3‟). The PCR was performed following protocol

developed by Fierer and coworkers (2007) with some modifications (100pM primer, 30 cycles

4

PCR cycles). The template DNA concentration for PCR reaction was used as 50ng and the PCR

program as denaturation at 950C for 5 minutes followed by 30 cycles of denaturation at 95

0C for

60 seconds, annealing at 540C for 30 seconds followed by extension at 72

0C for 90 seconds and

final extension at 720C for 10 min.

Sequencing and phylogenetic analyses:16S rDNAamplicons were custom sequenced at CIF,

UDSC, New Delhi, India. The resulting nucleotide sequences were assignedbacterial taxonomic

affiliations based on the closest match to sequences available at the NCBI database

(http://www.ncbi.nlm.nih.gov/) using theEzTaxon version 2.1 (www.eztaxon.org). Sequences of

bacteria obtained were deposited in the GenBank nucleotide sequence database.

Accession numbers: The sequences were submitted to GenBank under accession no JN084065-

JN084074, JQ713596-JQ713609, JQ713611, JQ751317, JF836006, JX233807 and KC511119-

KC511122

Construction of Phylogenetic Tree:

The 16S rRNA gene sequences were aligned using Multiple sequence alignment tool ClustalX

2.1 version. Phylogenetic and molecular evolutionary analysis was conducted using Phylip 3.69

(Tuimala. 2004) and MEGA 5.05 software version (Tamura et al. 2011). The phylogenetic tree

was constructed by neighbor-joining method using distance matrix from alignment.

RESULTS:

Saffron grows in light, friable soils that have a high nutrient content and thrives best in deep,

well drained clay-calcareous soils with a loose texture that permits easy root

penetration.Physiochemical analysis of both the soil samples is tabulated in table 1.Rhizosphere

and bulk soil of Saffron fromtwo fields Khrew (KR= Saffron rhizosphere from Wuyan, KB=

Bulk soil Khrew) and Wuyan (WR= Saffron rhizosphere from Wuyan, WB= Bulk soil Wuyan)

5

were compared for bacterial load by calculating CFU/ gm which was6.4 X 107

CFU/ gm and 1.4

X 106

CFU/ gm in WR and WB and 6.2X 107

CFU/ gm and 7.0 X 105CFU/ gm in KR and KB

respectively. A ~45 fold increase of bacterial load was observed in WR than WB (Ambardar and

Vakhlu 2013) whereas~88 fold was observed in KR. Even the bacterial load of WB was ~2 fold

more than that of KB but bacterial load of rhizosphere from both fields did not show much

difference.A total of 200 cultivable bacteria were selected from both the fields randomly

selecting 50 bacteria each from WR, WB, KR and KB of Saffron were taken up for further

analysis.To ascertain the taxonomic positions of isolated bacteria, gene sequence analysis

ofhypervariable region (V1-V3 region) of 16S rRNA was done in addition to microscopy,

biochemical characterization and the 16S rRNA gene identification, percentage sequence

similarity and GenBank accession number are given in Table 2. The bacterial strains isolated

from Wuyanand Khrewfield (WR+WB) belonged to 4 phyla namely Actinobacteria,

Bacteroidetes, Firmicutes and Proteobacteria out of which Actinobacteria, Firmicutes and

Proteobacteria belonged to WB, Bacteroidetes, Firmicutes and Proteobacteria belonged to WR

(Ambardar and Vakhlu 2013) whereas Actinobacteria, and Firmicutes to KB and Actinobacteria,

Bacteroidetes, Firmicutes and Proteobacteria to KR.Ninedifferent bacteria generanamely

Acinetobacteria, Arthrobacter, Brevibacterium, Bacillus, Chryseobacteria, Halomonas,

Pseudomonas, Serratiaand Streptomyceswere isolated from all the four soil samples withBacillus

genus was common in all the four soil samples.Phylogenetic analyses of 16S rRNA gene

sequence (V1-V3 region) of all the isolated bacteria cluster total rhizosphere bacteria and bulk

soil bacteria in separate clad thereby showing some evolutionary relationship among the

rhizobacteria (Fig 2) with Bacillus, Brevibacterium and Pseudomonas showing overlaps both in

bulk soil and rhizosphere of Saffron from Wuyan and Khrew together. The comparison of

6

bacteria between four soil samples showed that Chryseobacteria and Pseudomonas genera were

common between WR and KR, Arthrobacter, and BrevibacteriumbetweenWB and KB, Bacillus

and Pseudomonas between WB and WR and Bacillus and Brevibacterium between KB and KR.

Species level identification grouped all the 200 bacteria into 33 different bacterialspecies with

six different bacterial species from WR, ten from WB, six from KB and eleven from KR.

Although some genera were common among the four soil type but were different at species level

except Brevibacteriumhalotoleranswhich was common in WB, KB and KR,

Brevibacteriumhalotolerans and Brevibacteriumfrigoritolerans common in WB and KB; and

Bacillus aryabhattai common in WR and WB (Ambardar and Vakhlu 2013).

DISCUSSION

Roots emerge and wither in cyclic manner in Saffron but are able to harbor specific bacteria in

rhizospheric zone which is indicated by ~45 fold and ~ 88 fold increase in bacterial load in

rhizosphere than bulk soil of Wuyan(Ambardar and Vakhlu 2013) and Khrew respectively (fig

1). The number of microorganisms present in rhizosphere is higher for most of the plants

(Nannipieri et al. 2007) as in case of wild barley (Timmusk et al. 2011). Rhizodeposition

influences root- microbe interaction in various plants which is also true for saffron and results in

increase in the microbial biomass of specific bacteria in rhizosphere as compared to that of bulk

soil. The identification of random 200 bacteria was further confirmed by gene sequencing

analysis ofhypervariable region (V1-V3 region) of 16S rRNA in addition to microscopy,

biochemical characterization. Though, Sequencing of the entire 1,500-bp sequence is usually

required while describing a new species. However, for most of the bacterial isolates the initial

500-bp sequence (V1-V3 region) provides adequate differentiation for identification, as it has

substantial sequence difference between different strains. In addition, V1-V3 region 16S

7

rRNAgene shows slightly more diversity per kilobase sequenced than rest of the 16S rRNA gene

(Clarridge 2004).

Bulk vs Bulk soil:

Actinobacteria and Firmicutes were the common phyla between bulk soils of two fields with

Proteobacteria specific to bulk soil from Wuyan fields. Firmicutes consisting of single common

Bacillus genera which showed the prevalence of different species in different bulk soils

asBacillus aryabhattai and Bacillus methylotrophicus in WB (Ambardar and Vakhlu 2013) and;

Bacillus drentensis in KB.Firmicutes are abundant in bulk soil sample (Fierer et al., 2007) and

Bacillus methylotrophicusand Bacillus drentensis are reported from soil (Sharma et al., 2013;

Heyrman et al., 2004) and Bacillus aryabhattai from air samples (Shivaji, et al., 2009). In

addition of their presence in soil, Bacillus drentensis,Bacillus aryabhattai and Bacillus

methylotrophicus has been also reported from rhizosphere of Cactus (Garridoet al., 2012),

halophyte (Siddikee et al. 2010) and rice (Madhaiyan, et al., 2010) respectively.Actinobacteria

consisting of Arthrobacter and Brevibacterium was common in both the bulk soil with

Streptomyces specific to Khrew bulk soil. Actinobacteria phylum is reported to be isolated from

bulk soil of LoliumperenneandTrifoliumrepens (Janssen et al., 2002) and also from heavy metal-

contaminated bulk soil (Gremion et al.,2003). Arthrobacter being common genera was specific

to bulk soil at species level as Arthrobacterglobiformis was isolated from WB and

Arthrobacternitroguajacolicus from KB. Arthrobacterglobiformis and

Arthrobacternitroguajacolicus has been also isolated from soil (Casida et al., 1974; Kotouckova

et al., 2004) whereas A. nitroguajacolicus has been also isolated from rhizosphere of wild rye

(park et al., 2005). Brevibacterium was the only genus which was common between the bulk soil

of two fields at species level also. Brevibacteriumfrigoritolerans and

8

Brevibacteriumhalotolerans was isolated from the saffron bulk soil of both fields and are also

reported from arid soils (Delaporte and Sasson 1967). Streptomyces, another member of

Actinobacteria, was specific to bulk soil of Khrew only and comprised of Streptomyces

galilaeusand Streptomyces sanglieri. Streptomyces sanglieri, in addition to saffron bulk soil, has

also been reported from soil (Manfio et al., 2003) but Streptomyces galilaeus has not reported

from soil.The bulk soil from Wuyan field shows some distinction from Khrew by the presence of

Proteobacteria phyla comprising of Pseudomonas parafulva. Proteobacteria, being dominant in

rhizosphere, are also reported in bulk soil (Janssen et al., 2002) but Pseudomonas parafulva has

been reported from rhizosphere of wild rye (Park et al., 2005).

Rhizosphere vs rhizosphere:

Comparison of Rhizobacteria of Saffron from two different fields (Khrew and Wuyan) indicated

the presence of Bacteroidetes, Firmicutes and Proteobacteria phyla in common,with

Actinobacteria specific to saffron rhizosphere from Khrew fields only. Proteobacteria was the

dominant phyla in the rhizosphere from both fields and consisted of Pseudomonas and

Acinetobacteria from WR (Ambardar and Vakhlu 2013) and Pseudomonas, Halomonas and

Serratia from KR. Majority of rhizobacteria are reported from the three subdivisions of

Proteobacteria phyla (Fulthorpe et al. 2008), α-proteobacteria, β-proteobacteria and γ-

proteobacteria (Ahmad et al. 2008; Bhromsiri et al. 2010) which is true for saffron also, where

Gammaproteobacteria dominated the rhizosphere. Pseudomonas was the common genera in

both the rhizospheres but differed at species level as P.tremae, P. kilonensisand P.

koreensisisolated from WR and; P.mandeliiandP.moraviensis from KR.Pseudomonastremae has

been also reported from wild coffee (Muleta et al. 2008), P. moraviensisfrom banana (Ngamau et

al., 2013) but Pseudomonaskoreensis,Pseudomonas kilonensisand P.mandeliihave not been

9

reported from any rhizosphere though P. kilonensisand Pseudomonaskoreensishave been isolated

from the agricultural soils (Kwon et al. 2003).Acinetobacteriacalcoaceticuswas specific to

Saffron rhizosphere from Wuyan only, was also reported from the rhizosphere of wild rye (Cho

et al., 2010) and wheat (Prashant et al. 2009) and duckweed (Yamaga et al. 2010).Halomonas sp.

and Serratia consisting of S. ficaria and S. plymuthica were specific to KR. S. ficaria and S.

plymuthica were also reported from Angelica oilseed rape (Kalbe et al.,1996) and Grass

roots(Alstrom et al., 1987) respectively whereas Halomonassp fromxerophytic plant (Llamas et

al., 2006).Bacteroidetes, another common phylum,consists of Chryseobacteria genera which

being present in both the rhizosphere differed at species level. Chryseobacteriumelymi was

present in WR (Ambardar and Vakhlu 2013) whereas Chryseobacteriumindoltheticum in KR.

Chryseobacteriumelymiand Chryseobacteriumindoltheticum have been also reported from the

rhizosphere of wild rye (Cho et al., 2010) and garden lettuce (Young et al., 2004)

respectively.Similarly Firmicutesphyla consists of Bacillus genera which also differed at species

level in both rhizospheresas Bacillusaryabhattaifrom WR and Bacillus safensis and B.

megaterium from KR. Bacillus aryabhattai, has been reported from halophytic plants

rhizosphere (Siddikee et al. 2010), B. megateriumfrom soyabean(Zhang et al., 2000) and Bacillus

safensis from rhizosphere of wheat (Chakraborty et al., 2012). In addition to three common

phyla, Actinobacteriaphylum was also isolated from Saffron rhizosphere from Khrewfurther

consisting of Brevibacteriumhalotolerans. Actinobacteria have been also isolated from surface-

sterilized root tissues of wheat plants (Coombs and Franco 2003) and heavy metal-contaminated

rhizosphere (Gremion et al., 2003) but specifically Brevibacteriumhalotoleranshave been

reported from halophyte Prosopisstrombulifera (Sgroy et al., 2009).Nutrients concentration is

more in rhizosphere than in bulk soil thus is readily available to plants (Rengeland

10

Marschner2005). This high concentration of nutrients in the rhizosphere results in altered

abundance and composition of microbial communitieswhich differs altogether from the bulk soil.

Microbial community are influenced by root exudates which vary with different plants and their

species and thus rhizospherefavours the specific root associated microbial populations based on

modification of the root exudation profile (Hartmann et al., 2008). In the present study, bacterial

community of saffron rhizosphere from two different fields was altogether different despite

isolated from the same plant species thus showing difference at cultivar level. The influence of

microbial communities at the cultivar level has been also reviewed in the rhizosphere of

potatoes(Solanumtuberosum L.) including transgenic lines (Hartmann et al., 2009).

In the present study, only 33 different bacteria were isolated from bulk and rhizosphere of

Saffron from two fields as single media (LB agar) was used for isolation. It is well known fact

that the bacterial diversity could not be studied using culture media only as many bacteria resist

culturing in laboratory condition and thus we are able to isolate only 1% of total bacterial

diversity, leaving 99% unexplored (Amann et al., 1995). Modern techniques like culture

independent approach (Metagenomics) can be used to study the complete bacterial diversity

which isolate DNA directly from the soil/ water thus bypass culturing. Work has been also

initiated on thestudying the bacterial diversity from the metagenome of saffron rhizosphere and

bulk soil.

CONCLUSION:

Some researchers suggest that the rhizosphere microflora is soil dependent (Rengel et al., 2005)

while others suggest it is plant specific (Hartman et al., 2009). In the present study, even though

the same cultivar of Crocussativus was growm in two fields in district Pulwama of Kashmir, the

bacterial community in both bulk as well as rhizosphere soil of two fields was totally different at

11

species level and quite different at genus level. Preliminary study suggests that perhaps the soil

type play an important role in microbiology of both bulk and rhizosphere soil of Saffron fields.

ACKNOWLEDGEMENT:

Sheetal Ambardar is thankful to CSIR-UGC for Fellowship. We are also grateful to Department

of Biotechnology as some of the equipments used in the study were funded by DBT.

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19

TABLES:

Table 1: Soil analysis of soil from Wuyan and khrew

Sample pH Electrical

conductivity

Org.

Carbon

Ava. N Av. P Av.K Ca Mg Bulk

Density

units ds/m % Kg/ha Kg/ha Kg/ha ppm ppm gm/cc

Wuyan 7.35 0.13 1.36 306 26 504 3000 552 1.198

Khrew 7.57 0.14 1.94 407 35 508 2120 1458 1.082

Table 2: Bacterial isolates from bulk and rhizosphere soil of saffron from Wuyan and Khrew on

the basis of 16S rRNA sequence

S.No. Isolates Closest NCBI match/ Closest type strain Percentage

Identity

Accession

Number

WUYAN BULK SOIL

1 WBF1 Arthrobacterglobiformis 95.026 JN084065.1

2 WBF2 Bacillus methylotrophicus 98.561 JN084066.1

3 WBF3 Bacillus aryabhattai 100.000 JX233807

4 WBF4A Bacillus aryabhattai 98.569 JN084067.1

5 WBF4B Bacillus aryabhattai 100.000 JN084068.1

6 WBF5A Brevibacteriumhalotolerans 100.000 JF836006.1

7 WBF5B Brevibacteriumhalotolerans 100.000 JN084069.1

8 WBF6 Brevibacterium frigoritolerans 100.000 JN084070.1

9 WBF7 Pseudomonas parafulva 98.475 JQ751317

20

10 WBF8 Brevibacteriumhalotolerans 99.670 JN084071.1

WUYAN RHIZOSPHERE

11 WRF1 Acinetobactercalcoaceticus 99.745 JN084072

12 WRF2 Pseudomonas tremae 99.777 JQ713597

13 WRF3 Pseudomonas kilonensis 99.782 JN084073.1

14 WRF4 Chryseobacteriumelymi 99.125 JQ713598

15 WRF5 Bacillus aryabhattai 99.390 JN084074.1

16 WRF6 Pseudomonas koreensis 99.363 JQ713596

KHREW BULK SOIL

17 KBF1 Streptomyces galilaeus 91.929 -

18 KBF2 Brevibacteriumfrigoritolerans 100.000 JQ713599

19 KBF3 Streptomyces sanglieri 97.771 JQ713600

20 KBF4 Brevibacteriumhalotolerans 100.000 JQ713601

21 KBF5 Bacillus drentensis 97.968 JQ713602

22 KBF7 Arthrobacternitroguajacolicus 100.000 JQ713603

KHREW RHIZOSPHERE

23 KRF1 Brevibacteriumhalotolerans 100.000 JQ713604

24 KRF2 Pseudomonas mandelii 100.000 JQ713605

25 KRF3 Chryseobacterium sp. 94.286 JQ713606

26 KRF4 Pseudomonas moraviensis 100.000 JQ713607

27 KRF5 Bacillus safensis 97.785 JQ713608

28 KRF6 Chryseobacteriumindoltheticum 97.775 JQ713609

29 KRF7 Halomonasjohnsoniae 97.699 KC511119

30 KRF7A Bacillus megaterium 99.563 JQ7136011

31 KRF8 Bacillus safensis 100.000 KC511120

32 KRF10 Serratiaficaria 100.000 KC511121

21

FIGURES:

B R B R

Fig1: Comparison of bacterial load between bulk soil (B) and rhizosphere (R) from Wuyan

(left) and Khrew (right)

33 KRF10A Serratiaplymuthica 100.000 KC511122

22

Fig 2: Phylogenetic tree of bacterial isolates from bulk soil and rhizosphere of Saffron from

Wuyan and Khrew.

Pseudomonas

Brevibacterium

Bacillus

Chryseobacterium

Serratia

Arthrobacter

Streptomyces

BULK

RHIZOSPHERE

Halomonas

Acinetobacter

No: AU/Symposium/2014

Dated : 13-03-2014

TO WHOM IT MAY CONCERN

This is to certify that paper on Bacterial Diversity

in Rhizo-cormosphere of saffron by cultivation

dependent and independent approach authored

by Jyoti Vakhlu and Sheetal Ambardar presented

in IV International Saffron Symposium “Advances

in Saffron Biology, Technology and Trade”-is

accepted for publication in Acta Horticulturae .

The paper is under publication