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MSR Journal of Sciences

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ISSN 2394-1200 Vol No.1 Issue No.1

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Hon’ble ChairmanGEF

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MSRCASC

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Dr. Pushpa HHOD, Microbiology

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MSRCASC, Bangalore

Prof. S. G Prasanna KumarHOD, Chemistry/ biochemistry

MSRCASC, Bangalore

Prof. Asha K. KHOD, Biotechnology

MSRCASC, Bangalore

Dr. Vemula VaniAsst Professor

Dept of Microbiology MSRCASC, Bangalore

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Mr. Shiva KumarMr. Naveen Kumar

Ms. Sowmya R

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EDITORIAL

As an editor in chief of the MSR Journal of Sciences, I am writing to invite you to submit your articles of

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The scope of this journal has expanded to accommodate the consequences of the scientific revolution.

We encourage not only the traditional life sciences but also applied sciences and experimental research.

We envision the journal as the best place to publish all the level of scientific research.

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Thank you in advance for your valuable contributions to the MSR Journal of Sciences.

Dr. A. Nagarathna

Principal

1 Homology Modeling of Signal Peptidase I from Mycobacterium Tuberculosis 1 1* 2 3 4 5 Vemula Vani , Moyurakhi Gogoi , Pradeep Kumar , Swati Krishnan , Sanjay Prasad

2. Novel Textile Bio Colors from Fungi 111* 1 3 Sudha , Dr. Charu Gupta and Dr. Sunita Aggarwal

3. Artificial Neural Network: A Novel Method for Optimization of Bioproducts and

Bioprocesses: A Critical Review 211* 1 2 1 1 1 Upendra R.S. , Pratima Khandelwal , Zeinab Raftani Amiri , Rahila Banu , Aruna Barade , Veena.K ,

1 1 Gayathri.V , Yamini.D.E

4. “A New Approach: Exploring Honey Bee Venom (Apis melifera) As Anti-microbial, Anti-

inflammatory and Anti-arthritis Agent” 351* 1 1 1 Nitesh Gamare , Rajesh Banala , Ashwin Chougule , Mahesh Tengale

5. Preliminar Phytochemical Screening of Five Indian Medicinal Plants. 49 Prathiba H. D, and Prof. N. H. Manjunath*

6. Evaluation of Antibacterial Activity and Phytochemical Analysis of Vitex negundo

Selected Human Pathogens 57 G.L. Aruna* and Poojitha

7. Green Synthesis of Zno nanoparticles and Its Application in The Removal of Malachite

Green Dye 651* 2 3 4 5 6 Dr. Chandrapraba M N , Dr. Ahalya.N , Prashanth Kumar , Chaitra Barati , Rajani.D.M Vignesh.S

8. Regeneration of Bambusa Nutans in Vitro From Field Grown Nodal Explants. 711, 2 2, 3 *K. Chethan and T. S. Rathore

9. Effect of Computationally Synthesized Probable Drugs on Beta Toxin of Clostridium

perfringens 77

Prasanna D R*, Akshatha G, Ankita Sanjali, Madhuri D, Priyanka H L

10. Determination of Size of Foraging Population in Apis cerana indica and its Impact on the

Crop Productivity 89 A Nagarathna*

11. Combustion Synthesis and Characterisation of Y al o (yam) Nanopowders 974 2 91* 2 3 T. E. Kanakavalli , R. Harikrishna , A. Jagannathareddy

CONTENTS


HOMOLOGY MODELING OF SIGNAL PEPTIDASE I FROM

MYCOBACTERIUM TUBERCULOSIS1* 2 3 4 5 Vemula Vani , Moyurakhi Gogoi , Pradeep Kumar , Swati Krishnan , Sanjay Prasad

1, 2, 3,4 Department of Microbiology, M.S. Ramaiah College of Arts, Science and Commerce, Bangalore-54.5 Department of Inorganic and Physical Chemistry, Indian Institute of Science, Bangalore- 12.

1

MSRJournalofSciences1(1)2014:1-9ISSN:2394-1200

ABSTRACT

Tuberculosis, a multisystemic disease with myriad presentations and manifestations, is a most common

cause of infectious disease related mortality worldwide. As the conventional treatment is lengthy and

complex, there is pressing need for new drugs, preferably with noble modes of action to avert the

problem of cross-resistance. Several new targets have been proposed including proteins essential in the

protein secretion pathway such as type I signal peptidase (SPase I). SPase I is considered to be an

attractive target because it is essential, substantially different from the eukaryotic counterpart and

targeting SPase I might be able to reduce the persistence and shorten the therapy. As of now there is no

experimentally determined structure available for SPase I from Mycobacterium tuberculosis. The

objective of the present study is to determine the 3-dimensional (3D) structure of SPase I from

Mycobacterium tuberculosis using homology modeling. The sequence for the SPase I was retrieved

from UNIPROT database and sequence analysis was carried out using BLAST and FUGUE for the

selection of template. Crystal structure of type 1 signal peptidase from Escherichia coli in complex with

a beta-lactam inhibitor (1b12) was selected as a template. The protein modeling was performed using

M4T server ver. 3.0. The obtained 3D model of the SPase I was visualized and analyzed using Jmol.

This modeled protein structure was refined by loop modeling. Later, the quality of the protein structure

was verified by its energy and stereochemical properties. Further, the in sillico characterization of the

SPase I will be carried out. The 3D structure of SPase I, obtained from this study will be useful in

developing novel inhibitors using the methods of rational drug designing.

Keywords: signal peptidase I, tuberculosis, homology modeling, 3D structure.

Corresponding Author:

Dr Vemula Vani

Assistant Professor

Department of Microbiology

M. S. Ramaiah College of Arts, Science and Commerce, Bangalore - 54

E-mail: [email protected]

Phone: 9632119023

INTRODUCTION

In the recent past, due to advancements in genomics research, a very large number of protein sequences

have become available. The most challenging task is to deduce three dimensional (3D) structures of

these proteins.

The experimental methods to determine the protein 3D structure like X-ray crystallography, nuclear

magnetic resonance are technically demanding, time consuming and may not keep with which new

protein sequences are being discovered by genomics research. Although a large number of genes being

discovered, the number of protein structures being solved by experimental methods is limited.

Alternative strategies for structure prediction and modeling of proteins are computational methods.

The major computational methods for predicting the structure of proteins are ab initio methods and

homology modeling. Homology protein structure modeling remains the most accurate prediction

method.

Homology modeling exploits the fact that evolutionary related proteins with similar sequences have

similar structures. The degree of similarity is very high in the so-called “core regions” comprising of

secondary structural elements (α-helices and β-sheets) whereas the degree of similarity is usually low

in loop regions connecting the secondary structures. In homology modeling, prediction is made based 1

on information derived from known protein 3D structures . similar structures. The degree of similarity

is very high in the so-called “core regions” comprising of secondary structural elements (α-helices and

β-sheets) whereas the degree of similarity is usually low in loop regions connecting the secondary

structures. In homology modeling, prediction is made based on information derived from known 1

protein 3D structures .

The main steps to create a homology model are as follows: 1) Identification of structural homologues.

2) Selection of structural homologues used as templates for modeling. 3) Alignment of templates with

the protein sequence to be modeled. 4) Model building. 5) Evaluation and refinement of the model.

TB is still a major global health problem causing over 1 million deaths per year. An increasing problem

of drug resistance in the causative agent, Mycobacterium tuberculosis, as well as problems with the

current lengthy and complex treatment regimens, lends urgency to the need to develop new

antitubercular agents. Proteases play a central role in important cellular processes in all organisms

including protein turnover and the degradation of misfolded proteins, as well as gene regulation. M. 2tuberculosis has more than 100 genes encoding proteases or peptidases . The type I signal peptidase

(SPase I) plays a key role in the protein secretion process by cleaving the N-terminal signal peptide 3leading to release of the mature protein from the cytoplasmic membrane . Its activity is essential for the

4viability of all bacterial species tested including M. tuberculosis . Hence, SPase I is an attractive drug

target for TB. As of now, there is no three dimensional structure available for SPase I from

Mycobacterium tuberculosis. Thus, the objective of this study is developing the three dimensional

structure of SPase I from Mycobacterium tuberculosis using homology modeling.

MATERIALS AND METHODS

Retrieval of Spase I sequence from Uniprot database

The sequence details of the protein (SPase I) was retrieved from UniProt database. The UniProt

Knowledge base (UniProtKB) is the central hub for the collection of functional information on 5proteins, with accurate, consistent and rich annotation .

2

MSRJournalofSciences1(1)2014:1-9

Identification of template

For getting the homologous templates, PDB-BLAST (http://www.ncbi.nlm.nih.gov/BLAST) and

FUGUE were used. BLAST is a sequence similarity search program that can be used via a web interface 6, 7or as a stand-alone tool . It is a heuristic that finds short matches between two sequences and attempts

to start alignments from these 'hotspots' and also provides statistical information to help decipher the

biological significance of the alignment. FUGUE (http://www-cryst.bioc.cam.ac.uk/fugue/) was used

for recognizing distant homologues by sequence-structure comparison. It uses protein homology 8

recognition using environment-specific amino acid substitution tables from HOMSTRAD alignments .

Secondary structure prediction

SOPMA was used for secondary structure prediction of SPase I. SOPMA (Self-Optimized Prediction 9

Method with Alignment) is an improvement of SOPM method . SOPMA correctly predicts 69.5% of

amino acids for a three-state description of the secondary structure (alpha-helix, beta-sheet and coil) in 9

a whole database containing 126 chains of non-homologous (less than 25% identity) proteins .

Model building

For building the model of Spase I, an academic version of MODELLER (M4T ver-3.0) was used.

Multiple Mapping Method with Multiple Templates (M4T) (http://www.fiserlab.org/servers/m4t) is a

fully automated comparative protein structure modeling server. The novelty of M4T resides in two of its

major modules, Multiple Templates (MT) and Multiple Mapping Method (MMM). M4T server

performs three main tasks in an automated manner: (i) template search and selection performed by the

Multiple Template (MT) module; (ii) target sequence to template structure(s) alignment, performed by 10 11the Multiple Mapping Module (MMM) module and (iii) model building, performed by Modeller .

The software, Jmol version 14.0.11 was used to visualize and analyze the modeled Spase I. Jmol is free

software, an open source project in molecular visualization developed by a community of volunteers. It

is available for free at www.jmol.org. It is written in Java programming language, which makes it 12

compatible with all operating systems .

Loop modeling

Modeling of the erred loops in modeled SPase I structure was carried out using Swiss- PDB Viewer

version 4.1. Swiss-PDB Viewer (aka Deep View) is an application that provides a user friendly 13, 14

interface allowing analyzing several proteins at the same time . In this study, Swiss-PDB Viewer was

used to remodel the regions which showed instability in the Verify 3D graph.

Evaluation of the modeled structure

The evaluation of the obtained model for SPase I was done using Verify 3D program. The three-

dimensional (3D) profile of a protein structure is a table computed from the atomic coordinates of the

structure that can be used to score the compatibility of the 3D structure model with any amino acid

sequence. Three-dimensional profiles computed from correct protein structures match their own 15, 16, 17sequences with high scores .

The stereo-chemical quality of the SPase I structure was analyzed by Ramachandran plot using the

software RAMPAGE. RAMPAGE is an offshoot of RAPPER which generates a Ramachandran plot 18

using data derived by the Richardsons and co-workers . The Ramachandran diagram plots phi versus 19

psi dihedral angles for each residue in the input .pdb file. The diagram is divided into favoured,

3


allowed and disallowed regions. If the model is not convincing in terms of profile scores and stereo-

chemical quality, the modeling is done by rebuilding the erred regions.

RESULTS AND DISCUSSION

Sequence analysis

In this study, the complete sequence of the protein, SPase I from Mycobacterium tuberculosis was

retrieved from Uniprot database. Along with the sequence, the annotated information such as the

sequence length (294 aa), gene name (Lep B) and sub cellular location (cell membrane, single-pass type

II membrane protein) and sequence similarity (belongs to the peptidase S26 family) were retrieved

from Uniprot.

Identification of template

In order to search for the template for model building, the Spase I sequence was submitted to PDB-

BLAST server. To confirm the results obtained from PDB-BLAST, the sequence was also submitted to

FUGUE, the fold recognition program.

It was seen that, signal peptidase I from E. coli has 27% of identity among the homologous sequences

which resulted from PDB-BLAST server and the same protein showed the maximum Z score of 10.98

from FUGUE server indicating a strong match with the Spase I sequence.

Secondary structure prediction

The secondary structure of the SPase I protein was predicted by SOPMA. The predicted secondary

structure results revealed that the proportion of random coils, β turns, α helices and extended strands (β

folds) accounted for 62.59%, 3.74%, 14.97% and 18.71% of the secondary structure, respectively from

figure 1.

Figure 1: Secondary structure of S Pase I

The above mentioned evidences indicate that signal peptidase from E.coli with the PDB code 1b12 can

be taken as the template for homology modeling of the target sequence, Spase I.

The template for SPase I from E.coli : 1B12A, B

1B12A, B is the PDB code for the crystal structure of type I signal peptidase (beta- lactam inhibitor of oE.coli). The structure has been solved and refined at 1.9A resolution in complex with an inhibitor, a

beta-lactam (5S,6S penem) demonstrating that this residue acts as the nucleophile in the hydrolytic

mechanism of signal-peptide cleavage. Type I signal peptidases have been classified into the

evolutionary class of serine proteases SF, which utilize a Ser/Lys catalytic dyad mechanism as opposed

4


to the more common Ser/His/Asp catalytic triad mechanism. The E. coli SPase structure has a mainly b-

sheet protein fold, consisting of two large antiparallel b-sheet domains, two small 3 -helices 10

(consisting of residues 246-250 and 315-319), and one small a-helix (residues 280-285). There is one

disulphide bond, as was found in earlier biochemical studies, between Cys 170 and Cys 176. This bond

is located immediately before a beta-turn in the domain II beta-sheet.Type I signal peptidases are

membrane bound endopeptidase which function to cleave away the signal peptide from the

translocated preprotein, thereby releasing secreted proteins from the membrane and allowing them to 20,21

locate to their final destination in the periplasm, outer membrane, or extracellular surrounding .

Model building

The refined sequence-structure alignment as obtained by FUGUE server was used to construct 3D

models of SPase I using MODELLER (M4T ver.3.0). The target sequence alignment with templates

that were used for building the 3D model with PDB id 1b12 and 1kn9 is shown in figure 2.

A full atom model in PDB format for SPase I was obtained from M4T. The model generated from M4T

ver 3.0 was submitted to Verify 3D program and the graph was obtained (Figure 3). The graph revealed

that some of the regions in the modelled structure were not stable and such regions corresponded to the

regions of insertion and deletion. These regions were considered for loop modeling.

Figure 2: Alignment of multiple template sequences (1kn9D, 1b12C) with query sequence

(Spase I) obtained from M4T version 3.0

5


Loop modeling

The regions showing troughs in the Verify 3D graph of modelled SPase I structure were considered for

loop modeling using Swiss PDB Viewer (SPDBV). Table I shows the regions that were considered for

loop modeling.

While remodelling the erred loop regions, for each selected loop, anchor residues were carefully

selected and the loop database of SPDBV was scanned and one loop was selected based on its stereo-

chemical compatibility and its side chain interaction with the rest of the structure. The loops selected

were added to the model one at a time, and all the selected loop regions were remodelled. After

remodeling the loop regions, the model was subjected to energy minimization using Swiss PDB

Viewer. This process was repeated until the model obtained satisfied the criteria of Ramachandran plot,

Verify 3D graph and energy.

The Verify 3D graph for the finally obtained model is shown in figure 4. The regions of the troughs in

the verify 3D graph of the model, generated by Modeller ( figure 3) were found to be improved.

Compatibility scores above zero in Verify 3D graph indicating acceptable side chain environments and

reliability of the modelled structure for SPase I .

The number of residues found in the different categories of region of Ramachandran plot (Figure 5) in

the refined Spase I structure is shown in Table II.

Loop number

1

2

3

4

Residues number

19-22

64-69

169-177

197-200

Table I: The loop regions considered for remodelling using Swiss PDB Viewer

6


Table II: Residues found in the different categories of region of Ramachandran plot

These results indicate that the modelled structure of Spase I (Figure 6) is stereo-chemically satisfactory.

Figure 3: Verify 3D graph for the best model generated by MODELLER (M4T) for the target SPase I

Figure 4: Verify 3D graph for SPase I after remodeling the loop regions

Figure 5: Ramachandran Plot for the modeled protein, SPase I

7


Figure 6: The 3D structure of modeled SPase I as visualized using JMol.

CONCLUSION

Tuberculosis (TB) is among the top three leading causes of death by a single infectious agent

worldwide. There is a need to understand the complex biology and pathogenic potential of M.

tuberculosis in order to identify key pathways against which novel therapeutics can be developed. In M.

tuberculosis, over 250 proteins are exported across the cytoplasmic membrane through either type I or

type II signal peptidase-mediated mechanisms, and many of those proteins are important in bacterial

pathogenesis. The type I SPase is considered to be an attractive target because it is essential for the

survival, substantially different from the eukaryotic counterpart due to difference in structure and

localization, and its active site has ser/ lys catalytic dyad which is located at the outer leaflet of the

cytoplasmic membrane, permitting relatively easy access to potential inhibitors. Using the method of

homology modeling, the structure of SPase I from M. tuberculosis was predicted and the quality of the

structure was found to be convincing. This modelled structure of Spase I can be used in rational drug

designing for developing potential inhibitors. These inhibitors could help to shorten therapy by

targeting replicating and nonreplicating bacteria.

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Prisant, J.S. Richardson. 2003. Structure validation by Calpha geometry: phi,psi and Cbeta

deviation. Proteins: Struct. Funct. Genet. 50, 437-450.

19. Ramachandran, G.N., Sasisekharan, V., 1968. Conformation of polypeptides and proteins. Adv.

Protein Chem. 23, 283-438.

20. Mark Paetzel, Ross E. Dalbey & Natalie C. J. Strynadka. 1998. Crystal structure of a bacterial

signal peptidase in complex with a -lactam inhibitor. Nature 396, 186-190.

21. Mark Paetzel, Ross E. Dalbey, Natalie C.J. Strynadka. 2000. The structure and mechanism of

bacterial type I signal peptidases-A novel antibiotic target. Pharmacology & Therapeutics 87,

2749.

9


NOVEL TEXTILE BIO COLORS FROM FUNGI1* 1 3

Sudha , Dr. Charu Gupta and Dr. Sunit a Aggarwal1Department of Fabric & Apparel Science, Institute of Home Economics, University of Delhi,

F-4 Hauz Khas, New Delhi-110016, INDIA2Department of Fabric & Apparel Science, Institute of Home Economics, University of Delhi,

F-4 Hauz Khas, New Delhi-110016, INDIA3Department of Microbiology, Institute of Home Economics, University of Delhi,

F-4 Hauz Khas, New Delhi-110016, INDIA

ABSTRACT

The dyestuff industry at present is suffering from increase in costs of feedstock and energy for dye

synthesis and moreover they are under increasing pressure to minimize the damage to the environment.

The industries are continuously looking for cheaper and more eco-benign routes to existing dyes. Dyes

from natural sources such as plant and animal dyes are eco-friendly but have inherent drawbacks like

very low yield, non standardised, poor fastness properties and high cost. Therefore, there is an

emerging need for searching novel sources of textile dyes like from microorganisms i.e. bacteria, fungi,

yeasts and algae. Microbial dyes has several advantages over other natural dye sources viz.

independence from weather conditions, easy and fast growth even on cheaper waste substrates and

color of different shades. In the present study extracellular pigment was obtained from a fungi

Penicillium vinaceum under optimised fermentation conditions like media, time and temperature. It

produced two hues purple and brown when grown in Czapek Dox Broth (CZB) and Potato Dextrose

Broth (PDB) at 28 °C respectively. Highest optical density was attained in 17 days in stationary ± 2

cultures. The colored liquid was separated from the colorless mycelia and used as dye liquor for dyeing

unmordanted silk and wool fabrics at 70°80°C for about 45 min. Assessment of dyed fabrics in terms of

fastness revealed good to excellent wash and rub fastness. Percentage absorption and color value has

been estimated to be greater for wool than silk. The analysis by simple chromatography revealed multi

component nature of the pigment obtained.

Keywords : Rhizopus spp., Penicillium vinaceum, Color, Dyeing, Chromatography, Fastness

Corresponding author

Sudha Pandey

* Email: [email protected]

Phone : 7838422458

11


Introduction

The dyestuff industry is suffering from increase in costs of feedstock and energy for dye synthesis, and

is under increasing pressure to minimize the damage to the environment. The industries are 1.

continuously looking for cheaper, more eco-benign routes to existing dyes Natural dyes are eco-

friendly for the environment as compared to the synthetic dyestuff that can exhibit better 2,3biodegradability and generally have a higher compatibility with the environment . Natural dyes can be

obtained from vegetable sources like roots, stems, leaves, flowers and fruits of various plants or can be

obtained from animal sources like dried bodies of certain insects, shellfish, cochineal, lac etc. as well as 4

from certain microorganisms . It is a well known practice to extract the natural colours from the plant 5

sources but the yield is very low and they have low eco-efficiency . Therefore, there is an emerging

need for searching for new sources apart from these plant based natural dyes. Extraction of colours from

the microbial source is an upcoming field. Various microorganisms like bacteria, fungi, yeasts and

algae are coloured and natural colours can be extracted from these sources using simple and effective

protocols. Before extracting the colour from these microbes these are also looked for their safety and

efficacy. Some studies have confirmed the non toxicity and biodegradability of the fungal pigments.

Microbial cell production offers reliable scalable technology. The advantages of pigment producing

microorganisms include independence from weather conditions, easy and fast growth and colours of 6different shades can be obtained by growing on cheap substrates under controlled conditions . If

microbes are cultured in fermentation medium and their growth kinetics are optimized for maximum

pigmentation for possible use as textile colorants, they can prove to be 'bioengineered' textile dyes.

These can be standardized and subjected to direct experimental control. Thus, they can combine the

advantages of both plant based and synthetic dyes. Investigation in production and evaluation of

microbial pigment as textile colorants is currently being investigated at the British Textile Technology 7-10

Group .

Fungi are ecological interesting source of pigments, as some of these species are rich in stable colorants

such as anthraquinone. Anthraquinones are secondary metabolites produced from fungi that can be

used as textile colorant. A number of anthraquinone derivatives have been identified from various

species of fungi and lichens. These metabolites are of interest because many of them possess significant

antibiotic activity, primarily against Gram-positive bacteria and Pseudomonas aeruginosa. 11-13

Anthraquinones are also reported to have antiprotozoal and cytotoxic activities . In view of these

aspects, the present study involved the isolation of pigment producing fungi; optimize their colour

production and check in the dyeing ability of different fabrics with these colorants. Further dyed fabrics

were assessed for colour fastness and toxicity.


Fungal Isolates

Pigment producing fungi were isolated from air and soil of nearby locality using PDA (Potato Dextrose

Agar) plates (extract of 300 g peeled potato, 2.5 g glucose, 15 g agar in 1000 ml distilled water). For

isolation of fungi from air, PDA plates were exposed in air for 5 mins at different areas within the

college. For soil, samples were collected randomly and 1 g of each was dissolved in 10 ml of sterile

distilled water in a test tube. Then 0.1 ml of soil solution was spread onto the PDA plates with the help of

sterile spreader. All PDA plates were kept in B.O.D incubator at 28ºC±2ºC for 3-4 days. Different

fungal colonies appeared on PDA plates from which pure cultures of the pigment producing fungi were

12


obtained by transferring them onto fresh PDA plates and incubating at 28ºC±2ºC for 3-4 days. After

screening one member of samples color producing species was Rhizopus spp. obtained from soil.

Another fungal sample i.e Penicillium vinaceum used in the study was sourced from Division of Plant

Pathology, Indian Agricultural Research Institute, New Delhi, India.

Pigment production

For color production, mycelia disk (5mm diameter) from pure cultures of Rhizopus spp. and

Penicillium vinaceum was taken from PDA plates and inoculated individually in different culture

media viz Potato Dextrose Broth (PDB), Czapek Dox Broth (CZB) and Minimal Media Broth (MMB).

Incubation was done for 3 weeks at different temperatures i.e. 15°C, 28°C and 37°C to standardise the

optimum temperature. Incubation was also done both under stationary and shaking conditions to

maximise the color production.

After about 3 weeks of incubation fungal cultures showing color were filtered out using nylon mesh.

Further to extract color, fungal mycelia separated out from supernatant liquid was crushed using the

Homogeniser (Remi Motors). Crushed mycelia was then divided into 2 parts and to each part 10 ml of

methanol and 10 ml of chloroform was added and stirred using Magnetic stirrer (Remi). As no colorant

was extracted from the mycelia of two fungi after homogenisation it was discarded. Only colored

culture filtrate (supernatant) was used as dye liquor.

Material for dyeing

For dyeing purpose desized and scoured 100% cotton, silk and wool having thread count 130, 234 and

117 respectively were used. Metallic mordants like alum, copper sulphate and ferrous sulphate of

analytical grade were used for silk and wool, whereas, Harad (Myrobalan) was used for cotton. The

percentages of mordants taken were on the weight of the fabric (o.w.f) as 5%, 10% and 20% and the

method used for mordanting was pre-mordanting at a MLR 1:30 at 60°C for 30 min.

Dyeing

Before dyeing pH of the supernatant was checked and then 50 ml of each of the colored culture filtrate

(supernatant) was used to dye 1g unmordanted and mordanted silk, wool and cotton samples at 70°-80°

for 45 min. The dyed samples underwent sequential treatments: rinsing with cold water, washing in a

bath containing 3 g/l nonionic detergent at a material/liquor ratio of 1:30 at ambient temperature for 5

min, a second rinsing with cold water, and drying in air.

Analysis of dyed fabrics

Percentage Absorption

Percentage absorbance of the dyed fabrics was calculated using Spectro-photometer 107 (Systronic,

India) as:

O.D before dyeing O.D after dyeing

% absorption = ____________________________________× 100

O.D before dyeing

13


Color measurement

Color strength (K ⁄ S), L*, a* and b* values were calculated using computer color matching system

(Macbeth-color eye 3100).

Color fastness

The dyed fabrics were evaluated for color fastness to rubbing (crockMETER-1™ ISO 9001:2000

group) and colour fastness to washing using (digiWASH-INX™ ISO 9001: Certified group by

Paramount Instruments Pvt. Ltd., India).

Analysis of colorant via paper chromatography

A simple paper chromatography was carried out for separation of pigments with the help of different

solvents like: Water, Chloroform: Methanol: Acetic acid: Distilled water (25:15:4:2) and Butanol:

Acetic acid: Distilled water (60:15:25). Chromatography columns were prepared using these three

compositions and in each column a chromatography paper was set having concentrated dried spots of

the colorants created with the help of capillary tubes. Columns were then left untouched till solvent

ascended and various components separated out as spots or zones. After separation retention factor (Rf)

value was calculated for the separated components.

Results and Discussion

Fungal Pigments

Two fungi namely Penicillium vinaceum and Rhizopus spp. were grown in different media to produce

the pigments. It was found that color produced by both the fungi was extracellular. As no or little color

production was obtained after use of MMB (minimal media broth), shaking incubation conditions and

homogenisation and extraction from fungal mycelia; these were not used further in the study.

Penicillium vinaceum produced maximum pigmentation both in CZB and PDB at 37°C but results of

pigmentation were not consistent on repeating the experiment at this temperature. Hence 28°C was

considered the optimum temperature for maximum pigmentation and was used for further

experimentation. At 37°C Penicillium vinaceum produced two hues purple and brown in CZB and PDB

respectively. Rhizopus spp. grown in PDA at 15°C produced red hue. The incubation time for both the

fungi was selected by analysing the optical density (O.D) of culture filtrate incubated for different time

intervals at λmax 650 nm using Spectrophotometer. Highest optical density in culture filtrate of both

the fungi was attained within 17 days after which the value of optical density remained same (Figure 1

and 2). Hence, an incubation period of 17 days was chosen as best time period for growth of the fungi

viz Penicillium vinaceum and Rhizopus spp. for attaining the maximum pigmentation.

Figure 1: Optical density (O.D) of Penicillium vinaceum culture grown over a period of 21 days

14


Figure 2: Optical density (O.D) of Rhizopus spp. culture grown over a period of 21 days

Dyeing

Before dyeing, pH of the colored culture filtrate was also tested to see a change in pH. As quoted by 14

some researcher's fungus growing in the medium and the medium itself can alter its pH . The pH of the

culture filtrate obtained from Penicillium vinaceum both in CZB and PDB at 28°C was neutral i.e.

7.Whereas it was acidic i.e. 3 for Rhizopus spp. grown in PDB at 15°C. On dyeing only silk and wool

were dyed and cotton did not stained at all even when pH of the colored liquor was made neutral.

Unmordanted samples of silk and wool were successfully dyed

in hues of purple, brown and red respectively (Figure 3). Whereas, mordanting with metallic mordants

produced duller shades. This may be because of the colorant itself, as it is assumed to have some

proteolytic enzymes that aid in dyeing protein fibers. There may be a possibility that the complexes of

metallic mordants hinder the action of enzymes present in the colorant and the resultant dyeing is not

proper.

Figure 3: Wool and silk samples dyed with supernatant of Rhizopus spp. and

Penicillium vinaceum15


Analysis of dyed fabrics

Percentage Absorption

The optical density of all the culture filtrate before and after dyeing was recorded and used to calculate

percentage absorption for wool and silk fabric (Table I).

As evident from Table I the percentage absorption of colorant is more in wool than silk. This is because

wool has more amino acids and higher amorphous areas than silk. Absorbency of wool is greater than 15

that of silk . It was also found that percentage absorption of wool is more for colorant obtained from

Rhizopus than obtained from Penicillium vinaceum. It is because pH of Rhizopus spp. was 3 (more

acidic) whereas for Penicillium vinaceum it was 7 (neutral). The wool fibre contains equal amount of

amino and carboxyl groups which ionize and form a zwitter ion.

Table I: Percentage absorption of silk and wool dyed with Penicillium vinaceum

and Rhizopus spp.

At low pH the hydrogen ions are absorbed by carboxyl groups of wool protein (Keratin). At high

pH, the protein loses hydrogen ion leaving behind ionized groups. Thus wool absorbs maximum dye at 16

acidic medium . Overall it was found by visual evaluation that dyed samples were darker in shade but

the dye exhaustion is not complete so absorbance is not 100%. This shows that by standardizing the dye

recipe in terms of pH, time, temperature and addition of auxiliaries, we can improve the absorption of

the dye stuff to various fibres

Colour measurement

Table II summarises K/S, L*, a* and b* values of wool and silk using computer colour matching

system. From the table it is clear that wool has higher K/S value in all the cases than silk. This indicates

that the colour produced on wool is intense and bright than silk as shown in Figure 3.

Color fastness

As per the ratings of standard SDC Grey scale the dyed samples exhibited good to excellent rub and

wash fastness properties (Table III). It is evident from Table III that fastness to wash is excellent for all

the samples. Whereas, rub fastness to dry and wet rubbing showed varied results i.e. for dry rubbing

Fabric

Penicillium Vinaceum Rhizopus spp.

PDB (28˚C) CZB (28˚C) PDB (15˚C)

Optical

density

before

dyeing

Optical

density

after

dyeing

Absorption

%

Optical

density before

dyeing

Optical

density after

dyeing

Absorption

%

Optical

density before

dyeing

Optical

density after

dyeing

Absorption

%

Silk

1.695

0.995

41.29

1.521

0.821

46.02

1.233

0.636

48.41

Wool

1.695

0.732

56.81

1.521

0.711

53.25

1.233

0.478

61.23

16


Penicillium vinaceum samples fastness was recorded as 4/5 i.e. very less staining means very good dry

rub fastness and for Rhizopus spp. samples it was recorded as 5 i.e. no staining which means excellent

dry rub fastness. For wet rubbing Penicillium vinaceum samples fastness was ranged from 3/4 to 4 i.e.

considerable to less staining which means good wet rub fastness and for Rhizopus spp. samples it

varied from 4/5 to 5 i.e. very less staining to no staining which means very good to excellent wet rub

fastness.

Analysis of colorant via paper chromatography

Out of the three compositions used, only water composition revealed that the dye is multicomponent as

it is showing range of colors or color zones on the chromatograph having different Rf values. Spot-A of

Rhizopus spp. (PDB, 15°C) had just a single color component (orangish brown) at a Rf value 0.885

whereas, Spot-B and C of Pencillium vinaceum in PDB and CZB had two color components (orange

and brown) at a Rf value 0.596 and 0.783 and three components (pink, red and brown) at a Rf 0.42,

0.570 and 0.774 respectively (Figure 4).

Table II: K/S, L*, a*, b* values of dyed samples of silk and wool with Penicillium vinaceum

and Rhizopus spp.

Fabric

Penicillium vinaceum

Rhizopus spp.

PDB (28˚C)

CZB (28˚C)

PDB (15˚C)

K/S

L*

a*

b*

K/S

L*

a*

b*

K/S

L*

a*

b*

Silk

7.0

29.2

7

4.5

6

1.74

6.34

20.5

4

9.67

-2.46

8.89

34.1

7

22.5

2

11.46

Wool

12.1

2

21.4

0

3.8

6

0.992

8.98

18.9

8

13.4

5

-2.85

11.6

1

32.3

5

25.0

7

9.03

Conclusions

Textile bio-colorants were efficiently extracted from two fungi namely Penicillium vinaceum and

Rhizopus spp. The colorants obtained were extracellular and no intracellular pigment was extracted

from both the fungi. Further, it was found that physico-chemical growth conditions of the fungi can be

controlled and optimized to get maximum pigmentation. Penicillium vinaceum grown in CZB and PDB

at 28°C produced two hues purple and brown respectively. On the other hand, Rhizopus spp. grown in

PDA at 15°C produced red hue. For both the fungi the highest optical density was observed within 17

17


Table III: Colour fastness tests on wool and silk samples dyed with Penicillium Vinaceum and

Rhizopus spp.

* Standard fabric 1 is same as specimen and standard fabric 2 is cotton

days of incubation in stationary cultures after which no further change in colorant was seen. After

extraction of optimized colorants dyeing of silk and wool samples, with and without mordanting was

carried out. Mordanting seems to have no significant effect on the

Samples

Rub fastness

Wash fastness

Dry

Wet

Staining

Staining

Colour

Staining

Staining

on

standard

cotton

cloth

on

standard

cotton

cloth

change in

*specimen

on

*standard

fabric 1

on

*standard

fabric 2

Wool

(Pencillium vinaceum

CZB, 28˚C)

4/5

4

5

5

5

Silk


(CZB, 28˚C)

4/5

4

5

5

5

Wool


PDB, 28˚C)

4/5

3/4

5

5

5

Silk


PDB, 28˚C)

4/5

4

5

5

5

Wool

5

4/5

5

5

5

18


Figure 4: Paper chromatography of culture filtrate of Rhizopus spp. and Pencillium vinaceum-

Spot-A: Rhizopus spp. (PDB,15°C), Spot-B: Pencillium vinaceum (PDB, 28°C) and

Spot-C: Pencillium vinaceum (CZB, 28°C)

Spot-C Spot-A

Spot-B

Rf – 0.885

Rf – 0.596

Rf – 0.783

Rf – 0.774

Rf – 0.570

Rf – 0.472

dyeing performance as its inability to produce different hues and hence was eliminated from the study.

Without mordanting samples of silk and wool were successfully dyed into bright shades of purple,

brown and red corresponding to it supernatant colors at 70°-80°C for about 45 min. Dyed samples were

then subjected to spectrophotometer analysis to calculate the percentage absorption and color value of

the dyed samples and have been estimated to be greater for wool than silk. Fastness tests like rub and

wash fastness revealed good to excellent fastness of the dyed samples. Analysis of pigments by paper

chromatography revealed that the colored culture filtrate of Penicillium vinaceum is multi component,

whereas, that of Rhizopus spp. is a single color component. Ultimately, this can be concluded that

fungal sources can be exploited for color production for using as a textile dye under controlled

experimentation either in a small setup or on a mass scale basis in an eco friendly manner on wide

variety of substrates.

19


References

1. Duran, N., M.F.S. Teixeira, R. De Conti and E. Esposito, 2002. Ecological-friendly pigments

from fungi. Critical reviews in food and nutrition, 42:5366.

2. Tiwari, V. and P.S. Vankar, 2001. Unconventional natural dyeing using microwave and sonicator

with alkanet root bark: part II. Colourage, 48 (5): 493-499.

3. Gupta, D., S. Kumari, M.Gulrajani, 2001. Dyeing studies with hydroxyanthraquinones extracted

from Indian madder. Coloration Technology, 117: 333-336.

4. Atalla, M.M., E.A.M. El-khrisy, Y.A. Youssef and A.A. Mohamed, 2011. Production of textile

reddish brown dyes by fungi. Malaysian Journal of Microbiology, 7(1): 33-40.

5. Raisainen, R., P. Nousiainen and P.H. Hynninen, 2002. Dermorubin and 5-chlorodermorubin

natural anthraquinone carboxylic acids as dyes for wool. Textile Research Journal, 72: 973-976.

6. Gupta, C., A.P. Garg, D. Prakash, S. Goyal and Gupta S., 2011. Microbes as potential source of

biocolours. Pharmacologyonline, 2: 1309-1318.

7. Hamlyn, P.F., 1995. The impact of biotechnology on the textile industry. Textile Magazine, 3:6-

10.

8. Youssef, M.S., O.M.O. El-Maghraby and Y.M. Ibrahimn, 2008. Mycobiota and Mycotoxins of

Egyptian Peanut (Arachis hypogeae L.). International journal of Botany, 4 (4): 349-360.

9. Daniel, J.D., T.S. Silvana, F.H. Plinho and B. Adriano, 2007. Production of extracellular b-

glucosidase by Monascus purpureus on different growth substrates. Process Biochemistry, 42:

904-908.

10. Ferreira-Leitao, V.S., M E Andrade de Carvalho and E P S Bon, 2007. Lignin peroxidase

efficiency for methylene blue decolouration: Comparison to reported methods. Dyes and

pigments, 4: 230.

11. Nagia, F.A. and R.S.R EL-Mohamedy, 2007. Dyeing of wool with natural anthraquinone dyes

from Fusarium oxysporum. Dyes and Pigments, 75(3): 550-555.

12. Yagi, A., N. Okamura, H. Haraguchi, T. Abo and K. Hashimoto, 1993. Antimicrobial

tetrahydroanthraquinones from a strain of Alternaria solani. Phytochemistry, 33(1): 87-91.

13. Okamura, N., H. Haraguchi, K. Hashimoto and A. Yagi, 1993. Altersolanol-related antimicrobial

compounds from a strain of Alternaria solani. Phytochemistry, 34(4): 1005-1009

14. Cho, Y.J., J.P. Park, H.J. Hwang, S.W. Kim, J.W.M. Choi and J.W. Yun, 2002. Production of red

pigment by submerged culture of Paecilomyces sinclairii. Letters in Applied Microbiology,

35(3): 195-202

15. Gohl, E. P. G. and L. D. Vilensky, 2005. Textile Science: An Explanation of Fibre Properties.

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Bougainvillaea as wool colourant with mixed mordant. Indian Journal of Fibre and Textile

Research, 26: 432.

20


ARTIFICIAL NEURAL NETWORK: A NOVEL METHOD FOR OPTIMIZATION OF

BIOPRODUCTS AND BIOPROCESSES: A CRITICAL REVIEW

1* 1 2 1Upendra R.S. , Pratima Khandelwal , Zeinab Raftani Amiri , Rahila Banu ,

1 1 1 1Aruna Barade , Veena.K , Gayathri.V , Yamini.D.E1Department of Biotechnology, New Horizon College of Engineering, Bangalore, India, [email protected]

2 Department of Food Science, Faculty of Agricultural Engineering, Sari Agricultural Sciences and Natural Resources,

Sari, Iran

ABSTRACT:

Artificial Neural Network (ANN) is a computerized program designed to simulate the process in which

the Central Nervous System (CNS) functions. In a recent time ANN being increasingly used in

biotechnology and pharmaceutical research to predict the non-linear relationship between casual

factors and response variables. ANN has a remarkable ability to derive meaningful information from

complicated data. Medium formulation and optimization is essential for the success of an industrial

fermentation as it directly affects the time and cost of bio-products and most of the optimization

methods like conventional, Plackett burmann and Response surface methodologies finds a multi-

objective simultaneous optimization problem which can be solved through ANN. The potential

application of ANN methodology in the Biotechnology and pharmaceutical science, range from

interpretation of analytical data, optimization of drug production and drug dosage, optimization of

bioremediation process of polluted, waste water treatment, and also from design through bio-pharmacy

to clinical pharmacy. Present review focuses on introduction to ANN, ANN types, ANN working model,

various software tools used and some real time applications of ANN in optimization of bioproducts and

bioprocess.

Keywords: Artificial Neural Network, Biopharmaceutical, Bioproducts, Optimization, Simulation.

Corresponding Author :

Pratima Khandelwal

Professor and Head

Biotechnology Department

New Horizon College of Engineering

Bangalore


21

ISSN:2394-1200

INTRODUCTION

Neural networks arrived on the basis of central nervous system and the neurons are considered to be one 1

of its most significant information processing elements . The word network in the term 'artificial neural

network (ANN) refers a biologically inspired computational tool, simulating the connective behavior

of natural neurons, and is used in modeling of various systems. Its power resides on its ability to learn 2

from historical process data and to approximate linear and non linear functions . The universal agreed

definition is a network of simple processing elements (neurons) which can exhibit complex global 3

behavior, determined by the connections between the processing elements and element parameters . In

a neural network model, simple nodes are connected together to form a network of nodes hence the term 4 5"neural network" & .

In most cases an ANN is an adaptive system that changes its structure based on external or internal

information that flows through the network. In more practical terms neural networks are non-linear 6

statistical data modeling tools . They can be used to model complex relationships between inputs and 7

outputs or to find patterns in data . An ANN is typically defined by three types of parameters; 1.The

interconnection pattern between the different layers of neurons.2. The learning process for updating the

weights of the interconnections.3. The activation function that converts a neuron's weighted input to its

output activation. In modern software implementations of artificial neural networks the approach

inspired by biology has more or less been abandoned for a more practical approach based on statistics 8and signal processing .

Neural networks have been used successfully to a broad range of areas such as business, data mining,

drug discovery and biology. In medicine, neural networks have been applied widely in medical

diagnosis, detection and evaluation of new drugs and treatment cost estimation. In addition, neural

networks have begin practice in data mining strategies for the aim of prediction, knowledge discovery 10 11

9. Modeling and optimization are important aspects in the microorganisms development & .

Conventional optimization method (single variable optimization) is not only time-consuming and

tiresome but also unable to describe the complete effects of the parameters in the process, and ignores

the interactions between physicochemical parameters. In addition, the conventional method may lead 12 13to misinterpretation of results & . Statistical methods, such as, RSM and ANN are rapid and reliable

methods, which may be used to overcome the problem in conventional methods via decreasing the total

number of experiments, preparing short lists significant factors and process by regarding the reciprocal

interactions among the variables and to give an estimate of the united effects of these variables. ANNs 14

are methods that apply artificial learning tool for optimization . Besides microbiology, ANN has been 15 16

used in different scientific optimization processes successfully & .

Present review focused and emphasized on introduction to ANN, simulation studies, ANN working

model, various software tools used, various critical real time applications of artificial neural network in

biopharmaceuticals, bioproduct design and development, and Bioremediation process optimization.

1. TYPES OF ARTIFICIAL NEURAL NETWORK

Simple competitive networks: Used to analyze raw data of which has no prior knowledge. The only

possible way is to find out special features of the data and arrange the data in clusters so that elements

that are similar to each other are grouped together.

22


Adaptive Resonance Theory (ART) Networks: Adaptive resonance architectures are capable of

stable categorization of an arbitrary sequence of unlabeled input patterns in real time. Introduced by 17 18

Grossberg & . ART networks are biologically motivated and were developed as possible models of

cognitive phenomena in humans and animals.

Feed forward neural network: In this type connections between the units do not form a directed cycle,

the information moves in only one direction, forward, from the input nodes, through the hidden nodes

(if any) and to the output nodes. There are no cycles or loops in the network.

a) Single layer perceptron: The simplest kind of feed-forward network. Consists of a single layer of

output nodes; the inputs are fed directly to the outputs via a series of weights.

b) Multi layer perceptron: Consists of multiple layers of computational units, usually interconnected

in a feed-forward way.

Generalized regression neural network: A generalized regression neural network (GRNN) is often

used for function approximation. It has a radial basis layer and a special linear layer.

Feed forward back propagation neural network: Back propagation works by calculating the overall

error rate of a neural network. The output layer is then analyzed to see the contribution of each of the

neurons to that error. The neurons weights and threshold values are then adjusted, according to how

much each neuron contributed to the error, to minimize the error next time.

Time delay neural network: Is an artificial neural network architecture whose primary purpose is

to work on sequential data. The TDNN units recognise features independent of time-shift (i.e.

sequence position) and usually form part of a larger pattern recognition system. 3. VARIOUS

SOFTWARES USED IN ANN

Neural lab: Neural Lab is a free neural network simulator developed at the University of Guanajuato.

One of the main features is that it provides a visual environment to design and test artificial neural

networks. The tools allow reviewing and analyzing the structure of the training set, it is possible to see

the activation of the neurons for each case in the data set. The tutorial of Neural Lab provides some

examples in, prediction, data mapping, data classification and auto associative memory problems.

Neuro solution: Developed by Neuro Dimension. It combines a modular, icon-based (component-

based) network design interface with an implementation of advanced learning procedures, such as

conjugate gradients, Levenberg-Marquardt and back propagation through time. The software is used to

design, train and perform a wide variety of tasks such as data mining, classification, function

approximation, multivariate regression and time-series prediction. Neuro Solutions provides three

separate wizards for automatically building neural network models:

a) Data Manage R: Allows the user to import data from Microsoft Access, Microsoft Excel or text

files and perform various preprocessing and data analysis operations.

b) Neural Builder: Centers the design specifications on the specific neural network architecture the

user wishes to build. Once the neural network architecture is selected, the user can customize

parameters such as the number of hidden layers, the number of processing elements and the learning

algorithm.

c) Neural Expert: Used to solve Classification, Prediction, Function approximation or Clustering

based problems. There is also an optional beginner level that hides some of the more advanced

operations such as cross validation and genetic optimization.

23


Peltarion Synapse: Synapse is a component based development environment for neural networks and

adaptive systems. Allows data mining, statistical analysis, visualization, preprocessing, design and

training of neural networks an adaptive systems and the deployment of them.

Stuttgart Neural Network Simulator: SNNS is originally developed at the University of Stuttgart.

Network architectures and learning procedures are included is Back propagation (BP) for feed forward

networks, vanilla (online) BP, BP with momentum term and flat spot elimination, batch BP, Counter

propagation, Quick prop, Back percolation 1, RProp, Generalized radial basis functions (RBF), ART1,

ART2, ARTMAP, Cascade Correlation, Recurrent Cascade Correlation, Dynamic LVQ, Back

propagation through time (for recurrent networks)

Optimu Stock: is a neural network based forecasting application popular among stock market players.

It is used by technical and fundamental analysts alike.

Neuranus: (NEURAl Network User Simulator), allows the user to interact with the image for the

selection of the training sets, to create the network topology and perform the training algorithm, and to

realize in real time or near real time the results produced on the base of the choices performed in the

earlier steps.

Neuroph: It is an object oriented neural network framework written in Java (Fig 1). The latest version

2.7 has been released under the Apache License. Neuroph core classes correspond to basic neural

network concepts like artificial neuron, neuron layer, neuron connections, weight, transfer function,

input function, learning rule etc. Neuroph supports common neural network architectures such as

Multilayer perceptron with Back propagation, Kohonen and Hopfield networks.

Matlab: MATLAB has built-in neural network toolbox that saves you from the hassle of coding and

setting parameters Fig.1. Later on, advanced code can also be generated from where you can change the

parameters. Matlab toolbox is quite easier & self explanatory to understand the neural model execution.

Commonly used biological network simulators include Neuron, GENESIS, NEST and Brian.

Courtesy: www.google.co.in

Fig.1.Representative ANN Software commonly in use for media optimization of bioproducts

development

24


4. ARTIFICIAL NEURAL NETWORK MODELING

Neural network models in artificial intelligence are usually referred to as artificial neural networks

(ANNs). These are essentially simple mathematical models defining a function or a distribution over

or both and. ANN are forecasting methods that are based on simple mathematical models of the brain.

They allow complex nonlinear relationships between the response variable and its predictors. The

structure and topology of ANN referred to as a threshold unit and its function. It receives input from a

number of other units or external sources, weighs each input and adds them up. If the total input is above

a threshold, the output of the unit is one; otherwise it is zero. The threshold unit receives input from N

other units or external sources, numbered from 1 to N. Input i is called x and the associated weight is i

called w . The total input to a unit is the weighted sum over all inputs as in the following equationi

If this was below a threshold t, the output of the unit would be 1 and 0 otherwise. Thus, the output can be

expressed as in the following equation

Where is the step function, which is 0 when the argument is negative and 1 when the argument is non 19

negative. The so-called transfer function .

(ANN) refers to the interconnections between the neurons in the different layers of each system. A

neural network can be thought of as a network of “neurons” organised in layers. The predictors (or

inputs) form the bottom layer, and the forecasts (or outputs) form the top layer. There may be

intermediate layers containing “hidden neurons”. The very simplest networks contain no hidden layers 20

and are equivalent to linear regression. An example system has three layers . Fig.2 shows the neural

network version of a linear regression with four predictors. The coefficients attached to these predictors

are called “weights”. The forecasts are obtained by a linear combination of the inputs. The weights are

selected in the neural network framework using a “learning algorithm” that minimizes a “cost function”

such as MSE.

Fig. 2. Topological diagram illustrating the three layers of ANN.

25


Once we add an intermediate layer with hidden neurons, the neural network becomes non-linear. The

first layer has input neurons which send data via synapses to the second layer of neurons, and then via

more synapses to the third layer of output neurons. More complex systems will have more layers of

neurons with some having increased layers of input neurons and output neurons. The synapses store

parameters called "weights" that manipulate the data in the calculations. The adaptive weights are

conceptually connection strengths between neurons, which are activated during training and prediction 21.

5. REAL TIME APPLICATION OF ANN

Industrial Acids: Ricca et al, optimized the media constituents for citric acid production from oil palm

empty fruit bunches (EFB) as renewable resource under SSF using Aspergillus Niger and ANN

approach. ANN model was built using MATLAB software and dataset consists of 20 runs was used to

develop ANN. The determination coefficients (R2-value) for ANN and RSM models were 0.997 and

0.985, respectively, indicating the superiority of ANN in capturing the non-linear behavior of the

system. Validation process was done and the maximum citric acid production (147.74 g/kg-EFB) was

achieved using the optimal solution from ANN which consists of 6.1% sucrose, 9.2% mineral solution

and 15.0% inoculum . Kana et al., (2012) carried out optimization of citric acid production from

Aspergillus Niger MCBN297 using RSM and ANN coupling

Genetic Algorithm (GA) on seven process parameters. A

multilayer ANN was structured, trained on experimental data,

and served as fitness function for GA optimization. Two ANN

optimized media for citric acid production with predicted

values of 4.69 g/L each, gave experimental productions of 6.65

and 6.68 g/L respectively. ANN combined to GA are more

efficient in navigating the optimization search space for 22fermentation research and development .

Food Biotechnology: Amiri et al. developed synbiotic

acidophilus milk with probiotic cultures and prebiotics

satisfying functional dairy food properties. Two layer feed

forward ANN has been trained to generate new fractional factorial experiment to predict the sensory

score for colour, flavour, texture and OA for obtained samples based on inputs of probiotic and

prebiotic. The block diagram of proposed ANN has been illustrated in Fig.3.An 8 element vector is

considered as input layer of the network which are the most effective factors in the product

specifications. And the output layer is included a 4 element vector, which represents a product with the

score of 7, 7.5, 7.5, 7 for colour, flavour, texture and OA, respectively, in laboratory samples. From the

experimental data, it was found that, addition of inulin led to development of low calorie sweet

acidophilus milk which is of value for recommendation to both diabetic and calorie conscious 22

consumers .

Fig. 3. Block Diagram of proposed NN

Yu et al. used ANN for obtaining maximum soluble dietary fiber (SDF) production under SSF by

Hericiumer inaceus. Wheat bran (WB), Soybean meal along with four inorganic salts (KH2PO4,

ZnSO4, FeSO4 and MgCl ) were optimized using ANN and GA model. The ANN model was 2

26


constructed on the basis of data from the experiments, and it was found to possess excellent prediction

accuracy and generalization ability. The result of validation experiment was in close agreement with the

GA-predicted maximum SDF production. After optimization, parameters of the five media

supplemented to WB were (mg g-1 WB): soybean meal 124.3, KH2PO4 0.18, ZnSO4 0.6, FeSO4 0.2

and MgCl2, 1.2. The SDF production increased to 13.06 ± 0.51 g 100 g-1 in the validation experiment,

which was 4.68 fold as compared with the control.

Pharmaceutical Product development: ANN can be used in modeling of production, drug release and

drug stability of modified release solid dosage forms. ANN models are well established and could be

used in implementation of Quality by Design concept, i.e., understanding of Design Space and Quality 21

Risk Management for modified release formulations . Doreswamy and Chanabasayya, Studied the

application of neural networks for the prediction and analysis of antitubercular activity of Oxazolines

and Oxazoles derivatives by comparatively evaluate the performance of five neural network

techniques, Single hidden layer neural network (SHLFFNN), Gradient Descent Back propagation

neural network (GDBPNN), Gradient Descent Back propagation with momentum neural network

(GDBPMNN), Back propagation with Weight decay neural network (BPWDNN) and Quantile

regression neural network (QRNN) of artificial neural network (ANN) models. Predictive accuracy

was evaluated using the root mean squared error (RMSE), Coefficient determination, mean absolute

error (MAE), mean percentage error (MPE) and relative square error (RSE). It was found that all five

neural network models were able to produce feasible models. QRNN model was outperforms with all 24

statistical tests amongst other four models . Rajasimman and Subathra, applied statistical

experimental design for the optimization of five medium constituents (Starch, Soya bean meal,

K2HPO4, CaCO3 and FeSO4) for Gentamycin production by Micromsonospora echinospora subs

pallid (MTCC 708) in a batch reactor and the results are compared with the ANN predicted values. The

optimum values obtained by substituting the respective coded values of variables are: 8.9-g/L starch,

3.3-g/L soya bean meal, 0.88 g/LK2HPO4, 4.2 g/L CaCO3 and 0.033 g/L FeSO4. The analysis of the

data shows that optimized values of medium components give more production of gentamycin (1020 25

mg/L) in comparison with the conventional optimization methods . Dasari et al, compared the

performance of the Box- Behnken design of RSM and back propagation of ANN in the estimation of

fermentation performance parameters (moisture content, concentrations of glucose, ammonium nitrate

and methionine) for Cephalosporin C (CPC) production from Acremonium chrysogenum. Both models

provide quality predictions for the above four independent variables in terms of CPC production with

ANN showing more accuracy in estimation. When a global optimization routine was employed to

optimize the equation resulted from the neural networks, the optimum predicted antibiotic yield was

found to be 29.4 mg/g which is 14.8 % higher than the optimum value obtained from preliminary runs,

and 9.2 % higher than value obtained from Box-Behnken design of RSM. The superiority of ANN over

multi-factorial approaches would make the estimation technique a very helpful tool for fermentation 26monitoring and control .

Microbial growth: Ajdari et al, employed RSM and ANN to optimize the carbon and nitrogen sources

in order to improve growth rate of Monascus purpureus FTC5391, a new local isolate. The best models

for optimization of growth rate were a multilayer full feed-forward incremental back propagation

network, and a modified RSM model using backward elimination. The optimum condition for cell mass

production was, sucrose 2.5%, yeast extract 0.045%, casamino acid 0.275%, sodium nitrate 0.48%,

27


potato starch 0.045%, dextrose 1%, potassium nitrate 0.57%. The experimental cell mass production

using this optimal condition was 21 mg/plate/12days, which was 2.2-fold higher than the standard

condition. The results of RSM and ANN showed that all carbon and nitrogen sources tested had 27significant effect on growth rate (P-value < 0.05) .

Water treatment: Kardam et al, developed A two-layer ANN model to predict the removal efficiency of

Cd (II) ions from aqueous solution using shelled Moringa oleifera seed (SMOS) powder. The ANN

model was designed to predict sorption efficiency of SMOS for target metal ion by combining back

propagation (BP) with principle component analysis. A sigmoid axon was used as transfer function for

input and output layer. The Levenberg-Marquardt algorithm (LMA) was applied, giving a minimum

mean squared error (MSE) for training and cross validation at the ninth place of decimal. Sorption

studies led to the standardization of the optimum conditions as, metal concentration (25 mg/L), bio-

mass dosage (4.0 gm), contact time (40 min) and volume (200 mL) at pH 6.5 for maximum Cd removal

(85.10%). The formation of disinfection by-products (DBPs) in drinking water has become an issue of 28greater concern in recent years . Wassink, conducted bench-scale jar tests on a surface water to

evaluate the impact of enhanced coagulation on the removal of organic DBP precursors and the

formation of trihalomethanes (THMs) and haloacetic acids (HAAs). The results of this testing indicate

that enhanced coagulation practices can improve treated water quality without increasing coagulant

dosage. The data generated were also used to develop artificial neural networks (ANNs) to predict 29THM and HAA formation . Mirsepassi, applied back-propagation network ANN model to determine

and optimize operation parameters (alum and polymer dosages) of water treatment plants and enhance

the efficiency of the plant. The results showed that the ANN model was most promising. The correlation

coefficients (r) between the actual and predicted values for the alum and polymer dosages were both

0.97 and the average absolute percentage errors were 4.09% and 8.76% for the alum and polymer 30dosages, respectively . Mourab et al, studied the effects of process variables, pH, adsorbent mass,

initial concentration, and temperature, on the adsorption capacity of fluoride through three-levels, four-

factors Box-Behnken (BBD) designs. Same design was also utilized to obtain a training set for ANN.

The results showed that the ANN model was found to have higher predictive capability than RSM

model even with limited number of experiments and much more accurate in prediction as compared to

BBD. Sirisha et al, developed empirical models based on multiple regression and artificial neural

networks to predict the value of hardness with respect to the corresponding values of chloride, fluoride,

and calcium contents of the groundwater sample based on a region specific data. A thirty-point data set

consisting of data regarding chloride, calcium, fluoride and hardness is taken and is used in developing

the physical models for predicting the value of hardness based on the above-mentioned parameters.

Back Propagation Network of ANN is used for the study and the results are obtained in the ANN model

is encouraging (0.00054). Prediction using ANN is relatively better than that of regression model due to 31

its flexibility to map the inputs to outputs .

Industrial Enzymes: Khoramnia et al, evaluated the lipase production ability of a newly isolated

Acinetobacter sp. in submerged (SmF) and solid-state (SSF) fermentations using Coconut oil cake as a

cheap agro industrial residue. Multilayer normal and full feed forward backpropagation networks were

selected to build predictive models to optimize the culture parameters for lipase production in SmF and

SSF systems, respectively. The optimized values of learning rate and momentum for both fermentation

28


system networks were 0.15 and 0.8, respectively. The best topology was Gaussian transfer function

consisted of a 5-15-1 (inputs hidden layer-output neurons) for both SmF and SSF systems. The

optimum lipase production of Acinetobacter sp. in SmF was 32.2U/mg protein (3-fold increase) and for 32SSF 75.4U/mg protein (5 times increase) achieved . Muthuvelayudham and Viruthagiri, worked on

reducing the cost of cellulase production using mutant strains of T. reesei. by optimization of

fermentation conditions and modeling of the fermentation process. The feed forward back propagation

algorithm with one hidden layer was used in the training of the neural network, based on varying input/

output pair data sets. Logistic model and Luedeking-Piret model were found to be appropriate model

for obtaining kinetic parameters for best evaluation of fermentation process of converting cellulose to 33cellulase . Gawande and Kamat, tudied strains of Aspergillus terreus and A. niger to produce xylanase

on various lignocellulosic substrates using SSF. The effects of various parameters, such as moistening

agent, level of initial moisture content, temperature of incubation, inoculum size and incubation time,

on xylanase production were studied using ANN . The best medium for A. terreus and A. niger were

wheat bran moistened with 1:5 Mandels and Strenberg mineral solution containing 0·1% tryptone, at 35 7 8

°C, and at inoculum concentration 2x10 2x10 spores. Under these conditions, A. terreus produced 68·9 34

IU ml/l of xylanase, and A. niger, 74·5 IU ml/1, after 4 d of incubation . Rao et al, developed a hybrid

system of feed-forward neural network (FFNN) and genetic algorithm (GA) for enhanced alkaline

protease production by Bacillus circulans, optimized eight fermentation factors (incubation

temperature, medium pH, inoculum level, medium volume, carbon and nitrogen sources) and

constructed a '6-13-1' topology of the FFNN for identifying the nonlinear relationship between

fermentation factors and enzyme yield. FFNN predicted values were further optimized for alkaline

protease production using GA. Four different optimum fermentation conditions revealed maximum

enzyme production out of 500 simulated data. Concentration-dependent carbon and nitrogen sources,

showed major impact on bacterial metabolism mediated alkaline protease production. The alkaline

protease yield obtained in the validation experiments was 8320 Units, which were in close agreement

with the GA, optimized yield of 8283 Units. It can, thus, be seen that the usage of FFNNGA hybrid

methodology has resulted in a significant improvement in the alkaline protease yield (>2Æ5-fold) [35].

6. CONCLUSION

ANN is found to be applicable to analyze complex, nonlinear, and dynamic data with multiple inputs.

These make ANN valid as a tool to study biological process. ANN model developed by Ricca et al, was

a combination of Levenberg-Marquardt backpropagation training function, gradient descent with

momentum weight/bias learning function, consists a single hidden layer, ten hidden neurons and

LOGSIGTANSIG transfer functions and found to give the best performance of the neural network in

the production of citric acid by SSF. Kana et al, carried out a comparative modeling and optimization of

citric acid production from Aspergillus niger MCBN297 using RSM and ANN coupling GA on seven

process parameters and developed two ANN optimized media for citric acid production with predicted

values of 4.69 g/L each.

Amiri et al, applied two layer feed forward ANN model and developed synbiotic acidophilus milk with

probiotic cultures (Lactobacillus acidophilus, Bifidobacterium bifidum and Lactobacillus casei) and

prebiotics (7.5% honey, 9% inulin and 0.2% oat fibre) satisfying functional dairy food properties. The

29


studies concluded that the addition of inulin or honey had synergistic effect on the physico-chemical

and sensory quality of probiotic acidophilus milk. Yu et al, optimized soybean meal along with four

inorganic salts (KH2PO4, ZnSO4, FeSO4 and MgCl ) using ANN and GA model for obtaining 2

maximum soluble dietary fiber (SDF) production under SSF by Hericiumer inaceus. The ANN model

was constructed on the basis of data from the experiments, and it was found to possess excellent

prediction accuracy and generalization ability. The result of validation experiment was in close

agreement with the GA-predicted maximum SDF production 4.68 fold as compared with the control.

ANN can be used in modeling of production, drug release and drug stability of modified release solid

dosage forms. Doreswamy and Chanabasayya, studied the application of neural networks for the

prediction and analysis of antitubercular activity of Oxazolines and Oxazoles derivatives and by

comparatively evaluate the performance of five neural network techniques. The study found that

QRNN model was outperforms with all statistical tests amongst other four models. Rajasimman and

Subathra, applied ANN for the optimization of five medium constituents (Starch, Soya bean meal,

K2HPO4, CaCO3 and FeSO4) for Gentamycin production by Micromsonospora echinospora subs

pallid (MTCC 708) in a batch reactor. The analysis of the data shows that optimized values of medium

components give more production of gentamycin (1020 mg/L) in comparison with the conventional

optimization methods. Dasari et al, compared the performance of RSM and back propagation of ANN

in the estimation of fermentation performance parameters (moisture content, concentrations of

glucose, ammonium nitrate and methionine) for Cephalosporin C (CPC) production from Acremonium

chrysogenum. ANN optimum predicted antibiotic yield was found to be 29.4 mg/g which is 14.8 %

higher than the optimum value obtained from preliminary runs, and 9.2 % higher than value obtained

from Box-Behnken design of RSM. Ajdari et al, employed RSM and ANN to optimize the carbon and

nitrogen sources in order to improve growth rate of Monascus purpureus FTC5391, a new local isolate.

The results of RSM and ANN showed that all carbon and nitrogen sources tested had significant effect

on growth rate (P-value < 0.05).

In recent years, computer-based methods have been applied to many areas of environmental issues.

Kardam et al, developed A two-layer ANN model to predict the removal efficiency of Cd (II) ions from

aqueous solution using shelled Moringa oleifera seed (SMOS) powder. Sorption studies led to the

standardization of the optimum conditions for the maximum Cd removal (85.10%). Wassink,

conducted bench-scale jar tests on a surface water to evaluate the impact of enhanced coagulation on the

removal of organic DBP precursors and the formation of trihalomethanes (THMs) and haloacetic acids

(HAAs). The data generated were also used to develop ANNs to predict THM and HAA formation. The

results of this testing indicate that enhanced coagulation practices can improve treated water quality

without increasing coagulant dosage. Mirsepassi, applied back-propagation network model to

optimize operation parameters (alum and polymer dosages) of water treatment plants and enhance the

efficiency of the plant and ANN model has shown most promising results. Mourab et al, studied the

effects of process variables, pH, adsorbent mass, initial concentration, and temperature, on the

adsorption capacity of fluoride. The results showed that the ANN model was found to much more

accurate in prediction as compared to BBD. Sirisha et al, developed empirical models based on

multiple regression and ANN to predict the value of hardness with respect to chloride, fluoride, and

calcium contents of the groundwater sample based on a region specific data. Prediction using ANN is

relatively better than that of regression model due to its flexibility to map the inputs to outputs.

30


Numerous reports on media optimization using ANN make convenient to understand its applicability

for different biological responses particularly in microbiology. Khoramnia et al, studied multilayer

normal and full feed forward backpropagation networks to build predictive models to optimize the

culture parameters for lipase production under SmF and SSF systems using Acinetobacter sp. and

coconut oil cake as a cheap agro industrial residue. The optimum lipase production of Acinetobacter sp.

in SmF was 32.2U/mg protein (3-fold increase) and for SSF 75.4U/mg protein (5 times increase)

achieved. Muthuvelayudham and Viruthagiri, worked on reducing the cost of cellulase production

using mutant strains of T. reesei. by optimization of fermentation conditions and modeling of the

fermentation process. The feed forward back propagation algorithm with one hidden layer was used

and Logistic model and Luedeking Piret model were found to be appropriate model for obtaining

kinetic parameters for best evaluation of fermentation process of converting cellulose to cellulase.

Gawande and Kamat studied strains of Aspergillus terreus and A. niger to produce xylanase on various

lignocellulosic substrates using SSF. The effects of various parameters were studied using ANN. Under

the optimized conditions, A. terreus produced 68·9 IU ml/l of xylanase, and A. niger, 74·5 IU ml/1, after

4 d of incubation. Rao et al, developed a hybrid system of feed-forward neural network (FFNN) and

genetic algorithm (GA) for enhanced alkaline protease production by Bacillus circulans. The study

found that carbon and nitrogen sources, showed major impact on bacterial metabolism mediated

alkaline protease production. Keeping in mind the examples, mentioned in the manuscript, it can be

concluded that AAN can be used more widely in optimization of bioproduct and bioprcess

development.

Acknowledgment

We wish to express our sincere gratitude to Chairman, NHEI and Principal, NHCE, Bangalore for

providing us with all the facilities and support.

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33


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34


“A NEW APPROACH: EXPLORING HONEY BEE VENOM (Apis melifera)

AS ANTI-MICROBIAL, ANTI-INFLAMMATORY AND ANTI-ARTHRITIS

AGENT”1* 1 1 1Nitesh Gamare , Rajesh Banala , Ashwin Chougule , Mahesh Tengale

1REVA Institute of Science and management, Bangalore, India.

ABSTRACT:

Toxicity of bee venom is known to man since ages, which varies from mild inflammation to death. In

the present study the toxic potentialities of honey bee venom of Apis melifera was caried out invitro on

selected species of bacteria. The anti-microbial activity of Apis melifera bee venom was studied by

turbidometric bioassay and was observed in the sequence of S aureus > E.coli > Pseudomonas

aeruginosa. Minimum inhibitory concentration (MIC) was determined against S aureus using broth

dilution method at lowest dilution (540µg/30 µl). Furthermore, RNA extraction from S aureus grown

with and without venom was carried out and estimated. The result shows that bee venom has

significant anti-microbial effect and could be potential alternative antibiotic. Anti-arthritis & anti-

inflammatory properties of bee venom were known earlier which was confirmed through

bioinformatics.

Keywords: Bee venom, Apis melifera, MIC, Anti-arthritis, Anti-inflammatory.


Nitesh Gamare,

Revo Institute of Science and Management, Bangalore

Email: [email protected].

35


Introduction:

Honeybees are the earliest known social insects to man. They have survived alongside their ever-

changing environment for 120 million years. They are recognized and appreciated as the single most

important insect pollinators and thus, increase the productivity of food plants on earth (Jyothi, 1994).

Besides pollination, honeybees provide honey, bee wax, royal jelly, pollen, venom and propolis. The

venom gland of worker bee is located in posterior portion of the abdomen, between the worker's rectum 1

and ovaries . The two glands (Dufours and Venom gland) associated with sting apparatus of the worker

produce venom. The venom gland is a thin long, distally bifurcated integumentary gland with cuticular

lining. It consists of a secretary filamentous region, connected to a reservoir at its proximal portion, in 2which the venom is stored . The small flat cells also bearing canaliculi form the distal region of the

3reservoir where their products contributes to venom composition . The workers sting only once, which

leads to their death. Venom contains 88% water. At least 18 pharmacologically active components have 4been described so far; including various enzymes, peptides and amines . The glucose, fructose and

phospholipids contents of venom are similar to those in bee's blood. Venom from Apis melifera is

similar, but even the venoms from various races within each species are slightly different from each

other. Bee venom is haemorrhagic and contains apamine, melittin, phospholipase, hyaluronidase.

These oppose the inhibiting action of the nervous system and also stimulate the heart and adrenal

glands. Sulphur is the main element in inducing the release of cortisol from the adrenal glands which

protects the body against infections. The venom also contains mineral substances, volatile-organic

acids, formic acid and some antibiotics.

Venom is one of the products of honeybee, which is an important component in the pharmaceutical

industry. Use of naturally available substances as medicines, in Asia represents a long history of human

interactions with the environment. The medicinal value of these substances lies in some chemicals that

produce a definite physiological action on the human body. The venom production is usually complete

within two weeks and then glands start to degenerate in the adults. Not only has the age affected the

venom composition but also seasonal factors like availability of food sources etc. A newly emerged bee

has very little venom content, but the amount gradually accumulates with age, to about 0.3 mg in a 15 1

day old A mellifera worker bee , after the age of 18 days no additional venom is produced. 5Subsequently, the weight of the venom in the venom sac remains unchanged . The study of social

Hymenoptera (bees, wasps, and ants) venom proteins is of great interest, since these venoms can trigger

serious allergenic reactions in humans. The allergenic reactions of Hymenoptera venoms are caused

mostly by low molecular weight compounds, which can result in pain, local inflammation, itching, and

irritation as immediate responses that after some hours are attenuated. Melittin is the main compound

responsible for most of these reactions, and it is present in several bee venoms. Bee venom has

interesting pharmacological properties and is used in the treatment of various health conditions such as

arthritis, rheumatism, pain, cancerous tumors and skin diseases. Since ancient times Greeks, Romans,

Chinese and Egyptians have speculated about honey and bee product's curative properties. Honeybee

venom has been domesticated and a number of its antimicrobial peptides have been isolated, making it

the one used most often for treatment.

36

This observation led to an evaluation of the potential bacteriostatic and/or bacteriocidal characteristics

of bee venom and of melittin. Melittin is the largest single component (by weight) of bee venom;

it is a polyploid and of molecular weight 2850, and evidence suggests that in bee venom it exists mostly 8,9 10

as a tetramer . In 1941 Schmidt-Lange discovered that bee venom was antibacterial .17

Systems for recombinant glycoproteins have been developed . However, the potential of these cell

lines to create oligosaccharide structures which are immunogenic in man has not yet been elucidated. 11

Recent data suggest the existence of antibodies in sera of bee-sting-allergic patients , which are

directed against the highly heterogeneous N-glycan of the glycosylated variant of honey-bee (Apis

mellifera) venom phospholipase A2 (PLA2). Structural analysis of the PLA2 oligosaccharides

identified truncated pentasaccharide core units with fucose residues l,3- and/or l,6-linked to the

asparagine-bound N-acetylglucosamine.

Acne vulgaris is the most common skin disease that affects areas containing the largest oil glands, 12including the face, back, and trunk . Normal skin commensals including Propionibacterium acnes,

Staphylococcus epidermidis, Streptococcus pyrogenes and Staphylococcus aureus, proliferate rapidly 13,14during puberty and are often involved in the development of acne . P acnes is a Gram-positive

anaerobic bacterium that mostly resides in the pilosebaceous follicles of the skin. Although P acnes is a

member of the normal skin commensal, bacterial flora, it plays a critical role in the development of

inflammatory acne when it becomes overgrown and colonizes the pilosebaceous unit. On the other

hand, aerobic organisms such as S epidermidis, S pyrogenes and S aureus usually cause superficial 15infections within the sebaceous unit . It has also been widely accepted that inflammatory acne induced

by host immune reactions to acnes releases chemoactive factors that attract immune system cells such 16as neutrophils, monocytes, and lymphocytes .

For bee venom composition, melittin has been reported as the most abundant active component

possessing powerful cell lytic activity, especially on red blood cell membrane resulting in hemolysis 17,9and release of haemoglobin .

37


Table 1: Components of Bee venom

18Apipuncture is described in detail with principle is reviewed by Lee . In China a book by Chen Wei 19“Chinese Bee Acupuncture” has been published . The applied doses for adults are generally between

0.1-3 mg BV per treatments, the dose depending on the disease, higher doses (until 2-2.5 per treatment) 20being used in arthritis treatments . In one sting the maximum of about 50 to 100 μg per are applied, in

micropuncture much less BV is applied, depending on the stinging time about 1 to10 μg can be applied.

The lethal dose is about 2.8 mg/kg or 19 stings per kg, for a man of 75 kg meaning about 1400 stings.

Immunopathogenesis of Rheumatoid arthritis

Rheumatoid arthritis is a common destructive arthropathy of unknown etiology, strongly linked to the

MHC class II proteins HLA-DRB1*0404 and *0401. The joint changes are produced by the

hyperplasia of the synovial cells associated with increased vascularity and infiltration of inflammatory

cells forming pannus overlaying and destroying cartilage and bone. The infiltrating cells are primarily +

CD4 T-cells, which stimulate monocytes, macrophages and mast cells to secrete IL-1, IL-6, TNF and a

variety of chemokines, which recruit neutrophils into the joints. The IL-1 and TNF in the synovium

stimulate fibroblasts and chondrocytes to release tissue-destroying proteolytic enzymes which lead to

joint damage, and bone destruction follows the stimulation of osteoclasts by these cytokines. As the

malign pannus (cover) grows over the cartilage, tissue breakdown can be seen at the margin, almost

certainly as a result of the release of enzymes, ROIs and especially of IL-1, IL-6 and TNF. B-cells are 38


also activated and plasma cells are frequently observed. Secondary lymphoid follicles with germinal

centers may be present in the synovium. Immune complexes of rheumatoid factor and IgG may initiate

an Arthus reaction in the joint space leading to an influx of polymorphs. By releasing elastase,

collagenase and proteases these cells add to the joint destruction by degrading proteoglycan in the

superficial layer of cartilage.

Rheumatoid arthritis can be successfully treated with anticytokine therapy or by blocking T-cell

activation.

Now that the central role of cytokines in RA is appreciated, a number of anticytokine therapies have

been developed. These include a soluble TNF receptor-IgG1 fusion protein (Etanercept) which

neutralizes free TNF, and a chimeric monoclonal antibody against TNF itself (Infliximab). By blocking

TNF activity, its ability to activate the cytokine cascade of IL-1, IL-6, IL-8 and other inflammatory

cytokines is impeded, making this a valuable adjunct to the treatment of RA. Another approach is to

block the activation of CD28 on T-cells using a fusion protein made up of CTLA-4 and IgG1. This

competes with CD28 for binding to B7.1 (CD80) and B7.2 (CD86) and prevents T-cell activation. By

acting so early in the inflammatory cascade CTLA4Ig inhibits the secondary activation of macrophages

and B-cells and shows considerable promise in treating patients with RA.

Immunoglobulin G autosensitization and immune complex formation

Autoantibodies to the IgG Fc region, which is abnormally glycosylated, are known as antiglobulins or

rheumatoid factors, and are the hallmark of the disease, being demonstrable in virtually all patients with

RA. The majority are IgM antiglobulins, the detection of which provides a very useful clinical test for

RA. Immunoglobulin G aggregates, presumably products of the infiltrating plasma cells which

synthesize self-associating IgG antiglobulins, can be regularly detected in the synovial tissues and in

the joint fluid where they give rise to typical acute inflammatory reactions with fluid exudates.

Material and Methodology:

Venom collection:

Experimental colonies of honeybees (A. mellifera) were maintained. BV was collected by two methods.

Firstly with a Bee Venom Collector. The collected BV was diluted in cold water and then centrifuged at

10,000 g for 5 min at 4°C to discard residues from the supernatant. BV was lyophilized by a freeze dryer 21

and stored in a refrigerator for later use and the venom reservoirs were extracted at 4°C by dissecting

the stinging apparatus and stored at -20°C until required. Venom sacs were re-suspended in sterile water

and extracts of whole bee venom (WBV) were made by reservoir disruption under rapid defrosting and

light pressure by a glass rod. These samples were centrifuged at 10,000 g at 4°C for 5 min, and the 22

supernatants were used as protein and enzyme sources .

Collection of bacterial isolates:

The test clinical control isolates used in the present study were Pseudomonas aeruginosa, Escherichia

coli, and Staphlocccus aureus. These clinical isolates were identified based on the standard 23

microbiological techniques .

Maintenance of pure bacterial culture suspension in Nutrient Broth:

The collected clinical control microbial strains were maintained in the laboratory on Nutrient Agar (Hi-

39


Media) by SlantStreak technique for further pure cultures. The nutrient Agar Hi-Medium composed of

5 g peptone, 2 g Beef extract, 5 g Sodium chloride and 20 g Agar-Agar was dissolved in one liter of

double distilled water and pH was maintained at 7.0. The mixture of contents were later transferred into

a sterile conical flask and plugged with cotton for air tightening. The conical flask with contents was

autoclaved and flasks were cooled and stored at 5 to 10°C. Under sterile conditions, the contents when

needed were dissolved on heating mantle and 10 ml of medium was poured into sterile test tubes

and cooled in Laminar Air Flow by placing in slanting position. The solidified medium was streaked

with specific bacterial strains using sterile inoculation loop. The slants with strains were incubated in

Bacterial incubator at 35 to 37°C for a period of 24 to 48 h. The slants with strains were stored at 4°C.

Antimicrobial activity of bee venom:

Under aseptic conditions, pure colonies of Bacterial isolates from slants were picked with an

inoculating loop and suspended in 3 to 4 ml of nutrient broth in sterile test tubes and incubated for 24 h at 2437°C. The contents were transferred into sterile conical flask and plugged with cotton . From 3-4 ml

culture tube, 100 microlitre each culture was inoculated in two different flasks containing 20 ml

nutrient broth. In one flask 100 µl bee venom was added. Other flask was labeled control. Both the

flasks were incubated for 24 hours at 37°C. After incubation, 100 µl sample from each flask were spread

on nutrient agar plates. Incubate the plates at 37°C for 24 hours. Same procedure was followed for each

microbial culture to be tested.

Protein estimation:

The protein content in the honeybee venom samples was estimated by using Folin-Lowry's method as

standard at 660 nm.

Electrophoresis:

Sodium Dodecyl Sulphate Polyacrylamide Gel Electrophoresis (SDS- PAGE) was performed using

12% polyacrylamide at 120 V and 20 mA. Venom samples were dissolved in 20 μl of doubled distilled

water, 5 μl of sample buffer (0.001% mercaptoethanol, 75% of 0.313 M Tris- HCl and 10% glycerol)

and 0.001% bromophenol blue (pH 6.8). The samples were boiled for two minutes, shaken in vortex for

30 s and loaded onto the gel. The gels were stained in 0.25% Coomassie Brilliant Blue R-250 solution 22and destained with 30% methanol and 10% acetic acid to reveal proteins .

Effect of Bee venom on growth of S aureus

The MIC of honey bee venom was determined by incubating the fixed amount of bacterial culture (50

µl) with varying the concentration of honey bee venom. Dilution of honey bee venom was carried out as

shown in the table below.


Table 5: Dilution of venom

40

The varying concentrations were added to the fixed amount of bacterial culture in fixed amount of broth

and kept for incubation at 37°C for 24 hours. The results were obtained by taking the optical density at

660 nm using UV Visible spectrophotometer.

Insilico work on honey bee venom with respect to arthritis

Bioinformatics is seen as an emerging field with the potential to significantly improve how drugs are

found, brought to the clinical trials and eventually released to the marketplace. Computer Aided Drug

Design (CADD) is a specialized discipline that uses computational methods to simulate drug receptor

interactions. CADD methods are heavily dependent on bioinformatics tools, applications and 25databases .

Bioinformatics tools, biological databases like PubMed, PDB (Protein Data Bank) and software's like

Hex were applied in this investigation. Hex is an Interactive Molecular Graphics Program for

calculating and displaying feasible docking modes of pairs of protein and DNA molecules. Hex can also

calculate Protein-Ligand Docking, assuming the ligand is rigid, and it can superpose pairs of molecules 26using only knowledge of their 3D shapes .

Result and Discussion:

Venom Collection:

Both the methods, venom collector and dissection of venom sacs were employed to collect the venom

from honey bees. The venom was stored in -20°C for the further use.

Antimicrobial activity:

From the figures A to C', it was clearly observed that honey bee venom was found to be more effective

against S aureus followed by E coli, Pseudomonas aeruginosa. These results are in general agreement 27

with those found by who found that Mycobacteria and Staphylococci were affected by bee venom and 28

also showed that bee venom is less effective to E coli.

41


-0.2

0

0.2

0.4

0.6

0 5

0 1

00

15

0 2

00

25

0 3

00

35

0 4

00

45

0 5

00 Op

tica

l De

nsi

ty a

t

Protein concentration (mg/ml)

Sr no. Concentration (mg/ml)

Optical density

1. 50 0.034 2. 100 0.049 3. 150 0.126 4 200 0.166 5. 250 0.196 6. 300 0.202 7. 350 0.316 8. 400 0.344 9. 450 0.369 10. 500 0.402 11. Blank - Bee venom ? 0.201

Fig 1: Growth of bacteria on agar plat in presence of venom and without presence of venom. A and A': E coli with venom and without venom respectively. B and B': S aureus with venom and without venom respectively. C and C': Pseudomonas aeruginosa with venom and without venom respectively.

Protein estimation:

Protein was estimated by Folin-Lowry's method as depicted in table 7

Table 7: Protein estimation by Folin-Lowry's method

The above values states that concentration of protein in 0.1ml of venom is 0.2996 mg

Fig 2: Standard graph for protein estimation.

42


0

0.2

0.4

0.6

0.8

10 20

30

40

50

60

70

80

90

100

Op

tica

l De

nsi

ty a

t 6

60

nm

Concentration of bee venom (%)

MIC

Electrophoresis:

The SDS PAGE gel also confirmed the constituent proteins from the lyophilized crude venom such

as phospholipase A2, Melittin and some of the small peptides with molecular weight ranges of

35, 34, 30, 27, 31, 16, 15, 11, 9, 8, 7, 6, 5 and 4 KDa were observed (Figure 3).

Fig 3: SDS-PAGE (12%) was performed with molecular marker to know the presence of different

proteins and their molecular weight. M 1 KDa standard marker, 1 - Apis melifera

As the result was seemed to be the most sensitive against S aureus, dilution was prepared of Bee venom

and concentration of organism was maintained constant dilution as shown in table 1.

From the obtained results, bee venom seemed to be the most antibacterial tested substance, with

the lowest MIC values, since S aureus seemed to be the most sensitive (540 µg/ 30 µl i.e. 60%

concentration) for bee venom. The results for MIC of honey bee venom for S aureus are depicted in the

table 8 and figure 4.

Table 8: MIC of honey bee venom for S aureus

Concentration of venom

Optical density at 660 nm.

10% 0.589 20% 0.487 30% 0.382 40% 0.173 50% 0.069 60% 0.00 70% - 80% - 90% - 100% -

43


Insilico docking results:

The binding efficiency of melittin to different receptor is expressed in table 10, table 11, table 12 and

table 13. Docked structures are shown from figure number 15-22 and 3D-structure of receptor and

ligand are showed from figure 6-14.

44


Fig 15: Dock structure of IgG-IgM Complex with ligand Oxyphenbutazone.

45


Table 10: IgG-RF as receptor

Ligand E-value

Oxyphenbutazone -1.00

Melittin -515.18

Table 11: Melittin as ligand

Receptor E-value

RF -1.00

TNF -557.80

Table 12: CD-28 as receptor

Ligand E-valueCTLA4-IgG1 complex 0.00

Melittin -557.80

Table 13: Melittin as a ligand

Receptor E-value

COX 1 -318.75

COX 2 -648.05

The above result shows that the binding affinity of melittin with CD-28 is more when compared to other

receptors indicating that melittin interferes in T-cell activation by blocking the activation of CD28 on T-

cells. This competes with CD28 for binding to B7.1 (CD80) and B7.2 (CD86) and prevents T-cell

activation. By acting so early in the inflammatory cascade, it inhibits inflammation.

Mutant TNF is responsible for suppression of Tumor cell degeneration. Binding affinity with mutant

TNF of melittin may avoid Tumor generation, which may extend life of tumor patients.

Melittin show good binding affinity with RF, but Complex of melittin and RF also show binding with

IgG, which don't stop the cascade of arthritis, so this result was not taken into consideration.

Bee venom shows anti-inflammatory activity like that of glucocorticoid-and aspirin (Stefan Bogdanov

2011). Results obtained (table 13) depicts that binding affinity of melittin with COX-2 is satisfactory,

suggesting that Melittin from the bee venom shows the activity like aspirin.

The many kinds of prostaglandin are synthesized by a host of complicated biochemical pathways.

However, all pathways share a common stage facilitated by an enzyme called COX1 and COX2, whose

action melittin suppresses. Melittin works as enzyme inhibitor. It suppresses the action of the enzyme

COX2, stops the production of prostaglandin, thus disrupting the pathways to pain, inflammation.

Conclusion:

The study indicates that bee venom (Melittin) has potential as an anti-microbial, anti inflammation, anti

arthritis and anti tumor effect. The current investigation may lead in future to the newer eras of

medicine in the case of threatening diseases like AIDS and Cancers. The combination of wet lab and

insilico methods are a boon for drug discovery.

46


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48

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Vet. Sci., 36: 31-42.

PRELIMINAR PHYTOCHEMICAL SCREENING OF FIVE INDIAN

MEDICINAL PLANTS.

Prathiba H. D, and Prof. N. H. Manjunath

Department Of Biochemistry, Central College Campus, Bangalore University, Bangalore.

ABSTRACT

The use of traditional medicines holds a great promise as a easily available source as effective

medicinal agents to cure a wide range of ailments among the people particularly in tropical developing

countries like India. This present study deals with preliminary Phytochemical studies of some

common Medicinal plants viz. Abutilon indicum, Adathoda visca, Datura stramonium , Lantana

camara and tridax procumbens. Phytochemical analysis was carried out to understand the qualitative

existence of secondary metabolites in these plants. The plants have been screened for alkaloid,

flavonoid, saponin and tannin fractions . All the plant species showed the presence of these

phytochemicals, in varied quantities. Amongs the plants screened Datura Stramonium showed the

relatively higher percentage of Alkaloid and Flavanoid, then other plants. Adathoda visca showing

highest Flavonoids content. Amongs to the remaining plants, Abutilion indicum showed higher

amount of Alkaloids and Tannins. Lantana Camera and Tridax procumbens, showed lower level of

alkaloids, Flavonoids, Saponin and Tannin. In general, the yield obtained from these plant was quite

adequate thereby making further development of these herbal drugs economically feasible.

Key words: Medicinal plants, Adathoda visca, Abutilon indicum, Datura stramonium, Lantana

camara, Tridax procumbens, Phyochemicals, alkaloid, flavonoid, saponin, tannin.


Prathiba H D

Department of Biochemistry

Bangalore University, Bangalore.

Email : [email protected]

49


INTRODUCTION

Plants have provided man with all his needs in terms of shelter, clothing, food, Flavours and

fragrances as not the least, medicines. Plants have formed the basis of sophisticated traditional

medicine systems that have been in existence for thousands of years and continue to provide mankind 1with new remedies . In the recent years there has been an increasing awareness about the importance of

2medicinal plants . According to WHO medicinal plants would be the best source to obtain variety of

drugs. Medicine from plant sources have been use in Homeopathy, Ayurvedic, Allopathy and in

traditional medicine since time immemorial. Medicinal plants plays a significant role among the

traditional and modern systems. Their use have been multiplied through various researches and

application due to a number of side effects from use of synthetic drugs, antibiotics and high cost. The

curative properties of medicinal plants are mainly due to the presence of various complex chemical 4

substances of different composition which occur as secondary metabolites

Phytochemical which possess many ecological and physiological roles are widely distributed as plant 5constituents . Woody plants can synthesize and accumulate in their cells, a great variety of

phytochemicals such as alkaloids, flavonoides, tannins, cyanogenic, glycosides, phenolic compounds,

saponins and lignins. These compounds are known as secondary plant metabolites and have biological

properties such as antioxidant activity, antimicrobial effect, modulation of detoxification enzymes,

stimulation of the immune system, decrease of platelet aggregation and modulation of hormone 6metabolism and anticancer property .

Phytochemicals are basically divided into two groups i.e primary and secondary constituents

according to their functions in plant metabolism.Primary constituents comprise common

sugars,aminoacid proteins and chlorophyll while secondary constitutents consists of

bioactive substances include tannins, alkaloids, carbohydrates, terpenoids, steroids and

flavonoids, and so on. These compounds are synthesized by primary or rather secondary 3metabolism of plants . Secondary metabolites are chemically and taxonomically extremely diverse

compounds with obscure function. They are widely used in the human therapy, veterinary, agriculture, 8

scientific research and countless others .

In the present work, qualitative phytochemical analysis was carried out in Five plants. Abutilon

indicum, Adathoda visca, Datura stramonium, Lantana camara and Tridax procumbens. Which

have been known to posess medicinal property.


2.1 Plant Collection and Identification :-

The Plant materials Adathoda visca, Datura stramonium, Lantana camara, tridax procumbens, and

Abutilon indicum were collected from fields in and around Bangalore city.

2.2 Preparation of Plant Material :-

The leaves pluked from the plant were washed 2-3 times with running tap water and was then air dried

under shade. After complete shade drying the leaf material was ground in a the mixer to obtain the

powder and stored in plastic bags.

50


2.3 Extraction of Plant Material:-

Preparation of aqueous extracts:

Powdered leaf was homogenized with 5 gm of material was in 25 ml of water and the suspension was 0heat to 50- 60 c, and maintain for 15 minutes and then filtered. The filtrate was then centrifuged at 2500

rpm for 15 minutes and the clear supernatant was stored at 5° C until use .

2.4 Phytochemical Analysis: The phytochemical analysis was carried out according to the standard

methods with minor modification. Phytochemicals analysis of the crude powder of the Adathoda

visca, Datura stramonium, Lantana camara, Tridax procumbens, and Abutilon indicum, for the tests

of phytochemicals as a alkaloid, saponin, tannins, flavonoides and protein etc were made as shown

below.

2.4.1 Test for Alkaloides:

200 mg plant material were taken and added 10 ml Methanol and then filtered. After that 2 ml filtrate

were taken and added 1 % HCL with steam 1 ml filtrate and 6 drops Mayer΄s reagent/Wagners reagent/

Dragendorffs reagent. It produced creamish/Brown/Orange precipitate indicate the presence of

alkaloids.

Test for Saponins:

Approximate 0.5 ml filtered were taken and added 5 ml distilled water. Frothing persistence indicate 4presence of Saponins .

Test for Tannins:

200 mg plant material were taken and added 10 ml distilled water and then filtered. After that 2 ml

filtered were taken and added 2 ml FeCl Blue. Then black precipitate indicate the presence of Tannins 3

& Phenols.

Test for Flavonoides:

200 mg plant material were taken and added 10 ml Ethanol, then Tomato, Red colour indicate the

presence of Flavonoides, Glycoside

Quantitative analysis :

Alkaloids:

Alkaloids were Quantitatively determined according to the method of Harborne. Two hundred ml of

10% acetic acid in ethonal was added to 5g powdered sample, covered and allowed to stand for 4h.The

filtrate was then concentrated on a water bath 1/4 of its original volume. Concentrated ammonium

hydroxide was added drop wise to the extract until the precipation was complete The whole solution

was allowed to settle collected precipitates were washed with dilute ammonium hydroxide and then 5,6

filtered. The residue wsa dried, weighed and expressed as the alkaloids

Flavonoids:

To estimate flavonoids quantitatively,10 g powdered sample of each plant material was extracted twice

with 10 ml of 80% aqueous methanol at room temperature.The whole solution was filtered through

whatman filter paper No.1 the filterate was later transferred into crucibles evaporated to dryness on a 7water bath to a constant weight .

51


Saponins:

Quantitative determination of saponins was done according to Obadni and Ochuko.Twenty gram of

each powered sample was added to 100 ml of 20% aqueous ethanol and kept in a shaker for 30min.The

sample were heated over a water bath for 4h at 55°c.The mixture was then filtered and residue re-

extracted with another 200 ml of 20% aqueous ethanol.The combined extract were reduced to

approximately 40 ml over water bath at 90°c.The concentrate was transferred into a 250 ml separatory

funnel extracted twice with 20 ml diethyl ether. Ether layer was discarded while aqueous layer was

retained and 60 ml n-butanol was added to it.Then n-butanol extracts were washed twice with 10 ml of

50% aqueous sodium chloride. The remaining solution were dried in oven (40°C)to a constant weight. 8The saponin content was calculated as percentage of the initial weight of sample taken .

Tannin:

Tannin determination was done according to the method of Van-Burden and Robinson with some

modifications. Distilled water (50ml) was added to 500 mg of the sample taken in a 500 ml flask and

kept in shaken for 1h.It was filtered into a 50 ml volumetric flask and made up to the mark. Then 5 ml of

filtrate was pippeted out into a test tube and mixed with 2ml (10 fold dilution) of 0.1M FeCl3 in 0.1N 13HCL and 0.008M potassium ferrocyanide. The absorbance measured at 605 nm within 10 min .

52


Table 1 : PHYTOCHEMICAL CONSTITUENTS OF FIVE MEDICINAL PLANT

SL.

NO

Phytoconstituents

Adathoda

visca

Abutilon

indicum

Datura

stramonium,

Lantana

camara

Tridax

procumbens

1 ALKALOIDS

(a)Mayer’s test

(b)Dragndore’s

test

+

+

+

+

+

+

+

+

+

+

+

+

+

(c)Wagner’s test + +

2 FLANOIDS

(a)Shinoda test

(b)Alkline

reagent

(c)FeCl3 test

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+[Present] , - [Absent]

3 TANNINS

(a)Lead acetate

test

(b)FeCl3 test

+

+

+

+

+

+

+

+

+

+

4 SAPONINS

(a)Frathing test

+

+

+

+

+

5 AMINOACID

(a)Millons test

(b)Ninhydrin test

_

_

_

_

_

_

_

_

_

_

6 PROTEIN

(a)Biuret test

(b)Millons test

_

_

_

_

_

_

_

_

_

_

53


Table2:YIELD OF PHYTOCHEMICALS IN DIFFERENT PLANT EXTRACT SYSTEM.

SL.NO

PLANT

SPECIES

PHYTOCHEMICALS YIELD IN (mg)

ALKALOIDS FLAVONOIDS TANNINS SAPONINS

1 Adathoda visca

21 mg

64 mg 0.08 µ mole 13mg

2 Abutilon indicum 240 mg

61 mg 0.82 µ mole 12 mg

3

Datura stramonium,

298 mg 112 mg 0.31 µ mole 8 mg

4 Lantana camara 62 mg

23 mg 0.11 µ mole 9 mg

5 Tridax procumbens 61 mg

38 mg 0.05 µ mole 10 mg

Table 3: Date Showing Preliminary Phytochemicals Screening of The Leaf of Five Different

Plant Extracts In Different Solvent System.

S.

N

o.

Plant

Species

Seconda

Ry

Metabo

Lite

Aque

Ous

Extract

Chlorof

Orm

Extract

Ethan

Ol

Extract

Petrol

Eum

Ether

Extract

Metha

Nol

Extract

1 Abutilion

Indicum

ALKALOI

D

FLAVANO

ID

SAPONIN

TANNIN

_ _ _

+++

+++

+++

+++

+++

_ _ _

_ _ _

+++

+++

+++

+++

+++

+++

_ _ _

+++

+++

+++

+++

+++

54


2 Adathoda

Visca

ALKALOI

D

FLAVANO

ID

SAPONIN

TANNINS

_ _ _

+++

_ _ _

+++

_ _ _

_ _ _

+++

_ _ _

+++

+++

_ _ _

+++

+++

_ _ _

_ _ _

+++

+++

+++

+++

+++

3

DATURA

STRAMON

IUM

ALKALOI

D

FLAVANO

ID

SAPONIN

TANNINS

_ _ _

_ _ _

_ _ _

_ _ _

_ _ _

_ _ _

_ _ _

_ _ _

_ _ _

_ _ _

+++

+++

_ _ _

_ _ _

_ _ _

_ _ _

_ _ _

+++

+++

+++

4 Lantana

Camara

ALKALOI

D

FLAVANO

ID

SAPONIN

TANNINS

_ _ _

+++

_ _ _

+++

_ _ _

+++

+++

+++

+++

_ _ _

_ _ _

+++

+++

_ _ _

_ _ _

+++

+++

+++

_ _ _

+++

5 Tridax

procumbens

ALKALOI

D

_ _ _

+++

+++

+++

+++

+++

_ _ _

_ _ _

_ _ _

_ _ _

FLAVANO

ID

SAPONIN

TANNINS

_ _ _

+++

+++

+++

+++

_ _ _

+++

_ _ _

+++

_ _ _

+[Present] , - [Absent]

55


RESULTS AND DISCUSSION:

The present investigation was carried out on five plants to study the presence or absence of medicinally

active phytochemicals in the leaves of five different plant species. The Abutilon indicum Adathoda

visca, Datura stramonium, Lantana camara and Tridax procumbens, all the five plant species were

showing the presence of alkaloids, flavonoids, saponin, tannins,and it showing the absence of

amminoacids and proteins.The results are summarized in the table -1.Quantitative estimation of crude

phytochemicals from these five plants is given in table 2. Phytochemical analysis was carried out to

find out the qualitative and quantitative existence of secondary metabolites in them. The preliminary

screening of Alkaloid, Flavanoid, Saponin and Tannin in five different plant species has been carried

out. All the plant species showed the presence of the above mentioned phytochemicals, in different

quantities. Datura stramonium contained the higest percentage of Alkaloid and Flavanoid followed

by Adathoda visca, showing highest Flavonoids content. Amongs to the remaining plants, Abutilion

indicum showed higher amount of Alkaloids and Tannins. Lantana Camera and Tridax procumbens,

showed lower level of alkaloids, Flavonoids, Saponin and Tannin.Table-3 showing the premilinary

phytochemical screening of the leaf of five different plant extract in different solvent system.The

maximum yield was obtained in methanol and ethonolic extract of Adathoda visca, Abutilon indicum

and Datura stramonium.While minimum yield was obtained in aqueous extract and chloroform and

petroleum ether extract.So the moderate yield was obtained from the Lantana camara and Tridax

procumbens in methanol and ethanolic extract. In general, the yield obtained from these plant was quite

adequate thereby making further development of these herbal drugs economically feasible.

56


CONCLUSION:

The above results indicates that, the leaves of the plants investigated are rich in the alkaloids,

flavonoids, saponin and tannins They are known to show medicinal potential and physiological

activities. Thus the plants under investigation showed their medicinal potential and can be a source of

useful drugs for the treatment and prevention of various diseases and disorders. However, further

studies are required to isolate the active principal from the crude plant extract for proper drug

development. The isolation and purification of bioactive molecules are in process.

REFERENCES:

1. Gloria E.Barboza et al., 2009 Medicinal plants: A general review and a phytochemical

ethnopharemacalogical screening of the native argentine flora.v,34.

2. Dewick P.M.[1996] Tumor inhibition from plant; Tease and Evans

3. Krishnaiah, D, Devi, T, Bano, A and Sarbatly, R. [2009]. Studies on phytochemical constituents of

six Malaysian medicinal plants, J. Medicinal pl Research 3(2):67-72.

4. G A Ayoola, HAB Coker, phytochemical screening and antioxidant activities of some selected

Medical plants used for malaria therapy in southwestern Nigeria. j of pharmaceutical research,

2008; 7(3): 1019-1024.

5. rdHarborne JB:Phytochemical method 2005 A guide to modern techniques of plant analysis 3

edition. New Del hi:Springer Pvt. Ltd; 2005

6. Harbone, JB. 1973. Phytochemical methods, London, Chapman Hall Ltd. Pp.49-188.yes

7. Bohm BA, Koupai-Abyazani MR 1994: Flavonoids and condense tannins from leaves of

Hawaiia vaccinium vaticulatum andv calycinium. pacific sci, 48;458-463.

8. Obadoni BO, Ochuko PO: Phytochemical constituents of some Nigerian medicinal plants. Afr J

Biotechnol 2005, 4:685-688.+p

9. Van-Burden TP, Robinson WC 1981: Formation of complexs between protein and tannin acid.J

Agri food chem, 1:77-82.

EVALUATION OF ANTIBACTERIAL ACTIVITY AND PHYTOCHEMICAL

ANALYSIS OF VITEX NEGUNDO AGAINST SELECTED HUMAN

PATHOGENS

G.L. Aruna*and Poojitha

Department of Microbiology, Govt. Science Collage, Chitradurga - 577501 Karnataka,India.

*Author for correspondence:[email protected]

ABSTRACT:

The traditional medicine obtained from plants still plays an important role in the treatment of diseases.

This work is an attempt to compare the antibacterial activity of medicinal plants with antibiotics.

Thevitex negundo Linn. Plant was selected on the basis of their use in the treatment of infectious

diseases by local people. Aqueous, ethanol and acetone extracts ofvitexnegundo Linn.were examined

for their antibacterial activity by well diffusion method against selected human pathogens. The plant

has shown encouraging antibacterial activity. Phytochemical analysis of these extracts revealed the

presence of steroids, cardio lipids etc., and this study support to the traditional knowledge of local

users. Further study aimed at characterization of active agent from the plant extracts which exhibited

promising activities need to be carried out.

KEY WORDS: Antibacterial activity, Medicinal Plant, Phytochemicals and Antibiotics.

Coressponding Author:

G L Aruna

Department of Microbiology,

Govt. Science College,

Chitradugra,

Karnataka.


57


Introduction:1 The medicinal plants are the nature's gift to man . The use of medicinal plants as a source for relief

from illness can be traced back over five millennia to written document of early civilization in China, 2

India and north east. The potential of higher plants as a source of new drugs is still largely unexposed .

Among the estimated 2, 50,000 5,00,000 plant species only a small percentage has been investigated

phytochemically and the fraction submitted to biological or pharmological screening is even smaller. 3

Plants are used medicinally in different countries and are a source of many potent and powerful drugs .

The use of medicinal plant extracts for treatment has become popular when people realized that the

effective life span of antibiotic is limited and over prescription and misuse of traditional antibiotics are 4

causing microbial resistance . Such a fact is cause for concern because of number of patients in

hospitals who are suffering from infectious diseases due to multidrug resistant bacterial strains

resulting in high mortality. Therefore to reduce this problem, the alternative source of medicine must

be developed. For a long period plants have been a valuable source of natural products for maintenance

of human health, especially in last decade, with more intense studies for natural therapies. The use of

medicinal plants as antimicrobial agents increased worldwide. According to WHO medicinal plants 5

would be the best source to obtain a variety of effective drugs

Vitexnegundo Linn.is a large aromatic shrub distributed throughout India. This species is globally

distributed in Indo-Malesia, cultivated in America, Europe, Asia and West Indies. Within India, it is 6

found throughout the greater part of India, in the outer Himalayas . Itbelongs to family Verbenaceae 7commonly known as 'Five leaved chaste tree (English) . It is an aromatic large shrub or small tree

about 3m in height with quadrangular branches and almost found throughout India, ascending to

1500m in the outer Himalaya, fairly common in waste lands, on road side, the banks or streams or in 8,9moist places near deciduous forests .

Although, all parts of V.negundoare used as medicine in the indigenous system of medicine, the leaves

are the most potent for medicinal use. The decoction of leaves is used for treatment of inflammation,

eye-disease, toothache, leucoderma, enlargement of the spleen, ulcers, cancers, catarrhal fever,

rheumatoid arthritis, gonorrhea, sinuses, scrofulous sores, bronchitis fungal diseases and as tonics,

vermifuge, lactagogue, antibacterial, antipyretic, antihistaminic, analgesic, insecticidal, ovicidal, 6,7,11feeding deterrence, growth inhibition and morphogenetic agents anti-inflammatory, antioxidant

8and hepatoprotective disorders . The various chemical constituents like flavonoids, flavones,

glycosides, volatile oil, triterpenes, tannins terpenoids and alkaloids and many others were identified in 6,11 10this plant . It also finds use as a food crop and a source of timber .

The objective of this research was to evaluate the antibacterial activity and to identify the

phytochemical constituents of the selected vitexnegundoextracts used by local people against selected

bacteria, which were isolated from clinical samples.

Materials and Methods:

Collection and identification of plant material:

The vitexnegundo Linn.plant was collected from different parts of chitradurga district, Karnataka,

India and is identified and authenticated by Prof. R. K. Rangaswamy and Prof. Shankaramma Botany

Department, Govt. Science College, Chitradurga using the Gamble flora of Madras Presidency.

58


Preparation of plant extracts:

Fresh and healthy leaves of vitex negundo were collected and they were washed under running tap

water to remove soil particles and other dirt. The leaves were shade dried in the laboratory at room 0temperature (28± 2 C) for 3-5 days. The dried leaves were ground well into a fine powder using pestle

and mortar. The powder was stored in air sealed polythene bags at room temperature till the extract was

prepared.

A fixed weight (20g) of powdered plant material was soaked in 100 ml of three different solvents

separately such as water, ethanol and acetone for overnight. Thereafter it was shaken vigorously and

filtered using What mann filter paper No. 1 and the filtrate was allowed to evaporate for overnight so

that the volume of the extract becomes 1/4th of its original volume (12). Then the plant extracts were

used to determine their antimicrobial activity, phytochemical analysis and MIC.

Preliminary Phytochemical screening:

The leaf extracts were assayed for the presence of phytochemical constituents using the standard

methods described by Horborne16 and Kokateet al., with some modifications (13).

Table1: preliminary phytochemical screening of plant extracts

Antibacterial assay:

Preparation of bacterial cultures:

Clinical isolates of Staphylococcus aureus, Escherichia coli, and Klebsiellapneumoniaewere obtained

from vasavi clinical laboratory and sub-cultured on nutrient agar slants.

59


Preparation of bacterial inoculuum:

The selected bacteria such as Staphylococcus aureus, E. coli, klebsiellapneumoniaewere pre cultured 0on nutrient broth overnight and incubated at 37 C. The culture broths were centrifuged at 1000 rpm for

5 minutes, bacterial pellet was suspended in double distilled sterile water (12).

Antibacterial activity test:14, 15

In vitro, antibacterial activity was tested by well diffusion method using nutrient agar medium .

0.1ml of bacterial suspension was swabbed on agar plate using sterile cotton swab. Wells are cut into

the agar medium and loaded with respective plant extracts. Negative control was prepared using

respective solvent. Streptomycin was used as standard antibacterial agent. All the plates were kept for

incubation at 37°C for 24 h. After incubation, inhibition zones formed around the wells were measured

with scale in millimeter (Table 3). This study was performed in triplicates. Activityindex for each

extracts was calculated (Table 4) by the following standard formula

Activity index = Inhibition zone produced by extract/ Inhibition zone produced by produced by

standard (16)

Minimum Inhibitory Concentration (MIC):

MIC is defined as the lowest concentration of the plant extract where no visible growth is observed in

the test tube (bacteriostatic concentration). The method of Vollekaet al., (2001) modified by Usmanet

al., (2007) was used to determine the MIC of the plant extracts. In this method, 2-fold dilution 14,15

technique was used where the plant extract was prepared to the highest concentration of 20mg/ml

for all solvent extracts, and are serially double diluted (1-2) to a working concentration ranging from

20mg/ml to 0.625mg/ml using nutrient broth.

Later all the test tubes containing nutrient broth and plant extract in variable concentration were 0inoculated with 0.5ml respective test bacterial suspension. After 18 hours of incubation at 37 C, the test

tubes were observed for growth and turbidity was determined calorimetrically. The least concentration

of the plant extract (or highest dilution of plant extract) that completely inhibited the growth of the test

organism, i.e., where no turbidity was observed is the minimum inhibitory concentration (MIC) of the

plant extract. A control experiment was run in parallel to study the impact of the solvent alone (without

plant extracts) on growth of the test organisms. Solvents were diluted in a similar pattern with sterile 12nutrient broth followed by inoculation of test bacterial suspension and incubation .

Results

Phytochemical analysis

Table 2. Phytochemical analysis of vitexnegundoplant extracts

60


Fig. 1 Antibacterial activity of Vitexnegundo Linn.Plant extracts against selected bacterial

cultures.

Table 3:Antibacterial activity of Vitexnegundoplant extracts against selected bacteria.

Note: Values are mean of triplicates

Table 4: Activity index of Vitexnegundo Linn.Plant extracts against selected bacteria.

61


Graph 1.Graphical representation of MIC values of Vitexnegundo Linn.Plant extracts in mg/ml

against selected bacteria.

DISCUSSION

The major secondary metabolites like, alkaloids, flavonoids, saponins, phenols, terpenoids,

anthraquinones, proteins and aminoacids, carbohydrates and glycosides were assessed according to the 9standard procedure described by Harborne . Inour study preliminary phytochemical analysis of

different solvent extracts of Vitexnegundorevealed the presence of secondary metabolites as shown in

table 1. Srinivas et al., studied in vitro antibacterial activity of methanol, chloroform and hexane of

Vitexnegundo by agar diffusion method. They showed that the most susceptible gram positive bacteria

was Bacillus cereus, while the most susceptible gram negative bacteria was Klebsiella pneumoniae. 17

They suggest that Vitexnegundocan be used in treating diseases caused by test orgaanisms . Deepa et

al., reported the antibacterial activity of ethanol extract of Vitex negundoagainst Staphylococcus 18

aureusand E. coli .In the present study the Vitexnegundo plant extracts were assayed in vitro by agar

well diffusion method against three bacterial species as shown in table 2.The acetone extract has shown

highest inhibitory activity against Staphylococcus aureus and K. Pneumoniae. Comparitively ethanol

extract showed moderate and aqueous extract showed less inhibitory activity against all tested bacteria.

Our studies showed that the ethanol, aqueous and acetone extract of this plant were certainly much

better and powerful. This may due to the better solubility of their active components in these organic

solvents.

The MIC of different plant extracts as shown in graph1. Among different plant extracts tested

acetone extracts of Vitexnegundorelativelyhas lowest MIC of 5mg/ml against S. aureusandethanol

extract has MIC of 7.5mg/ml.

This study finding support to the traditional knowledge of local users.Vitex negundo plant extracts have

great potential as antimicrobial compounds against bacteria. Thus, they can be used in the treatment of

infectious diseases caused by antibiotic resistant bacteria. Further study aimed at characterization of an

active agent from the plant extracts which exhibited promising activities need to be carried out.

62


CONCLUSION:

Vitex negundo Linn.has got wide range of curative properties, which may be therapeutically beneficial

for overall health and wellness of population. Since the plant is easily available and no special

conditions are required to cultivate and collect the plant, it could be a better choice to treat the diseases.

Simultaneously, safety evaluations of plant need to be carried out carefully. References:

1. EL-Kamali, H.H. and Y. M. EL-amir, 2010. Antimicrobial Activity and Phytochemical

Screening of Ethanolic Extracts Obtained from Selected Sudanese Medicinal Plants. Current

Research Journal of Biological Sciences, 2:143-146.

2. Vashist, H. and A. Jindal, 2012. Antimicrobial Activities of Medicinal Plants. International

Journal of Research in Pharmaceutical and Biomedical Sciences, 3: 222-230.

3. Mahesh, B. and S. Satish, 2008. Antimicrobial Activity of Some Medicinal Plant against Plant

and Human pathogens. World Journal of Agricultural Sciences, 4:839-843.

4. Jayalakshmi, A., K. A. Raveesha and K.N.Amruthesh, 2011. Phytochemical investigations and

antibacterial activity of some medicinal plants against pathogenic bacteria. Journal of Applied

Pharmaceutical Science, 1:124-128.

5. Nascimento, G. G. F., J. Locatelli, P. C. Freitas and G. L.Silva, 2000. Antibacterial activity of

plant extracts and phytochemicals on antibiotic resistant bacteria. Brazilian Journal of

Microbiology, 31:247-256.

6. Ladda, P.L. and C. S. Magdum, 2012. Vitexnegundo Linn. : Ethnobotany, Phytochemistry and

Pharmacology- A Review. International Journal of Advances in Pharmacy, Biology and

Chemistry, 1: 111-120.

7. Gautam, K. and K. Padma, 2012. Evaluation of Phytochemical and Antimicrobial study of

Extracts of VitexnegundoLinn. Int. J. Drug Dev. and Res., 4(4): 192-199.

8. Singh, P., G. Mishra, S. Srivastava, S. Srivastava, K. K. Sangeeta, R. L. Jha1, Khosa.

Phytopharmacological Review of Vitex _egundo(Sambhalu)Pharmacologyonline,2: 1355-

1385.

9. Rose, C.M. and L. Cathrine, 2011. Preliminary Phytochemical Screening and Antibacterial

Activity on VitexNegundo.International Journal of Current Pharmaceutical Research,3(2):99-

101.

10. Vishwanathan, A. S. and R. Basavaraju, 2010. A Review on VitexnegundoL. A Medicinally

Important Plant. EJBS 3 (1): 30-42.

11. Amirtharaj. V.R, M. A. Reyaz, A. J. Kumar, M. Kaarthikeyan, Saivishwathdindu, N.S. Kumar,

2011. Preliminary Phytochemical Studies and Invitro Cytotoxic Activies on Vitexnegundo (L.).

International Journal of Research in Pharmaceutical and Biomedical Sciences, 2(4):1800-1804.

12. Aruna, G.L. M. Yadav and A. Fathima, 2012. In vitro antibacterial activity and phytochemical

analysis of some medicinal plant extracts against selected human pathogens. In proceedings of

National Conference on Biotechnological Approaches for Sustainable Environmental

Management, pp:47-55.

63



13. Kokate, C. K, A. P. Purohit, 2009. Pharmacognosy. Fourth Edition. NiraliPrakashan, Pune,

14. Mahida, Y and J.S.S. Mohan, 2007. Screening of Plants for their potential antibacterial

activity against Staphylococcal and Salmonella Spp. Natural Product Residence, 6:301-305.

15. Abhishek, R.U., R. Ashwin and T. P. Mahesh, 2011.Phytochemical analysis and antibacterial

efficacy of Baccaureacourtallensis. Medicinal Plants, 3:327-330.

16. Keerti, G. andK. Padma, 2012. Evaluation of Phytochemical and Antimicrobial study of

Extracts of Vitexnegundo Linn. International Journal of Drug Development and Research

4(4): 192-199.

17. Srinivas, P., R. S. Reddy, P. Pallavi, A. Suresh and V.Praveen, 2010. Screening for Antimicrobial

Properties of VitexNegundo. L.from rural areas of Warangal Dist/A.P. India. International Journal

of Pharma and Bio Sciences, 1(4): B 26-B38.

18. Deepa, M., P. R. Devi and P. Hariharan, 2012. Phytochemical screening and In vitro evaluation of

antimicrobial activity of Vitexnegundo Linn (Verbenaceae). InInternational Journal of Advanced

Life Sciences, 4:59-63.

64

GREEN SYNTHESIS OF ZnO NANOPARTICLES AND ITS APPLICATION

IN THE REMOVAL OF MALACHITE GREEN DYE1* 2 3 4Dr. Chandrapraba. M N , Dr. Ahalya.N , Prashanth Kumar , Chaitra Barati ,

5 6Rajani. D.M Vignesh. S

*Associate Professor, Department of Biotechnology, M.S. Ramaiah Institute of Technology, Bangalore2 Assistant Professor, Department of Biotechnology, M.S. Ramaiah Institute of Technology, Bangalore

3,4,5,6 Department of Biotechnology, M.S. Ramaiah Institute of Technology, Bangalore

ABSTRACT

Waste waters from textile industries contain a variety of polluting substances including dyes.

The environmental and subsequent health effects of dyes released in textile industry wastewater are

becoming a subject of great concern. An effective method of dye removal is hence required to address to

this problem. In the present work, ZnO nanoparticles, synthesized using Aloe vera leaf extract as both

reducing and capping agent, has been effectively used as an adsorbant for the removal of dye.

Characterization of the synthesized particles was done using X ray diffraction technique and Scanning

electron microscopy. The dye adsorption studies were carried out using Malachite green (fast green)

dye which is a widely used dye in textile industry. Effects of pH and initial concentration on the removal

of dye were studied. Dye removal efficiency of 85% was obtained under optimal conditions.

Desorption studies indicated the removal of up to 62% of the malachite green dye.

Keywords: Green Synthesis, ZnO nanoparticles, Malachite green, Aloe vera.

Corresponding author:

Dr. Chandrapraba. M N

Associate Professor,

Department of Biotechnology, MSRIT,

[email protected] (9980516932,)

65


Introduction

The effluent from textile industries carries a large number of dyes and other additives which are added 4

during the colouring process . This impacts colour, makes it aesthetically unpleasant and unfit to be

used for any other purpose. Most of these dyes do not degrade well even after proper treatments.

Nanotechnology provides challenging applications in finding solutions to these environmental

problems and is the study dealing with controlling matter on atomic and molecular scale. ZnO

nanoparticles can be regarded as the better and most important metal oxide nanoparticle for

applications in degradation of various dyes in water treatment, since it exhibits high catalytic efficiency,

high surface area for dye loading, strong adsorption ability, high fraction of atoms etc.

The “green route” for nanoparticle synthesis is achieving great interest because of eco-friendliness,

economic prospects, use of non toxic and safe reagents and feasibility. Among bio-organisms, plants 3 are the major source of reducing agents due to the simple procedures and inexpensive cost required .

Nanoparticles of zinc, silver, nickel, cobalt and copper have been synthesized using the plant species

such as Brassica Juncea (Indian mustard), Medicago sativa (Alfa alfa), Heliantus annus (Sunflower),

Azadirachta indica, Capsicum annum, Aloe barbadensis, Magnolia kobus and Diopyros kaki leaf

extracts. Apart from this Bacterium such as Pseudomonas aeruginosa, Bacillus subtilis & 1

Pseudomonas stutzeri have been used in synthesizing nanoparticles .

Aloe vera has been reported to possess immunomodulatory, anti-inflammatory, UV protective,

antiprotozoal and wound-burn healing promoting properties. Recently, the extract of Aloe vera plant

has been successfully used to synthesize single crystalline triangular gold nanoparticles (~50-350 nm in

size) and spherical silver nanoparticles (~15 nm in size) in high yield by the reaction of aqueous metal

source ions (chloroaureate ions for Au and silver ions for Ag) with the extract of Aloe vera plant. This 2

biosynthetic route has been extended to preparation of In O nanoparticles also (Maensiri et al., 2008).2 3

Malachite green dye (also known as Fast green), is a widely used textile dye, is a controversial dye 5

that is known to cause serious effects in aquaculture (Sudova et al, 2007). In the present work, ZnO

nanoparticles, synthesized using Aloe vera leaf extract as both reducing and capping agent, has been

effectively used as an adsorbent for the removal of dye.

Materials and methods

Synthesis of ZnO nanoparticles: Aloe vera hot extract was prepared by boiling 35g of Aloe vera

leaves in 100ml of distilled water. The resulting solution was filtered and the filtrate used as Aloe hot

extract. Aloe cold extract was prepared by homogenizing 35g of Aloe vera leaves in 100ml water and

filtering it. 30ml of these extracts was used as reducing agents to synthesize nanoparticles. 3g of zinc

nitrate was added to both extracts separately and kept at 60°C under vigorous stirring until dried. The

resulting powder was ground and calcined at 570°C in muffle furnace.

Characterization of nanoparticles: ZnO nanoparticles were characterized by X-Ray Diffraction.

X-ray diffraction patterns were recorded and compared with diffractometer using Cu k radiation

(=1.542Å) over a wide range of Bragg angles (10≤ 2 ≤ 80) with an accelerating voltage of 40KV and

current of 50mA. From XRD the crystallite size was calculated using the Scherrer's formula: P= (0.9)/

66


(cos), Where;P-crystallite size, - Wavelength (1.54Å), -Full maxima half width & -Diffraction angle.

The morphology of the samples was examined by scanning electron microscope (SEM). The images

were obtained by using a Zeiss Gemini Ultra 55 SEM. The measurements were carried out at the

acceleration voltage of 20 kV in the dark-field mode. Fourier transform infrared (FTIR) spectrum -1(Perkin-Elmer Spectrophotometer Spectrum One) in the range of 4000 180 cm was studied.The FT-IR

-1spectrum was measured between the wave number of 400 and 4000 cm .

Removal of dye using ZnO nanoparticles: In this study, the adsorption of the commercial dye

malachite green (acidic dye) onto ZnO nanoparticles has been investigated. Desired quantity of

nanoparticles was added to 50 mL of dye solution of varying concentrations and the suspension was

incubated for 2.5 h. At different intervals of time the aliquot was taken out, centrifuged for 5 min at 1500

rpm. The absorption spectra of the dye solutions were recorded and rate of decolorization calculated.

Desorption studies of the dye: Nanoparticles from the previous experiment were separated from

the dye solution by centrifugation and washed 2-3 times with double distilled water. Washed

nanoparticles were suspended in standard solutions of 1 N NaOH and 1N HCl and incubated for 2.5

hours. At different intervals of time the aliquot was taken out, centrifuged for 5 min at 1500 rpm and the

absorption spectra recorded.

Results and Discussion

Synthesis of ZnO nanoparticles: The zinc oxide nanoparticles synthesized were yellowish white

in colour and crystalline in nature.

Characterization of nanoparticles: The XRD analysis (Figure 1) showed distinct primary peaks

at 36.47º. The presence of sharp peaks and absence of unidentified peaks confirms the purity and 6,7

crystallinity and stable wurtzite phase of the ZnO nanoparticles (Sushil et al., 2010; Satyanarayana

et al., 2012). A definite line broadening of the XRD peaks indicates that the prepared material consist of

particles in nanoscale range. The crystalline size was determined using Debye Scherrer's formula and

was found to be 55nm.

0 10 20 30 40 50 60 70 80 90

0

2000

4000

6000

8000

31.9953

36.47447

47.763656.74221

63.06574

68.07188

HOT SAMPLE

Inte

nsi

tyo

rco

unt

sin

sec

ond

s

2 THETA degrees

67


Effect of concentration on dye removal efficiency of ZnO nanoparticles: Varying

concentration of the dye solution from 10 ppm to 120 ppm of pH 6 was prepared and 0.1 mg of the

nanoparticles were added and incubated at 32°C for 120 rpm for 2.5 hours. After 2.5 hours, the

absorbance was recorded at 620 nm and the percentage removal was calculated. Results obtained are

shown in Figure 2. The percentage of dye removal by synthesised nanoparticles was above 90% for

concentration of dye in the range 50-90 ppm.

Figure 3: Percentage dye removal by ZnO nanoparticles for varying dye concentrations.

Effect of pH on dye removal efficiency of ZnO nanoparticles: To study the effect of pH on

the decolourization efficiency, experiments were carried out at various pH values, ranging from 5 to 9

for constant dye concentration of 50 ppm and nanoparticle concentration of 0.1mg/ml. Results obtained

are shown in Figure 4. Percentage removal was above 85% in the pH range of 5 to 7. Beyond pH 7, the

removal efficiency decreased.

Figure 3: Percentage dye removal by ZnO nanoparticles for varying pH

68


Figure 1: XRD pattern of ZnO

The morphology and size measurements of the ZnO nanoparticles were determined by scanning

electron microscopy. The SEM images indicate that the particles are rod shaped and are well dispersed.

The morphology reveals that the nanoparticles are in the nano scale

Figure 2: SEM micrographs of Synthesized ZnO nanoparticles

FTIR studies: FT-IR absorption spectrum of ZnO nanoparticles is shown in Figure 5. The broad -1

at~3400 cm is attributed to the characteristic absorption of hydroxyls. is attributed to the characteristic -1

absorption of hydroxyls. The peaks which are located at~2900 cm are attributed to the asymmetric and -1 -1

symmetric stretching vibrations of CH mode. Peaks at 1572 cm 1376 cm are due to the stretching -1 -1

vibrations of C-O group. Furthermore, the peaks at 1458 cm and 787 cm correspond to CCl group. 4

-1 6 The peak at ~450 cm is the characteristic absorption band of ZnO (Sushil et al., 2010).

Figure 5: FTIR Spectrum of ZnO nanoparticles

69


Desorption Studies of the dye: Results of the desorption studies carried out at different pH i

s shown in Figure 6. From the graph, it is observed that the maximum dye desorption takes place at

pH 10.

0

10

20

30

40

0 2 4 6 8 10 12 14

pH

%de

so

rbed

Figure 6: Graph showing dye desorption at varying pH

Conclusions

Zinc oxide nanoparticles were synthesized by the bio-friendly approach using aloe vera plant extract. Detailed structural characterizations using the various characterization techniques demonstrated that the synthesized products are rod shaped and crystalline in structure and the average crystalline size as determined using Debye Scherrer's formula was found to be 55nm. The sharp peaks of the XRD patterns show the purity level of the ZnO nanoparticles. FTIR analysis determined that the synthesized

-1ZnO molecules showed absorbance at the resonant frequency of 450 cm which is the characteristic of their structure. The percentage of dye removal by synthesised nanoparticles was above 90% for concentration of dye in the range 50-90 ppm and the optimum pH range was found to be 5 to 7.

Acknowledgement

We would like to thank Alumini association of MSRIT for funding our project and helping us

economically.

References

1. Hasna Abdul Salam, Rajiv P., Kamaraj M., Jagadeeswaran P., Sangeetha Gunalan and Rajeshwari Sivaraj (2012) International Research Journal of Biological Sciences 1(5): 85-90.

2. Maensiri S, Loakul P, Klinkaewnarong, Phokha S, Promark V, and Seraphin S (2008) Indium Oxide nanoparticlesusing Aloe veraplant extract:synthesis and optical properties, Journal of optioelectronics and advanced materials 10: 161-165.

3. Ropisah Mie, Mohd Wahid Samsudin, Laily B. Din (2013) A Review on Biosynthesis of Nanoparticles Using Plant Extract: An Emerging Green Nanotechnology, Advanced Materials Research 667, 251.

4. Wang, C.X, Yediler, A, Lienert, D, Z. J. Wang, A. Kettrup (2002), Toxicity evaluation of reactive dye stuffs, oxilaries and selected effluents in textile finishing industry to luminescent bacteria vibrio fischeri. Chemosphere, 46, 339-344.

5. Sudova E, Machova J, Svobodova Z, Vesely T (2007) Negative effects of malachite green and possibilities of its replacement in the treatment of fish eggs and fish: a review, Veterinarni Medicina, 52 (12), 527539.

6. Sushil Kumar Kansal, Ahmed Hassan Ali, Seema Kapoor (2010) Photocatalytic decolorization of biebrich scarlet dye in aqueous phase using different nanophotocatalysts”, Desalination 259, 147155.

7. Satyanarayana Talam, Srinivasa Rao Karumuri and Nagarjuna Gunnam,(2012) Synthesis, Characterization, and Spectroscopic Properties of ZnO Nanoparticles, ISRN Nanotechnology, 1-6.


70

REGENERATION OF BAMBUSA NUTANS IN VITRO FROM FIELD GROWN NODAL EXPLANTS.

1, 2 2, 3*K. Chethan and T. S. Rathore 1 Department of Microbiology, M. S. Ramaiah College of Arts, Science & Commerce, MSR Nagar, Bengaluru.

2 thTree Improvement and Propagation Division, Institute of Wood Science & Technology, 18 Cross,

Malleswaram, Bengaluru.3 Arid Forest Research Institute, New Pali Road, Jodhpur, Rajasthan.

*Corresponding author: [email protected]

ABSTRACT

Bambusa nutans Wall. ex Munro. is an industrially important bamboo species used in construction,

making furniture, paper and pulp industry. An efficient and reproducible protocol has been developed

for in vitro propagation of B. nutans through axillary shoot proliferation. Nodal explants obtained from

ten years old field grown clumps, produced multiple shoots on Murashige and Skoog (MS) liquid

medium supplemented with NAA 0.25 mg/l + BAP 1.0 - 5.0 mg/l. Shoot multiplication experiments

were carried out with different concentration of 6-benzylamino purine (BAP), kinetin (Kn) and

thidiazuron (TDZ) with NAA. Different types of nutrient media (MS, B , SH, WP and Heller's) were 5

also tested for high frequency shoot multiplication. Highest rate of shoot multiplication (6-7 folds) was

obtained on MS liquid medium incorporated with TDZ within 4 weeks period at 25+2°c temperature

and 2500 lux intensity of light for 12 h photoperiod. In vitro shoot propagule (2-3 shoots/clump) of 3-4

cm in length exhibited high frequency rooting (100%) on MS/3 basal salts medium supplemented with

IBA, within 4 weeks period. This is the first report on in vitro propagation of B. nutans from field grown

mature clump.

Key words: Bambusa nutans, mature clump, in vitro regeneration.


Dr. K Chethan

Assistant Professor

Department of Microbiology,

M S Ramaiah College of Arts, Science and Commerec, Bangalore


71


INTRODUCTION

Bamboo constitutes a great extent to the economy of rural communities in developing countries,

particularly in the Asia Pacific region. Out of 75 genera and 1250 species of bamboo recorded from all 1over the world (Soderstrom and Ellis 1987), about 125 species belonging to 23 genera occur in India

with a wide range of distribution, covering an estimated area of 8.96 million hectares, which constitutes 211.71% of the forest area in deciduous and semi ever green regions of India . In order to meet the

increasing demand of bamboo, plantation of bamboo in forests and outside forestland is viable

alternative.

Bambusa nutans Wall. ex Munro is an economically and industrially important bamboo species of the

family Poaceae (Gramineae). It grows the best at altitudes of 500-1500 m. In India, commonly found in

the North-Eastern states, Odisha and West Bengal. The culm attain height up to 20 m, straight and

smooth. Gregarious flowering is at an interval of 35 years. This is one of the priority bamboo species

according to National Mission on Bamboo Application (NMBA), Government of India, New Delhi.

Long flowering cycle, short period of seed viability and vegetative propagation through culm cuttings,

limits the scope of bamboo improvement. Plant tissue culture approach has potential to over come the

above problems for mass production of high quality planting material of commercially important

bamboo species.

The present study was aimed to establish a reliable and reproducible protocol for in vitro propagation of

Bambusa nutans through axillary shoot proliferation from ten years old field grown superior

genotypes.


Nodal shoot segments were obtained from newly grown culm branches maintained in germ plasm bank

(IWST, Bangalore). Explants were surface sterilized using 70 % Ethanol and 0.075 % Mercuric

chloride. These explants were inoculated in shoot initiation medium (MS liquid + NAA 0.25 mg/l +

BAP 1.0 - 5.0 mg/l). In order to optimize shoot multiplication medium, different types of media (MS,

B , SH, WP and Hellers) and different concentration of BAP, Kn and TDZ are tested for high frequency 5

shoot multiplication. Various auxins (IAA, IBA, NAA and NOA) used in solidified MS/3 basal medium

for in vitro rooting experiments. Rooted shoots was transplanted into soil mixture consisted of sand, soil

and compost, grown under poly tunnel in green house for acclimatization.

DATA ANALYSIS

Experimental data was analyzed by one-way analysis of variance (ANOVA) using Excel version 5.0 3 and means were compared at 5% level of significance .


In this experiment, significant difference was observed in the effectiveness of the different cytokinins

used. In shoot initiation, MS liquid medium containing additives, NAA 0.25 mg/l and BAP 5.0 mg/l

proved the best in terms of multiple shoot induction within 3 weeks period (Fig. 1), than compared to

Kn. These results are supported by earlier reports, where BAP has been used for the multiple shoot 4,5,6

initiation in bamboo species (Sanjay et al. 2005, Ramanayake et al. 2006, Somashaker et al. 2008).

During multiplication among different nutrient media (MS, B , SH, WP and Hellers) and cytokinins 5

(BAP, Kn and TDZ) tested, MS liquid medium supplemented with additives + NAA 0.25 mg/l+ TDZ

72


0.25 mg/l proved the best in terms of enhanced shoot number as well as shoot length (4.57cm) than SH

and B media within 4 weeks period (Fig. 2). It is also significant that the shoot multiplication capacity 5

of the propagules in vitro was greatly influenced by the TDZ as compared to BAP and Kn. The results

7are in accordance that TDZ has been demonstrated as a high potent cytokinin in woody species .

Out of the various auxins used, high frequency (100%) rooting with 6-7 roots of 7-8 cm length was

achieved on MS/3 basal salts agar gelled medium supplemented with IBA 2.0 mg/l in 4 weeks period

(Fig. 3). This was followed by the medium containing NAA and NOA exhibited lowest rooting

response. Earlier reports on in vitro rooting also achieved in the modified MS medium containing

8,9,10auxins (IBA) with varied rooting percentage (Saxena and Bhojwani 1993 in D.longispathus of 73%,

Ramanayake and Yakandawala 1997 in D.giganteus of 77.5%, Ravi kumar et al. 1998 in D.strictus of

85-90%).

Hardening for 4 weeks in green house, followed by 2-3 weeks under 50% shade was found essential

before keeping in open nursery (Fig. 4). Based on the protocol developed, about 5000 plants were

produced.

CONCLUSION

In vitro regeneration through the use of axillary shoots not only results in the formation of multiple

shoots but also in successful root initiation and acclimatization in the green house. Experimental results

are important in the mass propagation of genetically uniform clones.

ACKNOWLEDGEMENT

Grateful to the Director and Group Co-ordinator Research of Institute of Wood Science and

Technology, Malleswaram, Bengaluru for providing facilities.

Fig 1. Effect cytokinins on shoot induction of B.nutans

73


Fig 2. Effect of cytokinins on shoot multiplication in B.nutans.

Fig 3. Effect of auxins on in vitro rooting from shoot clumps of B.nutans within 4 weeks

74


REFERENCES

1. Soderstrom TR, Ellis RP (1987) The woody bamboos (Poaceae: bambusacea) of Sri Lanka: A

morphological and anatomical study. Smithsonian contribution. No. 72.

2. Rai SN, Chauhan KVS (1998) Distribution and growing stock of bamboos in India. Indian

Forestry 118, 87-97.

3. Panse VG, Sulkatme PV (1978) Statistical methods of agricultural workers. ICAR publications,

New Delhi, pp.327.

4. Sanjaya, Rathore TS, Ravi Shankar Rai V (2005) Micropropagation of Pseudoxytenanthera

stocksii Munro. In vitro Cellular and Developmental Biology-Plant 41, 333-337.

5. Ramanayake SMSD, Yakandawala K (1997) Micropropagation of the giant bamboo

(Dendrocalamus giganteus Munro) from nodal explants of field grown culms. Plant Science

129, 213-223.

6. Somashaker PV, Rathore TS, Shashidhar KS (2008) Rapid and simplified method of

micropropagation of Pseudoxytenanthera stocksii. In: S.A. Ansari, C. Narayanan & A. K.

Mandal (Ed) Forest Biotechnology in India, Satish serial publishing house, Delhi, pp 165-182.

7. Huetteman CA, Preece JE (1993) Thidiazuron: a potent cytokinins for woody plant tissue

culture. Plant Cell Tissue Organ Culture 33, 105-119.

8. Saxena S, Bhojwani SS (1993) In vitro clonal multiplication of 4-year old plants of bamboo-

Dendrocalamus longispathus. In vitro Cellular and Developmental Biology-Plant 29, 135

142.

9. Ramanayake SMSD, Meemaduma VN, Weerawardene TE (2006) In vitro shoot proliferation

and enhancement of rooting for large scale propagation of yellow bamboo (Bambusa vulgaris

'striata'). Scientia Horticulture 110, 109-113.

10. Ravikumar R, Ananthakrishnan G, Kathiravan K, Ganapathi A (1998) In vitro propagation of

Dendrocalamus strictus Nees. Plant Cell Tissue and Organ Culture 52, 189-192.

75


77

EFFECT OF COMPUTATIONALLY SYNTHESIZED PROBABLE DRUGS

ON BETA TOXIN OF CLOSTRIDIUM PERFRINGENS

Prasanna D R*, Akshatha G, Ankita Sanjali, Madhuri D, Priyanka H L

Department of Biotechnology, Siddaganga institute of technology, Tumkur

ABSTRACT:

Clostridial gas gangrene is a highly lethal necrotizing soft tissue infection of skeletal muscle caused by

toxin and gas producing clostridium species.Clostridium perfringens is a gram positive, rod shaped,

anaerobic, spore forming bacterium of genus clostridium.Clostridium perfringens. In our present study

we followed procedure of computer aided drug design to find novel probable drug for the disease

Clostridial gas gangrene and mild enterotoxaemia. Computer aide drug design includes six basic steps

like Literature survey, Target identification, Target structure validation, Active site prediction, Lead

identification, Lead optimization and Docking. In literature survey using online sources as well as

scientific journals we found many toxins in Clostridium perfringens, finally as a part of our study beta

toxin was selected. In target identification the protein molecule responsible for disease is identified and

suitable template for the target is selected based on the identity where template structure is known.

Using target sequence and template structure model is obtained and it is validated for drug design

process. Active site is identified manually on basis of Cast p and Q-site finder. Lead compounds for

interaction with active sites are selected by pubchem and selected compounds are optimized for

effective interaction, then final process is docking to predict binding orientation of small molecules.

The small molecule of high energy value is selected as best compound. In future probable drugs for

remaining toxins are designed and by using these probable drug compounds a comparitive study can be

made and a novel drug can be designed and mild enterotoxaemia.

Keywords: Clostridial gas gangrene, Clostridium perfringens, Clostridium perfringens acting on

humans, drug design steps.


*Prassana D. RAssistant professor

Department of Biotechnology, Siddaganga Institute of technology, Tumkur.

E-mail: [email protected] Ph: 9902132693


Introduction:

The most common problem while starting any research to find a DRUG for any disease is TIME. To

start, the researcher has to test many number of drugs for safety on experimental organisms before it

could tested for clinical trials. To start with any research, one should go with trial and error protocol

which hosts low level of success rate. Main problem lies with investment and long running

experimental procedures. Finally settle with low level of success rate. Due to which researchers are

showing low level of response towards traditional method of drug discovery. In contrast bioinformatics

applications are emerged as a finix in solving problems of traditional drug discovery method. Meaning

to above sentence is stored in definition of Bioinformatics i.e Use of computers and its applications like

storage, retrieval, sharing and manipulation on biological macro molecules like DNA, Protein and

RNA. Same avoids 10 years of vigorous research in traditional drug discovery. Computer Aided drug

design is one among of all applications of Bioinformatics.

Present work of study is to find Probable drug for the disease Gas Gangrene. Bio-weapon will be in

many forms, it may be in the form of granules, powder, poisonous gases etc. One of the poisonous gas

used as bio-weapon is produced by Clostridium perfringens, a gram +ve, anaerobic and spore forming

bacilli commonly found throughout nature, responsible for the disease clostridium gas gangrene and

mild enterotoxaemia. Clostridium gas gangrene is a highly lethal soft tissue infection of skeletal muscle

caused by toxin and gas producing clostridium species.

Bacteria cause myonecrosis via specific exotoxins. These microorganisms enter the body via

significant skin breakage. Gangrenous infection by soil-borne bacteria was common in the combat

injuries of soldiers well into the 20th century, due to non-sterile field surgery and the basic nature of care

for severe projectile wounds. These projectile wounds was not cured by antibiotics which were present.

Then the infection did spread and soldiers died uncured. Then it was found that unhygienic condition

led to the proliferation.

Gas composition: 5.9% hydrogen, 3.4% carbon dioxide, 74.5% nitrogen and 16.1% oxygen was

reported in one clinical case.

Presently, 90% of contaminated wounds demonstrate clostridial organisms, but fewer than 2% develop

clostridial myonecrosis.

Clostridial myonecrosis deadly form of gangrene caused by clostridium perfringens that produce

toxins that cause tissue death.

The most important exotoxins and their biologic effects are as follows:

1 · Alpha toxin - Lethal, lecithinase, necrotizing, hemolytic, cardiotoxic

2· Beta toxin - Lethal, necrotizing

· Epsilon toxin - Lethal, permease

· Iota toxin - Lethal, necrotizing

· Delta toxin - Lethal, hemolysin

· Kappa toxin - Lethal, collagenase, gelatinase, necrotizing

· Lambda toxin Protease

78


In our material of study we shortlisted beta toxin because the work on alpha toxin was performed by

many research groups.

C. perfringens type C isolates are defined by production of two major toxins, α-toxin and β-

toxin.Clostridium perfringens type beta toxin cause severe, acute, necrotizing enteritis in livestock and 3

humans

The combination of aggressive surgical debridement and effective antibiotic therapy is the 4determining factor for successful treatment of gas gangrene

Materials and Methods:

Sequence analysis: CLC work bench

Target and template identification: Name of the Protein i.e beta toxin which was responsible for

causing gas gangrene was entered in the search option and searched for the protein name and organism

name from the hits obtained. Fasta format sequence of the accession number which contained required

information was downloaded, and this sequence will act as target sequence and through this sequence

template structure was searched so that structure of target can be modeled.

Target sequence was pasted in Blastp which was selected against PDB database and was searched.

Theoretically if identity < 20% abinitio method is followed.If 20 %< identity< 35% Fold

recognition/Threading method is followed.If identity >35% Homology modeling method is

followed.Practically we have look for more than 40% to fallow Homology modeling method. (Recent

advances says that homology modeling method can be followed even though there is very less identity)

The chosen HIT was the Template in which pdb file was downloaded from PDB site which contained

the detail of the structure and this was the template structure for modeling the protein structure.

Structure modelling using cph server 3.0: Sequence profiles have a broad application in field of

bioinformatics prediction algorithms dating back to the pioneering work by Rost and Sanders. The field

of protein structure prediction has largely benefited from this work, and most high performing

algorithms for protein homology modeling use sequence profiles as their main vehicle.

Target FASTA Format from SwissProt was copied and pasted.The result contained the optimum or the

best hit as a template will be compared with the template obtained via Blastp and also a 3D modeled

structure is given for the protein.

URL: CPH Server: http://www.cbs.dtu.dk/services/CPHmodels/

Active site identification: It was done manually.

Using Q-site finder: Q-SiteFinder is a new method of ligand binding site prediction. It works by

binding hydrophobic (CH3) probes to the protein, and finding clusters of probes with the most

favorable binding energy. These clusters are placed in rank order of the likelihood of being a binding

site according to the sum total binding energies for each cluster. Identifying the location of ligand

binding sites on a protein is of fundamental importance for a range of applications including molecular

docking, de novo drug design and structural identification and comparison of functional sites. It uses

the interaction energy between the protein and a simple van-der Waals probe to locate energetically

favorable binding sites. The modelled structure on the site was uploaded and submitted and the

structure was analysed to find the best active site, Volume and Surface area of active site and also the

amino acids present in that site was checked.

79


URL: Qsitefinder:http://www.bioinformatics.leeds.ac.uk/qsite finder Using castp (Computed Atlas

of Surface Topography of Proteins): Binding sites and active sites of proteins and DNAs are often

associated with structural pockets and cavities. CASTp server uses the weighted Delaunay

triangulation and the alpha complex for shape measurements. It provides identification and

measurements of surface accessible pockets as well as interior inaccessible cavities, for proteins and

other molecules. It measures analytically the area and volume of each pocket and cavity, both in solvent

accessible surface (SA, Richards' surface) and molecular surface (MS, Connolly's surface). It also

measures the number of mouth openings, area of the openings, and circumference of mouth lips, in both

SA and MS surfaces for each pocket.

The request of calculation for a particular molecule is submitted. The results will be emailed to you

including measured parameters for pockets, cavities and mouth openings, as well as listing of wall

atoms and mouth atoms for each pocket. In addition, a RasMol script sent through email will help you to

visualization the pocket of your interest.

URL : CASTp: http://sts.bioengr.uic.edu/castp/

The output of modeler is given as the input in CASTp in order to predict the ligand binding site in our

modeled target protein.

Target was uploaded,Option→ Jmol→Submit and Number of Pockets were shown depending upon

protein. By Clicking each Pocket, amino acids present in cavity was shown as sphere.

Lead identification: Once the therapeutic target was identified, then found one or more leads (e.g.,

chemical compounds or molecules) that interact with the therapeutic target so as to induce the desired

therapeutic effect, e.g., through antiviral or antibacterial activity.In order to discover the compounds

whose pharmacological properties are likely to have the required therapeutic effects, researchers must

test a large variety of them on one or more targets.

URL : Pubchem:http://pubchem.ncbi.nlm.nih.gov/

From the databases like emedicine, cdc, etc. the drugs currently used for the disease was found and was

searched for the similar compounds in PubChem and checked whether the drug follows Rule of

Five/Lipinski rule or not.

Lead optimization: Lead optimization was followed after lead identification. In lead optimization

researchers systematically modify the structure of the lead compound, docking each specific

configuration of a drug compound in a protein's active site, and then testing how well each

configuration binds to the site A few examples of bioinformatics tools that aid in lead optimization

efforts are BIOSTER, WABE, and ClassPharmer Suite. The objective of this drug discovery phase was

to optimize lead compounds i.e. new analogs with improved potency, reduced off-target activities, and

physiochemical/metabolic properties suggestive of reasonable in vivo pharmacokinetics. Mol

inspiration helps in optimizing lead by calculating molecular physicochemical properties relevant to

drug design and QSAR, including logP, molecular polar surface area (PSA), and the Rule of 5

descriptors. Lipinski's Rule of Five is a rule of thumb important for drug development where a

pharmacologically active lead structure is optimized step-wise for increased activity and selectivity.

The modification of the molecular structure often leads to drugs with higher molecular weight, more

rings, more rotatable bonds, and a higher lipophilicity.

80


Lipinski's Rule of Five states that, in general, an orally active drug has not more than 5 hydrogen bond

donors (OH and NH groups), not more than 10 hydrogen bond acceptors (notably N and O),a molecular

weight under 500 g/mol and a partition coefficient logP less than 5. The databases used was

Pubchem:http://pubchem.ncbi.nlm.nih.gov/

PreADMET:http://preadmet.bmdrc.org/preadmet/index.php

for lipinski rule: The name of drug was entered in pubchem and submitted.

for preadmet: The structure of drug was downloaded from Pubchem. After opening the preadmet tool

the structure was opened by clicking on ADME, Drug likeliness and Toxicity and clicked on calculate.

Docking using hex: Hex is an interactive molecular graphics program for calculating and displaying

feasible docking modes of pairs of protein and DNA molecules. Hex can also calculate small

ligand/protein docking (provided the ligand is rigid), and it can superpose pairs of molecules using only

knowledge of their 3D shapes.In Hex's docking calculations, each molecule is modeled using 3D

parametric functions which are used to encode both surface shape and electrostatic charge and potential

distributions. The parametric functions are based on expansions of real orthogonal spherical polar basis

functions. Essentially, this allows each property to be represented by a vector of coefficients. Hex's

surface shape representation uses a novel 3D surface skin model of protein topology, whereas the

electrostatic model is derived from classical electrostatic theory. By writing an expression for the

overlap of pairs of parametric functions, one can derive an expression for a docking score as a function

of the six degrees of freedom in a rigid body docking search. With suitable scaling factors, this docking

score can be interpreted as an interaction energy, which we seek to minimize. In fact, much of the early

development of Hex concentrated on displaying and superposing protein surface shapes using two

dimensional spherical harmonic expansions to represent surface shapes parametrically. This proved to

be a fast and accurate way to superpose pairs of similar protein molecules but this type of 2D surface

approach does not encode sufficient detail to give a viable docking algorithm. It was this observation

that prompted the development of our 3D density model of molecular shape.

URL: Hex: http://www.csd.abdn.ac.uk/hex/

The steps involved in HEX's Docking were:

Ø Open Hex 6.0 from the menu all Programs

Ø Open File menu in HEX 6.0 Open Receptor molecule

Ø Then Go to File menu Open Ligand molecule

Ø Select Control button in top Tool bar select Docking

Ø Suddenly Docking control window will Pop up default Click Activate

Ø Hex Progress window will pop up it will Progress Fourier transform steric scan Final search

Refinement Total docking.

Ø After completion of Docking output

81


Fig 5:Blastp result showing 15 best hits

Fig 6:The best hit with identity of 45% choosen as template.

Fig 7 :Template in PDB database and Template structure viewed via RASMOL

82


Result and Discussion: Target identification: The KEGG database, Swiss- Prot database showed

that Clostridium perfringens was mainly responsible for the disease clostridium gas gangrene and

mild enterotoxemia and the Target was Q46181 and Template was 2YGT.(fig 1 & fig 2)

Fig1: Swiss-Prot hits for the Protein Name.

Fig 2 : Details about the protein available in Swiss-Prot database Template identification :

Fig 3: FASTA format of the target sequence

83


Modelling using cphmodel 3.0 server: The structure is the modeled structure of target protein which

is modeled using template 2YGT in CPH Server. The amino acids present in the structure could was

easily viewed and the structure was easily analysed. (fig 8)

Structure validation and analysis: The Ramachandran plot value for Gonorrhea modeled structure is

87.2% and Quality Factor value is 90.775, that implies the quality of protein structure modeled is

satisfactory enough to use it for Drug Design.The value of ramachandran plot and quality factor should

be more than 80 to be satisfactory for Drug Design(fig 9& fig10)

Fig 8: modeled structure

Fig 9: SAVS result showing Quality factor

Fig 10:Ramachandran Plot for the modeled structure

84


Active site identification: Finally from the result,the predicted amino acid residues which may

contribute for the active site was found. The type of amino acids present were PHE71,THR72,

GLY96,ILE128,TRP209,MET267,MET286(fig 11 & fig 12) using qsite finder:

Fig11:Qsite Result and toggeled surface is the active site chosen

Using castp:

Fig12:Different active sites available and Active site chosen

Lead identification: Structures of the selected lead compounds (using PubChem)

penicilling chloramphenicol clindamycin

85


Sulphanamide formaldehydelinezolid

Metronidazole

Lead optimization: The lower the energy value more stable is the ligand with receptor i.e. the better

will the ligand react with receptor and the better will be the chances of curing the disease. For

clindamycin the E-value was -297.4 in HEX and the binding energy in Lead IT was -20.03 which was

satisfactory for its use as a drug for Gas gangrene.(fig 14 & fig 15)

Fig13: Lead Optimization result of drugs using PreAdmet Drug-Likeness tool

86


Fig14: HEX Docking result showing E value

Fig15: Lead IT results showing energy value

Conclusion:

Drug design using structure based approach saves both time and money for research related to finding

drugs for disease. Using this approach the Ligands were identified for the disease and then those ligands

were optimized to see whether they are suitable for human consumption or not. After that those ligands

were docked against the modeled structure of protein responsible for the disease, and according to the

docking score it could be shown which is the drug that can cure the disease more effectively.

Docking studies were carried out using Hex and Lead it. The energy values were found to be -20.03.The

probable drug molecules (clindamycin,linozidol,formaldehyde) can be used against gas gangrene. The

clinical trials can be carried out in this disease after getting the information about the remaining toxins.

Acknowledgement: we acknowledge to our Dr. Sree Sree Shivakumara Swamigalu, Founder

President, Sree Siddaganga Mutt,beloved Director, Dr. M. N. Channabasappa,Principal Dr.

Shivakumaraiah, Dr. B. S. Gowrishankar, Professor & Head, Department of Biotechnology, D. R.

Prasanna, Asst. Professor, Department of Biotechnology as our guide,KBITS and to all the teaching and

non-teaching staff.

87


References:

1. MasatakaOda, Michiko Kabura, Teruhisa Takagishi, AyakaSuzue, Kaori Tominaga, Shiori

Urano, Masahiro Nagahama, Keiko Kobayashi, Keiko Furukawa, Koichi Furukawa and Jun

Sakurai, 2012.Clostridium perfringens alpha toxin recognizes the GM1a-TrkA complex.Journal

of biological chemistry,287(39):33070-9.

2. Masahiro Nagahama, Shinya Hayashi, Shinsuke Morimitsu and Jun Sakurai,2003. Biological

activities and pore formation of Clostridium perfringens beta toxin in HL 60cells. Journal of

Biological chemistry,278: 36934-36941.

3. Anna Veshnyakoval, Jorg Piontek, Jonas Protze, Negar Waziri, Ivonne Heise and Gerd

Krause,2011. Mechanism of Clostridium perfringens enterotoxin interaction with claudin-3/-4

protein suggests structural modifications of the toxin to target specific claudins. Journal of

Biological chemistry, 287(3):1698-708.

4. Anna Veshnyakova, Jonas Protze, Jan Rossa, Ingolf E. Blasig, Gerd Krause and Joerg

Piontek,2010. On the Interaction of Clostridium perfringens Enterotoxin with Claudins. Toxins,

2:1336-1356.

5. C.M. Van Itallie, L. Betts, J.G. Smedley, B.A. McClane and J.M.Anderson,2008. Structure of the

claudin binding domain of Clostridium perfringens enterotoxin. Journal of Biological

chemistry,283:268-274.

6. Deiphine Autheman, Marianne Wyder, Michel Popoff, Katharina D'Herde, Stephan Christen and

Horst Posthaus,2013.Clostridium perfringens beta toxin induces necrostatin-inhibitable, calpain-

dependent necrosis in primary porcine endothelial cells.Pone, DOI: 10.1371

7. Leslie A. Mitchell and Michael Koval,2010. Specificity of Interaction between Clostridium

perfringens Enterotoxin and Claudin-Family Tight Junction Proteins. Toxins, 2:1595-1611.

8. M. Harada, M. Kondoh, C. Ebihara, A. Takahashi, E. Komiya, M. Fujii, H. Mizuguchi, S. Tsunoda,

Y. Horiguchi, K. Yagi and Y. Watanabe,2007. Role of tyrosine residues in modulation of claudin-4

by the C-terminal fragment of Clostridium perfringens enterotoxin. Biochemical

Pharmacology,73:206-214.

88


DETERMINATION OF SIZE OF FORAGING POPULATION IN APIS

CERANA INDICA AND ITS IMPACT ON THE CROP PRODUCTIVITY

A Nagarathna*,

* Professor, Dept of Biotechnology, M.S Ramaiah College of Arts, Science and Commerce,

MSRIT Post Bangalore- 560 054, Karnataka, India.

ABSTRACT:

Beekeeping is becoming an important component of present strategies for sustainable agriculture and

integrated rural development. The pollination activities of honeybee are important functions which

contribute to the sustainability and diversity of agricultural resource. The foraging behaviour is an

important aspect of their biology which enables them to adopt themselves to the available vegetational

and climatic conditions. The foraging activity of bees throughout the year gives an indication of the

adaptability of the bees in exploiting the bee forage of the locality. Extensive knowledge of the pollen

sources helps the beekeeping to exploit the sources to a maximum extent so as to develop stronger

colonies that are highly desirable from the point of their productivity.

The size of the foraging load at any given time of the day is related to the abundance of food sources.

The relative size of the foraging population and the weight of corbicular contents clearly demonstrated

that most pollen and nectar forages don't carry full loads.

Key words: Foraging population size, pollen, productivity.

*Corresponding Author:

Dr. A. Nagarathna,

Professor, Department of Biotechnology,

M.S. Ramaiah College of Arts, Science and Commerce, Bangalore.



89

ISSN:2394-1200

INTRODUCTION:

Honeybees are social insects distributed throughout the world. Pollination occurs through many agents;

however honeybees are important pollinating agents for many plants.

Foraging is a social enterprise which the bees collect pollen, nectar, water and propolis from plants. 1The art if collecting all these is called foraging and the bee is a forager . The foraging efficiency of

honeybee largely depends on the availability of bee forge, conditions of the colony and foraging range

of worker bees. The availability of pollen for foraging bees fluctuates from time to time of the year and 2also flowers of different plant species during different seasons .

Beekeeping is becoming an important component of present strategies for sustainable agriculture and

integrated rural development. The pollination activities of honeybee are important which contributes to

the sustainability and diversity of agricultural resource. Beekeeping helps in crop pollination and 3,4enhances productivity and thereby helping in conversation of forest and ecosystem.

MATERIALS AND METHODS:

The present investigations were carried out in different apiary sites viz., Hesargatta Village(HG),

Arkavathy Madhuvan(AK), Jnanbharathi(JB), Shivanahalli,(SH) and Shivakote(SK). At each site five

colonies of Apis Cerana (having 8 frames each) was selected for the study. The size of the foraging

population was estimated by the number of frames covered with bees on both sides.

The bees which foraged for pollen were seen alighting on any flower and collecting pollen from

another. The forages were evaluated for pollen loads. The returning forages were captured at the hive

enhance to collect the pollen load. The foraging activity of Apis Cerana was observed during different

hours of the day at regular intervals and throughout the year during the study period 2012-2013, number

of bees foraging for pollen was recorded. The size of foraging population varied during the course of the

study at different apiary sites. At regular intervals, pollen loads were collected from the foragers, 5packed and analyzed melissopalynologically following the method of .

RESULTS AND DISCUSSIONS:

The size of the foraging population at different hours of the day in different apiary sites show the size of

foragers were less in the morning with a gradual increase during the mid noon and thereafter it

continued to rise, before it actually ceases to function. The foraging rate was high at mid day as

compared to the morning and evening.

Amount of pollen foragers during different months of the year (Fig2) show the highest number of

foragers in the month of May and June followed by October November and from February to May there

is a steady increase. Foraging population was least recoding during July and August in the test apiary

sites HG. Similar observations were made for the Apiary site AK (Fig 4) where in the number of pollen

foragers were maximum from February to June, in July and august it was lowest and a steady rise was

seen from September to December.

Mean pollen load for Apiary site BG (Fig 6) showed an increase from February to May. the least being

in November and December, probably due to winter when flowering is very low. In apiary site HN(Fig

8) the peak pollen foraging was recorded between February and June however during July and August it

90


was the least due to the rainy season and a second peak was observed during November and December.

In apiary site SK(Fig 10) the peak foraging for pollen was seen between March to June with the least in

July and November, December, in other months moderate foraging activities were seen.

Mean pollen load carrying capacity during different hours of the day recorded at different apiary sites

was more or less the same with highest collection between 10.00-02.00 pm and subsequently declined

with another peak around evening before the cessation of foraging activity (Fig 1,3,5,7,9)

91


92


93


94


Maximum and minimum variation were recorded, this was due to the prevalent floral

diversity and its density in the study sites. These results are in concurrence with the finding of

95


6. A cerana showed greater foraging efficiency in spring, summer and autumn than in winter and rainy 7season . The relative size of the foraging population and the weight of corbicular contents clearly

8,9demonstrated that forages don't always carry full loads . Honeybees start foraging as early as 6.17 am 10 11. Foraging activity fluctuates from morning to evening. found high pollen collection in the early

morning while low amounts of pollen were collected in the afternoon and the present findings are in

tune of the same. Present study suggests that the availability of pollen sources affect the foraging

activity. The size of the foraging load at any given time of the day is related to the abundance of food

sources. The size of the foraging load and the foragers returning with different load size of pollen pellets

showed hourly variation. Thus the economy of the colony depends on the performance. The pollination

activity is important activities as they contribute to the sustainability and diversity of agricultural and

botanical resource there by contributes to the increased productivity and maintenance of biodiversity.

REFERENCES:

1. Gary NE. Activities and behavior of honey bees. 1992. In: Graham JM, editor. The Hive and the

Honey Bee. Dadant and Sons; pp. 269–373.

2. Kumar,J., and Kashyap,N.P.1996. Diversity of bee flora in lower Kulu valley, Himachal Pradesh

and its impact on honey production. Indian Bee J,. 58 (3) : 131-134

3. Mattu, V.K. 2009. Status, prospects and development strategies for organic beekeeping in the

Northwest Himalayan region. Proc. Int. Cong. Entomol. Punjabi University, Patiala,17-18

4. Mattu, V.K., Hem Raj., &Thakur, M.L. 2012. Foraging behavior of honeybees on apple crop and

its variation with altitude in Shimla hills of Western Himalayas, India . I.J.S.N, 3(1): 296-301

5. Suryanarayana M. C., Mohana Rao G. and Singh T. S. M. S. 1992. Studies on the pollen sources

for Apis cerana Fab and Apis mellifera L. bees at Muzaffarpur, Bihar, India. Apidologie. 23 : 33-46

6. Hamakawa,M and Morimoto,H.1967. Foraging behaviour of honeybee from April to November.

Japanese Journal of Tech. science, 124

7. Mattu V. K., and Verma L. R. 1985. Studies on the annual foraging cycle of Apis cerana F.in

Shimla hills of north west Himalayas. Apidologie. 16 : 1-18

8. Nunez. 1982. Honey foraging strategies at food source in relation to its distance from the Hive and

rate of flow of sugar .J. Apic. Res. 21 (3): 139-150.

9. Ravikumar R. 1992. Qualitative and quantitative studies on foraging cycle of Apis cerana in

Karnataka. Ph. D Thesis. Bangalore University 154.

10. Joshi N.C. and Joshi,P.C. 2010. Foraging behavior of apis spp.on Apple flowers in sub tropical

environment. New York science Journal, 3,71-76

11. Reyes- Carillo,J.C., Eischen,F.A.,Cano Riise,P Rochiguez Martinez R,Camberos. 2007. Pollen

collection and honeybee forage distribution in cantaloupe.Actazoologica Mexican 23: 29-36

96


COMBUSTION SYNTHESIS AND CHARACTERISATION OF Y Al O 4 2 9

(YAM) NANOPOWDERS

1* 2 3T. E. Kanakavalli , R. Harikrishna , A. Jagannathareddy

1Department of Physics/Electronics, M. S. Ramaiah College of Arts, Science and Commerce,

Bangalore 542Department of Chemistry, M. S. Ramaiah Institute of Technology, Bangalore 54

3Department of Physics, M. S. Ramaiah Institute of Technology, Bangalore 54

ABSTRACT

Yttrium Aluminum Monoclinic Y Al O (YAM) nanopowders have been synthesized 4 2 9

by a low temperature solution combustion method. This process is simple, fast and economic, does not

require high-temperature furnaces and complicated set-ups. Powder X-ray diffraction (PXRD) patterns

confirm the nano sized particles which exhibit monoclinic phase. The crystallite size estimated from

Scherrer's formula was found to be in the range ~ 38 nm.

Keywords: nanopowders, solution combustion method, diffraction pattern, crystallite size

*Corresponding author

T.E. Kanakavalli

E mail: [email protected]

Phone: 9480524160


97

ISSN:2394-1200

INTRODUCTION:

In the recent past, developments in nanotechnology have led to the synthesis of nanostructures

with unique optical and electrical properties, with significant prospect of applying them as building 1, 2

blocks in electronic and photonic devices . Fabrication of nanomaterials with controllable size and

shape has been of great scientific and technological interest due to their potential applications in nano

devices.

The Yttria – Alumina (Y O –Al O ) system is a promising material for many applications such 2 3 2 3

3as lasing materials, scintillation host and semiconductor processing technology . This system has three

phases: Yttrium Aluminum Garnet Y Al O (YAG), Yttrium Aluminum perovskite YAlO (YAP), and 3 5 12 3

Yttrium Aluminum Monoclinic Y Al O (YAM), with Y/Al ratios equal to 0.6/1, 1/1, and 2/1, 4 2 9

respectively. Generally, YAM is considered as an intermediate phase in the process of producing YAG.

Even if YAG is synthesized with a stoichiometric mixture of Y O and Al O , two detrimental phases 2 3 2 3

YAP and YAM often coexist as by-products. Crystal growth, physicochemical properties, optical

features and excellent laser characteristics of the two former compounds (YAG and YAP) doped with

rare earth ions are well documented. Knowledge on properties of YAM is considerably poor due to

serious problems encountered during synthesis and stability of a single-phase compound. It has been o

found that YAM undergoes the phase transition at about 1300 C forming YAG phase. In other studies 4, 5

easy decomposition of YAM to YAP and YAG has been observed . In view of problems mentioned

above, much more attention has been directed recently in the preparation of YAM at lower temperatures

so as to avoid occurrence of phase transitions.

Different physical or chemical synthetic approaches have been developed to produce nano-

sized particles including solid state reaction, thermal decomposition, sol-gel, precipitation and

solvothermal methods. Generally, these preparation methods involve complex procedures,

sophisticated equipment and rigorous experimental conditions. Most of these techniques require high

temperatures and long processing time. To achieve better quality nanostructured materials, low

temperature synthesizing procedure is desired. Obviously at higher temperature, the grain growth is

strongly stimulated and particles grow to sizes beyond nano regime. Solution combustion synthesis is

emerging as a promising technique for the preparation of nanopowders. This process is simple, fast and

economic, does not require high-temperature furnaces and complicated set-ups.

In the present paper, the preparation of nanocrystalline YAM powders by low temperature solution

combustion method is reported. The combustion reaction is initiated in a muffle furnace at

temperatures much lower than the phase transition of the target material. The synthesized powders are

characterized by using Powder X-ray diffraction (PXRD) technique.


Synthesis of YAM nanoparticles:

The chemicals used for the preparation of Y Al O were analar grade yttrium oxide [Y O , 99.99%, 4 2 9 2 3

Rolex Ltd.], aluminium nitrate [Al(NO ) , 99.99%, Sigma Aldrich], nitric acid [HNO 99.99%,Merk 3 3 3,

Ltd]. Oxalyl di-hydrazide(ODH)[C H N O ] was used as a fuel. For synthesis of Y Al O first yttrium 2 4 2 2 4 2 9,

oxide was converted into its nitrate salt by adding 1:1 HNO to Y O and heating the mixture on the sand 3 2 3

bath to evaporate the excess HNO to obtain clear transparent Y(NO ) . The reaction that occurs in the 3 3 3

process is given below;

98


Y O +6HNO →2Y(NO ) +3H O…………….(1) 2 3 3 3 3 2

Yttrium nitrate, obtained from Eq. (1) and solution containing stoichiometric amounts of aluminium

nitrate (Al O ), oxalyl di-hydrazide (ODH) are taken in a cylindrical petri dish of approximately 300 ml 2 3

capacity. The homogeneous solution was continuously stirred using magnetic stirrer to get

homogeneous redox mixture. The fuel: oxidizer ratio was calculated based on oxidizing and reducing 6valencies of the reactants by setting F/O = 1, as reported in the literature . The dish was introduced into

oa muffle furnace maintained at 500 C.

The solution initially undergoes dehydration followed by decomposition with the evolution of large

amounts of gases. The mixture then froths and swells forming foam, which ruptures with a flame and

glows to incandescence. During incandescence the foam further swells to the capacity of the container.

The entire combustion process normally takes about 5 minutes. The foam can be ground to obtain fine 0powder and it is calcined at 900 C for 3 hours in open air furnace.

The reaction for combustion synthesis in the present case can be written as

4 Y(NO ) + 2 Al(NO ) + 9 C H N O → Y Al O + 18 CO + 27 H O + 27 N3 3 3 3 2 6 4 2 4 2 9 2 2 2

Powder X-ray diffraction (PXRD):

The powder X-ray diffraction (PXRD) studies were carried out using Phillips X-ray diffractometer

(model PW 3710) with Cu Kα radiation (λ=1.5405Å).


PXRD pattern has been measured for assessing the overall structure and phase purity of the samples.

Fig.1 shows the PXRD patterns acquired from the sample. All the diffraction peaks in the pattern

corresponding to (1 1 0), (210), (310), (1 2 2), (32 0), (022), (2 3 0), (40 2), (1 3 2), (2 3 2), (1 5 2) and

(360) directions were indexed as monoclinic phase of YAM (JCPDF No. 78-2429) with space group

P2 /c. No other impurities and other compounds were observed within the detection limit of the XRD 1

technique. The deviation from perfect crystalline structure leads to broadening of the diffraction peaks.

The broadening of the diffraction peaks is an indication that the synthesized materials are in nanometer

regime. The crystallite size and lattice strain can be extracted from X-ray peak width analysis.

Crystallite size is a measure of the size of coherently diffracting domains. Crystallite size and lattice

strain affect the X-ray diffraction peak in different ways. Both these effects increase the peak width and

intensity accordingly.

On the full width at half-maximum (FWHM) of (1 2 2), (3 2 0) and (2 3 0) diffraction peaks, the

crystallite sizes of YAM nanostructures are estimated using the Debye-Scherrer's equation given

by, where λ represents the wavelength of the X-ray radiation, β is the full width at half qblcos9.0=D

maximum of diffraction peak (in radians) and θ is the scattering angle.

The average crystallite size is found to be in the range of ~38 nm.

99


Fig.1. PXRD patterns of YAM nanopowders

CONCLUSION:

YAM nanopowder has been synthesized by cost effective low temperature solution combustion

method using ODH fuel. PXRD profiles confirmed that the structures of the prepared products are

monoclinic phase (JCPDF No. 78-2429) without any secondary phases. The average particle size of the

synthesized powder determined by Debye-Scherrer's fomula is found to be in the range 38 nm. Further

scope of work includes synthesizing YAM nanoparticles along with various dopants by the solution

combustion method and to find optical properties of the synthesized materials.

REFERENCES:

1. Y.H. Tsang, A.E. El-Taher, T.A. King, S.D. Jackson. 2006. Efficient 2.96 lm dysprosium doped

fluoride fibre laser pumped with a Nd:YAG laser operating at 1.3 mm, Opt. Express, 14: 678–685.

2. Janisch R, Gopal P, Spaldin NA. 2005. Transition metal-doped TiO2 and ZnO - present status of

the field. J Phys: Condens Mat, 17:R 657-R689.

3. Z. Boruc, B. Fetlinski, M. Malinowski, S. Turczynski, D. Pawlak. 2012. Optical transitions

intensities of Dy3+:Y4Al2O9 crystals. Optical Materials, 34: 2002–2007

4. Gratzel M, (2001) Photoelectrochemical cells. Nature, 414: 338-344.

5. Ryba-Romanowski, R. Lisiecki, A. Rzepka, L. Lipin´ sk, A. Paja zkowska. 2009. Luminescence

and excitation energy transfer in rare earth-doped Y4Al2O9 nanocrystals. Optical Materials, 31:

1155–1162.

6. J. J. Kingsley and K.C. Patil. 1988. A novel combustion process for the synthesis of fine particle a-

alumina and related oxide materials, Mater. Lett. 6: 427 – 432.

100


M. S. RAMAIAH COLLEGE OF ARTS, SCIENCE AND COMMERCE Affiliated to Bangalore University, Re-Accredited with “A” Grade by NAAC,

M S R Nagar, MSRIT Post, Bangalore -560 054, Karnataka.

Phone : +91 80 2360 0966, Telefax : 080 2360 6905


www.msrcasc.edu.in

Master of Science (M. Sc.)

Ø M. Sc in Biotechnology

Ø M. Sc in Microbiology

Ø M. Sc in Bio Chemistry

Ø M. Sc in Applied Genetics

Ø M. Sc. Organic Chemistry

Master of Business Administration (MBA)

UNDERGRADUATE PROGRAMS

Eligibility: 10 + 2 and

Mathematics Compulsory

Eligibility: 10 + 2

Eligibility: 10+2 and

Biology Compulsory.

Bachelor of Arts (B.A)

�Ø Journalism, Psychology, Optional English

Bachelor of Commerce (B.Com)

Bachelor of Computer Applications (BCA)

Bachelor of Business management (BBM)

Bachelor of Science (B.Sc.)

Ø Electronics, Mathematics, Computer Science

Bachelor of Science (B. Sc.)

Ø Genetics, Microbiology, Biochemistry

Ø Bio technology, Microbiology, Chemistry

POST GRADUATE PROGRAMS

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