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Dr. Ashish Kumar Srivastava Scientific Officer Nuclear Agriculture and Biotechnology Division Bhabha Atomic Research Centre, Mumbai400085 [email protected] Mr. Sumit Gupta Scientific Officer Food Technology Division Bhabha Atomic Research Centre, Mumbai400085 [email protected] Er. Pramod Kumar Yadav (Bioinformatics & Computational Biology) Department of Computational Biology & Bioinformatics SHIATS, Post. Agriculture Institute, Allahabad 211007, India [email protected] Dr. G. J. Khan (Molecular Biology) Department of Biotechnology, L.N.Mithila University, Darbhanga, Bihar-846008,India [email protected] Harish Chandra Verma Scientist (Soft computing/Data mining) Central Institute for Subtropical Horticulture Rehmankhera, P.O.Kakori, Lucknow UP227107, India [email protected]

Dr. Rituraj Purohit (Bioinformatics and Pharmaceutical science) VIT University, Vellore Campus Vellore – 632014, Tamilnadu, India [email protected] Dr. Anamika Singh (Bioinformatics) Department of Biotechnology Amity University Campus, Malhaur Gomti Nagar Extension, Lucknow–226028 India [email protected] Dr. Dinabandhu Behera (Plant Molecular Biology & Genetic Engineering) National Research Centre on Plant Biotechnology Indian Agricultural Research Institute, New Delhi – 110012, India [email protected] Dr. Anand Singh Gaurav (Bioinformatics and Pharmaceutical Science) Shobhit University, NH-58, Modipuram Meerut – 250110, India [email protected] Mr. Sunil Kumar Prajapati (Pharmacology and Clinical Pharmacy) Maharana Pratap Group of institution Kothi, Mandhana, Civil Lines Kanpur-208001, India [email protected] Mr. Prashant Upadhyay (Industrial Pharmacy and Pharmaceutics) Department of Pharmaceutics, School of Pharmaceutical Science, IFTM University. Delhi Road (NH 24), Moradabad-244102 (U.P), India [email protected] Er. Vinay Kumar Rajak (Reservoir Engg. & Environmental Technology) Department of Petroleum Engineering, Indian School of Mines, Dhanbad, Jharkhand826004, [email protected]

Contents IJBBS: Vol. 1(2) June. 2013 (Page 129-256)

Editorial Length-weight relationships of Butter catfish, Ompok bimaculatus in Betwa River, India Abhishek K. Malakar, Pallavi, Wazir S. Lakra and R.M. Mishra Production of Biofertilizer from Agro- waste by using Thermotolerant Phosphate Solubilising Bacteria Ajay Kumar Singh, Harison Masih, Prerna Nidhi, Yashab Kumar, Jyotsana Kiran Peter and Santosh Kumar Mishra

129

135

Evidence of Genetic Polymorphism in Anopheles subpictus Populations from India 153 A.K. , Sharma, V. Tyagi, R. Yadav and D. Sukumaran Trichoderma induce Alteration of Serine/threonine and Tyrosine Phosphatase in Bipartite Interaction of Brassica Juncea Priyadarshni Kumar and Chandan Kumar

165

Comparison of Relative Performance of Predicting Protein Disordered Region Algorithms Deepalakshmi R and Jothi Venkateswaran C

173

A Computational Annotation of Expressed Sequence Tags (ESTs) from Labeo Rohita Sathish Kumar Ramaswamy, Nilavamuthan Chandrasekaran, Sindhu, Naganeeswaran Sudalaimuthu Asari Shantkriti Srinivasan and Shanmughavel Piramanayagam Species Information Retrieval Tool: A Bioinformatics tool for Leguminosae Family Sagar Patel, Hetalkumar Panchal, Jignesh Smart and Kalpesh Anjaria

179

187

Glycemic Index and Chemical Analysis of Products Prepared from Stevia Powder Angeleena, D. McCarty, Alok Milton Lal, S.D. McCarty and Satendra Singh Comparative Assessment of Bioactive Molecules in the Juices of Some Selected Fresh Fruits Sushma, Mohd. Arif Khan and Manish Kumar

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Structural and Functional Annotation of Uncharacterized Protein in Triticum aestivum 211 Gitanjali Tandon, Rajendra Sharma, A.K. Mishra and H. Chandrasekharan Effect of PGPR Isolates on Growth Promotion of Tomato (Lycopersicon Esculentum L.) Gurudayal Ram, Pramod W. Ramteke and Geeta D. Adhikari

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A Computational Analysis of Polymorphisms in Tumor Suppressor Genes Present in Colorectal Cancer Pathway Brij NathTewari and Bhartendu Nath Mishra

233

Editorial Bioinformatics through the history The term “Bioinformatics” was first used by Paulien Hogeweg and Ben Hesper in the beginning of 1970s, defining it as ‘‘the study of informatic processes in biotic systems’’. Although they have proposed the definition as above in article in Dutch language that is not generally accessible [1] but various public sources trace the origin of the term to publications by Paulien Hogeweg and Ben Hesper that appeared in 1978 [2, 3]. Their main aim was to combine pattern analysis and dynamic modeling and apply them to the challenge of unraveling pattern generation and informatic processes in biotic systems at multiple scales but now a days meaning of the term has been superseded as denoting the development and use of computational methods for comparative analysis of genome data. The long term goals they set for bioinformatics in the 1970s, were termed by them as the “horse part” and the “elephant”. The horse part is the “modeling morphogenesis, through the use of cell based models that incorporate some of the physical properties of cells [4]. Second but important part i.e. the elephant is “Constructive models of evolution”, are generally being created from studies on the evolutionary consequences of non-linear physical mapping includes both genotype and phenotype mapping [5-8]. Metabolic networks [9-10], regulatory networks [11-14] and chromosome organization [15-17] are also providing useful information in the above model construction. Many of the basic pattern analysis methods which are now being used in bioinformatics very often, were pioneered in the 1960s and further developed in the 1970s. A very notable work was of Margaret Dayhoff, who developed the first ever biological database known as “atlas of protein sequences and structure” [18]. It was built as a collection of sequences for investigating evolutionary relationships among proteins. In the 1970s and 1980s, novel modeling formalisms were actively explored and developed along with the development of pattern analysis methods. With help of the development in the in-silico technologies and wetlab methods sequence data was generated exponentially in the late 1980s and 1990s. It was the time when the term bioinformatics became mainstream, coming to mean the development and use of computational methods for data management and data analysis of sequence data, protein structure determination, homology-based function prediction, and phylogeny. But the information which is provided by the massive sequencing projects, and the related bioinformatics analysis to unravel the relationship in between function and evolution, is not really the ‘‘roots of bioinformatics’’, but rather they are the ‘‘trunk of bioinformatics’’ [19], Currently we are having a large set of fully sequenced genomes which also include human and it is expanding at very fast rate. This is providing a massive high-

throughput “omics” data, which is further available for comparative research and is presenting great challenge for bioinformatics. Recently, 2012 Nobel laureate Paul Nurse has emphasized about self-organization and the resulting counter-intuitive results, he argues that the next ‘‘quantum leap’’ in biology will come through studying information processing in biological systems. Also similarly Walter Gilbert another Nobel laureate has said-Most of the biological investigations in 21st century will be in silico. It is apparent that during the last 5 years, we have moved into a new stage, that can define our future strategy. By 2007, things had become more intelligent: text mining were used for decision making [20], ontology growth was manifold into every aspect of computing [21], and bioinformatics was distributive in the life sciences, for example, extending to biodiversity conservation planning [22] or synthetic biology [23]. Besides the more theoretical aspects of network biology [24], exemplified by gene and protein interaction networks, pressure mounted for support of translational medicine, ranging from structural variation [25] to cancer bioinformatics [26]. At the same time, new problems were emerging, related to next-generation sequencing efforts, ranging from re-sequencing to metagenomics [27]. The prediction in 2008 was that in 10 years, we will possess an adequate infrastructure for biological research [28], in a fusion of disciplines [29]. At present, we are facing an expansion of difficulty, ranging from genome assembly [30], protein design [31], or metagenomics [32] to genomic medicine [33], infectious disease [34], and phenotyping [35]. It can be concluded that whether bioinformatics in the wider sense of studying information processing in biotic systems is a quirk or a quantum leap, it is still an interesting area to understand and work in! There is lots and lots of data and software available and lot more need to be developed. More genomes, more database, more tools and analysis of all this available data and biology, physics and mathematics behind all this, which motivated us to start this new initiative to serve humanity in the coming decades and centuries.

Dr. Gulshan Wadhwa Department of Biotechnology, Ministry of Science & Technology, Government of India. New Delhi, India

References 1. Hesper B, Hogeweg P (1970) Bioinformatica: een werkconcept. Kameleon 1(6): 28–29. (In Dutch.) Leiden: Leidse Biologen Club. 2. Hogeweg P, Hesper B (1978) Interactive instruction on population interactions. Comput Biol Med 8: 319–327. 3. Hogeweg P (1978) Simulating the growth of cellular forms. Simulation 31: 90–96. 4. Anderson A, Chaplain M, Rejniak K, Fozard J (2008) Single-cell-based models in biology and medicine. Basel: Birkhauser Verlag. 5. Schuster P, Fontana W, Stadler P, Hofacker I (1994) From sequences to shapes and back: a case study in RNA secondary structures. Proc Biol Sci 255: 279–284. 6. Huynen M, Stadler P, Fontana W (1996) Smoothness within ruggedness: the role of neutrality in adaptation. Proc Natl Acad Sci U S A 93: 397–401.

7. van Nimwegen E, Crutchfield J, Huynen M (1999) Neutral evolution of mutational robustness. Proc Natl Acad Sci U S A 96: 9716–9720. 8. Huynen M (1996) Exploring phenotype space through neutral evolution. J Mol Evol 43: 165–169. 9. Kacser H, Beeby R (1984) Evolution of catalytic proteins or on the origin of enzyme species by means of natural selection. J Mol Evol 20: 38–51. 10. Soyer O, Pfeiffer T (2010) Evolution under fluctuating environments explains observed robustness in metabolic networks. PLoS Comput Biol 6: e1000907. doi:10.1371/journal. Pcbi.1000907. 11. Crombach A, Hogeweg P (2008) Evolution of evolvability in gene regulatory networks. PloS Comput Biol 4: e1000112. doi:10.1371/journal. Pcbi.1000112. 12. Draghi J, Wagner G (2009) The evolutionary dynamics of evolvability in a gene network model. J Evol Biol 22: 599–611. 13. Wagner A (2008) Robustness and evolvability: a paradox resolved. Proc Biol Sci 275: 91– 100. 14. Draghi J, Parsons T, Wagner G, Plotkin J (2010) Mutational robustness can facilitate adaptation. Nature 463: 353–355. 15. Crombach A, Hogeweg P (2007) Chromosome rearrangements and the evolution of genome structuring and adaptability. Mol Biol Evol 24: 1130–1139. 16. Hurst L, Pa´l C, Lercher M (2004) The evolutionary dynamics of eukaryotic gene order. Nat Rev Genet 5: 299–310. 17. Batada N, Hurst L (2007) Evolution of chromosome organization driven by selection for reduced gene expression noise. Nat Genet 39: 945–949. 18. Dayhoff, M. (1969) Atlas of Protein Sequence and Structure 1969. vol. 4, National Biomedical Research Foundation, Silver Spring, Maryland. 19. Hogeweg P (2011) The Roots of Bioinformatics in Theoretical Biology. PLoS Comput Biol 7(3): e1002021. 20. Perez-Iratxeta C, Andrade-Navarro MA, Wren JD (2007) Evolving research trends in bioinformatics. Brief Bioinform 8: 88–95. 21. Brewster C, O’Hara K (2007) Knowledge representation with ontologies: present challenges—future possibilities. International Journal of Human- Computer Studies 65: 563–568. 22. Faith DP, Baker AM (2007) Phylogenetic diversity (PD) and biodiversity conservation: some bioinformatics challenges. Evolutionary bioinformatics 2006. 23. Serrano L (2007) Synthetic biology: promises and challenges. Mol Syst Biol 3: 158. 24. Gatenby RA, Frieden BR (2007) Information theory in living systems, methods, applications, and challenges. Bull Math Biol 69: 635–657. 25. Scherer SW, Lee C, Birney E, Altshuler DM, Eichler EE, et al. (2007) Challenges and standards in integrating surveys of structural variation. Nat Genet 39: S7–15. 26. Hanauer DA, Rhodes DR, Sinha-Kumar C, Chinnaiyan AM (2007) Bioinformatics approaches in the study of cancer. Curr Mol Med 7: 133–141. 27. Pop M, Salzberg SL (2008) Bioinformatics challenges of new sequencing technology. Trends Genet 24: 142–149. 28. Stein LD (2008) Towards a cyberinfrastructure for the biological sciences: progress, visions and challenges. Nat Rev Genet 9: 678–688. 29. Smith TF (2008) Computational biology: its challenges past, present, and future. Proceedings of the 12th annual international conference on research in computational molecular biology. Singapore: Springer-Verlag. Pp 1–2. 30. Pop M (2009) Genome assembly reborn: recent computational challenges. Brief Bioinform 10: 354–366.

31. Suarez M, Jaramillo A (2009) Challenges in the computational design of proteins. J R Soc Interface 6 Suppl 4: S477–491. 32. Kyrpides NC (2009) Fifteen years of microbial genomics: meeting the challenges and fulfilling the dream. Nat Biotechnol 27: 627–632. 33. Auffray C, Chen Z, Hood L (2009) Systems medicine: the future of medical genomics and healthcare. Genome Med 1: 2. 34. Berglund EC, Nystedt B, Andersson SG (2009) Computational resources in infectious disease: limitations and challenges. PLoS Comput Biol 5: e1000481. 35. Thorisson GA, Muilu J, Brookes AJ (2009) Genotype-phenotype databases: challenges and solutions for the post-genomic era. Nat Rev Genet 10: 9–18.

International Journal of Bioinformatics and Biological Science: v.1 n.2 p.129-133 June, 2013

Length-weight relationships of Butter catfish, Ompok bimaculatus in Betwa River, India Abhishek K. Malakar1*, Pallavi1, Wazir S. Lakra2 and R. M. Mishra3 1

National Bureau of Fish Genetic Resources, Canal Ring Road, Lucknow-226002, Uttar Pradesh, India. 2 Central Institute of Fisheries Education, Versova, Andheri (W), Mumbai-400061, India. 3 Awadhesh Pratap Singh University, Rewa 486003, Madhya Pradesh, India.\ *

Corresponding Author: Abhishek K. Malakar [email protected] Received: 11 April 2013;

Accepted: 19 June 2013

ABSTRACT The length-weight relationships (LWRs) were studied of 116 fish samples of Ompok bimaculatus captured in the Betwa River (tributary of the Yamuna River) from December 2009 to January 2011. The estimated ranged of total length from 129.05 mm to 221.79 mm and weights from 21.16 g to 37.24 g, respectively. The relationship between total and standard lengths (TL and SL) was determined according to the power regression model. The b value in the length-weight relationship differed significantly between males and females (P>0.05). Lengthweight relationship showed positive allometric for males as W=-2.83×L3.35 (r2=0.95, n=64) and for female determined as W=-2.44×L3.09 (r2=0.94, n=52). The present study may be useful for basic information on the LWRs of indigenous Ompok bimaculatus fishes in Betwa River, India. Keywords: Ompok bimaculatus, length weight relationship, betwa river

INTRODUCTION Fish are one of the cheapest sources of protein in the world which contains necessary and essential nutrition for body (Davies 2009). Gharaei, et al., (2010, 2011) proposed, fish may play an important role in economic development of countries for both aquaculture and production of ornamental fish. Ompok bimaculatus (Bloch, 1794) is an indigenous and freshwater fish species belonging to the Family Siluridae under the Order Siluriformes. As a food fish it is delicious, tasty, nutritious catfish due to having larger concentration of lipoprotein and soft bone. In India, this species is distributed in the plains and sub mountain regions (Jayaram, 1999; Chakrabarty et al., 2007). Ompok bimaculatus is widely distributed throughout the India, Pakistan, Nepal, Bangladesh, Sri Lanka, Afghanistan, China, Thailand, Cambodia and Indonesia (Ng 2003). Ompok bimaculatus has been

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already declared as an endangered fish species from Western Ghats in India (CAMP 1998; Lakra and Sarkar 2006). A mathematical data of length-weight relationship (LWR) is a great importance in fishery assessments (Garcia et al., 1989; Haimovici and Velasco, 2000). Length and weight measurements in conjunction with age data can give information on the stock composition, age at maturity, life span, mortality, growth and production (Le Cren, 1951; Beyer, 1987; Bolger and Connoly, 1989). The deficiency of information on the biological aspects of the endangered fishes, the planning and implementation of species-specific conservation and management strategies could not be adopted till-date. Therefore, knowledge of LWR is most useful in fisheries science. Poor publish Information of Ompok as a genus and O. bimaculatus as a species create an ambiguity (Sarkar et al., 2010; Malakar et al., 2012). The present study was undertaken to provide the LWR of indigenous Ompok bimaculatus fish in Betwa River, India. MATERIALS AND METHODS A total of 116 specimens of Ompok bimaculatus were caught in River Betwa (tributary of the Yamuna River) at Bhopal, Madhya Pradesh (23°32' N and 77°48' E) in India using gill net at monthly intervals between December 2009 to January 2011. Sampled fishes were fixed with 10% formalin and transferred to the laboratory. The species were identified and the characters described by Talwar and Jhingran (1991); Jayaram (2010) and Froese and Pauly (2010). For each specimen, total length (TL) and standard length (SL), whole body wet weight (g) and sex was recorded. Total length (TL) of each fish was taken from the tip of the snout (mouth closed) to the extended tip of the caudal fin nearest 0.1 mm by digital caliper (Mitutiyo) and weighed to the nearest 0.01 g (total weight) by digital weighing machine (ACCULAB Sartorious Group). The length-weight relationship was estimated by using following equation: W = aLb, where the W is the body weight (g), L the total length (cm), “a” the intercept of the regression and “b” is the regression coefficient (slope) (Ricker, 1973). When b = 3, increase in weight is isometric. When the value of b is other than 3, weight increase is allometric (positive if b > 3, negative if b < 3) (Bagenal and Tesch 1978). The values of constant a and b were estimated from the log transformed values of length and weight to log W = log a + b log L (Beckman, 1948), via least square linear regression. The degree of association between the variables was computed by the determination coefficient, r2. Statistical analysis was done through Microsoft Office Excel 2010 and SPSS package version. RESULTS AND DISCUSSION A total of 116 specimens belonging to Ompok bimaculatus corresponding to Siluridae Family were used for calculation of length-weight relationships. The LWR differed significantly between males and females (P0.05) (Table 1). The parameter b value was observed as 3.35 for males and 3.09 for females. Therefore,

the b coefficient can be used in the pointed out length range, although sampling was carried out in various seasons. The length-weight relationship parameters would be treated as mean annual value. We determined a positive allometry power length weight relationship for average total species. The LWR results obtained could well be considered when fish populations are subject to fishing regulation, recovery programmes or other fisheries management activities in the respective rivers. According to Weatherley and Gill (1987) the annual LWRs could differ between seasons and years and many factors could contribute to these differences namely maturity, temperature, salinity, food availability and size. LWR may vary seasonally according to the degree of sexual maturity, sex, diet, stomach fullness, sample preservation techniques (Wootton, 1992), number of examined specimens, area/season effect sand sampling duration. In conclusion, our study has provided the basic information to fishery biologists about LWRs for Ompok bimaculatus in the Betwa River in India that would be beneficial for fishery biologists and conservationists to impose adequate regulations for sustainable fishery management and conservation. ACKNOWLEDGMENTS The authors are thankful to Director, National Bureau of Fish Genetic Resources (NBFGR), Lucknow for the providing support and laboratory facility. REFERENCES

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tions. Bulletin of the Fisheries Research Board of Canada, 191: 382. Sarkar, U.K., Gupta, B.K and Lakra, W.S., 2010. Biodiversity, ecohydrology, threat status and conservation priority of the freshwater fishes of River Gomti, a tributary of river Ganga (India). Environmentalist, 30, pp. 3-17. Talwar, P.K and Jhingran, A.G., 1991. Inland Fishes of India and Adjacent Countries. Vol. 2, Oxford and IBH Publishing. Co. Pvt. Ltd., New Delhi, India. Weatherley, A.H and Gill, H.S., 1987. The Biology of Fish Growth. Academic Press, London, pp: 14-21. Wootton, J.T., 1992. Indirect effects, prey susceptibility, and habitat selection: impacts of birds on limpets and algae. Ecology, 73: 981-991.



Production of Biofertilizer from Agro- waste by using Thermotolerant Phosphate Solubilising Bacteria Ajay Kumar Singh1*, Harison Masih1, Prerna Nidhi1, Yashab Kumar1, Jyotsana Kiran Peter1 and Santosh Kumar Mishra2 1

Jacob School of Biotechnology and Bioengineering, Sam Higginbottom Institute of Agriculture, Technology and Sciences-Deemed to be University, Allahabad-211007, India 2 Department of Biotechnology, IMS Engineering College, Ghaziabad, India *

Corresponding Author: Ajay Kumar Singh: [email protected] Received: 19 April 2013;

Accepted: 30 May 2013

ABSTRACT Biofertilizers are eco-friendly and cheap solution for the development of sustainable agriculture. Biofertilizers can also help small and marginal farmers to attain ultimate goal of increasing their crop productivity. The bacterial inoculation of vegetable and agricultural waste can be considered as organic compost containing phosphorus sources suitable to spinach plant growth. In order to prepare biofertilizer, thermo-tolerant phosphate-solubilizing bacteria Bacillus megaterium were collected. The strain possessed amylase, CMCase, and lipase activities and could solubilize calcium phosphate. During composting, biofertilizers inoculated with the tested bacterium namely Bacillus megatarium had a significantly higher temperature, pH, soluble phosphorus content, and germination rate than noninoculated biofertilizer. Adding these bacteria can shorten the period of maturity, improve the quality and increase the soluble phosphorus content. Therefore inoculating thermo-tolerant phosphate-solubilizing bacteria into agricultural and vegetable wastes represent a practical strategy for preparing multi-functional biofertilizer. Keywords: phosphate-solubilizing, thermotolerant, growth promoting

INTRODUCTION Bio-fertilizers are the microbial inoculants prepared from live or latent cells of effiecient strains capable of nitrogen fixing, phosphate solublising for enhancing growth of plants. They act as catalysts in providing valuable nutrients to the plant through phosphate solubilising, nitrogen fixing and growth promoting hormones. Chemical fertilizers are cost-intensive and lead to high yield in the short run. In the long run, however, they erode soil fertility and harm the natural predators of pests in the biosphere. All these lead to even greater use of chemical fertilizers and pesticides and therefore higher cost to farmers. The utility of bio-fertilizers has been validated

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through large-scale field trials by government and semi-government agencies as well as private bodies (Hassan et al., 2006). Phosphate-solubilizing microbes play important roles in phosphorus cycling in natural and agricultural ecosystems. Many bacteria and fungi are able to improve plant growth by solublising sparingly soluble inorganic and organic phosphates. Phosphatesolubilizing microbes can transform the insoluble phosphorus to soluble forms HPO2_ 4 and H2PO_4 by acidification, chelation, exchange reactions, and polymeric substances formation (Delvasto et al., 2006). Therefore, the use of phosphatesolubilizing microbial inoculants in agricultural practice would not only offset the high cost of manufacturing phosphatic fertilizers but would also mobilize insoluble phosphorus in the fertilizers and soils to which they are applied. Several microorganisms and their association with crop plants are being exploited in the production of biofertilizer. Several soil bacterial species of Pseudomonas, Bacillus etc. secrete organic acids and lower the pH in their vicinity to bring about dissolution of bound phosphates in soil. Increased yields of wheat and potato have been demonstrated due to inoculation of peat based cultures of Bacillus polymyxa and Pseudomonas striata (Wu et al., 2005). Compositing is a cost effective as well as environment friendly way of waste recycling. It is a process in which organic waste materials such as manure, leaves, sludge, fruits, paper, vegetables and food waste are biologically converted into an amorphous and stable humus like substance ( under conditions of optimum temperature, moisture and aeration) that can be handled, stored and applied without any hazardous environmental impacts. Composted organic waste materials are regarded to enhance crop yields compared to uncomposted /raw ones due to improvement in soil physical, chemical and biological properties and reduced mineralization rate (Ahmad et al., 2006). Food waste is less harmful to the environment than industrial waste. Thus, composting of food waste is attracting considerable attention because it would significantly reduce the amount of waste and the product can be used as compost or biofertilizer which can be handled, stored, transported and applied to the field without adversely affecting the environment (Debosz et al., 2002). During composting microbes produce organic acids that lower pH, enzymes, hormones and other biologically active substances which help in solublization of nutrients including rock phosphate and release P which is available during plant growth (Puente et al., 2004; Mohanty et al., 2006). The organic fertilizers had a significant direct and residual effect compared to inorganic single super phosphate on the biomass, P content and uptake in both groundnut and corn. The advantage of using these kinds of organic fertilizers is; they provide balanced nutrient supply, facilitate the growth of beneficial microorganisms and helps to suppress certain plant diseases and soil borne diseases (Sary et al., 2009). Spinach (Spinacia oleracea) belongs to the family Chenopodiaceae. It is a herbacious plant which produces edible leaves as an annual and seed as a biennial crop in the plains. The edible part of spinach is a compact rosette of leaves. It is commercially grown in the United States, Europe, India, etc. In India it is popular all over the country. 136 International Journal of Bioinformatics and Biological Science: v.1 n.2 p.135-152. June, 2013

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These kinds of fertilizers should fulfill the needs of green revolution in vegetable crop production. Thus with these fertilizers high yield/high quality/low cost/low environmental impact crops can be produced. Therefore the present study was carried out with the following objectives to characterize biofertilizer by thermotolerant phosphate solubilizing bacteria and assess the biofertilizer on Spinach plants growth. MATERIALS AND METHODS

Place of work The present study was carried out in the Department of Microbiology and Fermentation Technology, SHIATS, Allahabad, U.P (India). Procurement of test microbes, collection and maintenance For the study microbial culture Bacillus megaterium (MCCB-0208) was used. The mentioned bacterial isolate was collected from Microbial Culture Collection Bank, Department of MBFT, SHIATS. Bacillus megaterium was grown on nutrient agar media at 37°C for 24 hr and stored at 4°C. Enzyme activity Amylase production test α- Amylase activity was assayed by soluble starch Agar medium (Kammoun et al., 2008). Starch agar medium was melted, cooled to 45ÚC and poured into sterile Petri dishes, allowed to solidify. Using sterile technique, zig-zag streaking was done at the center of plate for bacterial inoculation and appropriately labeled plates were incubated for 48 hours at 37°C in an inverted position. The plates was flooded with iodine solution with the help of a dropper for 30 seconds and observed for halo zone formation.

Cellulase production test CMCase activity was determined by Mandels-Reese medium with cellulose as the sole carbon source for bacteria. The culture was inoculated in the centre and incubated at 30°C for 2 days. The Petri plate was flooded with Congo red solution (0.1%), and after 5 minutes the congo red solution was discarded and the plates was washed with 1M NaCl solution allowed to stand for 15-20 minutes. The plates was observed for halo zone formation (Lee et al., 2008). Lipase production test Lipase production test was carried out in the tributyrin agar medium. Medium was prepared, autoclaved at 121°C for 20 minutes, cooled at 40°C then poured in Petri plates. After solidifying the media, strain of Bacillus megaterium culture was streaked in center of petri plates with the help of inoculating needle and bacterial plate was incubated at 30°C for 2 days. Plates was observed for clear zone formation (Tsai et al., 2007)


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Qualitative estimation of Phosphate- solubilization The phosphate solubilization activity was tested on Pikovskaya agar medium. Bacterial culture was spot inoculated on Pikovskaya’s agar plates. Bacterial plates were incubated at 37°C for 2 days. Solubilization index was calculated by using following formula (Premono et al., 1996) SI = Colony diameter + halo zone diameter Colony diameter Quantitative estimation of Phosphate solubilization Broth phosphorus was determined by Ascorbic acid method (Watanabe and Olsen, 1965). Two solutions namely solution-A and solution-B were prepared. For solutionA 12 gm of AR grade Ammonium molybdate was dissolved in 250 ml of distilled water. In 100 ml of distilled water 0.291gm of Antimony potassium tartarate was dissolved separately, both the solutions were mixed and 148 ml of concentrated sulfuric acid was added and made the final volume up to 1 liter with distilled water. For solution-B 1.056 gm of Ascorbic acid was dissolved in 200 ml of solution A. For the quantitative measurement of phosphorus, Bacterial cultures were grown in Pikovskaya’s broth medium separately and incubated for 14 days with continuous shaking (120 rpm) at 37 ± 2°C. A 10 ml sample of each broth culture was taken in centrifugation tube and centrifuged for 15 min at 8000 rpm. 5 ml of supernatant was transferred in 25 ml volumetric flask. 0.5ml of 5N sulfuric acid was added to it and shaken for a while till carbon dioxide gas evolution disappeared. 4ml of Ascorbic acid solution was added to it and made the volume with distilled water. After 10 minutes the intensity of blue color was observed using colorimeter at 700 nm wavelength. The concentration of available phosphorus (mg/ml) will be calculated against standard phosphorus KH2PO4 curve. This procedure was repeated at every 2 days interval up to 14 days of incubation of bacterial culture in pikovaskaya’s broth. For standard curve of KH2PO4, 2.195gm KH2PO4 was dissolved in100 ml distilled water, transferred to a 1 lt. flask and 5ml of conc. H2SO4 was added, optical density obtained at different concentration at 700 nm wavelength. Equation resulted from standard curve of KH2PO4 Available Phosphorus = (X-0.006)/0.693, where X is optical density obtained at concentration of 1 mg/ml

Screening and compositions of Biofertilizer Vegetable and agriculture waste were collected from the proper sites of SHIATS campus. The wastes (50% agricultural wastes and 50% vegetable wastes) were air dried under sun light and properly crushed into the granules, roughly below 40 mm in diameter with the help of mixer-grinder and powder form of waste were sterilized in hot air oven at 161ÚC for 2-3 hours, for removing contaminations. Preparation of Biofertilizer Each gram of dry material was inoculated with tested microbial culture with at about

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1 × 107 cfu/gm for bacteria composting, at an initial moisture content of 60-65% in a 1000 ml conical flask. The composts were turned over every three to four days till 28 days. Incubation of 30°C was provided for bacterial composting, un-inoculated raw material was used as control (Yang and Chen, 2003). After 28 days Biofertilizers were packed in polybags (250gm / polybag) and stored at 4°C.

Tests during composting process Temperature measurement There may be variation in temperature due to decomposition of organic matter and release of heat. Therefore temperature variation was tested by mercury thermometer during biofertilizer preparation with thermotolerant phosphate solubilizing bacteria along with control i.e. without bacterial inoculation, at a regular interval of 7 days up to 28 days (Yang and Chen, 2003) pH measurement There may be variation in pH due to degradation of nitrogen containing materials, the formation of NH4+ ions and release of OH- by hydrolysis (Fageria and Baligar, 2001).The variation in pH of compost was tested at regular interval of 7 days upto 28 days by using pH-meter.

Plate 1: Bacterial Biofertilizer produced after 28 days of incubation.


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Plate 2: Packet of Bacterial Biofertilizer

Effect of biofertilizer on Spinach growth (Pot-experiment) 33 plastic pots of capacity 0.2kg each were taken. Soil was taken from agriculture field of SHIATS and prepared biofertilizer was mixed properly into it in the ratio of 1:10 (T1), 2:10 (T2), 3:10 (T3), 4:10 (T4) and 5:10 (T5) in pot-I, pot-II, pot-III, pot-IV and pot-V respectively. Pot-experiments were done for bacterial biofertilizer with different soil ratio combinations. Three replicates for each ratio were taken. Three pots were taken as control (C), having no addition of biofertilizer. Four Spinach seeds were sown in each pot and proper sterilized waterings were done throughout the season (Kukshal et al., 1977).

Growth parameters after germination of seeds Plant height The plant height was measured from the base of plant to the terminal growing point of the main stem at 30 days of transplanting. The average plant height was expressed in centimeters. Number of leaves per plant The fully opened and matured leaves were counted for each plant and its average was recorded as number of leaves per plant at 30 days after transplanting.

Leaf area Leaf area was calculated by Graph paper method (Sestak et al., 1971) at 30 days of transplanting by selecting one fully opened mature leaf from each replication and its average was presented as leaf area in sq. cm/ plant. Leaf area index (LAI)



The leaf area index was calculated by dividing the leaf area per plant by the land area occupied by the plant at 30 days after transplanting. 2 LAI=Leaf area (cm ) 2 Land area (cm )

Statistical analysis The data recorded during the course of investigation was subjected to one way ANOVA and the conclusion was drawn accordingly. (Fisher and Yates. 1968) RESULTS AND DISCUSSION

Enzyme production test In the present study the microorganisms, Bacillus megaterium was tested for amylase, cellulase and lipase production. Presence of yellow coloured halozone around the bacterial colonies indicated that Bacillus megaterium produced amylase (Plate 3). White coloured halozone appeared around the B. megaterium colonies indicated the production of cellulase, when the Mandel and Reese agar plates streaked with respective culture, and flooded with 0.1% Congo red solution (Plate 4). Clear zone appeared around the colonies confirms that the strain Bacillus megaterium were lipase producing, when the tributyrin agar plate streaked with bacterial culture and incubated for 48 hrs (Plate 5). Chang and Yang (2009) reported that B.coagulans, B. licheniformis, B.smithi, had similar kind of activities for amylase, CMCase and lipase production. The result showed that the test microorganisms had capacity to degrade different substrates by producing different enzymes but the diameter of halozone varied from organism to organism. Similar patterns of result were obtained by Gautam et al., (2012).

Plate 3: Starch hydrolysis by shown by clear zone around streaked bacterial culture Bacillus megatarium International Journal of Bioinformatics and Biological Science: v.1 n.2 p.135-152. June, 2013 141

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Plate 4: Clear zone around streaked culture of Bacillus megatarium showing cellulose degradation

Plate 5:Clear zone around streaked culture of Bacillus megatarium showing lipase production

Qualitative and quantitative estimation of phosphate solubilization The strain of Bacillus megaterium was tested for phosphate solubilization activity by plate and broth assay. Pikovskaya’s medium (PVK) was used to measure phosphate solubilizing activity. Solublization index based on colony diameter and holozone for each microorganism is presented in Table 4.1. Results showed that Bacillus megaterium was found phosphate solubilizer with SI= 2. Studies on agar plates revealed 142 International Journal of Bioinformatics and Biological Science: v.1 n.2 p.135-152. June, 2013


that phosphate solubilizing microorganisms formed clear zones by solubilizing suspended tricalcium phosphate. Halozone increased with increase in colony diameter. Fluctuations in solubilization index were observed during the 3 days observation period. According to Kucey et al., (1989) most of Phosphate solubilizing microbial strains lost their ability to form halozone by repeated subculturing .Similar results were found by Edi- Premono et al. (1996), Kumar and Narula (1999), Nautiyal (1999) and Chang et al.(2001). Phosphate solubilizing microbial strain of B. megaterium which showed efficient phosphate solubilizing activities in Pikovskaya’s agar medium was further tested for their activities in liquid medium. Broth assays were performed for quantitative estimation of phosphate solubilization by B. megaterium for 14 days. Available phosphates at a regular interval of 2 days up to 14 days are presented in Table 4.3. Amount of available phosphate was increasing upto 12 days in B. megaterium inoculated broth. Afterwards it decreased. Gaind and Gaur (1989) reported that the drop in solubilization after a maximum value might be attributed to deficiency in nutrients in the culture medium. The decrease in soluble P at later periods of incubation may be either due to decreased solubilizing activity or increased P absorption and refixation of solubilized P with metal ions present in the broth. This study indicates that the halozone criteria is not enough for phosphate solubilizing micro-organisms selection, as B. megaterium did not produce effective zone on pikovskaya’s plate could conversely solubilize significant amount phosphate in liquid media (Louw and Webley, 1959; Gupta et al., 1994). However, for screening a large number of microorganisms, this method can be regarded as generally reliable for isolation and preliminary characterization of phosphate solubilizing micro-organisms (Rodriguez and Fraga, 1999). Table 1: Qualitative estimation of phosphate solubilization. Micro-organisms Bacillus megaterium

Colony diameter

Halozone diameter

35mm

35mm

Solubilization Index (S.I.) 2

Table 2: Optical density values for standard curve of KH2PO4 Concentration(mg/ml) Blank 0.2 0.4 0.6 0.8 1

Optical Density 0.00 0.139 0.274 0.421 0.567 0.71


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Fig 6: Regression line drawn between different concentration of KH2PO4 and Optical densities Table 3: Available phosphate at different incubation period of B. megaterium inoculated Pikovskaya’s broth Incubation period (days) 2 4 6 8 10 12 14

Available Phosphate (mg/ml) 1.463 1.73 1.852 2.415 2.689 2.689 2.329

Fig 7: Available phosphate concentration at different incubation period of Bacillus megatarium inoculated Pikovskaya’s broth medium.



Tests during Preparation of Biofertilizer Bacterial inoculated biofertilizer were prepared by providing different condition of temperature, moisture and inoculum sizes. Data for variation in temperature have been presented in Table 3. During biofertilizer preparation, microbes decomposed the organic matter and released the fermentation heat (Yang and Chen, 2003). The temperature of Biofertilizer increased rapidly during composting up to 21 days. After that it decreased gradually for the maturation of biofertilizer. The temperature patterns were similar to the commercial composting process (Pai et al., 2003) and preparation of food waste biofertilizer (Tsai et al., 2007). During biofertilizer preparation, microbial inoculants need supplementation of bulk materials for their survival. They utilized the organic matter present in the waste as a source of their nutrient. The data for variation in pH have been represented in Table 4 and Fig 5. Initially the pH decreased with incubation period up to 14 and 21 days due to production of organic acid by the process of fermentation but increased thereafter. Microbial populations had a tendency to decrease suddenly from day 0 to day 14 due to the acidic environment, and then remain steady during the biofertilizer preparation. The pH of composts increased gradually over time to a neutral pH after 21 days composting due to the degradation of nitrogen-containing materials, the formation of NH4+ ions and the release of OH- by hydrolysis. The neutral or slightly alkaline pH of biofertilizer is beneficial in agriculture because of its contribution towards neutralization of the acidic agricultural soil (Huang, 1991; Fageria and Baligar, 2001). Table 4: Variation in pH of waste during composting by B. megaterium. Composting period(Days) 1st 7th 14th 21st 28th S.Ed± CD at 5%

pH of Agro-waste Bacterial compost 7.2 6.7 6.1 5.5 6.2 0.410 0.873

Control 7.2 7.1 7.2 7.1 7.3 0.082 0.174

Due to pH of Bacterial compost: F(cal), = 4.91> F(tab) = 3.48 (5% and 4 degree of freedom) – Significant ; CD= 0.873 Due to pH of Control: F(cal), = 2.1< F(tab) = 3.48 (5% and 4 degree of freedom) – Non-significant ; CD= 0.174


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Fig 8: Variation of pH of Agro-waste during composting by B. megaterium

Table 5: Temperature variation during composting of waste by B. megaterium Composting period (days) 1 7 14 21 28 S.Ed± CD at 5%

Temperature (ÚC) Bacterial composting 28 30 38.5 39 34 0.301 0.641

Control 28 29.5 28 28.5 28 0.468 0.996

Due to Temp. of Bact. compost: F(cal), = 536.029> F(tab) = 3.48 (5% and 4 degree of freedom) – Significant ; CD= 0.641 Due to Temp. of Control: F(cal), = 1489.25> F(tab) = 3.48 (5% and 4 degree of freedom) – Significant ; CD= 0.996

Fig 9: Temperature variation of Agro-waste during composting by B. megaterium 146 International Journal of Bioinformatics and Biological Science: v.1 n.2 p.135-152. June, 2013


Effect of prepared biofertilizer on Spinach plant growth In the present investigation effect of different combination of biofertilizer and soil were studied on the growth parameters of Spinach plant.Results in Table 6 and Fig 610 showed that Spinach growth parameters were significantly influenced by appling different combination of biofertilizer with soil. The best results of growth parameters Plant height, No.of leaves/ Plant, Leaf area, Leaf area index of Spinach plant were recorded by plants supplied with bacterial inoculated biofertilizer.The inoculation of B. megaterium had a more stimulating effect on the assimilation and solubilization of phosphate than uninoculated control. The lowest values of Spinach growth parameters were obtained by soil without inoculation (Control). It was obvious that addition of biofertilizer inoculated with thermotolerance phosphate solubilising microorganisms caused an enhancement in plant growth parameters. This study confirms that phosphate solubilizing microbial inoculants improved the estimated characters compared with untreated control. They also showed considerable differences among five treatments of different ratios of biofertilizer and soil. In bacterial biofertilizer applied soil of ratio 3:10 (T3) was considered the most effective treatment for growth characteres. These results may be attributed to the major role of plant metabolism according to Mengel and Kirkby (1982). Chaykovskaya et al.,(2001) reported that PSM increased phosphorus accumulation in plants, yeild of pea and barley.The bacterial strain was able to dissolve hard soluble organophosphates and increased growth and yeild of Spinach plant.The results revealed that the population of bacterial inoculation significantly increased in all the inoculated treatments, when compared to uninoculated control. The control plants showed very poor growth, which may be attributed to nutrient deficiency, e.g. the lack of available phosphate in the unfertilized soil. The above mentioned results are in harmony with those obtained by Abd EL-Kawy (1999) on geranium plants and Dessouky (2002) on Borage officinafis plant. Lyons and Breidenbach (1990) mentioned that phosphorus nutrition is doubly critical because the total supply of phosphorus in most soil is low and is not readily available for the plant use.


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Table 6: Growth of Spinach influenced by B. megaterium inoculated Biofertilizer Treatments (Biofertilizer:Soil) C (Contol) T1 (1:10) T2 (2:10) T3 (3:10) T4 (4:10) T5 (5:10) S. Ed± CD at 5%

Plant height(cm)

No. of leaves/Plant 30 DAT

Leaf area (cm2) 30 DAT

Leaf area index

4.63 8.83 9.66 8.73 7.56 3.1 0.547 1.164

5 7 6 8 7 6 0.133 0.284

3.75 11.16 8.41 13.29 8.66 4.20 0.075 0.159

0.09 0.29 0.21 0.34 0.22 0.10 0.149 0.149

Due to Plant height: F(cal), = 46.190> F(tab) = 3.11 (5% and 5 degree of freedom) – Significant ; CD= 1.164 Due to No. of leaves/ Plant: F(cal), = 123.75> F (tab) = 3.11 (5% and 5 degree of freedom)Significant; CD= 0.284 Due to Leave area: F(cal), = 5069.14> F (tab) = 3.11 (5% and 5 degree of freedom)- Significant; CD= 0.159 Due to Leaf area index: F(cal), = 16.186> F(tab) = 3.11 (5% and 5 degree of freedom)Significant; CD= 0.149

T1- 1:10, T2- 2:10, T3- 3:10, T4- 4:10, T5- 5:10, C- Control DAT- Days After Treatment Fig 10: Plant height of Spinach influenced by B. megaterium inoculated Biofertilizer (30 Days after treatment)



T1- 1:10, T2- 2:10, T3- 3:10, T4- 4:10, T5- 5:10, C- Control DAT- Days After Treatment Fig 11: No. of leaves/Plant of Spinach influenced by B. megaterium inoculated Biofertilizer at 30 Days after treatment

T1- 1:10, T2- 2:10, T3- 3:10, T4- 4:10, T5- 5:10, C- ControlDAT-Days After Treatment Fig 12: Leaf area of Spinach influenced by B. megaterium inoculated Biofertilizer 30 days after treatment

T1- 1:10, T2- 2:10, T3- 3:10, T4- 4:10, T5- 5:10, C- Control DAT-Days After Treatment Fig 13: Leaf area index of Spinach influenced by B. megaterium inoculated Biofertilizer 30 days after treatment. International Journal of Bioinformatics and Biological Science: v.1 n.2 p.135-152. June, 2013 149

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Fig 14: Growth of Spinach influenced by B. megaterium inoculated Biofertilizer on 30th day.

CONLUSION The cheapest and easily available waste materials (Agricultural and vegetable wastes) were utilized for the production of nutrient rich phosphate solublizing biofertilizer for better growth of Spinach plant. The efficacy of strain of Bacillus megaterium for different enzyme production was tested. The strain was able to produced Amylase, lipase and cellulase enzymes. Qualitative and quantitative estimation of phosphate solubilization were done for Bacillus megaterium and the solubilization index were calculated as 2. Inoculation with these bacteria into compost accelerated the decomposition of agricultural and vegetable wastes during the preparation of biofertilizers with high soluble phosphorus content. Growth parameters were observed after 30 days of germination of seeds with different treatments and control. In pot experiment of bacterial biofertilizer, maximum growth for plant height, No. of leaves/ Plant, Leaf area and leaf area index were observed in T3 (3:10) treatment, whereas T5 (5:10) showed the minimum growth for all parameters. Therefore, compost inoculated with thermo-tolerant phosphate-solubilizing Bacteria might be a good strategy for biofertilizer preparation The different organic materials such as vegetable and agriculture waste used in composting process were utilized and converted to nutrient rich biofertilizer hence, production of biofertilizer is an excellent waste recycling process and also help in the control of environmental pollution. Therefore phosphate solubilising microbial strain of Bacillus megaterium, and its application resulted faster growth of Spinach plant and hence can be used as biofertilizer for better growth of crops.



ACKNOWLEDGEMENTS The authors thank Pof.(Dr.)Rubina Lawrence, Head, Department of Microbiology & Fermentation Technology for her technical assistances and the Sam Higginbottom Institute of Agriculture Technology & Sciences (Deemed to be University), Allahabad for providing the financial supports and laboratory facilities . REFERENCES Abd EL-Kawy, M. 1999. A comparison on three geranium species and their response to NPK fertilization and micronutrients M.Sc Thesis, Faculty of Agriculture Cairo University.pp223-223 Ahmad, R., Khalid, A., Arshad, M., Zahir Z. A. and Naveed M. 2006. Effect of raw (uncomposted) and composted organic waste material on growth and yield of maize (Zea mays L). Journal of soil and environmental Sciences. 25(2): 135-142. Chang, C.H. and Yang, S.S. 2009. Thermo-tolerant phosphate-solubilizing microbes for multifunctional biofertilizer preparation. Bioresourch Technology.100:1648-1658. Chang, C.H., Hsieh, C.Y and Yang, S.S., (2001). Effect of cultural media on the phosphate solubilizing activity of thermo-tolerant microbes. Journal of Biomass Energy. China 20: 79–90. Chaykovskaya, L.A., Patyka, V.P. and Melnychuk, T.M. 2001. Phosphorus mobilizing microorganisms and their influence on the productivity of plants. In (W.J. Horst, Editions.) Plant, Nutrition-Food Security and Sustainability of Agroecosystems, pp: 668669. Debosz, K., Petersen, S.O., Kube ,L.K and Ambus, P.( 2002). Evaluating effects of sewage sludge and household compost on soil physical, chemical and microbiological properties. Applied Soil Ecology. 19:237–48. Dessouky, M.M. 2002. A comparative response of Borage officialis L. plant to the bio.-chemical fertilization and adenosine tri-phosphate (ATP) treatments. Bullettin of Faculty of Agriculture Cairo University.53: 613-638. Edi–Premono, Moawad M. A. and Vleck P.L.G. 1996. Effect of phosphate solubilizing Pseudmonas putida on the growth of maize and its survival in the rhizosphere. Indonasian Journal of Crop Sciences. 11: 13–23. Fageria, N.K. and Baligar, V.C. 2001. Improving nutrient use efficiency of annual crops in Brazilian and soils for sustainable crop production. Communication in Soil Science and Plant Analysis. 32: 1303-1319. Fisher, R.A. and Yates, F. 1990. Stastical table for biological agriculture and medical research. Alivas and Boyd Edinbury. 4: 251-267. Gaind, S., and Gaur, A.C. 1989. Thermotolerant phosphate solubilising microorganisms and their interaction with mung bean. Journal of Plant Soil and Environment . 133:141149. Gautam,S.P., Bundela, S.P., Pandey A.K., Jamaluddin, A. M. K., and Sarsaiya, S. 2012. Diversity of Cellulolytic Microbes and the Biodegradation of Municipal Solid Waste by a Potential Strain. International Journal of Academic Research. 2(6):330–333. Gupta, R., Singal, R., Shankar, A., Kuhad, R.C. and Saxena, R.K. 1994. A modified plate assay for screening phosphate solubilizing microorganisms. Journal of Genetics and Applied Microbiology. 40: 255-260. Hassan, A. H., Khreba, A. H., Emam, M. S. and Atala, S. A. 2006. Effect of biofertilizers, organic fertlizers and their interaction on the vegetative growth , yield and quality of artichoke flower head. Egyptian Journal of Applied Sciences. 21(11):185-200. Huang, S.N., 1991. Application of hog compost in crop production. In: Proc. Semin. Hog International Journal of Bioinformatics and Biological Science: v.1 n.2 p.135-152. June, 2013 151

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Evidence of Genetic Polymorphism in Anopheles subpictus Populations from India A.K. , Sharma *, V. Tyagi , R. Yadav and D. Sukumaran Vector Management Division, Defence R & D Establishment, Jhansi Road, Gwalior, Madhya Pradesh .India *

Corresponding Author: A.K. Sharma: [email protected] Received: 7 February 2013;

Accepted: 03 April 2013

ABSTRACT Anopheles subpictus is considered as a secondary vector of malaria with wider distribution, a prolific breeder in most part of India. Sibling species A of An. subpictus has been incriminated and established as a primary vector of malaria in some parts of India. Japanese encephalitis virus in India has also been isolated from 16 mosquito species including An. subpictus. An. subpictus is a major malaria vector in Sri Lanka. This species has also been reported to show insecticide resistant to DDT and dieldrin/HCH in various parts of India. Mosquitoes control remains the only viable strategy for preventing malaria and other mosquito borne-disease. Indiscriminate use of insecticides has resulted in the development of pesticide resistant strains and diminished the effectiveness of insecticides and moreover drug resistant strains of parasites that have further complicated the situation. An alternative strategy for vector control could be to exploit observed genetic variability in the vector populations. Moreover correct and precise identification of the target species has also medical and practical implications in vector control. In our present study, RAPD primers were screened to differentiate An. subpictus populations. POPGENE 1.31 software was used for statistical analysis and development of dendrogram based on RAPD fingerprints. In conclusion, we propose that our results on RAPD profiles of different An. subpictus populations provide evidence that there are enough and significant variations in the genomes of field collected populations from distant locations and the genetic pattern obtained in its various forms appears to be a major differentiating and orienting force for molecular changes in DNA across different populations. Keywords: Malaria, Anopheles subpictus, RAPD, genetic polymorphism

INTRODUCTION Anopheles subpictus is a species that is widely distributed in oriental regions and is a prolific breeder in most part of India during the rainy season. Sibling species A of An. subpictus (fresh water form) has been incriminated and established as a primary vector of malaria in Tarakeswar, west Bengal (chatterjee and Chandra, 2000), Orissa (Kumari et al., 2009), Jaffna area and is a well – established secondary vector of malaria in other parts of Sri Lanka (Kannathasan et al., 2008). Japanese encephalitis

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virus in India has been isolated from 16 mosquito species including An. subpictus (Samuel et al., 2000). This species has been reported to be resistant to DDT and dihedron/HCH in Gujrat (NMEP, 1991). Transmission of malaria can be reduced by adopting vector-control measures such as indoor residual spraying with insecticides, larval control measures and personal protection measures. The combination of tools and methods used to combat malaria now includes insects nets treated with long lasting insecticides and artemisinin based combination therapy, supported by indoor residual spraying of insecticide and intermittent preventive treatment during pregnancy. Vector control remains the most successful strategy for the suppression of mosquito-borne diseases. An alternative strategy for vector control could be to exploit observed genetic variability in the vector populations. Such studies are limited due to the limited knowledge of genome structure and complexity of mosquito species (Severson et al., 1994). Genetic diversity at its most elementary level is represented by differences in the sequences of nucleotides (adenine, guanine, thymine, and cytosine) that form the DNA within the cell of the organism. Genes regulate body size, shape, physiological processes, behavior traits, reproductive characteristics, tolerance of environmental extremes, dispersal and colonizing ability and many other traits which implicate that study of genetic diversity is important for understanding the biology of living organisms (Garros et al., 2004). The phenomenon of genetic variation has been estimated by several techniques including morphological studies, cytogenetics, protein electrophoresis and direct measurement of DNA variability. A molecular marker is a DNA sequence that is readily detected and whose inheritance can easily be monitored. Molecular marker consists of specific molecules which show easily detectable differences among different strains of a species or among different species. Population genetic studies require analysis of many individuals with multiple genetic markers like RAPD. It is rapid and relatively inexpensive compared with restriction fragment length polymorphism analysis or DNA sequencing (Hoy, 1994). Random amplified length polymorphic DNA (RAPD) (Welsh and Mc Cleland, 1990; Williams et al., 1990) is a polymerase chain reaction (PCR) technique that allows detection of many polymorphisms within the genomic DNA in a short time. In the present study we have used RAPD primers to understand the genetic polymorphism present in the An. subpictus population collected from different location. MATERIALS AND METHODS

Test Insect A total of 9 different locations representing Uttar Pradesh (2 locations namely- Agra & Muzaffarnagar) and Madhya Pradesh (6 locations namely- Dental hospital, Kachupura, Over bridge, New Defence Colony, DD Nagar & Rairu) alongwith laboratory reared An. stephensi, which is maintained in the lab since two decade has been taken as a reference population. The collection sites of mosquitoes of all sampling sites are shown in the map of India (Figure1). At every site only one time larvae collections was made from 3-4 closely located water bodies during the period from February to April, 2010. Larvae collected from each of these study area were pooled together respectively and a random sample was used for the study in order to minimize 154 International Journal of Bioinformatics and Biological Science: v.1 n.2 p.153-163. June, 2013

Evidence of Genetic Polymorphism in Anopheles subpictus Populations from India

relationship between mosquitoes. The adult mosquito emerged from these larvae collected were identified morphologically and representative samples are pinned as a voucher specimen and kept in the laboratory as reference collection. Mosquitoes were reared until adult stage under standard conditions (Temp 27o + 2oC, relative humidity 80 to 90% and 12-hrs light / dark cycle). The emerging virgin females were used for DNA extraction. The DNA extracted from single adult mosquito was used for further analysis.

Extraction of mosquito DNA The DNA extraction was done by using modified Coen method (Coen et al., 1982). Each sample (single adult mosquito) was homogenized in the micro centrifuge tube by adding 100µl lysis buffer. The homogenate was immediately kept on ice for 10 minutes and followed by heat treatment at 65ºC for 30 minutes. Subsequently, 30µl 5M potassium acetate was added and immediately transferred to ice for one hour followed by centrifugation at 13,000 rpm for 15 minutes at 10ºC. To the supernatant obtained, a double volume of absolute chilled ethanol was added for precipitation of DNA and kept tubes at -20ºC for overnight. After centrifugation at 13,000 rpm for 15 minutes at 10ºC, the precipitated DNA was washed in 70% ethanol twice. The pallet of DNA was allowed to air dry and finally dissolved in 50µl TE buffer. PCR amplification The PCR amplification reactions were performed in a final volume of 10µl reaction mixture per tube containing 1µl of 10X buffer, 1µl of MgCl2, 1µl of dNTPs, 0.5µl of Taq polymerase, primer 1µl, and 3.5µl of distilled water. Sterile distilled water was used to make the final volume. 10µl of master mix were taken in individual tubes with a negative control (PCR mix without DNA) was prepared to ensure purity of DNA. All PCR were performed using BIORAD, iCycler and reagents used for PCR were obtained from MBI Fermentas. For each PCR reaction 2µl of DNA samples from each sample of An. subpictus mosquito were used for PCR with Operon grade RAPD primers for screening as well as final genetic profiling of field collected mosquito samples. The temperature profile used were with initial denaturation at 94ºC for 4 minutes followed by 35 cycles of 94ºC for 1 minute, 40ºC for 1 minute, 72ºC for 2 minutes and a final extension step for 10 minute. Estimation of polymorphism and construction of phylogenetic tree: Photographs of gel were analyzed and polymorphism among species and the field samples were analyzed by scoring the polymorphic and monomorphic bands, ‘mono’ means same and ‘morphic’ means forms i.e., if same band of DNA is present in all individuals or the sample population under study. Whereas in polymorphic bands ‘poly’ means many and ‘morphic’ means forms is defined as discontinuous variation in a single population. The genetic distance and the polymorphism among the population were interpreted by using POPGENE version 1.31 software. A suitable phylogenetic was generated with the help of above referred software.


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RESULTS AND DISCUSSION A total of 50 Operon grade RAPD primers (Table 1) were screened with laboratory reared of mosquito species i.e., Culex quinquefasciatus, Aedes aegypti, Aedes albopictus, and Anopheles stephensi. Among these 50 primers only six primers OPAS 11, OPAS 12, OPAS 13, OPAS 15, OPM 12 and OPM 13 were able to generate clear, consistent and discrete banding pattern with the laboratory mosquito DNA. The polymorphism among four species was analyzed by scoring the polymorphic and monomorphic bands of selected primers. All these six primers revealed the percent polymorphism ranging from 94.7-100%. Table 1: The sequences of 50 RAPD-oligonucleotide primers screened to see genetic variability in Anopheles subpictus populations collected from different parts of India Primer

Sequence

Primer

Sequence

OPAK11 OPAK12 OPAK13 OPAK14 OPAK15 OPAK16 OPAK17 OPAK18 OPAK19 OPAK20 OPAS11 OPAS12 OPAS13 OPAS14 OPAS15 OPAS16 OPAS17 OPAS18 OPAS19 OPAS20 OPBD11 OPBD12 OPBD13 OPBD14 OPBD15

CAGTGTGCTC AGTGTAGCCC TCCCACGAGT CTGTCATGCC ACCTGCCGTT CTGCGTGCTC CAGCGGTCAC ACCCGGAAAC TCGCAGCGAG TGATGGCGTC ACCGTGCCGT TGACCAGGCA CACGGACCGA TCGCAGCGTT CTGCAATGGG AACCCTTCCC AGTTCCGCGA GTTGCGCAGT TGACAGCCCC TCTGCCTGGA CAACCGAGTC GGGAACCGTC CCTGGAACGG TCCCTGTGAG TGTCGTGGTC

OPBD16 OPBD17 OPBD18 OPBD19 OPBD20 OPM11 OPM12 OPM13 OPM14 OPM15 OPM16 OPM17 OPM18 OPM19 OPM20 OPX11 OPX12 OPX13 OPX14 OPX15 OPX16 OPX17 OPX18 OPX19 OPX20

GAACTCCCAG GTTCGCTCCC ACGCACACTC GGTTCCTCTC AGGCGGCACA GTCCACTGTG GGGACGTTGG GGTGGTCAAG AGGGTCGTTC GACCTACCAC GTAACCAGCC TCAGTCCGGG CACCATCCGT CCTTCAGGCA AGGTCTTGGG GGAGCCTCAG TCGCCAGCCA ACGGGAGCAA ACAGGTGCTG CAGACAAGCC CTCTGTTCGG GACACGGACC GACTAGGTGG TGGCAAGGCA CCCAGCTAGA

From the above six primers, primer OPAS 11 was selected for study of polymorphism in the field collected An. subpictus populations, this primer was selected on the basis of the clear, concrete and scorable fingerprint obtained by it. The DNA isolated from field populations were applied for RAPD-PCR using OPAS 11 primer and their genetic fingerprint was obtained on agarose gel (Figure 2). For inter population genetic variation studies, POPGENE 1.31 version software was used. RAPD banding information obtained from field population genome fingerprint was coded as a matrix of 1’s (band present) and 0’s (band absent) and used in computer 156 International Journal of Bioinformatics and Biological Science: v.1 n.2 p.153-163. June, 2013


Fig. 1: Map of India highlighting regions sampled for Anopheles subpictus population

Fig. 2: RAPD amplification profile of field population of Anopheles subpictus using primer OPAS 11; M- 100 bp plus DNA Ladder, 1-Dental Hospital, Gwalior; 2- Kachupura, Gwalior; 3- Overbridge, Gwalior; 4- Rairu, Gwalior; 5- Muzaffarnagar, Uttar Pradesh; 6- New Defence Colony, Gwalior; 7- DD Nagar, Gwalior; 8- Agra, Uttar Pradesh; B- Blank, An.- Anopheles stephensi Laboratory strain. International Journal of Bioinformatics and Biological Science: v.1 n.2 p.153-163. June, 2013 157

0.2412 0.1967 0.5596 0.3878 0.3365 0.2877 0.4418 0.4990

DH, GWL 0.7857 0.1133 0.4418 0.2877 0.3365 0.1967 0.2412 0.6242

0.8214 0.8929 0.4990 0.3365 0.2877 0.0741 0.2877 0.5596

0.5714 0.6429 0.6071 0.4990 0.5596 0.6242 0.5596 0.7673

K. PURA, OVERBRIDGE, RAIRU GWL. GWL 0.6786 0.7500 0.7143 0.6071 0.2877 0.3365 0.3878 0.6931

M. NAGAR (UP) 0.7143 0.7143 0.7500 0.5714 0.7500 0.1967 0.4418 0.3878

0.7500 0.8214 0.9286 0.5357 0.7143 0.8214 0.2877 0.4418

0.6229 0.7857 0.7500 0.5714 0.6786 0.6429 0.7500 0.7673

0.6071 0.5357 0.5714 0.4643 0.5000 0.6786 0.6429 0.4643 -

NEW DEF, REL PET, TAJ MAHAL, LAB GWL DD NAGAR AGRA (UP) An. St

1-Dental Hospital, Gwalior; 2- Kachupura, Gwalior; 3- Overbridge, Gwalior; 4- Rairu, Gwalior; 5- Muzaffarnagar, Uttar Pradesh; 6- New Defence Colony, Gwalior; 7- DD Nagar, Gwalior; 8- Agra, Uttar Pradesh; 9- Anopheles stephensi Laboratory strain.

DH, GWL K. PURA, GWL OVERBRIDGE, GWL RAIRU M. NAGAR(UP) NEW DEF, GWL REL PET, DD NAGAR TAJ MAHAL, AGRA (UP) LABAn. St.

Populations

Table 2: Distance matrix and Homology among the field populations of An. subpictus

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programs (written in note pad) written specifically for use with data generated by RAPD-PCR. The program RAPPIDIST was used to calculate Nei’s genetic distances (Nei, 1972) (Table 2) applying Lynch and Milligan’s (1994) correction, between the six field population and the laboratory reared An. subpictus population. For the statistical analysis, 28 bands or loci representing each of the seven population using OPAS 11 primer were taken, the average genetic distances between the populations was calculated. The average genetic distance between the populations was 0.5624 ranging from 0.1252-1.1015 (Table 2). Phylogenetic tree was generated with the help of POPGENE 1.31 version (Figure 3). The cluster analysis technique of unweighed pair- group method of arithmetic averages (UPGMA) and dice coefficient distance matrix method with appropriate bootstrap value was used to develop the trees (Figure 3). The consensus tree thus developed shows two clusters which are again branched as per their geographical distances.

Fig. 3: Phylogenetic tree representing Field populations of Anopheles subpictus

Fingerprinting genomes with arbitary primers is a versatile method for detecting genetic polymorphisms useful for population biology (Mc Clelland and Welsh, 1995). Most RAPD bands are dominant traits (Rafalski and Tinley, 1993) and their presence reflects priming sites flanking a segment of DNA suitable for amplification (Williams et al., 1990; Black, 1993). RAPD-PCR generates a fingerprint using arbitary selected primers and conditions of reduced stringency so the primer will initiate synthesis on DNA even when the match with the template is imperfect. The RAPD-PCR method has proved to be valuable in identifying large number of genetic polymorphisms in several insect species refractory to or little used for classical genetic analysis (Haymer, 1994). Similar work has been used for genetic fingerprinting (Apostol et al, 1993; 1994) they have also proven useful in detection and identification of cryptic species


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(Black et al, 1992, Willkerson et al, 1993; Kambhampati et al, 1992; Hill et al, 1994, Black and Munstermann , 1996). In case of mosquitoes (Kambhampati et al, 1992) several random primers have been used to distinguish between forest form and domestic form and also for other strains. In an attempt to separate members of the Anopheles gambiae complex, some random primers were screened to select profiles identifying each species. Field samples were developed in another species complex (Anopheles albitarsis complex), subgenus: Nyssorhipchus in South America. Here, four species including one undescribed species were also identifiable by some primers. Results presented in our study show that RAPD-PCR profile obtained by using primer OPAS 11 for Anopheles subpictus population collected from various different locations is capable to reveal the genetic polymorphism within this species. The observed genetic polymorphism in the populations may also affect the vector competence as the susceptibility of vector to transmit disease may also have genetic basis. The study carried out with RAPD primers are also able to differentiate the four species i.e., Culex quinque basciatus, Aedes aegypti, Aedes albopictus, and Anopheles stephensi, hence with the help of these RAPD primers one can demonstrate inter as well as intraspecific variation. The results of this study using RAPD markers revealed little to high genetic variations in An. subpictus with reference to their geographic distances between them. A number of similar studies have shown a considerable variation in the mean heterozygosity among the natural vector populations ranging from no demonstrable variations in Cx. pipiens pipiens (Farid et al., 1991, Sharma et al., 2009) to high level in Anopheles minimus (Komalamisra, 1989). Similar observations for genetic variations based on distance and geographic conditions were reported in Aedes aegypti populations at different locations by RAPD markers in Brazil (Ayres et al., 2003; Paduan et al., 2006) and Argentina (de Souza et al., 2001) and by allozymes in French Polynesia (Failloux et al., 1995). Besides various extrinsic environmental factors, frequent chemical insecticide pressure may also lead the genetic variation among the mosquito populations. In Brazil, such relationship were observed among the Anopheles subpictus (Ayres et al., 2004) and in India, similar observations were made for Culex quinquefasciatus populations (Sharma et al., 2009). The relationship of genetic variation of mosquitoes with its spatial and temporal difference in disease transmission is not fully understood. The possibility of variations in mosquito vectors for transmission of pathogen due to genetic differences among populations can not be ruled out. In conclusion, we propose that our results on RAPD profiles provide evidence that there are enough and significant variations in the genomes of field collected An. subpictus mosquito populations from distant locations and the genetic pattern obtained in its various forms appears to be a major differentiating and orienting force of molecular changes in DNA across different populations.



ACKNOWLEDGEMENT The authors are thankful to Prof. (Dr.) M. P. Kaushik, Outstanding Scientist & Director, DRDE, Gwalior, Madhya Pradesh, India, for interest and providing all necessary facility to conduct this research work. Sincere thanks also due to the scientists and supportive staff of Vector Management Division for their kind cooperation for carrying out the above work. REFERENCES Apostol, B.J., Black, W.C. IV, Miller, B.R, Reiter, P. and Beaty B.J. 1993. Estimation of the number of full sibling families at an oviposition site using RAPD-PCR markers: applications to the mosquito Aedes aegypti, Theoritical and Applied Genetics, 86: 991 – 1000. Apostol, B.L., Black, W.C., Reiter, P. and Miller, B.R. 1994. Use of randomly amplified polymorphic DNA polymerase chain reaction markers to estimate the number Aedes aegypti families at oviposition sites in San Jaun, Puerto Rico, American Journal of Tropical Medicine Hygiene , 51:89-97. Ayres, C. E. J., Melo-Santos, M. A. V., Prota, J. R. M., Solé-Cava, A. M., Regis, L. and Furtado, A. F. 2004. Genetic structure of natural populations of Aedes aegypti at the micro- and macro-geographic levels in Brazil, Journal of American Mosquito Control Association , 20: 350-356. Ayres, C. F. J., Melo-Santos, M. A. V., Cava, Sole A. M.and Furtado, A. F. 2003. Genetic Differentiation of Aedes aegypti (Diptera: Culicidae), the major dengue vector in Brazil Journal of Medicne Entomology, 40: 430-435. Black, W.C. 1993. PCR with arbitary primers: approach with care, Insect Molecular Biology 2: 1-6. Black, W.C. IV, and Munstermann, L.C. 1996, Molecular taxonomic and systematics of arthropod vectors. In BJ Beaty, WC Marquardt (eds), The Biology of Disease Vectors, University Press of Colorado, Niwot, CO, 438-470. Black, W.C. IV, Duteau, N.M., Puterka, G.J., Nicols, J.R. and Pettorini, J.M. 1992. Use of the random amplified polymorphic DNA polymerase chain reaction (RAPD –PCR) to detect DNA polymorphisms in aphids (Homoptera; Aphididae), Bulletin of Entomological Research , 82: 151-159. Chatterjee, S.N and Chandra, G. 2000. Role of An. subpictus as a primary vector of malaria in an area in India, Japan Journal of Tropical Medicine andHygiene, 28: 177-81. Coen, E.S., Stracha, T and Dover, G., 1982. Dynamics of concerted evolution of ribosomal DNA and histone gene families in the melanogaster species subgroup of Drosophila, Journal of Molecular Biology, 158: 7-35. De Sousa, G. B., Blanco, A. and Gardenal, C. N. 2001. Genetic Relationships among Aedes aegypti 9 Diptera: Culicidae) Populations from Argentina using Random Amplified Polymorphic DNA polymerase chain reaction markers, Journal of Medicinal Entomology , 38: 371-375. Failloux, A. B., Darius, H. and Pasteur, N. 1995. Genetic differentiation of Aedes aegypti, the vector of Dengue virus in French Polynesia, Journal of American Mosquito Control Association , 11: 457-462. Farid, H. A., Gad, A. M. and Speilman, A., 1991. A genetic similarity among Egyptian populations of Culex pipiens (Diptera: Culicidae), Journal of Medical Entomology, 28: 198-204.


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Trichoderma induce Alteration of Serine/ threonine and Tyrosine Phosphatase in Bipartite Interaction of Brassica Juncea Priyadarshni Kumar1 and Chandan Kumar2* 1

Deptt of Mol. Biol. and Genetic Engineering, G.B. Pant Univ. of Agril. & Technology, Pantnagar -263145, India 2 Radiopharmaceuticals Division, Bhabha Atomic Research Centre, Trombay, Mumbai – 400085, India *

Corresponding Author: Chandan Kumar: [email protected] Received: 2 April 2013;

Accepted: 07 June 2013

ABSTRACT Brassica is important edible oil crops of northern hemisphere of India and its major yield loss are due to the fungal pathogen. Biocontrol agent such as Trichoderma, enhances water micronutrient availability of the plants and induces systemic resistances against fungal pathogens by MAPK cascade. Dephosphorylation through tyrosine and serine/threonine phosphatase plays an important role in regulation of MAPK activity. To study tyrosine and serine/ threonine phosphatase, Brassica seeds were mixed with slurry of Trichoderma strains and sown in conical flasks. One and two week later protein was isolated from seedlings. MyBP was labeled with radioactive phosphorus through (γ33P)ATP at ser/thr position by PKA and at tyr position by Abl kinase which was used as substrate for the protein extracted from one and two week old Trichoderma inoculated seedlings. Ser/thr and tyrosine phosphatase remove inorganic 33PO4 from the respective 33P -labeled MyBP. Phosphatase activity was measured in terms of amount of free 33PO4 which was measured in scintillation counter. It was found that the phosphatase enzyme activity in control plants was significantly higher as compared to treated plants showing the activation of respective kinases. Out of the two strain of Trichoderma harzianum T43 is more potent than the T39 which may be able of provide added benefit to plants in longer duration of cultivation. Keywords: MAPK MyBP, bipartite interaction, phosphatase, brassica, trichoderma

INTRODUCTION Brassica is a very diverse group of edible oil crops, belong to the family cruciferae and genus Brassica, which oil content varies from 30 to 48%. The major fungal diseases of Brassica oilseeds are Albugo candida, downy mildew and black spot (Saharan et al., 1992) which cause severe crop yield loss. Biocontrol agents help in maintaining plant growth, development and acquiring resistance to various intruders.

Kumar and Kumar

Among the biocontrol agents the genus Trichoderma is a widely established and successful to interact with different organisms including plants and other microbes. It also enhances water micronutrient availability to the plants and induces systemic resistance. (Thrane et al., 1997 and Shoresh et al., 2005). They efficiently produce extra cellular enzymes like cellulase, chitinase, glucanase, protease etc (Benítez et al., 1998, Lifshitz et al., 1986 and Elad et al., 1982) and are instrumental in decomposing soil organic matter which helps in plant growth. Trichoderma sp. includes a number of strains, with the ability to produce antimicrobial antagonistic phytochemicals which acts against phyto-pathogenic microbes (Kubicek et al., 2001 and Fogliano et al., 2002) Hence, Trichoderma species not only improves overall plants growth but also help in defending from pathogenic organisms. The Trichoderma sp has the ability to systemically activate plant resistance mechanisms against different pathogens invasion and involves multiple signal transduction pathway (Mukherjee et al., 2003 and Mendoza et al., 2003). Mitogen-Activated Protein Kinases (MAPKs) have been established as a signal transduction components in a variety of plants. Activation of MAPKs requires phosphorylation on serine/threonine and tyrosine residues by upstream kinases, which has been proposed to play a role in cell cycle/ cell division (Thomas et al., 2000 and Jonak et a., 1993) hormone signaling and transduction of stimuli related to abiotic (Bogre et al., 1999 and Usami et al., 1995) and biotic stress (Bogre et al., 1997 and Adam et al., 1997). The activity of MAPK is strictly regulated via phosphorylation of the conserved TXY motif by an upstream MAPK kinase (MAPKK). Conversely, MAPKs activity are inactivated by dephosphorylation by protein phosphatase including tyrosine-specific phosphatase, serine/threonine-specific phosphatase and dual-specificity MAPK phosphatase (MKPs), which are highly specific to MAPKs. There are several families of protein phosphatase (Gupta et al., 1998) that catalyze the dephosphorylation of intracellular phosphoproteins, thereby reversing the action of protein kinases. Now a day many of the MAPK specific phosphatase is known like MP2C (Irute et al., 1998). In this present work we want to find the ser/thr and tyrosine phosphatase activity in Brassica seedlings induced by two different strain of Trichoderma so that it could be further studied its potential role and mechanism in induced resistance against plant pathogens. MATERIALS AND METHODS

Reagents All chemicals are procured from Sigma inc.USA, unless otherwise stated in the text. Protein ser/thr and tyr phosphatase assay systems were procured from New England Bio Labs inc. UK. [γ-33P] ATP was obtained from, Jonaki BRIT India. Plant material Seeds of Brassica juncea var. Varuna were kindly provided by Dr R.P. Awasathi, Professor of Plant Pathology from G.B Pant Univ. of Agril. &Tech., Pantnagar, Uttarakhand India.


Trichoderma induce alteration of phosphatase in Brassica

Trichoderma culture Trichoderma harzianum strains of T-43 and T-39 were procured from Bio control lab, Plant pathology, of G.B Pant Univ. of Agril. &Tech., Pantnagar, Uttarakhand. Inoculums concentration of Trichoderma harzianum cultures T-43 and T-39 were 12x108 and 12x108 cfu respectively. Trichoderma Treatment by Slurry method Brassica seeds (10-20) were soaked overnight and mixed with the slurry of Trichoderma strains T-43 and T-39. Seeds were sown in steam sterilized 250 ml conical flask containing agar-agar medium. The roots of treated plants were excised after 4-5 days of inoculation, sterilized (0.1% hypochlorite, 2min) and washed thrice with sterile water. The roots were cut into small pieces of 1cm in length and put on Trichoderma selective medium (TSM) to check the Trichoderma growth. Protein extraction One gm of cotyledon leaves were collected after one and two week of post treatment of Trichoderma and were crushed in liquid nitrogen. Powdered leaves are homogenized in 5ml of protein extraction buffer (Tris pH 7.4-10mM, NaCl-100 mM, EDTA -1mM, NaF -1mM, Na4P2O7- 20mM, Na3VO4 20mM, Triton100 -1%, Glycerol -10%, SDS -0.1%, Deoxycholate -0.5%, PMSF -1 mM and protease inhibitor cocktail) and centrifuged (20min, 10,000rpm, 4oC). The supernatant was transferred in eppendorf tube and stored at -80 oC for further use. Protein estimation was carried out by standard Bradford method (Bradford, 1976). The protein samples were used for the radioactive phosphatase assay. Radioactive Phosphatase assay of two ways interaction of BrassicaTrichoderma c-AMP dependent protein kinase (PKA) is serine/threonine specific kinase which phosphorylates the serine /threonine residue, unlike Abl protein tyrosine kinase (Abl) which is tyrosine specific kinase phosphorylates the tyrosine residue, Myelin basic protein (MyBP) was used as a substrate for the PKA and abl kinase which phosphorylats at serine/treonine and tyrosine residue in the presence of [γ- 33P] ATP. This phosphorylated MyBP was used a substrate for serine/threonine and tyrosine specific phosphatase (protein sample) which remove the 33PO4 group from the radiolabeled MyBP. Released inorganic 33PO4 was measured in scintillation counter which was the measure of enzyme activity expressed as percentage of ser/thr and tyrosine phosphatase. Enzymatic Labeling and purification of My BP MyBP was labeled with [γ-33P] ATP with PKA at serine/threonine position and with Abl at tyrosine position as following the protocol provided with kit. Briefly, following components {(PKA /MyBP or Abl /MyBP), 10x PKA (Abl) Buffer, ATP (10mM), [γ-33P] ATP and deionized water} were mixed in a 1.5 ml microcentrifuge tube in ratio of 4:2:2:1:11 v/v and incubated overnight at 30ºC. The reaction was terminated International Journal of Bioinformatics and Biological Science: v.1 n.2 p.165-172. June, 2013 167

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by adding of 10 ml of 100% TCA, kept on ice for (30 min). Labeled MyBP was purified by centrifugation (12,000Xg, 10 min, 4 ºC) because TCA precipitate the MyBP and unreacted [γ-33P] ATPs were present in the supernatant. Approximately 90% radioactive ATP (unreacted) are removed in this step. The supernatant was removed with a pipette tip without touching the pellet. The pellet was washed thrice by mixing on vortex) with 1ml of 20% TCA, again centrifuged (12,000Xg, 5min) and supernatant was discarded. Substrate solubilization buffer (0.5ml, provided with kit) was added to the tube and tapped with fingers for several times so that pellet was dissolved. The sample was transferred to 8kD molecular cut-off dialysis membrane and dialyzes against phosphate buffer for overnight. Buffer was changed 2-3 times to make radioactive MyBP free from (γ-33P)-ATP. Aliquot of 5 µl in duplicate was counted in scintillation counter to determine the incorporated phosphate concentration (µM) in MyBP. Incorporated Phosphate (γ-33P) concentration (µM) = {1/5*(Aliquots cpm/specific activity)}. Labeled MyBp was diluted in 1x protein phosphatase buffer to a concentration of 50 µM with respect to the incorporated 33PO4 and stored at 4º C for future use.

Assay of protein serine/threonine and tyrosine phosphatase Protein samples (crude extract of protein) were diluted to 1- 4 fold in phosphatase assay buffer. Reaction was set up along with the blank (without protein sample) and kept on ice in a 1.5 ml microcentrifuge tube. Reaction tubes were pre-incubated for 2-5 min at 30ºC and reaction was initiated by the addition of 10 µl of substrate (33P-MyBP) and incubated for 10 min at 30ºC. The reaction was terminated by adding 200 µl of cold 20% TCA, vortex and placed on ice for 10-20 minutes and spun at (12,000Xg for 5 min). TCA supernatant (200 µl) was aspirated with the pipette tip and added to 20ml aqueous scintillation cocktail (procured from M/s SRL, Mumbai, India). The activity in supernatant and total radioactivity in 10 µl of the substrate (33P-MyBP) was measured in liquid scintillation counter. Radioactive count of 33PO4 are expressed as % enzyme activity [(100x {CPM of Released 33PO4/CPM of 10 µl of substrate (33P)-MyBP}]. Statistical analysis All results are presented as mean ± sd of at least three independent experiments. Statistical analysis was performed using one way ANOVA test where p value kept 99 % pure.

Assay of protein serine/ threonine and tyrosine phosphatase Percent enzyme activity in control samples (without Trichoderma treatment) of one and two week old seedlings were 23.2 ± 0.62 and 27.31± 0.76 respectively (Figure 1.).While seedlings grown in presence of T39 strain, the protein serine/ threonine phosphatase after one and two week old seedlings are 10.72 ± 0.62 (p