Vol. 1, Issue 3
IRSAPS Bulletin
Sep-Dec 2011 http://www.irsaps.org
(A periodical published by Indian Research Scholars’ Association for Promoting Science)
Three-dimensional structure of tRNA-enzyme complex, anticodon stem loop (ASL) of tRNA containing hypermodified nucleoside, hn6Ade at 3'-adjacent (37th) position in the anticodon loop of tRNA
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IRSAPS Bulletin Volume 1, Issue 3 Issue Editor: Prof. A. K. Gade Release date: 30th January 2012 This journal is published by Indian Research Scholars’ Association for Promoting Science. To join IRSAPS, please visit: http://www.irsaps.org 1st Issue: January-April 2nd Issue: May-August 3rd Issue: September-December Statement on current journal’s policy: IRSAPS Bulletin does not have a peer review policy for articles. Authors are solely responsible for the authenticity of content and correctness of all articles. It is a free open source online journal. Nevertheless, readers and authors are referred to the announcement for a change in Journal’s policy. Authors are requested to follow ethical guidelines, failing to which may lead to rejection of manuscripts and withdrawal of published articles.
Cover page details: Three-dimensional structure of tRNA-enzyme complex, anticodon stem loop (ASL) of tRNA containing hypermodified nucleoside, hn6Ade at 3'-adjacent (37th) position in the anticodon loop of tRNA are shown. The hypermodified nucleoside hn6Ade found in the anticodon loop of hyperthermophilic organisms. Courtesy: Bajarang V. Kumbhar and Kailas D. Sonawane*, Structural Bioinformatics Unit, Department of Biochemistry, Shivaji University, Kolhapur, Maharashtra, India. Contact person: Dr. Kailas D Sonawane. E-mail: kds_biochem@unishivaji.ac.in.
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Associate Editors and Editorial Board Members* 1. Dr. Amit K. Chattopadhyay School of Engineering and Applied Sciences Mathematics (NCRG) Aston University Birmingham B4 7ET, UK E-mail: akchaste[at]gmail.com
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Center 5323 Harry hines Blvd Dallas, TX 75235 USA E-mail: sonikasaddar[at]gmail.com 13. Dr. T. Govindaraju Assistant Professor Bioorganic Chemistry Lab New Chemistry Unit Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR) Jakkur, Bangalore 560064, India Tel: +91 80 2208 2969
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Announcement The publication of IRSAPS Bulletin will be discontinued from the next issue. IRSAPS Bulletin is now re-christened as ‘Journal of Interdisciplinary Science’ which will become a peer reviewed international journal with ISSN/IBN number. The journal will be initially released as online open source journal. In view of this development, the reviewing policy of the journal has been changed with immediate effect. All research manuscripts submitted for publication in the journal will now subject to peer reviewing. However, current policy will continue for all non-research articles like science news. All articles must be in the new Journal format, which will soon be made available at http://irsaps.org. Now onwards, authors are also required to send a signed copyright agreement form. All communications related to the new Journal will initially be operated from the following branch offices: 1. Department of Chemical Sciences
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Journal of Interdisciplinary Science We invite research and review articles for the introductory issue of ‘Journal of Interdisciplinary Science’. Readers are requested to visit the journal website (will be available soon) for further announcements. We look forward for your active cooperation.
IRSAPS Bulletin 2011, Vol. 1, Issue 3
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Contents
1.
Editorial
1
2.
Magnetic nanocomposite films
2
3.
Molecular Modeling Study of Hypermodified Nucleic Acid Base 3-hydroxynorvalylcarbamoyl adenine, hn6Ade Present at 3'-adjacent Position in Anticodon Loop of Hyperthermophilic tRNAs
8
4.
Microbial Genomics Tool (MGT 1.0) for Bacterial Codon Usage Analysis
16
5.
An Applicaton of Radon And Wavelet Transforms for Image Feature Extraction
20
6.
Use of Proteinase Inhibitors from Okra for Inhibiting the Helicoverpa armigera (Hubner) gut
Proteinases
7.
25
Science cartoons
IRSAPS Bulletin 2011, Vol. 1, Issue 3
B.i
Š IRSAPS
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Few lines from the editorial desk……………! All over the world is celebrating the year
acid
base
N6-(3-
2011 as the international year of chemistry, with the
hydroxynorvalylcarbamoyl) adenine.
The
third
motto for the occasion “Chemistry – our life, our
article discusses a new microbial genomics tool for
future”, which signifies the importance of chemistry
the codon usage analysis while fourth article is on
for our existence and in our life. Chemistry is
the application of wavelet and Radon for the rotation
considered as the Central Science among the three
and translation invariant image transform analysis
branches of science. IRSAPS has been doing its bit in
and their use for image enhancement and feature
the promotion of chemistry by publishing articles,
extraction. The fifth article provides a detail study of
conducting webinars and more emphasis is been
the protease inhibitors based insect resistance
given to popularizing science as a whole. In this issue
management strategies.
IRSAPS has aptly decided to amalgamate the From the next issue, IRSAPS Bulletin will go chemistry
and
life
together
to
focus
on
“Biochemistry” for the current issue along with the
international and it will be released as „Journal of Interdisciplinary Science‟. Accordingly, we plan to
articles from other branches of science as well. This publish articles covering broader subject areas. We journal is providing a platform to promising young hope the current issue will give a glimpse of what researchers from all fields of science, engineering journal
is
aiming
to
bring
the
flavor
of
and medicine for publishing their research work and interdisciplinary science into a single platform. We ideas, for the cause of promoting science. hope everyone will enjoy reading it and appreciate it In this issue there are five scientific articles
as a source of promoting science. We look forward
covering magnetic nanocomposite films, Radon and
for the active participation from the scientific
Wavelength
community.
transforms,
protease
inhibitors,
molecular modeling and Microbial genomics and couple of science cartoons. The first article is a brief review on magnetic nanocomposite films and their
-Aniket K. Gade
applications, while the second article is about molecular modeling studies of hypermodified nucleic
1
Magnetic nanocomposite films Hardeep Kumar Institute of Physics, University of São Paulo, São Paulo 05508-090, Brazil Email: hsehgal_007@yahoo.com
This is an article focusing on practical applications of certain nanocomposite (NC) materials, together with a brief overview of the basic principle of their operation. The nanocomposites have been categorized in to two broad sub-classes - (I) magnetic multilayers and (ii) granular nanocomposites. It has been shown that the operational principle of both sub-classes of NCs rely on the mechanism of magnetoresistance (both GMR and TMR), a quantum mechanical phenomenon that characterizes a relatively low resistant electrical conductivity for parallel spin ferromagnets as opposed to antiparallel spin orientations. The latter half of the article shows practical applications of such conformational magnetization. It has been argued that electronic devices functioning around the 1 GHz frequency range would benefit from the usage of FM-I granular films, a particular variety of soft materials, a property attributed to a combination of electromagnetic shielding and extraordinary Hall resistivity.
400 AD) and Maya blue, a blue dye used by the Mayas (in
1. Introduction Nearly all natural and synthetic materials are
700 AD) are of particular interest. The multifunctional
heterogeneous, i.e. they are microscopically built by
properties of the NCs are often complex relations defined
different components or phases. In nanocomposite (NC)
by varying sizes, shapes and relative fractions of the
materials, one of the solid constituents traditionally exhibits
constituent components. The possibility of realizing unique
a nanoscale structure, with length scales up to 100 nm. The
properties of NCs leads to pave the way to a broad range of
concept
technological applications ranging from aerospace [1], gas
of
enhancing
properties
and
improving
characteristics of materials through creation of multi-phase
(a)
sensing [2], data storage [3,4], automobile industry [5],
(b)
A B A B A
A
B
Fig. 1 Schematic illustration of (a) multilayer and (b) granular nanocomposites (NCs). ‘A’ and ‘B’ represent the constituents of the NCs, with the dimensions of at least both/one of A and B in nanometer range in multilayer/granular NCs. NCs is not new. The idea has been practiced ever since
medical [6], non-linear optics [7], to solar energy
civilization started and humanity began producing efficient
applications [8]. The NCs can be processed in the bulk or
materials for functional purposes. Typical examples of
thin film form, but in order to realize compact and reduced
naturally evolved nanocomposites (NCs) can be found in
size technological devices/components the research in thin
the form of bone, tooths etc. Among the early examples of
films is under more attention. In NC thin films the
human made NCs, the tempera colours used in the Ajanta
constituent components can be arranged principally in two
caves (200 BC), the Lycurgus Cup made by the Romans (in
ways:
2
Fe Cr Fe
H= 0
Fe Cr Fe
H = - 40 KG
Fig. 2 Schematic illustration of GMR effect in Fe /Cr multilayer [9] (i) Multilayer NCs:
Layer by layer arrangement of
Magnetoresistance or GMR. The physical origin of GMR
constituents, thickness of each layer (constituent) is in the
can be attributed to the influence of the electron spin the
nanometer range (Fig.1 (a)).
electronic transport in ferromagnetic conductors i.e. spin
(ii) Granular NCs: When one of the dimensions is in the
dependent scattering at the interfaces and on bulk of the
nanometer range i.e. zero dimensional or one dimensional
multilayer structures.
or two dimensional (see Figure 1(b).
The tunnel magnetoresistance (TMR), which is
2. Magnetic Nanocomposite films
the newest type of the magnetoresistance effect, has
2.1. Magnetic Multilayers
attracted more interest than AMR and GMR because of its
One of the important phenomena discovered in magnetic multilayers eg. Fe/Cr is the Giant magnetoresistance discovered by Baibich et al. [9] and simultaneously by Binash et al. [10] in 1988. Magnetoresistance (MR) is the change in electrical resistance of a conductor by a magnetic field. In non-magnetic conductors, it is relatively small. In magnetic materials and magnetic multilayers, the spin polarization of the electrons leads to large MR effects in small magnetic fields. The variation of the resistance as a function of the magnetic field observed by Baibich et al. for Fe/Cr multilayers at 4.2 K is shown in Figure 2. When the magnetic field is increased, the configuration of the magnetizations in neighboring Fe layers changes from antiparallel to parallel, leading to a drop in the resistance (see Figure 2). Since the reduction of the resistance is significant [9, 10], this effect has been called Giant
high magnetoresistance ratio at room temperature. The multilayered device of the tunnel magnetoresistanec structure consists of two ferromagnetic electrodes separated by a very thin nonmagnetic insulator layer. The tunnel current through the insulator layer depends on the magnetization direction of the two ferromagnetic electrodes relative to each other in the presence of an external magnetic field. Imposing the spin conservation constraint on the tunneling process, the tunneling conductance can be written as a sum of two independent conduction channels: one channel for each spin direction. The relative variation of conductance and the density of states (DOS) of each spin channel are then linked as follows in the Jullière formula:
TMR=
2P 1 P 2 1− P1 P 2
, where
Pi =
Di Di
Di + Di
3
Fig. 3 Schematic of spin dependent tunneling: Density of states (DOS) of two ferromagnetic electrodes in antiparallel and parallel configuration in FM/I/FM layer [11]. The
D1 ( )
and D2
( ) are
DOS of the two
ferromagnetic electrodes at the Fermi level for the two spin directions. Figure 3 shows the density of states for both
the other hand, in parallel configuration, minority electrons (spin up or spin down) would pass into minority states and majority electrons would pass into majority states leading
`
Fig. 4 Schematic illustration of (a) multidomain, (b) single domain structures for bulk and NPs; each arrow represents the magnetic moment of an atom, (c) Critical size of single domain and superparamagnetism of several materials, (d) shows the coercivity of magnetic NPs as a function of size, and (e) the corresponding hysteresis loops as a function of size [11] ferromagnetic electrodes in anti-parallel and parallel
to high tunneling conductance/current.
configurations. In the antiparallel configuration, majority
2.2. Magnetic granular films
(minority) electrons from the first electrode would seek minority (majority) empty states in the second electrode
Magnetic granular films are the nanocomposte films with a typical combination of magnetic nanoparticles
which would lead to low tunneling conductance/current. On
4
(MNPs) embedded at random in an immiscible non-
applications of FM-I granular films include high coercivity
magnetic matrix (Figure 1(b)), exhibit a wide range of
that is required for information storage, high permeability,
novel properties associated with MNPs. First, MNPs can
high resistivity for shielding and bit writing at high
respond to an external magnetic field without physical
frequencies, MR sensors and read heads, high sensitivity
contact, making them attractive for remote applications.
Hall sensors [13]. In addition, FM-I granular films are
Second, as the size of the MNPs reduces from the bulk to
reported to be potential candidates for field emission and
the nanoscale, different magnetic properties, compared with
solar energy applications also.
their bulk counterparts, can be obtained. When particle size
It is very important to prepare FM-I granular
is smaller than a critical size (Dcrit) as in Figure 4(c), multi-
films with controlled MNP size, uniform composition and
domain magnetic structures in the bulk (Figure 4(a)) will
uniform thickness for most of the applications. A large
become single domain (Figure 4(b)). In the vicinity of Dcrit ,
number of physical techniques like sputtering (radio
the coercivity of MNPs is largest and will decrease as
frequency and ion-beam), thermal co-evaporation, Pulsed
particle
the
laser deposition (PLD) and ion-implantation; and chemical
superparamagnetic limit (Dsp), as defined in Figure 4(c) for
routes eg. spin-coating and dip-coating have been used to
various materials, below which the coercivity is zero for all
prepare FM-I granular films of different materials.
sizes at room temperature (see Figure 4(d)) [12].
Amongst these techniques, sputtering is the best in terms of
Superparamagnetism is a unique property of single domain
film thickness and composition uniformity, and large area
MNPs, and is determined by size, temperature and
deposition. In the following sections the important areas of
measurement time. Finally, and more intriguingly, the
application of FM-I granular films will be discussed.
properties of MNPs are tunable as a function of particle
3. Applications of FM-I granular films
size, particle size distribution and interparticle interactions.
3.1 Tunneling Magnetoresiatance
size
decreases,
until
it
reaches
Depending upon nature of non-magnetic matrix, two types of granular films can be considered:-
Gittleman et al. in early 70s [14], they suggested spin-
(1) Ferromagnetic metal-Metal (FM-M) granular films, where immiscible matrix is a noble metal eg. Au, Cu and Cr etc.
dependent tunneling as the origin of MR effect and hence it was attributed as tunneling magnetoresistance (TMR) phenomenon. But the magnitude of TMR was even less
(2) Ferromagnetic metal-Insulator (FM-I) granular films, where immiscible matrix is an insulator (I) eg. SiO2, Al2O3, MgO, ZrO2 etc. by Abeles et al. In the recent times, these granular films have attracted a considerable attention because they exhibit a wide variety of interesting properties in magnetism and magneto-transport,
than AMR in Ni-Fe alloys, so was not of interest till Fujimori et al.’s report of large TMR in Co: Al-O system [15]. In FM-I granular films giant magnetoresistance is
The work on FM-I granular films was pioneered
which
suggest
their
prospective
applications in multiple fields. For instance, MNPs embedded in either insulating or metallic matrix show peculiar magnetic or magneto-transport properties like enhanced
TMR was studied first in Ni-SiO2 granular films by
coercivity,
superparamagnetism,
high
permeability, high resistivity, GMR or TMR and giant Hall effect (GHE). Further, out of FM-M and FM-I granular films, FM-I granular films show superior magnetotransport (GHE and magnetoresistance) properties. The attractive
observed when the volume fraction (xv) of magnetic particles is below percolation threshold (xp), caused by spin-dependent tunneling of conduction electrons at the metal-insulator interfaces [15]. FM-I granular films are important to study as it enriches the mechanism of TMR and of observation of interesting effects like coulomb blockade due to electrons tunneling into small metal particle. Recently, the enhancement of MR caused by the cotunneling effect with Coulomb blockade and other magnetotransport properties, such as spin injection and accumulation effect, has been found in granular films [16].
3.2 Extraordinary Hall effect 5
The Hall effect in semiconductors is the basis of many
With the development of telecommunication technology
devices in measuring magnetic fields. In nonmagnetic
and highly integrated electronic devices, electromagnetic
metals, the ordinary Hall coefficient is low because of the
shielding has been intensively studied in the past years to
high carrier density. The stronger effect that Hall
satisfy
discovered in ferromagnetic conductors came to be known
electromagnetic
as the extraordinary Hall effect (EHE) or anomalous Hall
components from possible electromagnetic interference. It
effect (AHE). Hall resistivity for magnetic materials is
is well known that a highly permeable material can increase
expressed as:
the inductance of an inductor, generally by a factor of the
ρ xy = R0 B + Rs μ0 M
the
requirements
of
radiation
reducing
and
undesirable
protecting
delicate
relative permeability of the material. Thus a substantial
(1)
increase in inductance and hence in the quality factor can where B is the magnetic induction, M is the magnetization,
be obtained if no extra losses are produced by the magnetic
(b)
(a)
Fig. 5 (a) HRTEM micrograph, and (b) The dependence of complex permeability (FeCo)57:(SiO2)43 granular film.
i
on frequency f for the
μ0 is the magnetic permeability of free space, R0 is the
material. The two main loss mechanisms in an inductive
ordinary Hall coefficient and Rs is the extraordinary Hall
material at high frequencies are the ferromagnetic
coefficient. The first term represents the ordinary Hall
resonance FMR frequency and eddy current losses.
effect
while
the
second
term,
coming
from
the
(2)
extraordinary/spontaneous Hall effect, is a characteristic of ferromagnetic materials, and is proportional to its magnetization. The origin of the EHE lies in the spin-orbit
Whereas,
μ'r =
Ms +1 μ0 H k
(3)
interaction present in a ferromagnet. Rs obey a power law
is derived from the Landau-Lifshitz-Gilbert equation. Hk is
relationship with the electrical resistivity, given by Rs=αρn,
the anisotropy field and the gyromagnetic factor. It is thus
where α is a constant. Smit’s classical asymmetric
in general necessary to maximize MS and pick a reasonable
scattering gives the exponent n=1 while the quantum
value for Hk (trade-off between and fFMR) in order to
mechanical side-jump scattering theory yields n=2. It is
achieve a high FMR frequency. Eddy current losses are
reported that both ordinary and extraordinary Hall
minimized by having a high resistive material and a small
resistivity increases
~
102-103
and
103-104 times,
respectively for FM-I granular films (Ni-SiO2, Co-SiO2, etc.) In the vicinity of percolation threshold (xp) compared to the corresponding bulk FM material [17].
3.3 High frequency applications
characteristic dimension (e.g. layer thickness). Of course, a '
'
high relative permeability μr is desirable, since μr is directly related with the level of the output signals of the RF magnetic devices. The possible material candidates for high frequency applications are:-
6
'
(i) Ferrites: Ms is small => μr
is low and fFMR is also
relatively low. Therefore, bulk ferrites are not widely used in high-frequency applications, although they are mostly insulators (ii) Ferromagnetic metal/alloys: Ms is large and Hk is
μ'r
small =>
is large. But small resistivity (ρ) value
films GMR (TMR) effect is observed for FM volume fraction, xv<xp. In FM-I granular films an enhancement in ordinary (x102-103) and extraordinary Hall resistivity (x103-104) than corresponding FM is observed near percolation threshold (xv<xp) than corresponding FM counterpart and can be used in Hall sensors applications. FM-I granular films are best soft materials for integrated
implies Large eddy current losses. Therefore FM metals are
electronic devices employed
not suitable for practical use in high frequency applications.
applications for electromagnetic shielding.
(iii) FM-I granular films (xv>xp): The FM-I granular films
5. References 1. Voevodin A. A., O’Neill J. P., and Zabinski J. S. Surface and Coatings Tech. (1999) 116, 36. 2. Juli´an Fern´andez C. de, Manera M. G., Spadavecchia J., Maggioni G., Quaranta A., Mattei G., Bazzan M., Cattaruzza E., Bonafini M., Negroa E., Vomiero A., Carturan S., Scian C., Della Mea G., Rella R., Vasanelli L., and Mazzoldi P. Sensors and Actuators B (2005) 111, 225. 3. Huajun Z., Jinhuan Z., Zhenghai G., and Wei W. J. Magn. Magn. Mater. (2008) 320, 565. 5. Usuki A., Kawasumi M., Kojima Y., Okada A., Kurauchi T., and Kamigaito O. J. Mater. Res. (1993) 8, 1174. 6. Benzaid R., Chevalier J., Saâdaoui M., Fantozzi G., Nawa M., Diaz L. A., and Torrecillas R., Biomaterials (2008) 29, 3636. 7. Tatsuma T., Takada K., and Miyazaki T., Adv. Mater. (2007) 19, 1249. 8. Wang M., Lian X., and Wang X. Curr. Appl. Phys. (2009) 9, 189. 9. Baibich M. N., Broto J. M., Fert A., Nguyen Van Dau F., Petroff F., Etienne P., Creuzet G., Friederich A., and Chazelas J. Phys. Rev. Lett. (1988) 61, 2472. 10. Binash G., Grünberg P., Saurenbach F., and Zinn W. Phys. Rev. B (1989) 39, 4828. 11. Schuhl A. and Lacour D., C. R. Physique (2005) 6, 945. 12. Wen, T. and Krishnan K. M. J. Phys. D: Appl. Phys. (2011) 44, 393001. 13. Kumar H., Ghosh S., Bürger D., Zhou S., Kabiraj D., Avasthi D. K., Grötzschel, R., and Schmidt H. J. Appl. Phys. (2010) 107, 113913. 14. Gittleman J. I., Goldstein Y., and Bozowski S. Phys. Rev. B (1972) 5, 3609. 15. Fujimori H., Mitani S., and Ohnuma S., Mater. Sci. Eng. B (1995) 31, 219. 16. Yakushiji K., Ernult F., Imamura H., Yamane K., Mitani S., Yakanashi K., Takahashi S., Maekawa S., and Fujimori H. Nat. Mater. (2005) 4, 57. 17. Denardin J. C., Knobel M., Zhang X. X., and Pakhomov A. B. J. Magn. Mater. (2003) 262, 15. 18. Ge S., Yao D., Yamaguchi M., Yang X., Zuo H., Ishii T., Zhou D., and Li F. J. Phys. D: Appl. Phys. (2007) 40, 3660.
consist of nano sized particles, which are separated by insulating regions. This microstructural feature leads to achieve a high resistivity (ρ). Secondly, if the size of NPs is reduced less than a critical length known as exchange length (Lex), exchange coupling between the magnetic particles takes place. This forces the magnetizations of particles to be aligned parallel, therefore, leading to a cancellation of magnetic anisotropy and the compensation of the demagnetization effect of individual particles. As a result, the average anisotropy (Hk) of the film and hence the coercivity Hc reduce considerably. Thus, the FM-I granular '
films are expected to have a high μr value and low eddy current losses even in the high frequency region. Bulk Co, Fe and FeCo, have the highest M s values of 2.3, 2.1 and 1.79 emu/cc, respectively among the magnetic materials and one expects good high frequency response of FM-I granular films based on Co, Fe, FeCo. There are many works on Co, Fe and FeCo based FM-I (where I: SiO2, Al2O3, ZrO2 etc.) granular films in literature for high frequency applications [17]. Figure 5(a) and (b) shows the HRTEM micrograph and The dependence of complex permeability
μ= μ' − i μ ' ' on frequency f for
the (FeCo)57:(SiO2)43 granular film, it is clear from Figure 5(b) that this granular system can be used upto 1 GHz range [18].
4. Summary In this article we have mainly focused on two kinds of magnetic nanocomposite (NC) structures: (i) Multilayer
in
near 1GHz range
and (ii) granular NCs . In FM-M (FM-I) based granular
7
Molecular modeling study of hypermodified nucleic acid base 3hydroxynorvalylcarbamoyl adenine, hn6Ade present at 3'-adjacent position in anticodon loop of hyperthermophilic tRNAs Bajarang V. Kumbhar and Kailas D. Sonawane* Structural Bioinformatics Unit, Department of Biochemistry, Shivaji University, Kolhapur. 416 004, India Phone: +91 9881320719, +91 231 2609153, Fax No: +91 231 2692333 *Email: kds_biochem@unishivaji.ac.in Conformational
preferences
of
hypermodified
nucleic
acid
N6-(3-
base
hydroxynorvalylcarbamoyl) adenine, hn6Ade have been investigated theoretically using PCILO, RM1 and HF-SCF methods. Automated geometry optimization using Density Functional Theory (B3LYP/6-31G** basis set) has also been made to compare the salient features. Molecular dynamics (MD) simulations have been performed on the preferred conformations of hn6Ade
to
find
out
the
hydration
effect.
The preferred
conformation
of hn6Ade is
such
that
the
N6-(3-
hydroxylnorvalylcarbamoyl) side chain spreads away ‘distal’ from the five membered imidazole moiety of adenine. The atoms N(6), C(10) and N(11) of ureido group as well as amino acid atoms such as C(12) and C(13) remains coplanar with the purine base in the preferred conformations. The most stable structure of hn 6Ade is stabilized by the intramolecular interactions between N(1)…HN(11) which would be useful to protect the N(1) site of adenine from participating in the usual Watson-Crick base pairing at 3'-adjacent (37th) position of anticodon loop of tRNA. This may help maintain proper reading frame of mRNA during protein biosynthesis process. MD simulation study of hn6Ade reveals that free rotations around the bond N(11)-C(12) could be possible. The characteristics feature of this modified base is the presence of methyl group which is involved in the interaction between O(13)…HC(15). These interactions could play an important role in the stabilization of tRNA structure at elevated temperatures in case of hyperthermophilic organisms.
position in anticodon loop of tRNA of hyperthermophilic
1. Introduction The hypermodified nucleosides naturally occur at th
th
34 and 37 positions in the anticodon loop of tRNA from all domains of life.
1-3
bacteria
and
archaea.4
The
anticodon
3'-adjacent
modifications help define reading frame for the codon-
These modified components are
anticodon interaction by preventing extended Watson-Crick
derivatives of the four common ribonucleosides. Most of
base pairing whereas, the modifications present at 34th
the modifications involve simple alkylation, hydrogenation,
position may restrict or enlarge the scope of wobble base
thiolation or isomerization of these four common
pairing.5-7
ribonucleosides in the base and the 2'-hydroxyl group of the
Transfer RNA which recognizes codons starting
ribose. However, some modifications involve complex
by U contain hydrophilic modified nucleosides such as
chemical modifications which are characterized by the
t6Ade, m6t6Ade and mS2t6Ade occurs at the 3'- adjacent
presence of diverse functional groups in base substituents,
position of anticodon loop of tRNA.3,8 The orientation of
such tRNA components are referred as hypermodified
the N(6) substituent in t6Ade, m6t6Ade, and mS2t6Ade has
N6-(3-
been found to be ‘distal’ (spreads away from the N(7) of
hydroxynorvalylcarbmoyl) adenine, hn Ade and its 2-
adenine ring) in the crystal structure9 as well as predicted
N6-(3-hydroxynorvalylcarbmoyl)
theoretically by using quantum chemical PCILO method.10-
nucleosides.
Hypermodified
nucleosides 6
methylthio
derivative
adenine, mS2hn6Ade which occur at the 3'-adjacent (37th)
11
In these modifications the N(6) substituent spreads away
8
Fig. 1 Atom numbering and nomenclature for the various torsion angles of hn6Ade. A fully extended (all trans) but proximal conformation is shown here. from the five membered imidazole moiety of the adenine
away from the five membered imidazole moiety of adenine
ring and becomes inaccessible for participation in the usual
preventing N(6)H and N(1) site in the usual Watson-Crick
Watson-Crick base pairing with codons and thus help
base pairing.
define the proper reading frame for the codon-anticodon
1.
interaction during protein biosynthesis process.
Nomenclature, Conventions and procedure Figure 1 depicts
the
atom
numbering
and
The previous studies on the conformational
identification of the various torsion angles describing
preferences of the hypermodified bases i 6Ade and its
rotations around the respective acyclic chemical bonds. In
2 6
mS i Ade along with its hydroxylated derivatives like cis6
6
2
6
2
6
io Ade, trans-io Ade, cis-mS -io Ade and trans-mS io Ade 2
along with various forms of the lysidine (k C) have been studied computationally energetic
conformations
12-13
. Recently, multiple iso-
of
wybutine 2
(yW)
14
and
2
the
N(6)
substituent
torsion
angle
[N(1)C(6)N(6)C(10)] describing rotation around the bond C(6)-N(6) and measures the orientation of the bond N(6) and
C(10)
with
respect
to
the
C(6)N(1) from the cis (eclipsed,0) position in the right-
conformational preferences of m G and m 2G have also
hand sence of rotation. Likewise,
been reported.15
angles [C(6)N(6)C(10)N(11)],
The structural significance of hn6Ade has not
the
the
torsion
[N(6)C(10)N(11)C(12)],
[C(10)N(11)C(12)C(13)],Ө[N(11)C(12)C(13)C(14)],
been investigated by any experimental methods. Hence,
ψ1[C(12)C(13)C(14)C(15)], ψ2[C(13)C(14)C(15)H],
present study has been performed to understand the
ω[C(12)C(13)O(13)H], φ1[N(11)C(12)C(16)O(16a)],
conformational preferences of hypermodified nucleic acid
φ2[C(12)C(16)O(16a)H] define
the
rotation
of the
base, N6-hydroxynorvalylcarbamoyl, hn6Ade using various
successive chemical bonds along with the main extension
energy calculation and MD simulation methods. It is also of
of the substituent. The extended conformation with the
interest to find out the structural role of hydrophobic –
adopted convention has been chosen initially as a reference
CH2CH3 group present in the side chain of hn6Ade. It has
point in the energy calculations. The standard bond length
found that 3-hydroxynorvalylcarbamoyl substituent spreads
and bond angle values are retained from the earlier
9
Table 1 Torsion angle values of the starting structures obtained by PCILO method (Conformer I and II) for hn 6Ade molecules Sr. No hn6ade molecule:
Torsion Angle (degree)
Relative Energy
I
01800θ=300, 0, 0, ω60 φ1= 90 φ2=180.
0.0
III
01800θ=300, 0, 0, ω0φ1=300φ2=180.
2.0
investigation
t6Ade9
on
hydroxynorvalylcarbamoyl)
N6-(3-
then again optimized at ab-initio level using Hartree-Fock
is
SCF (6-31G**) method.24 In this way most stable structure
because 6
adenine,
hn Ade
an
analogue of N6-threonylcarbonyl adenine, t6Ade.
of hypermodified nucleoside, hn6Ade obtained by HF-SCF
2.1. Conformational search and geometry optimization
(6-31G**) method using the PC Spartan Pro version 06
The conformational space has been searched for
V1.1.0 software.
6
the modified nucleic acid base hn Ade using quantum chemical PCILO method.
16-18
2.2. Molecular dynamics simulation study
This method has been found
To investigate the hydration effect on the
useful in conformational analysis of many bio-organic
modified base hn6Ade we performed molecular dynamics
molecules including nucleic acid constituent.
19-20
In PCILO
(MD) simulation study using Sybyl 7.3 commercial software
optimized
energy
optimized preferred conformations of modified base
calculation and energy correction terms up to third order
hn6Ade used as a starting geometry for molecular dynamic
are retained for each conformation. In logical selection of
simulation. Kollman-all-atom force field26 with Gasteiger-
grid points approach is used for searching the most stable
Marsilli charges and TIP3P model water has been chosen
21
for molecular dynamics simulation study. Minimal cubic
Conformational search by PCILO method resulted into two
periodic boundary conditions of diameter 35.968Å have
structure
throughout
and
the
the
conformational
alternative
stable
structure.
from
Tripos,
Inc.25
method polarities of each bond in the molecule are
The
PCILO-RM1-HF
Table 2 Full geometry optimization calculation using semi-empirical RM1and PM3 methods over the PCILO starting conformer I and II of hn6Ade Torsion Angle (degree) Conformer hn6Ade RM1
PM3
conformations
Relative Energy
α
β
δ
θ
ψ1
ψ2
ω
φ1
φ2
I II
2 1
357 337
171 171
281 284
295 293
188 187
181 180
71 72
48 233
186 175
0.00 1.06
I III
12 11
336 335
160 162
279 283
308 303
202 201
181 180
56 54
71 251
184 172
22.62 22.92
(conformation I
and
II)
and
these
been applied. Trajectories are taken for time span of 10 ps.
conformations are then used as starting structures for the
The constant temperature (canonical ensemble) simulation
full geometry optimization calculations using PM3
22
and
at 300 K were used along with 8 Å-non bonded cut off and
RM123 methods in order to find out the most stable
dielectric function ‘constant’ held at 1. For temperature
6
structure of hn Ade. The lowest energy stable structure is
ramp from 0 K to 200 K, 10 ps interval of 50 K and for 200
10
Fig. 2 Most stable structure of hn6Ade (PCILO conformer I) obtained by PCILO-RM1-HF 6-31G** optimization (α=1º, β=356º, γ=173º, δ=276º, θ=297º, ψ1=185º, ψ2=180º, ω=59º, φ1=52º, φ2=182º). K to 300 K, 10 ps interval of 25 K temperature steps were
using semi empirical PCILO method. The relative energy
used. The other usual conditions applied includes 1 fs time
difference between conformations I and II found below 2.0
step, initial Boltzmann velocity distribution, and shake
Kcal/mol
algorithm for hydrogen atoms, 10 fs non-bonded update
considered as starting conformers in this study. The
with scaled velocities. To remove steric clashes initially,
structural properties of hn6Ade are not studied by
5000 cycles of steepest descent minimization steps were
crystallographically or by using NMR. Hence, in order to
applied to the whole system. This minimized system
search the whole conformational space of hn6Ade, the full
considered for 200 ps equilibration protocol followed by
geometry optimization has been performed over the
5000 cycles of steepest descent energy minimization.
conformation I and II (Table 1) using semi-empirical RM1
Finally system is subjected for 1ns of production run time
and PM3 methods and results are shown in (Table 2). The
(Table 1). These two conformations are
Table 3 Geometrical parameters for hydrogen bonding interactions in the PCILO-RM1-HF of hn6Ade (Figure 2). Atom involved Distance Distance Angle 1-2-3 atom Pair atom Pair 1-2-3 1-2 A° 2-3 A° degree N(1)...HN(11) 2.026 0.996 132.21 O(16b)...HO(13) 2.441 0.945 121.01 O(13)...HC(15) 2.600 1.083 95.00
optimized stable conformations Figure Ref. 2 2 2
by maintaining all parameters as described above. All
relative energies of geometry optimized conformations
calculations were performed on HP xw8600 workstation.
using RM1 and PM3 methods are then compared to
3. Results and Discussion
indentify energetically stable conformer of hn6Ade. It has
3.1. Conformational search by PCILO method
been revealed that conformation I obtained by PCILO-RM1
Table 1 depicts the torsion angle values of conformation I,
optimization is found energetically stable conformation as
and II of hypermodified nucleoside hn6Ade obtained after
compared to conformation II (Table 2). Hence, PCILO-
the multidimensional conformational search carried out
RM1 optimized conformation I (Table 2) is then subjected
11
Table 4 Geometrical parameters for the torsion angles and hydrogen bonding interactions for average structure and snapshot structures after molecular dynamics simulation study of PCILO-RM1-HF optimized stable structure of hn6Ade. Average/ snapshots structures (ps)
Torsion angle (degree) θ=277, , , ωφ1=54 φ2=239. θ=179, , , ω φ1=127φ2=340 θ=181, , , ωφ1=287 φ2=15
0-300
350
550
Atoms involved (1-2-3)
Distance atom pair 1-2 (A)
Distance atom pair 2-3 (A)
Angle 1-2-3 (degree)
Figure No.
N(1)...HN(11) O(10)...HO(13)
2.248 1.701
1010 0.960
119.41 166.13
3a
N(1)...HN(11)
2.401
1.010
127.79
3b
N(1)...HN(11) O(13)...HN(11)
2.058 2.574
1.010 1.010
132.22 92.27
3c
torsion angles describing the conformation
Self Consistent Field (HF-SCF) method using 6-31G**
β=356º,γ=173º, δ=276º, θ=297º, ψ1=185º, ψ2=180º, ω=59º,
basis set to find out preferred most stable conformation of
φ1=52º, φ2=182º. This most stable conformation of hn6Ade
hypermodified nucleoside, hn6Ade.
may be compared to the crystal structure of t6Ade9-10, an
3.2. Geometry optimized stable conformation of hn6Ade
analogue
The predicted most stable structure of the hypermodified
hydroxynorvalylcarbamoyl side chain spreads away ‘distal’
of
hn6Ade.
The
are
α=1º,
to full geometry optimization with the help of Hartree-Fock
N(6) substituent
3-
Fig. 3 A) 1ns MD simulated average structure of hn6Ade at 0-300 ps. B) Snapshot structure of hn6Ade at 350 ps. C) Snapshot structure of hn6Ade at 550 ps. nucleic
acid
base
N6-(3-hydroxynorvalylcarbamoyl)
adenine, hn6Ade obtained by PCILO-RM1-HF (6-31G**) optimization is depicted in Figure 2. The optimized
from the five membered imidazole moiety of adenine ring as observed in N(6)–threonylcarbamoyl adenine t6Ade 6 6
2 6
9-10
,
11
m t Ade and mS t Ade. This kind of orientation prevents
12
extended Watson-Crick base pairing of adenine base at 3'-
the molecule. The interaction between O(13) and HC(15)
adjacent (37th) position and thus avoid misrecognition of
of hydrophobic –CH2CH3 group observed the in most
codons. The intramolecular interactions (Table 3) between
stable and alternative stable conformations of hn6Ade could
N(1)…HN(11), O(16b)…HO(13) and interaction between
play an important role during codon-anticodon interactions
O(13)…HC(15) may provide stability to the structure
in hyperthermophiles. This extra hydrophobic group
(Figure 2). Due to series of conjugated bonds extending the
present in hn6Ade as compared to t6Ade may be helpful for
partial double bond character from the adenine ring through
the growth of hyperthermophilic bacteria and archaea at
N(6), C(10), O(10) and N(11) the torsion angles α, β and γ
elevated temperatures.4
are essentially constrained to adopt planar cis or trans
3.3. MD simulation of PCILO-RM1-HF optimized
orientation. In addition to this the strong steric repulsion
stable structure of hn6Ade
from proximal orientation of N(6) substituent atoms to N(7)
Molecular dynamics (MD) simulation has also
Fig. 4 Molecular dynamics simulation results of hn6Ade. A) Stabilization in α torsion angle. B) Stabilization in torsion angle. C) Stabilization in torsion angle. D) Fluctuations in torsion angle. E) Fluctuations in torsion angle. F) Fluctuations in 1 torsion angle. G) Fluctuations in 2 torsion angle. H) Fluctuations in torsion angle. I) Fluctuations in 1 torsion angle. J) Fluctuations in 2 torsion angle. K) Fluctuations in hydrogen bonding interaction between N(1)…HN(11). ruled out the trans orientation of α torsion angle. The 6
been performed to explore the conformational space of
hydrophobic –CH2CH3 group of hn Ade prefers extended
hypermodified nucleic acid base hn6Ade using Sybyl7.3
conformation forming an intramolecular interactions within
software.25 The PCILO-RM1-HF optimized stable structure
13
(Figure 2) is used as starting geometry for 1ns MD
(0-300 ps) as well as snapshot structures 350 ps and 550 ps
simulation study. The results of torsion angle and
maintained the uriedopurine ring as well as hydrogen
geometrical parameters for the average structure and
bonding interaction between N(1)…HN(11) (Figure 3A, B
snapshot structure are shown in the Figures 3A-C and
and C). The geometrical parameters for torsion angle
Table 4. We analyzed average structure at 0-300 ps and
values and hydrogen bonding interaction for the snapshot
snapshot structures at 350 ps and 550 ps to compare the
structure are listed in the Figures 3B-C and Table 4. The
conformational preferences of most stable structure
above discussed snapshot structure taken at 350 ps (Figure
obtained by PCILO-RM1-HF optimization (Figure 2). The
3B) and 550 ps (Figure 3C) clearly show that the norvalyl
average
‘distal’
group is free to rotate around the bond N(11)-C(12), these
orientation of the N(6)-substituted side chain i.e. spreads
and
snapshot
structures
maintain
results are in close agreement with experimental study of
away from the five membered imidazole moiety of adenine
modified base t6Ade.27
ring as observed in the most stable structure of hn6Ade
The fluctuations in the torsion angle Figure
(Figure 2). The uriedopurine ring as well as intramolecular
4 (A-B) maintained well during 1ns MD simulation study
interaction between N(1)…HN(11) (Figure 4K) are well
whereas torsion angle Figure 4C fluctuates between
maintained during 1ns molecular dynamics simulation
180 which indicates that the uriedopurine ring as well as
period as observed in the PCILO-RM1-HF optimized
hydrogen bonding interaction between N(1)…HN(11)
structure (Figure 2).
(Figure 4K) would be important for the orientation of the
The average structure (0-300 ps) having torsion
N(6)-substituted side chain to ‘trans’ whereas other torsion
angle values are (θ=
angles show fluctuations over 1ns MD simulation period
277, , , ω23φ1=54φ2=239). The
(Figure 4). The torsion angle Figure 4Dand θ Figure
torsion angle values for α, β, γ, φ1 and show small
4E maintained their initial values up to 500 ps and then
differences whereas the θ and ψ1 changes by 20, ω varies
fluctuates between 120-180after ps till end of the
about 30 whereas φ2 shows large variation as compared to
simulation period. The next torsion angle φ1 Figure
stable structure (Figure 2). The average structure is
4Ifound well maintained up to 0-300 ps after that it
stabilized from the hydrogen bonding interactions between
fluctuates between -120 to -150 up to 500ps.Torsion
N(1)…HN(11) and O(10)…HO(13) (Figure 4A and Table 4). The snapshot structure taken at 350 ps (Figure 3B) also shows basic interaction
between
N(1)…HN(11) as
observed in (Figure 2). The 3-hydroxylnorvalycarbamoyl side chain maintains ‘distal’ orientation (Figure 3B and Table 4). The next snapshot structure taken at 550 ps also maintains
interaction
between
N(1)…HN(11)
and
O(13)….HN(11) as shown in (Figure 3C). The interaction between O(13)….HN(11) suggest that the hydroxyl group ‘HO(13)’ of norvalylcarbamoyl group of hn6Ade orient towards the N(1) site of adenine and could play an important role to prevent extended Watson-Crick
angles
N(11)-C(12) are possible in case of hn6Ade also similarly as explained in the crystal structure of t6Ade.27 The average
4FFigure
4GFigure
4HandFigure 4J show fluctuations between 180. The fluctuations of torsion angles as shown in (Figure 4DJ) and above discussed snapshot structures (Figures. 3B, C), it clearly indicates that the norvalyl group of hn6Ade is free to rotate around the bond N(11)-C(12) as similarly shown in previous experimental study of t6Ade
27
which is
6
an analog of hn Ade having extra methyl group. The hydrophobic –CH2CH3 group of hn6Ade point towards the N(1) site of adenine and thus could interact with codons if present at 3'-adjacent side of anticodon loop of tRNA. 4. Conclusions
hydrogen bonding from 3'-adjacent site of anticodon loop of tRNA. This proves that the rotations around the bond
Figure
Conformational preferences of modified base, 6
hn Ade performed using PCILO method followed by semi empirical RM1-HF optimization along with molecular dynamics simulation study shows that N(6) substituted 3-
14
hydroxynorvalylcarbamoyl side chain of hn6Ade prefers ‘distal’ conformation. The most stable and alternative stable conformations are stabilized by the hydrogen bonding interaction between
N(1)…HN(11) of 3-
hydroxynorvalylcarbamoyl
chain
side
which
is
a
characteristic feature of uriedopurine as found in earlier studies on t6Ade
10,
mS2t6Ade and m6t6Ade.11 This
intramolecular interaction may help prevent extended Watson-Crick base pairing at 3´-adjacent (37th) position during codon-anticodon interactions. In addition to this the most stable structures of 3-hydroxynorvalylcarbamoyl substituent of hn6Ade (Figure 2) shows intramolecular interaction
between
O(16b)…HO(13)
and
a
weak
interaction between O(13)…HC(15) which might play an important role in the stabilization of tRNA structure of hyperthermophilic organisms at higher temperature range. Molecular dynamics (MD) simulation study clearly shows that the norvalylcarbamoyl group of hn 6Ade is free to rotate around the bond N(11)-C(12) similarly as observed in earlier experimental study of modified base t6Ade.27
Intramolecular
interactions
between 6
N(1)….HN(11) and O(13)….HN(11) of hn Ade also maintained during MD simulation study as observed in PCILO-RM1-HF
preferred
structure.
The
extended
orientation of hydrophobic –CH2CH3 group of hn6Ade towards the N(1) site of adenine base might provide hydrophobic environment at 3'-adjacent site of tRNA anticodon loop during codon-anticodon interactions. Such orientation of –CH2CH3 group could also play an important role in the translocation process in order to have smooth and
in
phase
protein
biosynthesis
process
of
hyperthermophiles at elevated temperatures. Acknowledgements KDS is gratefully acknowledged to Department of Science and Technology (DST), New Delhi (No.SR/FT/LS028/2007) and University Grants Commission, New Delhi for financial support under the scheme UGC SAP DRS-I sanctioned to Department of Biochemistry, Shivaji University, Kolhapur. BVK is gratefully acknowledged to
References 1. Motorin Y., Bec G., Tewari R., and Grosjean H. RNA. (1997) 3, 721. 2. Morin A., Auxilien S., Senger B., Tewari R., and Grosjean H. RNA. (1998) 4, 24. 3. Persson B. C. Mole Microbiology. (1993) 8, 1011. 4. Reddy D. M., Crain P. F., Edmonds C. G., Gupta R., Hashizume T., Stetter K. O., Widdel F., and McCloskey, J. A. Nucleic Acids Res. (1992) 20, 5607. 5. Agris P. F. Prog. Nucleic. Acid. Res. Mol. Biol. (1996) 53, 79. 6. Chheda G. B., Hall R. H., Magrath D. I., Mozejko J., Schweizer M. P., Stasiuk L., and Taylor P. R. Biochemistry. (1969) 8, 3278. 7. Schweizer M. P., Chheda G. B., Baczynskyj L., and Hall R. H. Biochemistry. (1969) 8, 3283. 8. Davis D. R. in Modification and Editing of RNA, Grosjean H., Benne. R Eds.: ASM Press: Washington, (1998) P 85. 9. Parthasarathy R., Ohrt J. M., and Chheda G. B. Biochemistry (1977) 16, 4999. 10. Tewari R. Ind. J. Biochem. Biophys. (1987) 24, 170. 11. Tewari R. J. Biomol. Struct. Dyn. (1990) 8, 675. 12. Sonawane K. D.; Sonavane, U. B. and Tewari R. J. Biomol. Struct. Dyn. (2002) 19, 637. 13. Sonawane K. D. and Tewari R. Nucleos. Nucleot. Nucleic. Acids. (2008) 27, 1158. 14. Kumbhar N. M. and Sonawane K. D. J. Mol. Graphics. Modell. (2011) 29, 935. 15. Bavi R. S., Kamble A. D., Kumbhar N. M., Kumbhar. B. V., and Sonawane K. D. Cell Biochem. Biophys. (2011) 61, 507. 16. Masson A., Levy B., and Malerieu J. P. Theor. Chim. Acta. (1970) 18, 193. 17. Diner S., Malrieu J. P., and Claverie P. Theor. Chim. Acta. (1969) 13, 1. 18. Diner S., Malrieu J. P., Jordan F., and Gilbert M. Theor. Chim. Acta. (1969), 15, 100. 19. Pullman B., and Pullman A. Adv. Protein. Chem. (1974) 28, 347. 20. Pullman B., and Saran A. Prog. Nucleic. Acid. Res. Mol. Biol. (1976) 18, 215. 21. Tewari R. Int. J. Quant. Chem. (1987) 31, 611. 22. Stewart J. J. P. J. Comp. Chem. (1991) 12, 320. 23. Rocha G. B., Freire R. O., Simas A. M., and Stewart J. J. P. J. Comp. Chem. (2006) 27, 1101. 24. Hehre W. J., Radom L., Schleyer P. V. R., and Pople J. A. In Ab Initio Molecular orbital Theory, Wiley, New york, (1986). 25. Tripos International (2006) SYBYL 7.3, Tripos International, South Hanley Rd., St. Louis, Missouri, USA 26. Weiner C. J., Kollman P. A., Case D. A., Singh U. C., Ghio C., Alaqom G., Protera S., and Weiner P. J. J. Am. Chem. Soc. (1984) 106, 765. 27. Murphy F. V., Ramakrishnan V., Malkiewicz A., and Agris P. F. Nat. Struct. Mol. Biol. (2004) 11, 1186.
University Grants Commission, New Delhi for providing fellowship as a Project Fellow under the scheme UGC SAP DRS-I.
15
Microbial Genomics Tool (MGT 1.0) for Bacterial Codon Usage Analysis Rajendra Verma1, Ragini Gothalwal1, Kamalraj Pardasani2, Anil Prakash, Kishor Shende1* 1. Bioinformatics Center (SubDIC), Dept. of Biotechnology, Barkatullah University Bhopal M.P. India 2. BIF, Department of Applied Mathematics, MANIT, Bhopal M.P. India
*E-mail: kishor556@hotmail.com
Richard Grantham (1980) proposed genome hypothesis stating that codon catalogue can be a genomic feature to study the genome variability. New genome sequencing technology has resulted into flood of genome sequences in databases. Looking towards the need of future of bacterial genome analysis, MGT (Microbial Genomics Tool) development was initiated. MGT is developed with java programming language with user-friendly interface. It can calculate codon usage frequency and indices. It also gives nucleotide composition with e-translated protein sequences. It produces result in MS-Excel and text file format, which can be further processed for statistical
analysis.
This
software
is
open
access
and
it
can
be
obtained
from
Source
forge
web
site
(http://sourceforge.net/project/micromictool/). It has the utility in research and teaching of bacterial genomics. Future development plan for MGT is inclusion of statistical analysis application and microarray analysis. an organism helps in understanding the basis of molecular
1. Introduction
biology of gene regulation and gene expression. This can Over the past three years, parallel DNA sequencing platform have reduced the cost of DNA sequencing. Next
indirectly help in understanding the morphology, physiology and phylogeny of organisms2-8.
generation sequencing has the potential to dramatically accelerate biological and biomedical research, by enabling the 1
The controversial ideas of Kimura, Kings and Jukes on
comprehensive genome sequencing and analysis . More than
natural evolution led some early detractors to postulate that
7000 sequenced bacterial genomes are available at NCBI ftp site
usage of synonymous codon in protein coding genes is not
(http://ncbi.nlm.nih.gov). Chromosomal DNA stores (RNA in
necessarily random and that codon composition could be biased
RNA viruses) genetic information to carry out cellular processes
towards the codons that would match the tRNA pool of the host
and same is transferred generation after generation. Three
organism. This prediction was partially confirmed by Grantham
important
and
and his co-workers. They compiled codon usage table for all the
translation are meant to transfer the genetic information to form
sequences genes available at that time and proposed that each
functional assembly, a cell. Translation is the process where
genome has a particular codon usage signature that reflects
signals in nucleotide sequence form is converted to sequence
particular evolutionary forces acting with that genome2-4.
amino acid. There are 64 possible codons and 20 amino acids;
Consequently they proposed „Genome Theory‟. According to
hence the code is redundant and multiple codons can specify the
this theory “Codon usage pattern of a genome was a specific
same amino acid according to Wobble hypothesis given by
characteristic of an organism”. Organism specific codon usage
Crick. Multiple codons coding for single amino acid are called
pattern suggested that the variation in codon usage pattern might
synonymous codons. The correspondence between codons and
be correlated with variation in tRNA abundance2-3, which
amino acids is nearly universal among all known living
ultimately affects the gene expression4. Early studies of E. coli
organisms with a few variations. Different organisms often
codon usage pattern showed remarkable variation in strongly
show different preferences for synonymous codons called as
and weakly expressed genes4. A modulation of the coding
codon usage bias. The codon usage patterns differ significantly
strategy according to expression was proposed such that codons
depending upon several factors such as mutational bias, natural
found in abundant mRNA were under selection for optimal
selection for translation optimization. Codon usage analysis in
codon-anticodon pairing4. A later study in E. coli found that the
processes
viz.
replication,
transcription
16
variation in codon usage is dependent on translational level and
of them can calculate only CAI (Codon Adaptation Index)
the codon usage of abundant protein genes could be
values. ACUA11 is not updated since long and some of its
distinguished from other genes9.
application doesn‟t work.
Codon usage pattern analysis is a method to understand the
This project was undertaken to develop a software tool
bacterial genome. Various codon usage indices are formulated
MGT (Microbial Genomics Tool) that will be user friendly and
to understand the codon usage bias and factor that affect this
will provide the output format suitable for statistical analysis in
biasing. Codon usage indices such as RSCU (Relative
most of the Windows based statistical analysis software tool.
Synonymous Codon Usage), CAI (Codon Adaptability Index),
The provision is also made to implement the inclusion of new
CBI (Codon Bias Index), Third nucleotide composition of codon
applications which will be developed in future. MGT was
(GC3), Nc (Effective number of codon), Fop (Optimal Codon
planned for open access.
usage) etc. These indices can help to understand the factor shaping the codon usage in different species, organisms, genera or even different cellular processes in single organisms. Increased involvement of computers and computational techniques led to development of many user friendly software tools.
Simultaneously
the
advancement
of
Information
technology also led to storage of complex data and tools for this type of data analysis. In 1999 CodonW was developed by John Peden for comprehensive analysis of codon usage frequency. This is designed to simplify the Multivariate Analysis (Correspondence Analysis) of codons and amino acids usage. It
2. Methodology 2.1. MGT Development MGT is a standalone software tool developed in java programming
language
on
NetBeans
IDE
7.0.1
(http://netbeans.org ). User-friendly interface was developed using Java swing package. MGT interface is shown in Fig-1. Installation package of MGT was created in MSI format (Windows Installation Technology) using software “Advanced Installer 8.6” trail version. (http://www.advancedinstaller.com) 2.2. Codon Usage Indices
also calculates standard indices of codon usage. It has both menu and command line interface10. Lacks of user friendly
2.2.1. Relative Synonymous Codon Usage (RSCU)5: RSCU is
interface is the main demerit of this tool but still it widely used
calculated as the observed codon usage divided by the average
11
software can calculate most
codon usage for that amino acid (equation). A value of 1.00 is
of codon usage indices, codon frequency, RSCU, CAI values,
obtained if all codons for a particular amino acid are used
C3s, G3, T3, and A3s; and also the result can be visualized in
equally. RSCU removes the influence of amino acid
MS-Excel file. Major drawback is it lacks the major multivariate
composition that is present in raw codon usage data.
for codon usage analysis. ACUA
analysis algorithms and also it is not updated since long. EEq. (2.2.1)
CAI12 server side tool is to estimate an expected value of Codon Adaptation (eCAI). JCat13 is a novel tool, which calculates the codon usage adaptability of a target gene to its potential
Where, Xij is the frequency of the jth codon for the ith amino
expression host. It is a server side web application, designed in
acid, encoded by in synonymous codons.
14
15
java. Other software such Jemboss and BioEdit can calculate codon usage frequency and RSCU values, but they can process only a single sequence or sum of codon frequency of all the ORFs (Open Reading Frames) present in the file. Most of these softwares are suitable enough to work on specific single task.
2.2.2. Synonymous site composition statistics15: The GC3 value is the fraction of codons, which are synonymous at the third codon position and have either a „G‟ or a „C‟ at that codon position. Similar way AT3s can be calculated.
CodonW is one suit able to calculate most of the codon usage indices. But it has command line operation without any graphical interface. EMBOSS18, Jembos14 and BioEdit15 can calculate only codon usage frequency and RSCU values. Some
Eq. (2.2.2)
17
Where, NNU, NNG, NNC etc. refer to the total number of
„Browse‟ tab. „Result Bar‟ is text box which can visualize the
codons of that form
results of calculations. The third portion is „Tool Box‟ divided
2.2.3. Effective Number of Codons (Nc)15: The effective
into two parts. First part is „Codon Analysis‟ that contains 3 tabs corresponding to 3 different applications. „Codon Table‟ tab
number of codons provides a way to quantify differential codon
calculate codon usage frequency, „RSCU‟ tab calculate RSCU
usage of a particular gene to the equal use of synonymous
value table and „Other Value‟ tab calculate the GC3, AT3, Nc,
codons. Nc is an estimate of the strength of general codon usage
Enc values etc. Second portion is of „Nucleotide Composition‟
bias. It may be influenced by mutation biases and/or selection
containing 2 tabs. „N Table‟ tab calculate the individual
for particular codons. The genetic code has five amino acid family types (non-synonymous, 2- fold, 3-fold, 4-fold and 6-fold synonymous amino acids). The Nc value is calculated as the
nucleotide composition and percent AT and GC contents of gene. „Translation‟ tab perform the e-translation of the ORF sequence and returns the protein sequence.
arithmetic average of all non-zero homozygosity values for each of the amino acid family types.
Eq. (2.2.3.1) Where, Fi - average homozygousity for the class with „i‟ synonymous codons Homozygosity for each amino acid is estimated from the squared codon frequencies.
Eq. (2.2.3.2) Where, k - number of synonyms; n - total usage of k-fold synonymous amino acid; F - homozygosity; Pi = frequency of usage of „ith‟ synonymous codon.
Fig. 1 MGT 1.0 interface showing tab and text boxes for different applications. The result output is shown in „Result Bar‟ text box. The result is also shown in MS-Excel file. 3.2. Program input MGT accepts nucleotide sequence as input text file with ORFs nucleotide sequences in fasta format as shown in Fig-2.
Expected value of Nc if codon bias is solely a function of GC3s.
The sequence file is loaded through „Browse‟ tab. Multisequence is also accepted by MGT, which is present in CodonW but it is command line and total codon frequency is obtained for either single or number of nucleotide sequences in Jemboss and
Eq. (2.2.3.3) Where, S - frequency of G+C (i.e. GC3) 3. Result and Discussion
BioEdit. 3.3. Program output MGT software calculates average and percentage codon frequency; codon usage indices such as AT3, GC3, RSCU, Nc
3.1. Microbial Genomics Tool (MGT) interface
and ENc value. Nucleotide composition calculation includes frequency of nucleotides (A, T, G and C), AT-percent and GC-
MGT 1.0 interface has two main menus „File‟ and „Help‟ (Fig-
percent values. Output can be visualized in „Result Bar‟ text box
1). „Fasta File Only‟ field is to access the input file through
18
Fig. 2 Input file with Fasta formatted nucleotide sequence. and also can be saved in MS Excel file format (Fig-3). As the
(http://sourceforge.net/project/micromictool/), a site for open
result file is tab delimited it can be further processed for
source software. This software is at infancy stage and future
advanced statistical analysis by suing any window based
plan includes addition of applications for statistical analysis of
Statistical software.
codon usage data and microarray data. Acknowledgments The
author
is
grateful
to
BTISNET,
Department
of
Biotechnology, Government of India New Delhi for their constant and encouraging support. We also acknowledge the Sourceforge team (http://sourceforge.net/) for providing server place to make software open access. References 1. 2. 3. Fig. 3 Result window showing results in (A) Result Bar (B) Result in MS-Excel file. 4. Conclusion
4. 5. 6. 7.
version is provided with user-friendly graphical interface.
8. 9. 10. 11.
Multiple sequences can be passed to tool and tabulated result for
12.
Microbial Genomic Tool (MGT 1.0), the first version is developed for In-silico research bacteria genomic study. This
each sequence can be obtained. It provides application for calculation of codon frequency, RSCU, AT3, GC3, Nc Enc, nucleotide composition and percent values. MGT development was initiated with the future intention to provide multiapplication software for bacterial genome analysis. MGT1.0 is open access and can be obtained from sourceforge site
13.
14. 15. 16. 17.
Shendur J. and Hanlee J. Nature Biotechnol. (2008) 26, 1135. Grantham R. C., Gautier and Gouy M. Nucleic Acids Res. (1980) 8, 1892. Grantham R. C., Gautier, Gouy M., Mercier R. and Pave A. Nucleic Acids Res. (1980) 8, r49. Grantham R. C., Gautier, Gouy M., Jacobzone M. and Mercier R. Nucleic Acids Res. (1981) 9, r43. Sharp P. M. and Li W. H. J. Mol. Evol. (1986) 24, 28. Sharp P. M. and Li W. H. Nucleic Acid Res. (1987) 15, 1281. Sharp P. M., Bailes E., Grocock R. J., Peden J. F. and Sockett R. E. Nucleic Acids Res. (2005) 33, 1141. McInerney J. O. Bioinformatics (1998) 14, 322. Gouy, M. and Gautier C. Nucleic Acids Res. (1982) 10, 7055. Peden J. http://www.sourceforge.net/ (2005). Umasanker V., Vijay K., Arun K. and Dorairaj S. Bioinformation (2007) 2, 62. Garcia-Vallve S., Puigbo P., Bravo I. G. BMC Bioinformatics (2008) 9, 65. Grete A., Hiller K., Monice, Much R., Nortemann B., Dietmar C., Hempel and John D. Nucleic Acid Res. (2005) 33, W536. Carver T. and Bleasby A. Bioinformatics (2003) 19, 1837. Hall T. A. J. Nuclic Acid SYMP (1999) 41, 95. Wright, F. Gene (1990) 87, 23. Rice P., Longden I. and Bleasby A. Trends Genet. (2000) 16, 276.
19
An application of radon and wavelet transforms for image feature extraction Heena Patel*, Saurabh dave, Himanshu Patel, and Chintan dave Ganpat University, Kherva, India
*hkp01@ganpatuniversity.ac.in In this paper we proposed wavelet and Radon for the rotation and translation invariant image transforms analysis and their use for image enhancement and features extraction. Main focus of this paper is to use two-dimensional Radon and wavelet transforms to form fundamental mathematical tools in these areas. Results are verified in the MATLAB environment both for data and for analysis of biomedical images.
preprocessing.
1.1 Introduction
Individual
features
are
obtained
by
connection of these blocks using a wavelet decomposition The Radon transform is named after the Austrian mathematician Johann Karl August Radon (December 16, 1887 – May 25, 1956). The main application of the Radon transform is CAT scans, where the inverse Radon transform is applied. The Radon transform can also be used for line detection. Radon
transform
block into the second level. Two features obtained by this decomposition are sum of squared image component coefficients evaluated in the first and the second decomposition level by high-pass filters both for image columns and rows. Results of features’ variance with application of different methods are displayed both
forming
a
very
important
mathematical tool used in tomography is based upon works of Johann Radon born in 1887 Litomerice. His doctoral
graphically and in the tables.
(a)
dissertation has been defended in Vienna in 1910 and his most appreciated works were devoted to integral geometry. The Radon transform1 belonging to this category introduced in 1917 is defined as a collection of 1D projections around an object at angle intervals θ. The Radon transform of a two-dimensional (2-D) function f(x, y) is defined as:
(b)
R(θ,r)R(θ,r)[f(x,y)]=
f ( x, y) (r x cos y cos )dxdy
Eq. 1 Where, r is the perpendicular distance of a line from the origin and θ is the angle formed by the distance vector. The present work allows features extraction by blocks
Fig. 1 (a) An example of a set of parallel lines for a chosen θ = 45◦ in the (x, y) plane and (b) the localization of corresponding points in the (θ, r) plane in which the discrete Radon transform is evaluated.
of Radon transform, wavelet transform and blocks of image
20
For the constant value of Θ the set of parallel lines for
(a)
different values of r are presented in Figure 1(a). The parallel lines are used for the integration of the given
20
image. The plane (x, y) is transformed in this way to the
40
plane (θ, r). The transformation proceeds by integration of
60 80
the given image along parallel lines in the plane (x, y) and
100
resulting value is then marked in the graph as a point for a
120
given θ and r as depicted in Figure 1(b). Each point has a
20 40 60 80 100120
different intensity of color, depending on its value, having value 0 corresponding to black and 1 corresponding to
(b)
white color presented in Figure 3(b). A discrete Radon transform called Hough transform has been introduced in 20
1972 by R. Duda and P. Hart 2, 3 as a tool for image features
40 60 80 100 120 20 40 60 80 100120
(c)
60
Fig.2 Block diagram of the proposed technique
x'
-50
40
0
extraction. 20
50
1.2 Simulation of Radon Transform
0
50
100
150
(degrees)
In the Simulink environment there is no block for the Radon transform. A general block called ”Matlab function” can be used instead. This block has a single input and single output. Parameters of this block include: • Name of existing function of Matlab library or name of the created function as M-file • Output dimensions specified for returned single value
Fig. 3 Visualization of (a) input MR image rotated by θ= 90◦, its (b) Radon transform depicted as points for θ= 0◦ − 180◦ and for the same r for each θ, and (c) inverse Radon transform.
output variable is frame-based. Input image and images after transformation are visualized in the matrix viewer presented in Figure 3 and sent to workspace in direct and inverse radon transform.
• Choice to Collapse 2-D results to 1-D 1.3 Radon Transform to Detect Lines The Matlab function or M-file use every ”Matlab function” block for processing of the input value. Figure 2
The Radon transform is closely related to a common
presents block diagram of the direct and inverse Radon
computer vision operation known as the Hough transform.
transform of MR image and visualization of Radon
You can use the radon function to implement a form of the
transform image. Input image is loaded as a constant and
Hough transform used to detect straight lines adjusted to
21
function that limits the duration of the analyzed signal
uses fixed size windows that cannot be suite the speed of
segment.
the changing phenomena observed in the input signals.
(a)
Wavelets solve this problem by using the so called
50
100
150 50
100
150
(b)
50
Fig. 5 Wavelet decomposition mother wavelet which can be scaled and translated to
100
achieve both time localization and multi-resolution. The 150
decomposition stage results in this way in four images 50
100
150
representing all combinations of low-pass and high-pass initial image matrix. The reconstruction stage includes row
Fig. 4 (a) Original image (b) edge image.
(a) 1.4 Principles of Image Wavelet decomposition Wavelet functions used for signal analysis are derived from the initial function W(t) forming basis for the set of functions.
Wa,b(t)=
1
1 W ( (t ď&#x20AC; b)) a a
Eq. 2
For discrete parameters of dilation a=2m and translation
(b)
b=k 2m. Wavelet dilation, which is closely related to spectrum compression, enables local and global signal analysis. The principle of signal and image decomposition for resolution enhancement is presented in Figure 4.The wavelet transform has gained a great deal of interest due to its time localization and multiresolution properties.
4-7
Fourier transforms (FT) lack time localization as frequency components are attributed to the entire time signal and not to specific parts of it. Windowed Fourier Transforms (WFT) achieves this localization by using a window WFT
Fig. 6 (a) 2-Level and 4-Level Decomposition. (b) 2level Decomposition of reference image
22
upsampling at first and row convolution in stage R.1. The
”Transpose” enables matrix transposition. In our diagram it
corresponding images are then summed. The final step R.2
enables matrix transposition after column downsampling to
assumes column
with
proceed row decomposition. We transpose matrix after the
reconstruction filters followed by summation of the results
row decomposition to visualize matrix right. Diagram for
again. In the case of one-dimensional signal processing,
one decomposition and reconstruction levels is presented in
steps D.2 and R.1 are omitted.
Figure 5.
upsampling and
convolution
Table 1 STD computed from rotated MR image features. STD of MR Image Features
The whole diagram for image decomposition into the second level and its reconstruction is presented in Figure 6. Block diagrams mentioned above have been created to obtain definition of features of rotated images. We compare
Feature-1
Feature-2
DWT
0.0013
0.0254
diagonal DWT transform coefficients in the first and the
RT-DWT
2.97 X 10-5
0.0023
second decomposition levels using MR images obtained by
the standard deviation (STD) of the sum of squared
rotation from 0 to 180 degrees with step 10◦ using (i) diagram with the plain DWT,(ii) diagram for the Radon. 1.5 Simulation of Wavelet Transform in Simulink Environment
2. Results
Wavelet transform diagram was created with blocks of
Thanks to the objective confrontation of STDs, Table 1 is
Simulink library. Block”DWT” computes the discrete
the bright example that the Radon transform is a powerful
Fig. 7 Individual Simulink blocks which create one level of wavelet decomposition and reconstruction.
wavelet transform using a filter bank with specified
tool expressively contributing to image analysis. The
highpass and lowpass filters. The filters can be user-defined
improvement of the STD between the plain DWT and RT-
or formed by wavelets of the Wavelet Toolbox. The output
DWT by an order has been verified. We achieved also a
is set to ’Multiple ports’. It enables to see each sub band as
small improvement by denoising of the magnetic resonance
a frame-based vector or matrix. The common block
image. Therefore image enhancement is very desirable
23
(d)
(e)
(f)
Fig. 8 Visualization of (a, d) input MR image, (b, e) wavelet decomposition, and (c, f) image wavelet reconstruction here. We also tested with simulink of MATLAB (Figure 7)
and translation invariant feature selection using appropriate
and also using other images (Figure 8) which is in built in
image transforms.
MATLAB. Image preprocessing allows further research devoted to the optimization of wavelet coefficients thresholding to denoise the original image.The proposed method of image features extraction allows the estimation of the rotation invariant image features and moreover it is very flexible as it allows the use of different wavelet
4. References 1. Bracewell R. N. Fourier Analysis and Imaging. Kluwer Academic Press, (2003). 2. Choi D. I. and Park. S. H. IEEE Trans.Neural Networks, (1994) 5, 561.
functions and different rotation steps in case of the Radon
3. Duda R. O. and Hart P. E. Comm. ACM, (1972) 15, 11.
transform.
4. Gavlasov´a A. and Proch´azka A. Simulink modeling of radon and wavelet transforms for image feature extraction, Institute of Chemical Technology, Department of Computing and Control Engineering.
3. Conclusions The above results show the importance of wavelet and Radon for the rotation and translation invariant image transforms analysis and their use for image enhancement and features extraction. The major finding of the present work is to use two-dimensional Radon and wavelet
5. Malviya A. and Bhirud S. G. International Conference on Emerging Trends in Electronic and Photonic Devices & Systems, ELECTRO-2009. 6. Ramprasad P., Nagaraj H. C. and Parasuram, M. K. International Journal of Computer Science (2009) 4, 2.
transforms to form fundamental mathematical tools. It is
7. Wikipedia. Johann Radon.
assumed that further studies will be devoted to feature
http://en.wikipedia.org/wiki/Johann Radon.
based image segmentation and further methods of rotation
24
Use of proteinase inhibitors from okra for inhibiting the Helicoverpa armigera (Hubner) gut proteinases Shilpa K.Udamale and M.P.Moharil* Biotech Centre, Department of Botany, Dr. Panjabrao Deshmukh Agricultural University Akola, Maharashtra- 444 104, India *Email: mpmoharil@gmail.com The Abelmoschus esculentus, okra, genotypes and its wild relatives were analyzed for the presence of trypsin, chymotrypsin and Helicoverpa gut proteinases (HGPs) inhibitors (HGPIs), with the aim to identify potent inhibitors of H. armigera gut proteinases. Proteinase Inhibitors (PIs) obtained from wild relatives of okra exhibited stronger inhibition of HGPs than the PIs obtained from genotypes of okra. In in vitro inhibitory assay against HGPs, A. tuberculatus 90396 and 90515, wild relatives of okra, showed high tryptic inhibitory (71.8% and 69.2%), chymotryptic inhibitory (68.5% and 66.2%) and Helicoverpa gut proteinase activity (70.2% and 68.2%). Electrophoretic studies showed the variation in trypsin inhibitors (TIs), chymotrypsin Inhibitors (CIs) and HGPIs isoforms in wild relatives of okra, whereas, its genotypes of okra mostly showed monomarphic profile. Maximum eight HGPIs isoforms were found in A. tuberculatus (90396 and 90515). In insect bioassay studies, significant reduction in weight of H. armigera larvae were found, when larvae fed on PIs obtained from A. tuberculatus (90396 and 90515). Thus result of the present investigation indicate that, further exploration of PIs obtained from
A.
tuberculatus (90396 and 90515) will be helpful for developing PIs base insect resistance management strategies. Plant synthesizes various proteinaceous compounds
1. Introduction Helicoverpa
armigera,
(Lepidoptera:
against an insect attack, among the several plant defense
Noctuidae), a highly devastating polyphagous crop pest,
proteins. Proteinase inhibitors (PIs) are abundantly
has a broad host spectrum causes a significant yield losses
present in seeds and storage tissues represents up to 10
in many agriculturally important crops like cotton,
per cent of the total protein (Casaretto and Corcuera 5).
chickpea,
pigeonpea,
okra,
PIs act as antimetabolic proteins, which interfere with
sorghum,
pearl
corn,
maize,
groundnut
the digestive process of insects. PIs are particularly effective against phytophagous insects and micro-
worldwide are directed against H. armigera which
organisms. The defensive capabilities of PIs rely on
resulted into high levels of insecticide resistance in this
inhibition of proteinases present in insect guts or
pest. Insecticide resistance in H. armigera is widespread
secreted by micro-organisms, causing a reduction in the
problem in India, Pakistan, China, Australia, Thailand
availability of amino acids necessary for their growth
Indonesia
sunflower
tomato,
(Volpicella et al.1). Thirty percent of all pesticides used
and
millet,
Hubner
2
(Ahmad ).
Bacillus
and development. Most PIs interact with their target
thuringiensis (Bt) either in the form of formulation and
proteinases by contact with the active (catalytic) site of
transgenic plant may lead to develop resistance in insect in
the proteinase resulting in the formation of a stable
a short period of time. Since many insect pests have
proteinase-inhibitor complex that
is incapable of
developed resistance to Bt like chemical pesticides (Oppert
enzymatic
and
et al.3). Therefore, it is important to search and develop
Preliminary studies on presence of proteinase inhibitors
alternative
and
from seeds of okra by Ogata et al, 7 showed that PIs from
natural plant
okra inhibited both bovine trypsin and chymotrypsin,
defense system, promises to lead in this aspect in near
which are typical digestive enzymes. This study showed
methods
The
and
of controlling
use
of
these
proteinase inhibitors (PIs), constituent of
pest
activity
(Lawrence
Koundal 6).
4
future (Mosolov and Valueva ).
25
that okra seeds contain PIs of trypsin, chymotrypsin
bromophenol blue (Gujar et al,9). After electrophoresis,
which constitute the defense machinery.
SDS-polyacrylamide gel was washed in 2.5% Triton X-100
In the present work, different okra genotypes and
for 10 min to remove SDS, then incubated in 2% casein in
it’s wild relatives were screened for the presence of PIs.
Glycine-NaOH (10 pH), gel was then stained with
Several potent and high potential PIs were identified in
coomassie brilliant blue R-250. HGPs bands were revealed
wild relatives of okra. Bioassays were performed to
as white bands with dark blue background.
ascertain the potency of the okra inhibitors in inhibiting
2.4 Proteinase and PI assays
the growth of H. armigera larvae. This outcome can be
Total proteinase activity was measured by azocaceinolytic
exploited for planning the strategies for developing
assay (Marcheti et al, 10). For azocaceinolytic assay, midgut
insect resistance transgenic plants in future.
homogenate was mixed with (130 µl) of Tris-HCl buffer,
2. Material and Methods
pH 9. To the above mixture, 100 µl of 2% azocasein was
2.1 Seed material and PI extraction
added and incubated for 1 hr at 370 C. The reaction was stopped by adding 500 µl of 5% ice cold trichloroacetic
Seeds of the different genotypes of okra were kindly provided by Senior Research Scientist, Chilli and Vegetable Research Unit, Dr. PDKV, Akola and wild relatives were obtained from National Bureau of Plant Genetic Resources (NBPGR), Raichur and NBPGR, Akola. Dry seeds were grounded to a fine powder, defatted and depigmented with several washes of acetone and hexane. The solvent was filtered off and seed powder was obtained upon air drying. The powder was mixed with five volumes of 0.1M Sodium Phosphate Buffer (SPB) pH, 6.8 and kept overnight at 4°C for extraction with intermittent shaking. The suspension was centrifuged at 12,000 rpm for 20 min at 4oC and the supernatant was stored in aliquots at -20oC. The protein content of the extract was determined by Bradford’s method (Bradford 8).
acid (TCA). After centrifugation at 14000 rpm for 15 min at 40 C, an equal volume of 1M NaOH was added to the supernatant an absorbance was measured at 420 nm. The protease activity of sample was calculated using trypsin standard curve in terms of tryptic unit (TU). Tryptic and Chymotryptic activities were estimated using the
chromogenic
substrates
N--Benzyl-L-argine
p-
nitroanilide (BApNA, Sigma) and N-Succinyl-Ala-AlaPro-Leu-p-nitroanilide(SAApLNa,
Sigma),respectively,
dissolved 100 mg/ml in dimethyl sulfoxide . Midgut supernatant were diluted 1:100 in buffer containing (200 mM Tris, pH-8.0, 20 mM CaCl2) and 50 µl were added to microplate well and 50 µl BApNA for tryptic and SAApLNa for chymotryptic were added after 30 second incubation at 370C, absorbance was estimated at 405 nm.
2.2 Extraction of HGPs
For the inhibitory assays, a suitable amount of inhibitor and
Late third or early fourth instar larvae, from homogenous
HGPs extract was preincubated for 30 min at 370 C prior to
culture of H. armigera were dissected and mid-gut was
the
0
addition
of
substrate.
H.
armigera
trypsin
isolated and stored frozen at -78 C. Required gut tissue was
,chymotrypsin and total gut proteinase inhibitory activities
homogenized in 1 volume of 0.2M glycine-NaOH buffer
were estimated by using substrate BApNA, SAApLNa and
o
(pH 10.0) and kept for 2 h at 10 C. The suspension was
azocasin. 30 µl proteinase inhibitor and 50 µl gut extract
centrifuged at 12,000 rpm for 20 min and the supernatant
were preincubated for 30 min. at 370 C. after that 50 µl
was used as a source of HGPs.
substrate were added to each well after 1 min incubation at
2.3 Electrophoretic visualization of HGPs
370 C, the reaction was terminated by addition of 500 µl of
HGPs were detected by using by SDS-polyacrylamide
5% TCA and absorbance was monitored at 405 nm. For
gel. Enzyme extracted from the mid gut of H. armigera
total gut proteinase inhibitory activity, after adding 5%
larvae was diluted and electrophoresed on 12% SDS-
TCA centrifuged it and 50 µl of 1 N NaOH were added
polyacrylamide gels along with treatment buffer 60mM
and absorbance was estimated at 405 nm. One proteinase
Tris-HCl pH 6.8, 2%SDS, 20% glycerol and 0.1%
unit was defined as the amount of enzyme that increases
26
protocol given by Bhavani et al,12. Fresh and soft seeds of pigeonpea were pressed by thumb and forefinger gently and put into multiwell rearing tray for releasing larvae. PIs obtained from A. tuberculatus 90396 and A. tuberculatus 90515 (50 μg of protein concentration) were loaded between the cavity of two crushed grains with the help of micropipette. Second larval instar of H. armigera was selected to start bioassay. Constant exposure of PI was maintained during whole experiment up to pupation of larvae. The observations of larval weights were taken after every 24 hrs after ingestion of food. Control population was also maintained simultaneously without PIs. The observation on larval mortality, larval weight, pupal weight, number of malformed pupae and malformed adult were also recorded. CRD design was used for statistical analysis. 3. Results and discussion 3.1 Activity and visualization of gut proteinases of H.
Fig.1 Electrophoretic visualization of H. armigera gut proteinase isoforms .
armigera
absorbance by 1 OD/ min and one PI unit was defined as the amount of inhibitor that causes inhibition of 1 unit of
Total gut proteinase (azocaseinase), trypsin like proteinases (BApNAase)
and
chymotrypsin
like
proteinases
(SAApLNase) activities present in gut of H.armigera were
proteinase activity under the given assay conditions. 2.5 Electrophoretic visualization of TIs , CIs and HGPIs
assayed (Table 1). Total Proteinases activity was observed
isoforms TIs,CIs and HGPIs isoforms were detected by using 10%
Table 1: H. armigera gut proteinases activity
11
polyacrylamide gel having 1% gelatin (Felicioli et al, ). Sr.
The respective gels were transferred to solutions containing
No.
0.1 % trypsin or 0.1 % Chymotrypsin or HGP extract of equivalent activity, and incubated for 1 hrs with constant
1
Proteinases Total
shaking. The gels were washed with warm water, fixed in
Proteinase
10 % TCA, stain with Coomassie Brilliant Blue R-250 and
activity
destained. Isoforms were revealed as blue bands against
2
white background.
(U/gut)a 2.15 ± 0.001
1.97 ± 0.003
activity
2.6 Bioassay of PIs against H. armigera larvae
3
Bioassay was carried out at insect rearing facility of Department of Entomology, Dr. PDKV, Akola.
Tryptic
Enzyme activity
Eggs,
Chymotryptic
1.84 ± 0.002
activity a
All figures are mean of triplicate ± SE.
neonate and early instars larvae of H.armigera were collected from experimental field of Dr. PDKV, Akola. This culture was maintained in the laboratory at 27oC at
to be 2.15 U/gut, among it tryptic activity was found to be
80% relative humidity on fresh and soft seeds of pigeonpea
slightly higher (1.97U/gut) than chymotryptic activity (1.87
until further use. Bioassay was carried out according to
U/gut). Electrophoretic visualization of H. armigera gut
27
1. A .tuberculatus 90396 , 2. A. tuberculatus 90515, 3. A .tuberculatus 90400, 4. A.. tuberculatus 140957, 5. A. tuberculatus 90402, 6. A. ficulneus 140986, 7. A. tetraphyllus 90398, 8. A .tetraphyllus 90461, 9. A. tetraphyllus 90386, 10. A. ficulneus 41748, 11. A. ficulneus 141042, 12. A. tetraphyllus 92503, 13. A. ficulneus 210361, 14. A. tetraphyllus 90404, 15. A. ficulneus 140947, 16. A. angulossus 203832, 17. A. angulossus 203863, 18. A. angulossus 470751, 19. A. manihot 141019, 20. A. manihot 141045, 21. A. angulossus 203833, 22. A. angulossus 203834, 23. A. manihot 141012, 24. A. moschatus 140985, 25. A. moschatus 141056, 26. A. moschatus 141065, 27. A. moschatus 470737, 28. A. moschatus 470747, 29. A. manihot 329394, 30. Arka bahar, 31. Parbhani kranti, 32. AKO â&#x20AC;&#x201C;107, 33. Arka anamika, 34. AKO37, 35. Pusa A-4, 36. AKO-111, 37. AKO-102, 38. Adunika, 39. VRO-3. M- Standard Molecular Weight Marker
Fig.2 Helicoverpa gut proteinase inhibitors (HGPIs) isoforms from different genotypes and wild relatives of okra (Plate 2) proteinase isoforms were also carried out by 12% SDS-
proteinases of H. armigera gut, he showed ten isoforms of
polyacrylamide (Figure 1). As reveled from the Plate 1,
proteinases in the gut of H. armigera.
total H. armigera gut proteinase activity was distributed in
The presence of proteinases of different specificities in
ten isoforms, ranging from molecular weight 118.0 kDa to
the midgut has great significance for the survival and
16.2 kDa. The apparent density of P 1, P2, P3, P7, P8 and P9
adaptation of phytophagous insects on several host plants.
found to be high, while that of P 4, P5, P6 and P10 were low.
The adaptation of pests to host plant PIs probably results
Earlier studies on proteolytic activity of lepidopeteran
from the selection pressure acting on an entire insect
insect gut showed that, insect gut comprises of many
population when they encounter PIs of their host plants
isoforms of proteinases having diverse properties and
(Harsulkar et al,16). Thus, ten isoforms of HGP found in
specificities (Johnston et al,13). Harsulkar et al,14, studied
present investigation supported the polyphagous nature of
the isoforms of gut proteinases of H.armigera, their study
Helicoverpa armigera.
revealed that H.armigera gut proteinase activity was
3.2 Electrophoretic profiles of TIs, CIs and HGPIs
distributed in six isoforms. Similarly, Potdar
15
studied
isoforms from different genotypes of okra and its wild relatives
28
Table 2 Helicoverpa gut proteinase inhibitory potential of PIs isolated from okra genotypes and its wild relatives. Sr. No
Genotype
HGP tryptic inhibitory activity (%)
HGP chymotryptic inhibitory activity (%)
HGP total proteinase inhibitory activity (%)
1 2 3 4 5 6 7 8 9 10
A .tuberculatus 90396 A. tuberculatus 90515 A .tuberculatus 90400 A.. tuberculatus 140957 A. tuberculatus 90402 A. fiulneus 140986 A. tetraphyllus 90398 A .tetraphyllus 90461 A. tetraphyllus 90386 A. fiulneus 41748
Wild relatives of okra 71.8±0.001 68.4±0.004 69.2±0.003 66.2±0.004 62.4±0.005 59.3±0.005 67.0±0.006 62.7±0.003 60.4±0.006 60.2±0.004 54.4±0.005 50.2±0.003 49.4±0.002 51.7±0.003 48.0±0.001 50.5±0.005 51.2±0.005 51.4±0.005 46.1±0.002 44.1±0.003
70.2±0.002 68.3±0.003 61.4±0.001 60.6±0.004 62.2±0.002 46.1±0.002 43.1±0.005 46.9±0.002 45.0±0.003 38.1±0.003
11 12
A. fiulneus 141042 A. tetraphyllus 92503
39.9±0.003 44.5±0.001
42.5±0.003 41.8±0.003
42.7±0.002 43.8±0.002
13 14
A. fiulneus 210361 A. tetraphyllus 90404
47.0±0.006 44.5±0.004
42.6±0.002 43.3±0.002
40.5±0.004 46.9±0.003
15 16
A. fiulneus 140947 A. angulossus 203832
43.4±0.003 65.3±0.004
41.8±0.002 55.5±0.006
43.5±0.002 59.1±0.003
17
A. angulossus 203863
53.0±0.002
50.9±0.003
46.1±0.001
18 19
A. angulossus 470751 A. manihot 141019
50.2±0.002 47.3±0.002
49.4±0.003 48.7±0.003
47.3±0.003 43.8±0.002
20 21
A. manihot 141045 A. angulossus 203833
42.4±0.002 51.5±0.003
47.9±0.001 45.6±0.003
42.9±0.003 51.5±0.001
22
A. angulossus 203834
48.0±0.003
42.6±0.004
46.5±0.003
23 24
A. manihot 141012 A. moschatus 140985
57.9±0.002 49.4±0.001
45.2±0.003 49.8±0.001
45.4±0.003 44.3±0.002
PIs 25 were isolated from ten genotypes of okra45.9±0.003 and 29 A. moschatus 141056 26 A. moschatus 141065 50.1±0.002 wild relatives by the method given by Felicioli et al,11. Gel
weight 25.1 kDa to 6.3 kDa) with43.5±0.002 variable intensities. A. 44.1±0.002 51.7±0.004 43.3±0.003 tuberculatus (90396, 90515, 90400, 140957 and 90402)
27 A. moschatus 470737 54.0±0.003 co-polymerized with 1 percent gelatin was used for the 28 A. moschatus 470747 52.4±0.004 detection of TIs, CIs and HGPIs bands. 29 A. manihot 329394 42.7±0.004 All wild relatives of okra showed variability in terms Genotypes of okra
54.7±0.002 48.4±0.003 reported maximum (five) CIs isoforms. While, A.ficulneus 52.0±0.002 50.7±0.003 (140986, 141042, 210361, 140947), A. tetraphyllus 40.3±0.002 41.9±0.003 (92503), A. moschatus (141065) and A. manihot (329394)
of the number intensities 30 and Arka bahar of TIs bands. A. tuberculatus 53.7±0.004 31 Parbhani krantihighest (six) TIs isoforms, 63.8±0.005 90396 and 90515 exhibited 32 AKO -107 53.9±0.004 A.angulossus (203832) showed four TIs isoforms, whereas, 33 Arka anamika 55.1±0.001 A. ficulneus 141042, 210361 and 140947)50.5±0.004 and A. 34 (41748, AKO-37 tetraphyllus exhibited the minimum (one) TIs 35 (90404) Pusa A-4 51.9±0.002
exhibited46.0±0.002 only one CIs isoform. Different okra genotypes 46.6±0.002 62.1±0.003 58.4±0.004 exhibited maximum number of (four) of CIs isoforms, 50.1±0.003 56.8±0.003 except Arka bahar which showed only one CIs isoform. 51.7±0.003 52.6±0.002 Results clearly indicate the potentiality of A .tuberculatus, 48.6±0.002 51.5±0.001 to search45.6±0.004 new and potent proteinase inhibitors. This is also 54.5±0.002
AKO-111 57.9±0.003 isoforms.36All okra genotypes showed monomorphoic PIs 37 AKO-102 65.6±0.002 profile i.e. four TIs isoforms were detected in all genotypes 38 Adunika 60.0±0.003 of okra 39 with dark intensity, except Arka bahar63.9±0.002 which VRO-3
50.9±0.005 62.2±0.003 confirmed by our studies on TIs and HGPIs isoform. 56.7±0.003 60.3±0.001 To determine specificities of PIs towards HGP 55.7±0.001 63.3±0.003 isoforms,61.9±0.002 PIs extract were 62.9±0.002 resolved on gelatin-
showed less intense TIs isoforms.
polyacrylamide gel. Further, it incubated with HGP extract
Similarly, gelatin co-polymerized polyacrylamide gel
obtained from mid gut of Helicoverpa larvae (equal TI
electrophoresis showed wide range of CIs (molecular
units), HGPI bands were visualized as described in
29
materials and methods. Plate 2 (a, b, c) represents the electrophoretic profile of HGPIs in seed extracts of okra and itâ&#x20AC;&#x2122;s wild relatives (Figure 2). The tuberculatus group showed presence of high activity HGPIs bands as compared to okra and other wild relatives. Among the wild relatives of okra A.tuberculatus (90396 and 90515) showed eight HGPIs isoforms, whereas, A.tuberculatus (90402) exhibited seven HGPIs band followed by A.tuberculatus (90400 and 140957) showed six HGPIs isoform and five HGPIs isoform was found to be in A.angulossus (203832), whereas, A. ficulneus (41748 and 210361) and A.manihot (329394) showed that only one HGPIs isoform Plate 2 (a, b). Different genotypes of okra showed variable number of HGPI isoforms with different intensities.
AKO-111,
AKO-102,
Addunika,
VRO-3
reported maximum (five) HGPIs isoforms with high intensity. Also Parbhani Kranti AKO-107, Arka anamika, AKO-37 possessed four HGPIs isoforms, whereas, Arka bahar consists only one HGPIs isoform Plate 2(c). These results clearly showed that PIs from wild relatives of okra A. tuberculatus (90396 and 90515) exhibited strong inhibitory potential against HGP. A similar observation were also reported in pigeonpea by Choughule et al,17
showed that pigeonpea cultivars
exhibited monomorphism in TIs and CIs isoforms,
Fig. 3 Effect of okra PIs on the growth and development of H. armigera larvae (Plate 3).
whereas, diverse proteinase inhibitory profiles in pigeonpea wild relatives. Patankar et al,18 also observed significant
genotypes. The variation observed in the wild relatives of
variation in the TIs isoforms from wild Cicer species.
okra is considered significant, as TIs are known to serve as
However, they have observed great conservation of TIs
defense proteins against herbivores (Ryan21).
isoforms in the mature seeds of the chickpea cultivars. A
Wild relatives of okra A.tuberculatus (90396 and
similar observation exists in pigeonpea where TIs and
90515) showed eight HGPIs isoforms with high intensity,
chymotrypsin inhibitors are conserved in matured seeds of
whereas, AKO-111, AKO-102, Addunika, VRO-3 reported
the cultivated pigeonpea, whereas, a high level of diversity
maximum (five) HGPIs isoforms while, Arka bahar
19
exsist in uncultivated species of Cajanus (Kollipara et al, , Pichare and Kachole
20
consists only one HGPIs isoform (Plate 2). These results
). The variation observed in wild
clearly showed that PIs from wild relatives of okra A.
Cicer species is considered significant, as the TIs are
tuberculatus (90396 and 90515) exhibited strong inhibitory
known to serve as a defense proteins against herbivores
potential against HGP. Earlier studies on electrophoretic
(Ryan 1990). Cicer reticulatum and Cicer arietinum
profiles of HGPIs of pigeonpea and itâ&#x20AC;&#x2122;s wild relatives.
showed similar TIs band patterns, which suggests that
Rhynchosia group showed presence of high activity HGPIs
Cicer reticulatum is genetically closer to Cicer arietinum.
bands (5) as compared to pigeonpea and other wild
Thus, this studies can also be used for karyotyping the
Cajanus species (Chougule et al, 17).
30
Table 3 Day-wise reduction in weight of H.armigera larvae feed with okra PIs of A. tuberculatus (90396) and A. tuberculatus (90515). Age (DAI)a 1 2 3 4 5 6 7 8 9 10 11 12 13 Pupa Mean F-test
Weight of larvae (mg) when fed with A. tuberculatus (90396) A. tuberculatus Control (without PI) (90515) 23.8 25.3 27.3 28.2 31.0 31.7 38.3 40.0 43.3 47.1 50.1 55.0 54.7 56.7 72.3 71.0 89.7 98.7 91.0 120.3 138.0 116.0 130.0 162.7 121.3 141.3 186.7 127.6 156.0 221.3 144.7 180.0 259.7 156.3 211.7 297.7 176.3 224.7 330.3 178.0 225.7 329.3 98.2 120.2 161.0 Age Variety Interaction significant significant significant
SE CD at5% a
1.78 4.96
3.72 10.32
Meanb
25.5 30.3 40.5 50.7 61.2 86.4 116.4 136.2 149.8 168.3 194.8 221.9 243.8 244.3
6.45 17.88
DAI- Days after ingestion of proteinase inhibitor, bMean of all the survival larvae
3.3 Inhibitory potential of PIs from different genotypes
(AKO-37) to 63.9% (VRO-3). Amongst different wild
of okra and its wild relatives against Helicoverpa gut
relatives of okra, minimum inhibitory potential (39.9%) of
proteinases.
tryptic activity was observed in PIs of A. tuberculatus
Several genotypes of okra and its wild relatives were
(141042) and maximum tryptic inhibitory potential
analyzed for their inhibitory potential against HGP activity.
(71.80%) was observed in PIs of A. tuberculatus (90396)
Inhibition capacity of okra PIs towards HGP was evaluated
followed by A. tuberculatus (90515) i.e. 69.2%. Similar
by in-vitro micro plate adopted enzyme assays. Low
trend of inhibition was observed in case of Helicoverpa gut
concentration of proteinase inhibitors (30Âľg) was used to
chymotryptic activity and Helicoverpa gut total proteinase
obtain inhibition of tryptic, chymotryptic and total gut
activity.
proteinase activity. Control was maintained without any PIs and its activity was considered as 100%.
Earlier studies on wild relatives of pigeonpea showed more than 70 percent inhibition, whereas, cultivars showed
Helicoverpa gut consist of both tryptic and
around 50 percent inhibition of HGP (Chougule et al,
17
).
chymotryptic activity. Tryptic activity was slightly higher
Moreover, the proteases from H. armigera were inhibited
than chymotryptic activity. Therefore, inhibitory potential
upto 85 percent by AKTI at a concentration 45Âľg ml-1
of PIs towards trypsin as well as chymotryptic activity was
(Zhou et al,22). Previous study showed that the C. annum
considered to be useful potent PIs.
PIs inhibited more than 60 percent total proteolytic activity
Table 2 summarizes the inhibitory potential of PIs
(Tamhane et al,23 ). 72 percent total gut activity was
obtained from various okra genotypes and its wild relatives against
Helicoverpa
tryptic
activity,
inhibited by chickpea PI (Harsulkar et al,16).
Helicoverpa
H. armigera is a polyphagous pest and possesses
chymotryptic activity and total proteinase activity. A close
different types of proteinases in its gut (Harsulkar et al.,16),
examination of data revealed that different okra genotypes
the effectiveness of okra wild PIs offers good gene pool for
possessed tryptic inhibitory activity ranges from 50.5%
the development of H. armigera (Bhendi fruit borer)
31
resistant okra varieties, similarly it offers good source to isolate PIs genes for developing insect resistance transgenic plants against H.armigera. 3.4. Effect of okra PIs obtained from A. tuberculatus (90396 and 90515) on fitness parameters of H. armigera
blackish malformed pupae, which the normal pupal were Table 4: Effect of okra PIs on the growth and development of H.armigera . Growth and developmental parameters
Bioassay results of PIs showed significant reduction in weight of H. armigera larvae when fed on PIs obtained from A. tuberculatus 90396 and 90515 (Table 3 and 4, Figure 3). Also, effects on different parameters of H. armigera were recorded like viz. larval mortality, pupation rate, reduction in pupal weight, malformed pupae, pupal mortality and malformed adult. 3.4.1. Day-wise reduction in weight of H.armigera larvae feed with okra PIs of A. tuberculatus (90396 and 90515)
Larval mortality % Larval wt. reduction % (Control= 330.3mg larval Wt.) Reduction in pupal wt. % (Control 329.3mg) Malformed pupae %
Proteinase inhibitors A. tuberculatus (90396)
A. tuberculatus (90515)
40
30
53.4
68.0
54.1
68.5
60
50
10
10
30
20
The data (Table 3) on insect weight was affected by feeding with PIs obtained from A. tuberculatus (90396) and A. tuberculatus (90515), wild relatives of okra, indicated significant difference among the treatments. The wild
Pupal Mortality % Malformed adult %
relative A. tuberculatus (90396) was found most effective. The mean of insect weight was 98.2mg at 13 DAI, indicating significant reduction than the larvae fed on PIs obtained from A. tuberculatus (90515) and artificial diet without PIs. The second factor i.e. age also showed significant difference indicating that the weight of the insect was directly proportional to the age of the insect. The interaction studies reveled that there was significant reduction in insect body weight, when larvae fed with A. tuberculatus (90396) even at 12, 13 day old larva as well as pupal stage. 3.4.2. Effect of okra PI on the growth and development of H. armigera 53.4% and 68.0% weight reduction was observed in larvae fed on A. tuberculatus (90396) and A. tuberculatus (90515) PIs containing diet (Plate 3b). Larval mortality was observed at 11 days after ingestion which on up to 40% in A. tuberculatus (90396) and 30% in A. tuberculatus (90515), whereas, in control no larval mortality was recorded. The larvae fed on proteinase inhibitor obtained from
dark brown (Plate 3d). Pupation rate was lower in population fed on PIs of A. tuberculatus 90396 (60 %) followed by population fed on PIs of A. tuberculatus 90515 (70%) than control. In addition to this, significant decrease in pupal weight 54.1% and 68.5% was also observed in population fed on A. tuberculatus (90396 and 90515) as compared to control (Plate 3). 60% and 50% malformed pupae were found in population fed on PIs of A. tuberculatus
(90396)
and
A.
tuberculatus
(90515),
respectively compared to control. Whereas, pupal mortality was only 10 per cent (Plate 3d and 3e). Okra PIs also exhibited adverse effect on adult emergence. After emergence adults were found to be malformed (Plate 3f). 53.4% and 68.0% weight reduction was observed in larvae fed on A. tuberculatus (90396) and A. tuberculatus (90515) PIs containing diet (Plate 3b). also larval mortality was observed up to 40% on A. tuberculatus (90396) and 30% on A. tuberculatus (90515), whereas, in control no larval mortality was recorded. Pupation
rate significantly
decreases and 60% and 50% malformed pupae were found in population fed on PIs of A. tuberculatus (90396) and A.
A. tubercualtus (90396) and A. tubercualtus (90515) forms
32
tuberculatus (90515). Okra PIs also exhibited adverse effect on adult emergence. The disruption of amino acid by the inhibition of protein digestion through PIs is the basis of PIs based defense in plants, however, in nature it might be coupled with other factors. To evaluate in vivo effects of okra PIs on H. armigera feeding assays were conducted with added inhibitor protein in the diet.
Larval growth and
development were dramatically reduced when larvae fed on okra PIs diet. Reduced feeding of larvae was observed in case of PIs incorporated diet than control the adverse effects were significant at a higher concentration of PIs doses. Significant difference in larval mortality was also evident. This can be explained as larval stage is very crucial for accumulating nutrients and energy, which is used for pupal and adult development. Starvation and added stress on gut proteinases expression system to synthesize new and higher amounts of proteinases could be the possible reason for arrested growth and mortality of H. armigera larvae. Other researchers also observed growth and retardation and mortality with PI doses to H. armigera and other insects (Kranthi et al, Shukla et al,
25
and Bhavani et al,
24
, Tamhane et al,
23
,
12
). Another interesting
observation was that the inhibitor caused a high ratio of deformities in pupae and adult (Plate 3 (d and f)), such types of result were also shown by Franco et al,26. They reported 50 % deformities in pupae and 81% in adult due to SKTI inhibitor. The requirement of lower PIs (50Âľl) in diet for maximum effect on H.armigera growth retardation indicates its high specificity towards HGPs.
4. Conclusions After extensive In vivo and in vitro screening of PIs from several cultivated and wild relatives of okra in present study, PIs from A. tuberculatus (90396 and 90515) were found to possess potential, so as to explore it in future
5. References 1.Volpicella M L, Ceci R, Cordewener J., America T., Gallerani R., Bode W., Jongsma M. A. and Beekwilder J. Eur. J. Biochem (2003) 270,10. 2. Ahmad M. J. Agric. Res (2007) 45, 56. 3. Oppert B., Kramer K. J., Beeman R. W., Johnson. D. and Mc Ganghay W. H. J. Biol. Chem. (1996) 272, 23473. 4. Mosolov V. V. and Valueva T. A. Appl. Biochem. Microbiol (2008) 44, 233. 5. Casaretto J. A. and Corcuera L. J. Biol. Res. (1995) 28, 239. 6. Lawrence P. K. and Koundal K. R. Electron. J. Biotechnol (2002) 5, 93. 7. Ogata F., Imamura H., Hirayama K. and Makisumi S. Agric. Biol. Chem (1986) 50, 2325. 8. Bradford M. Anal Biochem (1976) 72, 248. 9. Gujar G. T. and Chandrashakar K. Indian J. Expt. Biology (2004) 42, 164. 10. Marchetti S., Delledonne M., Fogher C., Chiaba C., Chieja F., Savazzini F. and Glordano A. Theor. Appl. Genet (2000) 101, 519. 11. Felicioli R., Garzelli B., Vaccari L., Melfi D. and Balestreri E. Anal Biochem (1997) 244, 176. 12. Bhavani P., Chumki B. and Theertha P. ArthropodPlant Interactions (2007) 1, 255. 13. Johnston K. A., Lee M. J., Gatehouse J. A. and Anstee, J. H. Insect Biochem (1991) 21, 389. 14. Harsulkar A. M., Giri A. P., Gupta V. S., Sainani M. N., Deshpande V. V., Patankar A. G. and Ranjekar P. K. Electrophoresis (1998) 19, 1397. 15. Potdar S. S. (2008) M. Sc. Thesis (unpublished), Dr P. D. A. U., Akola. 16. Harsulkar A. M., Giri A. P., Patankar A. G., Gupta V. S., Deshpande V. V., Ranjekar P. K. and Sainani M. N. Plant physiology (1999) 121, 497. 17. Chougule N. P., HivraleV. K., Chhabda P. J. and Giri A. P. Photochemistry (2003) 64, 681. 18. Patankar A. G., Giri A. P., Harsulkar A. H., Sainani M. N., Deshpande V. V., Ranjekar P. K. and Gupta V. S. Insect Biochem. Mol. Bio. (1999) 31, 453. 19. Kollipara K. P., Singh L. and Hymowit Z. T. Theor Appl Genet (1994) 88, 986. 20. Pichare M. M. and Kachole M. S. J. Biochem Biophys Methods (1994) 28, 215. 21. Ryan C. A. Annual Rev Physiol (1990) 28, 425. 22. Zhou J. Y., Liao H., Zhang N. H., Tang L., Xu Y. and Chen, F. Biotechnol. Lett (2008) 30, 1495. 23. Tamhane V. A., Chougule N. P., Giri A. P., Dixit A. R., Sainani M. N. and Gupta V. S. Bichimica ET Biophysica Acta (2004) 1722, 156. 24. Kranthi K. R., Jadhav D. R., Wanjari R., Kranthi S. and Russel D. J. Econ. Ent. (2002) 94, 253. 25. Shukla S., Arora R. and Sharma H. C. Plant Biotechnology (2005) 22, 1. 26. Franco O. L., Dias S. C., Magalhaes C. P., Monteiro A. S., Melo F., Oliveira Neto O. B., Monnerat R. G. and Grossi-de-sa M. F. Pytochemistry (2004) 65, 81.
for developing PIs based management strategies of lepidopteran pest general and H. armigera in particular.
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