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Trends in Biotechnology & Biological Sciences Volume 1

Number 1

November, 2014

CONTENTS MINI REVIEW 1.

Codon Usage Data Base Explorer (CUDBE): A Codon Usage Database of Firmicutes Bacteria Kishor Shende, Anil Prakash and Kamalraj Pardasani

1

2.

Heavy Metal Contamination and Health Hazards: A Review Sabina Khanam

5

RESEARCH PAPERS 3.

Performance of Yield and Quality in Advanced Lines of Fine Rice (Oryza sativa) M. Atiqur Rahman, Md. Sarowar Hossain, Imtiaz Faruk Chowdhury and H. Mehraj

9

4.

Morphological and Molecular Identification of Trichoderma asperellum Tasp/8940 Mohammad Shahid, Mukesh Srivastava and Anuradha Singh

19

5.

Entomopathogenicity and Cross Infectivity of EPNs on Cardamom Shoot/Panicle/ Capsule Borer Conogethes punctiferalis Guenee’ under Laboratory Conditions. M.A. Ansar Ali, T.manoharan and S. Kuttalam

25

6.

Growth Performance, Photosynthetic Efficiency and Pigment Concentration of Glycine max (L.) Merr., as affected by Alphamethrin, a Synthetic Pyrethroid Insecticide Fozia Bashir, Faisal Zahid and M. Iqbal

29

Subscription Order Form Instructions to the Authors

29

Trends in Biotechnology & Biological Sciences 1(1), ISSN PRINT : 2394-5508, 1-4, 2014

MINI REVIEW

Codon Usage Data Base Explorer (CUDBE): A Codon Usage Database of Firmicutes Bacteria KISHOR SHENDE1, ANIL PRAKASH2 AND KAMALRAJ PARDASANI3 1

Department of Biotechnology and Bioinformatics Center, Barkatullah University, Bhopal Department of Microbiology, Barkatullah University, Bhopal 3 Department of Applied Mathematics, MANIT, Bhopal email : [email protected] 2

ABSTRACT CUDBE (Codon Usage Database Explorer) as an effort to create a catalog of tabulated codon usage and compositional values of ORFs of firmicutes bacterial genomes. Currently CUDBE is comprised of codon usage data of phylum firmicutes bacteria. The data is tabulated using MS-Excel sheet individually for each bacterial genome. Database is in Compact Dick form designed using html web page as interface and MS-Excel sheet as backend. Database can explored through hyperlinks of web pages and tables MSExcel sheet. The data can be further analyzed by other computational software that uses MS-Excel file as an input. The database can be obtained in CD database from Bioinformatics Center, Barkatullah University Bhopal. Key words

Codon, Firmicutes, Bacteria

The Phylum Firmicutes (Latin: firmus = strong and cuties = skin, referring to cell wall) is comprised of bacteria with low G+C content of genome. They are Gram positive, either cocci or rods or spindle shaped. They are ecologically, industrially and medically important group of bacteria found in diversified environmental conditions and many are pathogenic (Tortora, 2004). Phylum Firmicuteswas divided into three main classes as Bacilli, Clostridia and Mollicutes (Bergey’s Manual), but recently class Mollicute have been replaced by new class ‘Erysipelotrichi’ (Bergey’s Manual) due to unique phenotypic properties like lack of rigid cell wall (Ludwig and Scheifer, 2005).Phylum Firmicutes bacteria are ecologically, industrially and medically important for human being. Genus Bacillus comprised of diversified bacteria important in industrial, agricultural, medical and ecological field. Most of them are soil dwelling, helps in improving the soil fertility and also produces plant growth promoting hormones. Most of the bacteria are given importance for genome sequencing belongs to the genus Bacillus, Lactobacillus, Staphylococcus, Streptococcus, Clostridiums and Mycoplasma (Erysipelotrichi)(Bergey’s Manual).

Genetic information is passed from one generation to next through replication process and to the proteins and protein assembly through the processes transcription and then by translation (Snyder and Champness, 1997). Different organisms often show particular preference of a particular codon over its synonymous codon is called as Codon Bias. Synonymous codon usage is non-random and species specific. As the numbers of sequenced genes grew, it became evident that synonymous codons are not used equally (Ikemura, 1981; Grantham, et al., 1981). Connections have also been demonstrated between codon usage and: (i) gene length (Moriyama and Powell, 1998); (ii) gene location on the chromosome (Daubin and Perriere, 2003); (iii) the strand it resides on (James, and Gary, 2010); (iv) need for specific secondary structures in mRNA (Mondal, et al., 2008); and (v) characteristics of the gene’s protein product, such as its hydrophobicity (D’Onofrio, et al., 1999) or secondary structure elements (Oresic and Shalloway, 1998).Number of indices were proposed to partition the influence of functional constraint, natural selection, mutational pressure and physical stability of the double stranded genomes in the evolution of the genes and genomes of organism. The frequency of codon use in each organism is made searchable through World Wide Web Site. Various codon usage indices has been formulated and used to explore the codon usage variation as, Relative Synonymous Codon Usage (RSCU) (Grantham, et al. 1974), GC percentage of the whole genome (Jukes and Bhushan, 1986), Codon Adaptive Index (CAI) (Sharp and Li 1987), Effective Number of Codons (ENC or Nc) (Wright,1990). Codon Usage Database (Nakamura, et al., 2000) is an extended form of initially developed CUTG (Codon Usage Translated from Genbank) by Ikemura and Tashimichi. The codon catalog given in Codon Usage Database is the overall use of the codons obtained by summing codons frequencies of all sequenced genes in the genome that characterize the species or genus or functional

2

Trends in Biotechnology & Biological Sciences 1 (1), 2014

Fig. 1. Interface of CUDBE (Codon Usage Explorer Database) showing listing of firmicutes Bacteria with hyperlink to respective MS-EXCEL table.

processes. The aim of current work was to catalog the codon usage variations data generated using various measures, of the bacteria from the phylum firmicutes at genus, species and functional process (COG categories) level. This will help in assessing the codon usage pattern and its implication on gene expression patterns in firmicutes bacteria.

Data Generation and Data tabulation These 103 Firmicutes genome sequences (Table 3.1) comprised of, 74 genomes of 33 species of class Bacilli, 15 genomes of 10 species of class Clostridia and 14 genomes of 12 species of class Mollicutes (Now Erysipelotrichi) cataloged. The data files related to

Fig. 2. Interface of CUDBE showing codon usage table in MS-Excel sheet

SHENDE, et al., Codon Usage Data Base Explorer (CUDBE): A Codon Usage Database of Firmicutes Bacteria

3

Fig. 3. Interface of CUDBE showing codon usage table in MS-Excel sheet

Firmicutes genomes, genes, gene product, structural genes and annotations were obtained from NCBI ftp site (ftp://ftp.ncbi.nlm. nih.gov/genomes/Bacteria/). The data was generated using the software tools, CODONW (Peden, 1999), and in-house developed software tool BIOCOM (Unpublished). ORFs were analyzed for various parameters related with codon usage analysis at genome and process level. The data was classified on the basis of COG categories and tabulated using MS-EXCEL (Microsoft INC, USA).

Database Architecture and Utility Database was developed on auto-bootable CD, using MS-EXCEL as back end and web page as an interface, with browsing nature. Database was based on simple flat file type linked to text on html pages (Fig. 1). Respective tables of codon usage values and genomic composition of each bacterium were created. ORFs were placed as records and genomics features as columns. Genomics features as, compositional analysis (Nucleotide composition, AT%, GC%), AT and GC Skewness, Codon Adaptive Index, Codon Bias Index, Amino acid composition, actual and percent frequency of codon, RSCU (Relative Synonymous Codon Usage) (Grantham, et al. 1974), Nc, Enc (Wright,1990), GC and AT compositional attributes of 1st, 2nd and 3rd position of codons, AROMA, gravy and axis values of correspondence analysis. Besides, the

ORFs information is also provided as, location of ORFs, strand, gene, COG code, PID number and product (Fig. 2). The database is simple and can be easily explored through the html interface. The data can be accessed in the form of MS-Excel sheet to perform further computational analysis using various criteria with user’s own set of parameters. The graphical presentation of process wise cluster analysis and compositional analysis are also provided with database. Best view of this database is given in Mozilla Firefox. The snapshot of the designed database is given in Fig. 3. A database named CUDBE (Codon Usage Database Explorer) was created to store the codon usage related data of genomes Firmicutes bacteria. Database is simple and easy to explore. The data files are in MS-Excel that provides flexibility for performing various computational analysis using different statistical methods and criteria by other software. The Codon Usage Database is not updated further after 2007 and also it only provides the codon usage catalog of whole genome, not the individual gene, process wise cataloging. Therefore this under development database CUDBE will facilitated the study of optimization of codon usage in gene under the process of cloning in foreign host and laterally transferred gene in firmicutes bacteria.

4


LITERATURE CITED Daubin, V. and Perriere, G. 2003. Molecular Biology of Evolution. 20(4):471-483. D’Onofrio, G., Jabbari, K., Musto, H. and Bernardi, G.. 1999. Gene. 238(1):3-14. Grantham, R. 1974. Science. 185:862-864 Grantham, R.C., Gautier, Gouy, M., Jacobzone, M. and Mercier, R. 1981. Nucleic Acids Research. 9: r43-r75.

Moriyama, E.N. and Powell, J.R.. 1998. Nucleic Acids Research. 26(13):3188-3193. Nakamura, Y., Gojobori, T. and Ikemura, T. 2000. Nuclic. Acid Research. 28:292. Oresic, M. and Shalloway, D. 1998. Journal of Molecular Biology. 281(1):31-48. Peden, J. 1999. Analysis of codon usage. PhD Thesis. University of Nottingham.

Ikemura, T. 1981. Journal of Molecular Biology. 151:389-409.

Sharp, P.M. and Li, WH. 1987. Nucleic Acids Research. 15:12811295.

James, J.D. and Gary, J.O. 2010. Molecular Biology Evolution. 27(4):800-810.

Snyder, L. and Champness W. 1997. Molecular Genetics of Bacteria. ASM press public. Washington DC. pp. 13-27.

Ludwig, W. and Schleifer, K.H. 2005. Oxford University Press, New York, pp. 70–98.

Tortora, G.J., Funke, B.R. and Case, C.L., 2004. Microbiology: An Introduction. 8th edition. pp: 211-220.

Mondal, U.K., Sur, S., Bothra, A.K. and Sen A. 2008. Indian J. of Medical Microbiol. 26(4):313-321.

Wright, F. 1990. The ‘effective number of codons’ used in a gene. Gene. 87:23-29. Received on 07-09-2014

Accepted on 16-09-2014


MINI REVIEW

Heavy Metal Contamination and Health Hazards: A Review SABINA KHANAM Deptt. of Zoology, D.G. College, Kanpur

ABSTRACT Heavy metals have been used by humans for thousands of years. Although several adverse health effects of heavy metals have been known for a long time, exposure to heavy metals continues, and is even increasing in some parts of the world, in particular in less developed countries, though emissions have declined in most developed countries over the last 100 years. Toxic metals can be present in industrial, municipal, and urban runoff, which can be harmful to humans and aquatic life. Heavy metals within water supply, seriously effect health. Severe effects on humans include reduced growth and development, cancer, organ damage, nervous system damage, and in extreme cases, death. Exposure to some metals, such as mercury and lead, may also cause development of autoimmunity, in which a person’s immune system attacks its own cells. This can lead to joint diseases such as rheumatoid arthritis, and diseases of the kidneys, circulatory system, and nervous system. Heavy metals in the environment are caused by air emissions from coal-burning plants, smelters, and other industrial facilities; waste incinerators; process wastes from mining and industry. Key words

heavy metals, health, environment.

The term “heavy metals” refers to any metallic element that has a relatively high density and is toxic or poisonous even at low concentration. “Heavy metals” is a general collective term, which applies to the group of metals and metalloids with atomic density greater than 4 g/cm3 , or 5 times or more, greater than water. However, being a heavy metal has little to do with density but concerns chemical properties. Heavy metals include lead (Pb), Cadmium (Cd), mercury (Hg). Heavy metals occur as natural constituents of the earth crust, and are persistent environmental contaminants since they cannot be degraded or destroyed. To a small extent, they enter the body system through food, air, and water and bioaccumulate over a period of time.

Heavy Metal Emission Heavy metals can be emitted into the environment by both natural and anthropogenic causes. The major causes of emission are the anthropogenic sources

specifically mining operations. In some cases, even long after mining activities have ceased, the emitted metals continue to persist in the environment. Hard rock mines operate from 5-15 years until the minerals are depleted, but metal contamination that occurs as a consequence of hard rock mining persist for hundreds of years after the cessation of mining operations. Apart from mining operations, mercury is introduced into the environment through cosmetic products as well as manufacturing processes like making of sodium hydroxide.

Human Exposure Through Food, Air and Water Heavy metal pollution of surface and underground water sources results in considerable soil pollution and pollution increases when mined ores are dumped on the ground surface for manual dressing. Surface dumping exposes the metals to air and rain thereby generating much AMD. When agricultural soils are polluted, these metals are taken up by plants and consequently accumulate in their tissues. Animals that graze on such contaminated plants and drink from polluted waters, as well as marine lives that breed in heavy metal polluted waters also accumulate such metals in their tissues, and milk, if lactating. Humans are in turn exposed to heavy metals by consuming contaminated plants and animals, and this has been known to result in various biochemical disorders. In summary, all living organisms within a given ecosystem are variously contaminated along their cycles of food chain.

Human Exposure Through Industrial Products Industrial products that are used in homes, and which have been produced with heavy metals are sources of human exposure to such heavy metals. Mercury exposure is through disinfectants (like mercurochrome), antifungal agents, toiletries, creams and organo-metallics; cadmium exposure is through nickel/cadmium batteries and artist paints; lead exposure is through wine bottle wraps, mirror coatings, batteries, old paints and tiles and linolein amongst others. Infants are more susceptible to the endangering effects of exposure to heavy metals.

6


Table 1. Guideline in drinking water by the World Health Organization (WHO) and National Agency for Food and Drugs Administration and Control (NAFDAC), Nigeria. Heavy metal

Max. acceptable conc. (WHO)

Max. acceptable conc. (NAFDAC)

Lead Cadmium

0.01 mg/l 0.003 mg/l

0.0 mg/l 0.0 mg/l

Mercury

0.001 mg/l

0.0 mg/l

List of Drinking Water Contaminants Contaminants Potential Health Effects from Ingestion of Water

Sources of Contaminant in Drinking Water

Lead

Corrosion of household plumbing systems; erosion of natural deposits

Health Effects Lead: Occupational exposure to inorganic lead occurs in mines and smelters as well as welding of lead painted metal, and in battery plants. Low or moderate exposure may take place in the glass industry. High levels of air emissions may pollute areas near lead mines and smelters. Airborne lead can be deposited on soil and water, thus reaching humans via the food chain. Up to 50% of inhaled inorganic lead may be absorbed in the lungs. Adults take up 10–15% of lead in food, whereas children may absorb up to 50% via the gastrointestinal tract. Lead in blood is bound to erythrocytes, and elimination is slow and principally via urine. Lead is accumulated in the skeleton, and is only slowly released from this body compartment. Halflife of lead in blood is about 1 month and in the skeleton 20–30 years (WHO 1995). The symptoms of acute lead poisoning are headache, irritability, abdominal pain and various symptoms related to the nervous system. Lead encephalopathy is characterized by sleeplessness and restlessness. Children may be affected by behavioural disturbances, learning and concentration difficulties. In severe cases of lead encephalopathy, the affected person may suffer from acute psychosis, confusion and reduced consciousness. People who have been exposed to lead for a long time may suffer from memory deterioration, prolonged reaction time and reduced ability to understand. In less serious cases, the most obvious sign of lead poisoning is disturbance of haemoglobin synthesis, and long-term lead exposure may lead to anaemia.

Cadmium

Mercury (inorganic)

 Infants and children: Delays in physical or mental development; children could show slight deficits in attention span and learning abilities  Adults: Kidney problems; high blood pressure Kidney damage

Kidney damage

Corrosion of galvanized pipes; erosion of natural deposits; discharge from metal refineries; runoff from waste batteries and paints Erosion of natural deposits; discharge from refineries and factories; runoff from landfills and croplands

Cadmium: Natural as well as anthropogenic sources of cadmium, including industrial emissions and the application of fertilizer and sewage sludge to farm land, may lead to contamination of soils, and to increased cadmium uptake by crops and vegetables, grown for human consumption. The uptake process of soil cadmium by plants is enhanced at low pH. Cigarette smoking is a major source of cadmium exposure. Biological monitoring of cadmium in the general population has shown that cigarette smoking may cause significant increases in blood cadmium (BCd) levels, the concentrations in smokers being on average 4–5 times higher than those in non-smokers (Jarup, et al., 1998). Despite evidence of exposure

KHANAM, Heavy Metal Contamination and Health Hazards: A Review

from environmental tobacco smoke, however, this is probably contributing little to total cadmium body burden. Food is the most important source of cadmium exposure in the general non-smoking population in most countries. Cadmium is present in most foodstuffs, but concentrations vary greatly, and individual intake also varies considerably due to differences in dietary habits. Women usually have lower daily cadmium intakes, because of lower energy consumption than men. Gastrointestinal absorption of cadmium may be influenced by nutritional factors, such as iron status (Flanagan, et al., 1978). Inhalation of cadmium fumes or particles can be life threatening, and although acute pulmonary effects and deaths are uncommon, sporadic cases still occur (Barbee et al. 1999). Cadmium exposure may cause kidney damage. Mercury: The mercury compound cinnabar (HgS) was used in pre-historic cave paintings for red colours, and metallic mercury was known in ancient Greece where it (as well as white lead) was used as a cosmetic to lighten the skin. In medicine, apart from the previously mentioned use of mercury as a cure for syphilis, mercury compounds have also been used as diuretics [calomel (Hg2Cl2)], and mercury amalgam is still used for filling teeth in many countries (WHO 1990). Metallic mercury is used in thermometers, barometers and instruments for measuring blood pressure. A major use of mercury is in the chlor-alkali industry, in the electrochemical process of manufacturing chlorine, where mercury is used as an electrode. The largest occupational group exposed to mercury is dental care staff. During the 1970s, air concentrations in some dental surgeries reached 20 ìg/m3, but since then levels have generally fallen to about one-tenth of those concentrations. Acute mercury exposure may give rise to lung damage. Chronic poisoning is characterized by neurological and psychological symptoms, such as tremor, changes in personality, restlessness, anxiety, sleep disturbance and depression. The symptoms are reversible after cessation of exposure. Because of the blood–brain barrier there is no central nervous involvement related to inorganic mercury exposure. Metallic mercury may cause kidney damage, which is reversible after exposure has stopped. It has also been

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possible to detect proteinuria at relatively low levels of occupational exposure. Metallic mercury is an allergen, which may cause contact eczema, and mercury from amalgam fillings may give rise to oral lichen. It has been feared that mercury in amalgam may cause a variety of symptoms. This so-called ‘amalgam disease’ is, however, controversial, and although some authors claim proof of symptom relief after removal of dental amalgam fillings[14], there is no scientific evidence of this (Langworth, et al., 2002).

LITERATURE CITED Barbee, Jr, J.Y. and Prince, T.S. 1999. Acute respiratory distress syndrome in a welder exposed to metal fumes. South Med J. 92: 510–2 Battarbee, R., Anderson, N., Appleby, P., Flower, R.J., Fritz, S., Haworth, E., Higgit, S., Jones, V., Kreiser, A., Munro, M.A., Natkanski, J., Oldfield, F., Patrick, S.T., Richardson, N., Rippey, B. and Stevenson, A.C. 1988. Lake Acidification in The United Kingdom, ENSIS, London. http://www.geog.ucl.ac.uk/~spatrick/f_r_pubs.htm Flanagan, P.R., McLellan, J.S., Haist, J., Cherian, M.G., Chamberlain, M.J. and Valberg, L.S. 1978. Increased dietary cadmium absorption in mice and human subjects with iron deficiency. Gastroenterology, 74: 841–6 Garbarino, J.R., Hayes, H., Roth, D., Antweider, R., Brinton, T.I. and Taylor, H. 1995. Contaminants in the Mississippi River, U. S. Geological Survey Circular 1133, Virginia, U.S.A. (www.pubs.usgs.gov/circ/circ1133/) Habashi, F. 1992. Environmental Issues in the Metallurgical Industry –Progress and Problems, Environmental Issues and Waste Management in Energy and Mineral Production. Balkama, Rotherdam, pp. 1143 -1153. Hawkes, J.S. 1997. Heavy Metals, J. Chem. Educ. 74(11): 1374. Horsfall, M.N., Jr., Spiff, A.I. 1999. Speciation of Heavy Metals in Intertidal Sediments of the Okirika River System (Nigeria), Bull. Chem. Soc. Ethiop. 13(1): 1–9. Hossn, E., Mokhtar, G., El-Awady, M., Ali, I., Morsy, M. and Dawood, 2001. A. Environmental exposure of the pediatric age groups in Cairo City and its suburbs to cadmium pollution. Sci Total Environ; 273: 135–46 Hutton, M. and Symon, C. 1986. The Quantities of Cadmium, Lead, Mercury and Arsenic Entering the U.K. Environment from Human Activities. Sci. Total Environ. 57: 129-150. Institute of Environmental Conservation and Research INECAR 2000. Position Paper Against Mining in Rapu-Rapu, Published by INECAR,Ateneo de Naga University, Philippines (www.adnu.edu.ph/Institutes/Inecar/ pospaper1.asp) Jarup, L., Berglund, M., Elinder, C.G., Nordberg, G. and Vahter, M. 1998. Health effects of cadmium exposure—a review of the literature and a risk estimate. Scand J Work Environ Health; 24 (Suppl 1): 1–51 Langworth, S., Bjorkman, L., Elinder, C.G., Jarup, L. and Savlin,

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Trends in Biotechnology & Biological Sciences 1 (1), 2014 P. 2002. Multidisciplinary examination of patients with illness attributed to dental fillings. J Oral Rehabil; 29: 705–13

Washington,Center for Water and Watershed Studies Fact Sheet, University of Washington, Seattle.

Lenntech, Water Treatment and Air Purification 2004. Water Treatment,Published by Lenntech, Rotterdamseweg, Netherlands

Seidal, K., Jorgensen, N., Elinder, C.G., Sjogren, B. and Vahter M. 1993. Fatal cadmium-induced pneumonitis. Scand J Work Environ Health; 19: 429–31

(www.excelwater.com/thp/filters/Water-Purification.htm).

Trueby, P. 2003. Impact of Heavy Metals on Forest Trees from Mining Areas. In: International Conference on Mining and the Environment III, Sudbury, Ontario, Canada. (www.xcd.com/sudbury03/prof156.html).

Lindh, U., Hudecek, R., Danersund, A., Eriksson, S. and Lindvall A. 2002. Removal of dental amalgam and other metal alloys supported by antioxidant therapy alleviates symptoms and improves quality of life in patients with amalgam-associated ill health. Neuroendocrinol Lett; 23: 459–82 McCluggage, D. 1991. Heavy Metal Poisoning, NCS Magazine,Published by The Bird Hospital, CO, U.S.A. (www.cockatiels.org/articles/Diseases/metals.html).

United Nations Environmental Protection/Global Program of Action 2004. Why The Marine Environment Needs Protection From Heavy Metals, Heavy Metals 2004, UNEP/ GPA Coordination Office (http://www.oceansatlas.org/ unatlas/uses/uneptextsph/wastesph/2602gpa.)

Nriagu, J.O. 1989. A global Assessment of Natural Sources of Atmospheric Trace Metals, Nature, 338: 47-49.

WHO 1995. Lead. Environmental Health Criteria, vol. 165. Geneva: World Health Organization.

Nriagu, J.O. and Pacyna, J. 1988. Quantitative Assessment of Worldwide Contamination of Air, Water and Soil by Trace Metals, Nature, 333:134-139.

WHO 1992. Cadmium. Environmental Health Criteria, vol. 134. Geneva: World Health Organization.

Peplow, D. 1999. Environmental Impacts of Mining in Eastern

WHO 1999. Methyl Mercury. Environmental Health Criteria, vol. 101. Geneva: World Health Organization. Received on 03-09-2014



Performance of Yield and Quality in Advanced Lines of Fine Rice (Oryza sativa) M. ATIQUR RAHMAN1, MD. SAROWAR HOSSAIN1, IMTIAZ FARUK CHOWDHURY2 * AND H. MEHRAJ3 1

Department of Genetics and Plant Breeding; Department of Agronomy; 3 Department of Horticulture, Sher-e-Bangla Agricultural University, Dhaka-1207, Bangladesh email: [email protected] 2

ABSTRACT The experiment was conducted in the experimental Farm, Sher-e-Bangla Agricultural University, Bangladesh during the period from July to December 2011 to study the variability of yield attributes in advanced lines of fine rice (Oryza sativa). Seven newly developed lines of fine rice variety were used as test crop for this study. These were S-1, S-2, S-5, AL-33(II), AL-36, AL-42(II), AL-44(I) and Chinigura. The longest plant height (137.4 cm) was observed in Chinigura whereas the shortest plant height (106.0 cm) was found in S-2 lines. Maximum grain yield (634.3 g/m2) was found in S-2 while the lowest yield (534. g/m2) in AL-33(II). The S-1, S-2, S-5, AL-33(II) and AL44(I) lines showed maximum percentage of hulling and milling but lower head rice recovery than the check Chinigura. Long slender grains and superior cooking performance over Chinigura, all were superior for length of cooked rice elongation, kernel elongation ratio and volume expansion. The S-1, S-2 and AL-42(II) lines showed low GT which take short time for cooking. Key words

aromatic fine rice, grain yield, quality

Rice (Oryza sativa) belongs to Poaceae family is the staple food in Bangladesh and covers 75% of total cropped area (Rekabdar, 2004) of which around 27% is occupied by fine rice varieties (BBS, 2003). Bangladesh produces several fine aromatic rice varieties with excellent eating qualities. The consumers judge the quality of rice on the basis of size and shape of rice grain. The preference for grain size and shape can vary from one group of consumer to another group of consumers (Khush et al., 1999). Quality of rice may be considered from the view point of size, shape and appearance of grain, milling quality and cooking properties (Dela Cruz and Khush, 2000). Most of rice varieties have been developed traditionally by selection,

hybridization and back crossing with locally adapted high-yielding lines. Yield and quality performance of some dwarf advanced lines of Basmati rice (developed by Sher-e-Bangla Agricultural University) were not evaluated. Considering the above facts, present research work has been undertaken in order to evaluate yield and variability of different advanced fine rice.

MATERIALS AND METHODS The experiment was conducted at experimental farm, Sher-e-Bangla Agricultural University (SAU, Dhaka, Bangladesh during the period from July to December 2011 to study the variability of yield and quality attributes in advanced lines of fine rice (Oryza sativa). Seven advanced fine rice lines of 10th generation viz. S-1, S-2, S-5, AL-33(II), AL-36, AL-42(II) and AL-44(I) were collected from Department of Genetics and Plant Breeding and Chinigura rice were collected from Bangladesh Rice Research Institute and used as check variety for this study. The experiment was laid out in Randomized Complete Block Design with three replications. Each plot size was 2.5 m × 2.0 m. Urea (150 kg/ha), TSP (100 kg/ha), MP (100 kg/ha), gypsum (60 kg/ha), zinc sulphate (10 kg/ha) and borax (150 kg/ha) were applied (BRRI, 2012). The entire amount of TSP, MP, gypsum, zinc sulphate and borax were applied during the final preparation of plot land. Mixture of cowdung and compost was applied @ 10 t/ha during 15 days before transplantation. Urea was applied in three equal installments at after recovery, tillering and before panicle initiation. Rice seedlings were transplanted in lines each having a line to line 30 cm and plant to plant 25 cm distance. Flood irrigation was given to maintain a constant level of standing water up to 6 cm in the early stages to enhance tillering and 10-12 cm in the later stage to discourage late tillering. The field was

10


finally dried out 15 days before harvesting. Data were collected on plant height, number of effective tillers/ hill, number of in-effective tillers/hill, number of total tillers/hill, panicle length, number of filled spikelets/ panicle, number of unfilled spikelets/panicle, number of total spikelets/panicle, weight of 1000 seeds, grain yield/plant, grain yield/m2 (converted into yield/ha) and grain yield/plot.

Elongation ratio was calculated by dividing the length of cooked kernel by its original length.

Hulling percentage was measured by using following formula

Elongation index was calculated by dividing the length/breadth ratio of the cooked kernel by length breadth ratio of the original raw kernel.

Weight of brown rice

Hulling % =

Elongation ratio (ER) =

Where, L0 and L1 are kernel length before and after cooking, respectively

× 100

Weight of rough rice x 100

LI

The brown rice obtained after hulling was passed through “Stake Rice Whitening and Caking Machine” for 5 minutes to obtain uniformly polished grains and the weight of polished grains was recorded. Milling outturn was calculated as Milling outcome =

Weight of milled rice

× 100

Weight of rough rice

Head rice recovery was calculated in percentage as Milling outcome=

Weight of milled rice

× 100

Weight of rough rice

Ten rough, ten brown and five polished kernels from the bulk sample of each entry were measured for their length and breadth by slide calipers. L/B ratio of rough rice = (Grain length/ Grain breadth) × 100 Individual kernels of the sample were taken separately in long labeled test tubes and presoaked in 5 ml of tap water for 30 minutes. After that, the tubes were placed in a water bath maintained at boiling temperature for 8-9 minutes. After cooking the test tube were taken out and cooled under running water for two minutes. Cooked kernels were taken out of the tubes and excess water was removed with a blotting paper. Length and breadth of cooked kernels were measured as above. L/B ratio of cooked kernel was computed according to following formula: L/B ratio =

Grain length Grain breadth

(LI) (L0)

Elongation index (EI) = L0

B1 B0

Water absorption (uptake) percentage was measured as the volume of water needed to cook 1 gm of rice in a definite period of time and temperature. For cooking, rice samples were taken in long test tube and pre-soaked in slightly excess but uniform quantity of water (10 ml) for five minutes and were placed over a water bath maintained at boiling temperature (100ÚC) for 6 to 7 minutes. The sample tubes were then out and cooled under room temperature for 10 minutes. Water absorption was calculated in percentage as, Water absorption % =

W2 - W1 ×100 W1

After recording the weight of uncooked samples, their volume was determined by displacement of water method using a finely graduated narrow cylinder of 5 ml capacity. After cooking, final volume of the above sample was recorded and volume expansion percentage was calculated Volume expansion % =

V2 - V1 ×100 V1

Alkali spreading value was determined according to procedure of Little et al. (1958). A sample of eight whole milled rice kernels from each entry was placed in small petriplates (5 cm wide) containing 10 ml of 1.7% potassium hydroxide (KOH) solution. The petriplates were covered and placed in an incubator maintained at 30± 1ÚC for 16 hours as suggested by

RAHMAN, et al., Performance of Yield and Quality in Advanced Lines of Fine Rice (Oryza sativa)

11

Fig. 1. Comparison of panicle of newly developed advanced fine rice and check 1 = Chinigura; 2 = AL-44(I); 3 = AL-44(II); 4 = AL-36; 5 = AL-33(II); 6 = S-5; 7 = S-2 and 8 = S-1

Zaman (1981). After 16 hours of incubation, the petriplates were gently taken out from the incubator. Alkali spreading values of six grains of each entry were recorded separately and mean was calculated on a 7 point numerical scale proposed by Jennings et al. (1979). For determination of cooking time, the rice samples were taken in long test tube with water and placed in water at boiling temperature (1000C) on Hot plate. When the starch granules are disappeared then rice samples were seems to be cooked. Time was determined by stop watch. Collected data were analyzed by using MSTAT-C and SPSS computer package program. The significance of the difference of means was estimated by Duncan’s Multiple Range Test (DMRT) at 5% level of probability (Gomez and Gomez, 1984).

RESULTS AND DISCUSSION Plant height (cm): Significant variation was recorded for different newly developed advanced fine rice lines and check in plant height. Tallest plant (137.4 cm) was observed in Chinigura whereas shortest from S-2 lines (106.0 cm) which was statistically similar with other lines (Table 1). Khush, 1999 reported that short stature reduces the susceptibility of rice crop to lodging. Number of effective tillers/hill: Maximum number of effective tillers/hill (20.9) was recorded in Chinigura while minimum number (9.1) was found in AL-44(I) which was statistically identical (9.3 and 9.8) with AL36 and AL-42(II) (Table 1). Highly significant variation was found among twelve genotypes of coarse rice in number of effective tillers/plant (Zahid et al., 2005). Shah, et al., 1999, Prasad, et al., 2001 and Hassan, et al., 2003 found significant heritability among the

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Table 1. Performance of growth and yield contributing characters of some newly developed advanced fine rice and check Advanced line and check S-1 S-2 S-5 AL-33(II) AL-36 AL-42(II) AL-44(I) Chinigura LSD0.05 CV(%)

Plant height (cm) 112.1 106.0 107.9 111.4 106.2 111.7 106.4 137.4 7.6 6.5

b b b b b b b a

No. of effective tillers/hill

No. of ineffective tillers/hill

No. of total tillers/hill

13.0 16.6 15.6 15.9 9.3 9.8 9.1 20.9 2.1 9.4

0.8 0.4 0.6 1.3 0.4 0.4 0.6 1.0 0.1 5.9

13.8 17.0 16.2 17.3 9.7 10.2 9.7 21.9 2.5 6.1

c b bc bc d d d a

c e d a e e d b

c b bc b d d d a

Panicle length (cm) 29.6 28.5 26.4 30.0 29.8 31.8 28.8 27.5 0.1 5.9

d f h b c a e g

No. of filled spikelets/ panicle 137.1 c 102.6 d 95.9 e 110.5 d 154.1 b 138.2 c 152.5 b 199.4 a 8.9 7.2

In a column means having similar letter(s) are statistically similar and those having dissimilar letter(s) differ significantly at 0.05 level of probability

genotypes for yield and yield contributing traits. Higher numbers of productive tillers are responsible for higher yield (Padmavathi et al., 1996). According to new plant type concept reduced tillering habit (6-10 tillers/plant) would give higher yield than the modern varieties having 20-25 tillers (Khush, 1999) because only 14-15 of these tillers produced panicles which were small and rest remaining were unproductive. Number of ineffective tillers/hill: Maximum number of ineffective tillers/hill was recorded in AL-33(II) (1.3) while minimum from S-2, AL-36 and AL-42(II) (0.4) (Table 1). Number of total tillers/hill: Maximum number of total tillers/hill was recorded in Chinigura (21.9) while minimum in AL-36 and AL-44(I) (9.73) which was statistically similar with AL-42(II) (10.2) (Table 1). Panicle length (cm): Panicle length varied significantly due to different newly developed advanced fine rice lines and check (Table 1). Data revealed that average panicle length was 29.0 cm for studied lines and check variety. Longest panicle (31.8 cm) was recorded in AL42(II) while shortest panicle (26.4 cm) in S-5. Panicle length under the genetic control and could be use in the selection of a desirable trait (Tahir, et al., 2002). Environmental conditions, time of sowing and transplantation also responsible for variation which affect the expression of genes. Photoperiod, leaf area index, sink and source relationship, inter-competition among plant population and plant density also affect

crop performance. Plants with large panicles tend to have a high number of fertile grains (Padmavathi, et al., 1996). Comparison of panicle of fine rice is presented in Fig. 1. Number of filled spikelets/panicle: Maximum number of filled spikelets/panicle was found in chinigura (199.4) whereas minimum in S-5 (95.9) (Table 1). Tahir et al. (2002) reported highly significant variation for grain per panicle for different genotypes. Others factors i.e. soil fertility, plant nutrients, translocation and weather condition might also responsible. Number of unfilled spikelets/panicle: Maximum number of unfilled spikelets/panicle was recorded in AL-33(II) (67.5) whereas minimum was attained in S2 (21.7) (Table 2). Number of total spikelets/panicle: Maximum number of total spikelets/panicle was found in Chinigura (242.7) which was statistically similar with AL-44(I) (207.7) whereas minimum in S-2 (124.3) (Table 2). Similar findings also reported by Chauhan, et al., 1986; Janagle, et al., 1987. Weight of 1000-grains: Weight of 1000-grains varied significantly due to different newly developed advanced fine rice lines and check (Table 4). The highest weight of 1000-grains (3.0 g) was recorded in S-2 which was statistically similar (2.9 g) with S-5, while the lowest weight (1.3 g) was observed in Chinigura. Highly significant variation among different traits and observe


13

Table 2. Performance of yield contributing characters and yield of some newly developed advanced fine rice and check No. of unfilled spikelets/ panicle

No. of total spikelets/ panicle

Weight of 1000-grains (g)

S-1

27.1

de

164.2

c

2.8

ab

28.6

S-2

21.7

e

124.3

d

3.0

a

S-5

31.3

d

127.2

d

2.9

AL-33(II)

67.5

a

178.0

bc

AL-36

38.6

c

192.7

AL-42(II)

31.3

d

AL-44(I)

54.6

Chinigura

43.3

LSD0.05

5.3

35.6

0.2

3.9

40.3

0.3

CV(%)

10.3

11.8

5.1

10.6

6.6

6.6

Advanced line and check

Yield (g/plant)

Yield (g/m2)

Yield (t/ha)

ab

611.3

ab

6.1

a

32.0

a

634.3

a

6.3

a

a

31.4

a

617.7

ab

6.2

a

2.6

bc

21.7

d

498.3

d

5.0

c

bc

2.8

ab

25.8

bc

552.3

bc

5.5

bc

169.5

bc

2.6

bc

22.9

cd

534.3

c

5.3

c

b

207.1

ab

2.5

c

27.5

ab

577.3

b

5.8

b

c

242.7

a

1.1

d

28.2

ab

603.3

ab

6.0

a


that these traits are under the control of genotypic difference among the genotypes (Tahir, et al., 2002). Grain yield/plant: Grain yield/plant varied significantly due to different newly developed advanced fine rice lines and check (Figure 5). Maximum grain yield/plant (32.0 g) was found in S-2 which was statistically similar with S-5, S-1, Chinigura and AL-44(I) (31.4 g, 28.6 g, 28.2 g and 27.5 g respectively) while minimum from AL-33(II) (21.7 g) which was similar (22.9 g) to AL42(II). Significant variation for grain yield/plant was also found among the twelve genotypes of coarse rice (Zahid, et al., 2005). This variation in grains yield might be due to environment (Mahpattra, 1993) or correlation of grain yield/plant with various yield contributing characteristics like; fertility of soil, grains/panicle, filled grains /panicle and grain weight. Mirza, et al., 1992 reported positive correlation among number of panicle/ plant, panicle length, number of grains/panicle and 1000-grain weight and grain yield/plant. Grain yield/m2: Grain Maximum grain yield was found in S-2 (634.3 g/m2) which was statistically similar with S-5, S-1, chinigura and AL-44(I) (617.7 g/ m2, 611.3 g/ m2, 603.3 g/ m2 and 577.3 g/ m2 respectively) while minimum in AL-33(II) (498.3 g/m2) which was similar to AL-42(II) (534.3 g/m2) (Table 2). Grain yield/hectare:: Maximum grain yield was found

in S-2 (6.3 ton/ha) which was statistically similar with S-5 (6.2 ton/ha), S-1 (6.1 ton/ha), chinigura (6.03 ton/ ha) and AL-44(I) (5.8 ton/ha) while minimum in AL33(II) (5.0 ton/ha) which was statistically similar with AL-42(II) (5.3 ton/ha) (Table 2). Hossain (2004) reported that higher biological yield did not always contribute higher yield and it is desirable to select hybrids having higher spikelet fertility combined with high biomass and harvest index than those producing lower biological yield with higher harvest index. Positive association between grain number per panicle and grain yield has been reported by number of workers (Chauhan et al., 1986; Janagle et al., 1987).

Evaluation of mean performance of quality characters (before cooking) Hulling: Hulling of rice for different advanced line of rice varied significantly. Maximum hulling (77.9%) was recorded from S-2 and minimum from AL-33(II) (69.9%) (Table 3). Milling outturn: Maximum milling return (70.4%) was found from S-2 and minimum from AL-36 (61.4%) (Table 3). A good milling quality includes high whole kernel recovery and less of broken rice. While milling recovery as a whole mainly depends upon the hull content this varies from 18 to 26% and the nature of alluron layer. Ahuja et al. (1995) reported a range of 67 to 71%

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Table 3. Mean performance of quality characteristics before cooking of some newly developed advanced fine rice and check Advanced lines and check

Grain dimension of rough rice Hulling (%)

Milling (%)

HRR (%)

Length (mm)

Breadth (mm)

L/B ratio

S-1

77.7

a

69.5

a

62.5

b

12.6

a

2.6

a

4.9

a

S-2

77.9

a

70.4

a

61.7

b

12.5

a

2.5

ab

5.0

a

S-5

77.5

a

69.7

a

61.1

b

11.6

bc

2.5

abc

4.7

a

AL-33(II)

76.9

a

68.2

a

47.1

c

12.0

ab

2.4

abc

4.9

a

AL-36

69.9

c

61.4

c

35.6

d

11.0

c

2.2

d

5.1

a

AL-42(II)

72.3

b

64.6

b

59.0

b

10.0

d

2.3

bcd

4.4

b

AL-44(I)

77.1

a

69.1

a

58.1

b

10.1

d

2.2

cd

4.5

b

Chinigura

76.3

a

69.0

a

68.3

a

5.8

e

2.5

abc

2.4

c

Mean

75.7

67.7

56.7

10.7

2.4

4.5

Range

69.7-77.9

61.4-70.4

35.6-68.3

5.8-12.6

2.2-2.6

2.4-5.1

CV(%)

4.1

3.8

5.0

3.4

5.2

5.3


for milling recovery in Basmati varieties. Head rice recovery (HHR): Maximum HHR (68.3%) was recorded from chinigura while minimum from AL36 (35.6%) (Table 3). For commercial success of a rice hybrid it must possess high total milled rice and whole kernel (HRR) turnout. If fine has higher broken percentage its marketability will be reduced. Head rice recoverability is an inherited trait although environmental

factors such as temperature and humidity during ripening and post harvest stages are known to influence grain breakage during milling (Shobha Rani, 2003). Grain dimension and length-breadth ratio of rough rice: Longest rough rice (12.6 mm) was found in S-1 which was statistically identical with S-2 (12.5 mm)and AL-33(II) (12.0 mm) followed by S-5 (11.6 mm) whereas shortest in Chinigura (5.8 mm) with 10.7 mm

Table 4. Mean performance of quality characteristics before cooking of some newly developed advanced fine rice and check Advanced lines and check S-1 S-2 S-5 AL-33(II) AL-36 AL-42(II) AL-44(I) Chinigura Mean Range CV(%)

Brown rice Length (mm) 8.5 8.9 8.7 8.9 8.7 7.7 8.0 4.4 8.0 4.4-8.9 3.9

ab a a a a c bc d

Breadth (mm) 1.7 1.8 1.7 1.6 1.7 1.7 1.7 1.4 1.7 1.4-1.8 2.1

b a ab c b ab b d

L/B ratio 5.0 5.1 5.1 5.6 5.2 4.5 4.7 3.1 4.8 3.1-5.6 8.2

b b b a b c c d

Milled rice (uncooked rice) Length Breadth L/B ratio (mm) (mm) 8.4 b 1.7 b 5.1 b 8.5 b 1.7 a 5.0 b 8.5 b 1.7 b 5.1 b 8.1 c 1.6 b 5.1 b 9.1 a 1.7 ab 5.4 a 7.1 d 1.6 b 4.3 c 8.0 c 1.7 b 4.8 bc 4.1 e 1.4 c 2.9 d 7.7 1.6 4.7 4.1-9.1 1.4-1.7 2.9-5.4 3.4 4.1 6.8



average length (Table 3). Similar conclusion was also done by Vijayakumar et al. (1997). Maximum breadth of rough rice was found in S-1 (2.6 mm) which was statistically similar with S-2 (2.5 mm), S-5 (2.5 mm), Chinigura and AL-33(II) (2.4 mm) whereas minimum in Al-36 (2.2 mm) which was statistically similar with AL-44(I) (2.2 mm) and AL-42(II) (2.27 mm) with 2.4 mm average (Table 3). Higher milling percentage may not yield higher head rice recovery as it depends on grain dimension also. Grain size and shape, hardness, percentage or absence of abdominal white, moisture content, harvest precision, storage conditions, processing and type of mills employed have direct effect on head rice recovery (Bhattacharya, 1980). In general, varieties with long bold grains and those having white centers give lower head rice yields. Varieties possessing medium slender, long slender and translucent grains give high head rice yields. Varieties with high protein content also suffer less breakage. Sun cracking which is caused alternate drying and wetting of grains due to delayed harvest also adds more breakage of grain (Shobha Rani, 2003). Maximum length-breadth ration was found from AL-36 (5.1) which was statistically similar with all lines except AL-42(II) and AL-44(I) while minimum from Chinigura (2.4) (Table 3). An inverse relationship was found between HRR% and grain L/B ratio by Viraktamath, 1987 and Yadav and Singh, 1989. Grain dimension and length-breadth ratio of brown rice: Longest brown grain was found in S-2 (8.9 mm) while shortest Chinigura (4.4 mm) with 8.0 mm average length (Table 4). Maximum breadth of brown grain was found in S-2 (1.8 mm) which was statistically similar with S-5 and AL-42(II) (1.7 mm) whereas minimum in Chinigura (1.4 mm) and with 1.7 mm average breadth (Table 4). Maximum length-breadth ratio of brown rice was found from AL-33(II) (5.6) and minimum from Chinigura (3.1) (Table 4). Grain dimension and length-breadth ratio of milled rice: Longest milled grain was found in AL-36 (9.1 mm) while shortest in Chinigura (4.1 mm) with 7.72 mm average. Maximum breadth of milled grain was found in S-2 (1.7 mm) while minimum in Chinigura (1.4 mm) (Table 4) with 1.62 mm average. Maximum length-breadth ratio of brown rice was found from AL36) (5.4) and minimum from Chinigura (2.9) (Table 4). Grain dimension and length-breadth ratio of cooked rice: Longest cooked rice length was found from S-1 (12.2 mm) which was statistically similar with

15

S-2 (12.1 mm) and AL-33 (II) (11.4 mm) while shortest from Chinigura (5.7 mm) with 10.6 mm average (Table 5). Maximum breadth of milled grain was found in S-2 (2.4 mm) while minimum in AL-36 (2.0 mm) with 2.2 mm average (Table 5). Maximum length-breadth ratio of brown rice was found from AL-36 (5.4) and minimum from Chinigura (2.6) (Table 5). Kernel elongation ratio: Maximum kernel elongation ratio was found from S-1 (1.5) and minimum from AL-36 (1.2). Elongation ratio (L1/L0) is a measure of kernel elongation upon cooking resulting from swelling of starch granules by uptake of water (Juliano, 1979). Pilaiyar (1988) proposed elongation ratio to be best index of quality compared to elongation index and proportionate change. Significant association of L/B ratio with kernel elongation was reported by Deosarker and Nerkar, 1994. Chauhan, et al., 1995 pointed out significant positive correlation between kernel elongation and cooked kernel length. Kernel elongation was primarily influenced by kernel shape and size. Water absorption: Maximum water absorption was found from Chinigura (209%) while minimum recorded from AL-42(II) (114%) (Table 5). Water uptake is considered an important economic attribute of rice as it gives indirect measure of volume increase on cooking. Water uptake shows a positive and significant influence on grain elongation, while volume expansion did not influence grain elongation as reported by Sood and Siddiq (1986). Earlier studies of rice in general suggested the extent of variation for this character to range between 194 to 250% (Juliano, et al., 1969). Working with a larger number of scented basmati varieties Sood and Siddiq, 1980 have reported still wider range (74439%) of variation for this character. Zaman, 1981 reported that the good cooking rice varieties have water absorption value ranging between 174% and 275%, whereas majority of those showing pasty appearance have value as high as from 300 to 570%. He concluded that high water absorption is relatively less desirable characteristics and it would be desirable to select a variety or hybrid with moderate water absorption. Volume expansion: Maximum volume expansion was found from AL-33(II) (6.7%) while minimum from Chinigura (1.9%). Volume expansion of kernels on cooking is considered another important measure of consumer preference. More volume of cooked rice from a given quantity is a matter of great satisfaction to an average rice consumer irrespective of the fact whether the increased volume is due to length-wise or

16


Table 5. Mean performance of quality characteristics after cooking of some newly developed advanced fine rice and check Advanced line and check

Cooked rice Length (mm)

Breadth (mm)

L/B ratio

Kernel Water elongation absorption ratio

Volume expansion

Alkali Cooking time spreading (minute) value

S-1

12.2

a

2.3

ab

5.2

a

1.5

a

186

b

3.3

c

6.2

b

16.0

d

S-2

12.1

a

2.4

a

5.1

b

1.4

b

125

d

2.6

d

6.2

b

16.0

d

S-5

11.2

bc

2.2

abc

5.1

b

1.4

b

168

c

3.2

c

5.7

c

17.0

c

AL-33(II)

11.4

ab

2.3

abc

4.9

b

1.5

a

126

d

6..66

a

4.0

d

17.0

c

AL-36

10.9

c

2.0

d

5.4

a

1.2

c

178

b

2.7

d

5.7

c

17.0

c

AL-42(II)

9.8

d

2.1

bcd

4.6

c

1.4

b

114

e

2.5

d

6.3

a

16.0

d

AL-44(I)

9.9

d

2.0

cd

4.7

c

1.3

c

190

b

4.5

b

3.5

e

18.0

b

Chinigura

5.7

e

2.2

abc

2.6

d

1.4

b

209

a

1.9

e

2.0

f

19.0

a

Mean

10.6

2.2

4.5

1.4

162

265.9

4.9

17.0

Range

5.7-12.2

2.0-2.4

2.6-5.4

1.2-1.5

114-209

66.7-445.5

2.0-6.3

16.0-19.0

CV(%)

4.7

3.9

7.9

5.7

3.44

5.7

3.6

4.9


breadth-wise expansion. Volume expansion by and large is determined by water uptake, however, subject to the influence of kernel texture (Zaman, 1981). Varieties showing volume expansion of 500% and above are considered desirable. Earlier studies with randomly selected varieties have shown volume expansion to vary between 295% and 714% (Juliano, et al., 1969). Alkali spreading value: Maximum alkaline spreading value was found from AL-42(II) (6.3) while minimum from Chinigura (2.0) (Table 5). The gelatinization temperature (GT) is considered to be yet another major

index of cooking quality of rice. The time required for cooking by the gelatinization temperature. The GT of rice varieties ranging from 550C to 790C are grouped into low (55-690C), intermediate (70-740C) and high (74-790C) (Juliano et al., 1969). High GT rice becomes excessively soft when overcooked, elongated less and requires mire water and time for cooking as compare to those with low or intermediate GT (Table 6). Rice varieties that have low GT start to swell at low temperature during cooking than rice varieties that have intermediate of high GT (Nagato and Kishi, 1966). Rice varieties having intermediate GT produces good cooked rice.

Table 6. Classification of newly developed advanced fine rice and check variety on the basis of Alkali spreading score, Alkali value and GT types Advanced lines and check

Alkali spreading value

Range

Alkali digestion

GT types

S-1

6.17

6.0-6.3

High

Low

S-2

6.17

6.0-6.3

High

Low

S-5

5.67

5.5-6.0

Intermediate

Intermediate

AL-33(II)

4

3.7-4.3

Intermediate

Intermediate

AL-36

5.67

5.5-6.0

Intermediate

Intermediate

AL-42(II)

6.33

6.0-6.7

High

Low

AL-44(I)

3.5

3.0-4.0

Low

High

Chinigura

2

2

Low

High

GT= Gelatinization temperature


17

Cooking time: Maximum cooking time was found from Chinigura (19 minutes) while minimum recorded from S-1, S-2 and AL-42(II) (16 minutes) (Table 5). Cooking time is important as it determines the tenderness as well as stickiness of cooked rice to great extent. Milled rice has high protein content or a high GT required much water and a longer time to cook than rice with lower values (Juliano et al., 1969). Cooking time of rice grain depends on coarseness of the grain and it’s GT. The color and gloss of the grain was also intensively correlated to the exposure of microwave heating. The leaner kernel elongation after cooking is compared with the original length of kernel before cooking (Irshad, 2001).

Deosarker, B.D. and Nerkar, Y.S. 1994. Correlation and path analysis for grain quality charaters in indica rice. J. Maharastra Agric. Univ., 19(2): 175-177.

Chinigura exhibited better performance in respect of plant height, number of effective tillers/hill, number of total tillers/hill, number of filled spikelet/ panicle and number of total spikelets/panicle but S-1, S-2 and S-5 lines higher yielded than Chinigura. S-1, S-2, S-5, AL-33(II) and AL-44(I) lines showed higher percentage of hulling and milling but lower head rice recovery than Chinigura. All lines had long slender grains except Chinigura. Superior cooking performance over Chinigura was observed in all the lines for length of cooked rice elongation, kernel elongation ratio and volume expansion. S-1, S-2 and AL-42(II) lines showed low GT which is preferred by the consumers.

Jennings, P.R. Coffman, W.R. and Kauffman, H.E. 1979. Rice Improvement. IRRI, Manila, Philippines. pp. 186.

LITERATURE CITED Ahuja, S.C., Panwar, D.V.S., Uma Ahuja and Gupta, K.R. 1995. Basmati rice-The Scented Pearl. CCS Haryna Agricultural University, Hisar, India. pp.63. BBS, (Bangladesh Bureau of Statistics) 2003. Statistical Yearbook of Bangladesh. Stat. div. Min. Planning, Dhaka, Bangladesh. pp. 72-78. Bhattacharya, K.R. 1980. Breakage of rice during milling: a review. Trop. Sci., 22: 255-278. BRRI (Bangladesh Rice Research Institute). 2012. Adhunik Dhaner Chash. Joydebpur, Dhaka. pp. 10. Chauhan, J.S. Chauhan, V.S. and Lodh, S.B. 1995. Cooking quality components and their interrelationships with some physicchemical characters of rainfed and upland rice grain. Oryza. 32(2): 79-82. Chauhan, S.P., Singh, R.S. Maurya, D.M. and Vaish, C.P. 1986. Character association in upland rice cultivars of India. Intl. Rice Res. Newsl., 11(4): 8. Dela Cruz N. and Khush G.S. 2000. Rice grain quality evaluation procedures. In: Aromatic rices. Ed. Singh RK, Singh US, Khush GS. Oxford and IBH publishing Co. Pvt. Ltd. New Delhi. pp. 15–28.

Gomez, K.A. and Gomez, A.A. 1984. Statistical Procedure for Agricultural Research (2nd edn.). Int. Rice Res. Inst., A Willey Int. Sci., pp. 28-192. Hossain, M.S. 2004. Development and evaluation of three line rice hybrids using inter sub-specific (indica/japonica) derivatives. Division of Genetics. Indian Agricultural Research Institute, New Delhi. 11002. Irshad, A. 2001. Factors affecting rice grain quality. DAWN Group of Newspapers. Janagle, R.D., Ugle, S.D. and Dumbre, A.D. 1987. A study of cause and effect relationship among quantitative traits in upland paddy. J. Maharashtra Agric. Univ., 12(1): 31-34.

Juliano, B.O. 1979. The chemical basis of rice quality. The workshop on “Chemical Aspects of Rice Grain Quality”. IRRI, Manilla, Philippines. pp. 69-90. Juliano, B.O., Onate, L.U. and Del Mundo, A.M. 1969. Relation of starch composition, protein content and gelatinization temperature to cooking and eating qualities of milled rice. Food Technol. 19: 1006-1011. Khush, G.S., Paule C.M. and Delacruz, N.M. 1999. Rice grain quality evaluation and improvement of IRRI. In Proc. on Workshop of Chemicals Aspects of Rice Grain Quality. 21– 31. Los Banos, Philippines Little, R.R., Hilder, G.B. and Dawson, E.H. 1958. Differential effect of dilute alkali on 25 varieties of milled rice. Cereal Chm., 35: 111-126. Mahapatra, K.C. 1993. Relative usefulness of stability parameters in assessing adaptability in rice. Indian J. Gen. and Pl. Breed. 53(4): 435-441. Mirza, J.M., Ahmad Faiz and Abdul Majid. 1992. Correlation Study and Path Analysis of Plant Height, Yield and Yield Component. Sarhad J. Agric: 8(6): 647-651. Nagato, K. and Kishi, Y. 1966. On the grain texture of rice. Vertical difference of cooking characteristics of milled white rice (in Japanese, English summary). Proc. Crop Sci. Soc. Jap., 35: 245-256. Padmavathi, N., Mahadevappa, M. and Reddy, O.U.R. 1996. Association of various yield components in rice (Oryza sativa L.). Crop Research (Hisar). 12(3): 353-357. Pilaiyar, P. 1988. Quality characteristics of Tamil Nadu Rices. Madrass Agric. J., 75(9-10): 307-317. Rekabdar, M.F.H. 2004. Dhan Chaser Nana Kotha. Krishikatha. 67(2): 39-40. Shah R., M.Z. Sulemani, M.S. Baloch and G. Hassan. 1999. Performance of course rice genotypes in the plains of D.I. Khan, Pakistan. Pak. J. Biol. Sci. 2(2): 507509. Shobha Rani, N. 2003. Quality considerations in developing rice hybrids. In: Winter school on advances in hybrid rice technology. Org. DRR, Hyderabad, India. pp. 145-159.

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Sood, B.C. and Siddiq, E.A. 1980. Studies on component quality attributes of basmati rice (Oryza sativa L.). Zpfarzenz Chtg., 84: 294-301. Sood, B.C. and Siddiq, E.A. 1986. Current status and future outlook for hybrid rice technology in India. In: Hybrid Rice Technology, Directorate of rice research, Hyderabad, India. ip. 1-26. Tahir, M., Waden, D. and Zada, A. 2002. Genetic variability of different plant yield attributes in rice. Sarhad J. Agric., 18(2): 13-17. Vijayakumar, C.H.M., Ahmed, M.I., Viraktamath, B.C. and Ramesha, M.S. 1997. Heterosis: early prediction and relationship with reproductive phase. Intl. Rice Res. Newsl., 22(2): 8-9.

Viraktamath, B.C. 1987. Heterosis and combining ability studies in rice with respect to yield, yield components and some quality characteristics. Ph. D. Thesis. IARI, New Delhi. Yadav, T.P. and Singh, V.P. 1989. Milling characteristics of aromatic rices. IRRN. 14(6): 7-8. Zahid, A.M. Akhtar, M. Sabrar, M. Anwar, M. and Mushtaq, A. 2005. Interrelation-ship among Yield and Economic Traits in Fine Grain Rice. Proceedings of the International Seminar on Rice Crop. October 2-3. Rice Research Institute, Kala Shah Kau, Pakistan. pp. 21-24. Zaman, F.U. 1981. Genetics studies of some of the cooking and nutritive qualities of cultivated rice (Oryza Sativa L.) Ph. D. Thesis. University of Bihar, Buzaffarpur, India. Received on 05-09-2014



Morphological and Molecular Identification of Trichoderma asperellum Tasp/8940 MOHAMMAD SHAHID, MUKESH SRIVASTAVA, AND ANURADHA SINGH Biocontrol Laboratory, Department of Plant Pathology, Chandra Shekhar Azad University of Agriculture & Technology, Kanpur-208002, Uttar Pradesh, India. email: [email protected]

ABSTRACT Most isolates of the genus Trichoderma were found to act as mycoparasites of many economically important aerial and soil-borne plant pathogens. Trichoderma has gained importance as a substitute for chemical pesticides and hence an attempt was intended to corroborate the positive relatedness of molecular and morphologicalcharacters. A fungal strain of Trichoderma asperellum Tasp/8940 was isolated from a soil sample collected from CSA Farm, Kanpur, Uttar Pradesh, India. The universal primers were used for amplification of 18SrRNA gene fragment and the strain was characterized by using 18SrRNA gene sequence with the help of ITS marker. It is proposed that the identified strain Trichoderma asperellum Tasp/8940be assigned as the type strain of a species of the genus Trichodermabased on phylogenetic tree analysis together with the 18SrRNA gene sequence search in Ribosomal Database Project, small subunit rRNA and large subunit rRNA databases. The sequence was deposited in GenBank with the accession number KC800921. Thus an integrated approach of morphological and molecular markers can be employed to identify a superior strain of Trichodermafor its commercial exploitation. Key words

18S ribosomal RNA gene, Trichoderma,ITS, Biocontrol, Antagonist.

Trichoderma, commonly available in soil and root ecosystem, has gained immense importance since last few decades due to its bio-control ability against several plant pathogens. Some strains of Trichoderma like T.harzianum, T. atroviride, T. viride, T. virens and T. koningii are efficient bio control agents which have the ability to inhibit pathogen growth in the soil, hence improving the overall health of the plant. Antagonistic microorganisms such as Trichoderma reduce growth, survival of pathogen by different mechanism like competition antibiosis, mycoparasitism, hyphal interactions and enzyme secretion. Such micro organisms are now available commercially and are used in crop management and practices Kubicek, et al., 2000. The use of Trichoderma species as biological

control agents has been investigated for over 70 years but it is only relatively recently that strains have become available commercially. Biocontrol agents are widely regarded by public as “natural” and non-threatening products, although risk assessments must clearly be carried out on their effects on non-target organisms. Moreover, knowledge concerning the behaviour of such antagonists is essential for their effective use.The morphological and microscopic figure of Trichoderma asperellum Tasp/8940 is given figure 1 (a-b). Accurate and definitive fungal identification is essential for correct disease diagnosis, treatment of associated with fungal infections. Characterization of fungal species using classical methods is not as specific as the genotyping methods. Genotypic techniques involve the amplification of a phylogenetically informative target, such as the small-subunit (18S) rRNA gene reported by Woese CR, et al., 1977. rRNA is essential for the survival of all cells, and the genes encoding the rRNA are highly conserved in the fungal and other kingdoms. The sequences of the rRNA and proteins comprising the ribosome are highly conserved throughout evolution, because they require complex inter- and intramolecular interactions to maintain the protein-synthesizing machinery reported by Sacchi, et al., 2002, Hillis, et al., 1991 and Woese, et al., 1987. Trichoderma species are common soil inhabitants and are effective in providing bio-control of soil borne pathogens due to antagonistic behaviors. The major aspect of successful biological control strategies includes the production, formulation and delivery system of bio-agents.The internal transcribed spacer (ITS) region of the rDNA is perhaps the most widely sequenced DNA region in fungi. It has typically been most useful for molecular systematic study at species level, and even within species found by Ospina-Giraldo, et al., 1998, Kubicek, et al., 2000, Kulling-Gradinger, et al., 2002 and Lee, et al., 2002 attempted a first phylogenetic analysis of the whole genus of Trichodermausing sequence analysis of the ITS region of rDNA.

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Fig. 1. (a-b). Trichoderma asperellum Tasp/8940 strain in PDA medium and microscopic image

In this study, the method of isolation and identification of an unknown fungal fromCSA Farm ofKanpur district using 18SrRNA gene sequence as reported in bacterial rRNA gene found by Srivastava, et al., 2008 to characterize the strain Tasp/8940as a member of the Trichoderma spp. The soil sample has received great attention from the public, due to its potential for biodiversity and biological conservation. The internal transcribed spacer (ITS) region of the rDNA is perhaps the most widely sequenced DNA region in fungi. It has typically been most useful for molecular systematic study at species level, and even within species Kindermann, et al., 1998 attempted a first phylogeneticanalysis of the whole genus of Trichodermausing sequence analysis of the ITS1 region of rDNA.

MATERIALS AND METHODS Isolation and identification of Trichoderma : Soil samples were collected from various experimental fields of Indian CSA Farm of Kanpur district.Isolate of Trichoderma species was isolated and identified in potato dextrose agar (PDA) with low sugar medium reported by Hiney M, et. al.(1992). The identification of Trichoderma isolates were confirmed both by morphological and molecular characters (ITS) NCBI, GenBank Accession Number KC800921 and reconfirmed by Indian Type Culture Collection (ITCC), IARI, Pusa, New Delhi Accession Number allotted 8940 and finally deposited to NBAIM, Mau Accession Number allotted F-03108 DNA isolation of Trichoderma : Pure culture of the target fungal was grown overnight in liquid PD Broth medium for the isolation of genomic DNA using a method described by Hiney et. al. (1998). Molecular characterization : The total genomic DNA was extracted from isolate of Trichoderma

asperellumTasp /8940based on Cetrimide Tetradecyl Trimethyl Ammonium Bromide (CTAB) mini extraction method of Crowhurst et al. (1995) with minor modification. Agarose gel electrophoresis : Ten microlitre of the reaction mixture was then analyzed by submarine gel electrophoresis using 1.0% agarose with ethidium bromide at 8 V/cm and the reaction product was visualized under Gel Doc/UV trans-illuminator. Internal Transcribed Spacer region : The internal transcribed spacer (ITS) regions of the rDNA repeat from the 3’end of the 18S and the 5’end of the 18S gene were amplified using the two primers, ITS-1 and ITS-4 which were synthesized on the basis of conserved regions of the eukaryotic rRNA gene reported by Zhang, 2000. The PCR-amplification reactions were performed in a 50 ml mixture containing 50 mMKCl, 20 mM Tris HCl (pH 8.4), 2.0 mM MgCl2, 200 mM of each of the four deoxynucleotide triphosphates (dNTPs), 0.2µmM of each primer, 40 ng/ml of template and 2.5 U of Taq polymerase. The cycle parameters included an initial denaturation at 94°C for 5 min, followed by 40 cycles of denaturation at 94°C for 1 min, primer annealing at 55°C for 2 min and primer extension at 72°C for 3 min and a final extension for 10 min at 72°C. Amplified products were separated on 1.2% agarose gel in TAE buffer, pre-stained with ethidium bromide (1mg/ml) and electrophoresis was carried out at 60 volts for 3 hours in TAE buffer. One Kb ladder (MBI, Fermentas) was used as a marker. The gel was observed in a transilluminator over ultra violet light. The desired bands were cut from the gel with minimum quantity of gel portion using QIAGEN gel extraction kit. Purification of PCR product : The PCR product was purified by Qiagen gel extraction kit using the following protocol described below. The DNA fragment was excised from the agarose gel with a clean sharp scalpel.

SHAHID, et al., Morphological and Molecular Identification of Trichoderma asperellum Tasp/8940

Then the gel slice was weighed in an eppendorf. We then added 3 volumes of buffer QG to 1 volume of gel (100 mg ~ 100 ml). The mixture was then incubated at 50°C for 10 min. The gel was dissolved by vortexing the tube every 2 to 3 min during the incubation until the mixture color is uniformly yellow. We then added 1 gel volume of isopropanol to the sample and mixed. A QIAquick spin column is then placed in a 2 ml collection tube provided. The sample is applied to the QIAquick column followed by centrifugation for one minute so that DNA binds to the column. The flow-through is discarded and the QIAquick column is placed back in the collection tube. We then added 0.75 ml of buffer PE to QIAquick column and centrifuged for 1 min to wash. The flow through is again discarded and the QIAquick column centrifuged for an additional 1 min at 10,000 x g. The QIAquick column is now placed into a clean 1.5 ml eppendorf. We then added 50ml of buffer EB (10 mM Tris-Cl, pH 8.5) to the center of the QIAquick membrane and centrifuged the column for 1 min to elute DNA. DNA sequencing of the 18SrDNA fragment : The 18SrDNA amplified PCR product (100 ng concentration) was used for the sequencing with the single 18SrDNA 20F Forward, ITS1 primer: 5'TCCGTAGGTGAACCTGCGG-3’and 22R Reverse ITS4 primer: 5'-TCCTCCGCTTATTGATATGC3’synthesized by DNA Sequencer by (Merck laboratory, Bangalore). Sequence Analysis : A comparison of the 18SrRNA gene sequence of the test strain was done using BLAST against non-redundant nucleotide (nr/nt) database observed by Thompson, et al., 1994. A number of Trichodermasequences were selected on the basis of a similarity score of 90% with database sequences. Multiple sequence alignment of these selected homologous sequences and 18SrRNA gene sequence of test strain was performed using Clustal W reported by Saitou, et al., 1987. Subsequently, an evolutionary distance matrix was then generated from these nucleotide sequences in the dataset. A phylogenetic tree was then drawn using the Neighbour Joining method reported by Tamura, et al., 2007. Phylogenetic and molecular evolutionary analyses were conducted using MEGA (Molecular Evolutionary Genetics analysis) version 4.0 reported by Altschul, et al., 1997. We again compared the 18SrRNA gene sequence of test strain with different set of sequence database such as small subunit ribosomal RNA (SSUrRNA) and large subunitribosomal RNA (LSUrRNA) using Ribosomal

21

RNA BLAST reported by Cole, 2009. 18SrRNA gene sequence of test strain is also compared against those sequences, in Ribosomal Database Project found by Wang Q, et. al. (2007) by using the RDP Classifier checks Program. The annotated information for the sequence in the database to which 18SrRNA aligns is used for the fungal identification.

RESULTS AND DISCUSSION A total of 641bp of the 18SrRNA gene was sequenced and used for the identification of isolated fungal strain. Subsequently, 18SrRNA gene sequence based phylogenetic tree showing the relationships between the test strain T asp /8940 and selected representatives of the genus Trichodermais given in Figure 1. It is evident from phylogenetic analysis of 18SrRNA gene that the isolate Tasp/8940 represents a genomic species in the genus Trichoderma. Comparison of teststrain against known sequences of SSUrRNA and LSUrRNA databases showed that the gene sequence of isolate T asp /8940 has 90% sequence similarity (Score=546 bits, Expect=0.0) with 18SrRNA gene sequence of Trichoderma (Genbank Acc. No.: KC800921). Thus, data shows that the isolate Tasp/8940 is a member of the genus Trichoderma. Similarity rank program classifier available at the Ribosomal Database Project classified the isolate Tasp/8940 as a novel genomic species of the genus Trichoderma with a confidence threshold of 90% (Figure 2). The 18SrRNA gene sequence of isolate Tasp/8940 was deposited in GenBank and allotted the accession number KC800921. Molecular analysis using Internal Transcribed Spacer region (ITS): Ribosomal RNA (rRNA) sequence analysis has been well-documented as a means of determining phylogenetic relationships in all of the major organismal domains. Variable sequences within the mature small subunit (SSU) and large-subunit (LSU) rRNA genes have been found to be appropriate for assessing subgeneric relationships in many eukaryotes. One of these variable regions, the D2 region of the LSU, has been used to determine phylogenetic has been used to determine phylogenetic relationships in a number of pathogenic fungal genera found by Logrieco, et al., 1995. The ITS region of the rDNA operon, which is more variable than the D2 region, has proven useful in distinguishing relationships at the species level found by Kusaba, et al., 1995. The genetic variability within 69 bio-control isolates of Trichoderma collected from different geographic locations and culture collections and their phylogenetic

22


Fig. 2. Phylogenetic and molecular variability analysis of Trichoderma species

analysis were done with the help of the sequence data obtained from the inter Transcribed spacer 1(ITS1) region of Ribosomal DNA and a fragment of the translation elongation factor 1(tef1) and reported that more than 50% of the potential bio-control strains were grouped within Trichoderma section Pachybasium reported by Hermosa, et al., 2004. Trichoderma isolates with different bio-control capabilities and identification by molecular methods and further characterized into three main clades by internal transcribed spacer (ITS) Sequence analyses. Consequently, a reliable phylogenetic tree was constructed containing isolates belonging to the T. harziunum clade reported by Maymon, et. al., 2004. Molecular phylogenetic analyses of biological control strains of Trichoderma (Ascomycetes, Hypocreales) strains that have warted conidia are traditionally identified as T. viride, the type species of Trichoderma. However, two morphologically distinct types of conidial warts (I and II) have been found. Because each type corresponds to a unique

mitochondrial DNA pattern, it has beenquestioned whether T. viridecomprises more than one species. Combined molecular data (sequences of theinternal transcribed spacer 1 [ITS-1] and ITS-2 regions and of part of the 18SrRNA gene along with resultsof restriction fragment length polymorphism analysis of the endochitinase gene and PCR fingerprinting), morphology, physiology, and colony characteristics distinguish type I and type II as different species. Type I corresponds to “true” T. viride, the anamorphic of Hypocrearufa. Type II represents a new species, T. asperellum, which is, in terms of molecular characteristics, close to the neotype of T. hamatum. Analysis of ITS1-5.8S-ITS2 region of the cDNA showed that approximate 600 bp and size variation was observed. Restriction analysis of this region showed that inter and intra -specific polymorphism found by Latha, et al., 2002. Trichoderma has attained importance for substitute of chemical pesticides and hence an attempt was intended to corroborate the positive relatedness of

SHAHID, et al., Morphological and Molecular Identification of Trichoderma asperellum Tasp/8940

molecular and morphological characters. A fungal strain of Trichodermalongibrachiatum28CP/7444 was isolated from a soil sample collected from Barabanki district of Uttar Pradesh, India. The universal primers were used for amplification of the 28S rRNA gene fragment and strain characterized by using 28SrRNA gene sequence with the help of ITS marker. It is proposed that the identified strain Trichodermalongibrachiatum 28CP be assigned as the type strain of a species of the genus Trichodermabased on phylogenetic tree analysis together with the 28S rRNA gene sequence search in Ribosomal Database Project, small subunit rRNA and large subunit rRNA databases. The sequence was deposited in GenBank with the accession number JX978541. Thus, an integrated approach of morphological and molecular markers can be employed to identify a superior strain of Trichodermafor its commercial exploitation. Previously similar results were also reported by Shahid, et al., 2013, 2014 and they also concluded that most of the Trichoderma species are morphologically very similar and were considered as a single species for many years. Since new species were discovered, a consolidated taxonomical scheme was needed, proposed and defined nine morphological species aggregates. DNA methods brought additional valuable criteria to the taxonomy of Trichoderma which are being used today for studies that include identification and phylogenetic classification.

ACKNOWLEDGEMENT The authors are grateful to the financial support granted by the ICAR under the Niche Area of Excellence on “Exploration and Exploitation of Trichoderma as an antagonist against soil borne pathogens” running in Department of Plant Pathology,C.S.A University of Agriculture and Technology,Kanpur-208002, U.P., India.

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Entomopathogenicity and Cross Infectivity of EPNs on Cardamom Shoot/ Panicle/Capsule Borer Conogethes punctiferalis Guenee’ under Laboratory Conditions. M.A. ANSAR ALI1*, T.MANOHARAN 2, AND S.KUTTALAM3 1,2 &3- Department of Agricultural Entomology, CPPS, Tamil Nadu Agricultural University (TNAU),Coimbatore-641003,India. *email: [email protected]

ABSTRACT Small cardamom shoot,panicle and capsule borer C. punctiferalis is a serious, major pest and successful biological control with the EPNs (native isolates) of Heterorhabditis indica (ICRI- EPN 18) and Steinernema bicornutum ( ICRI- EPN 35) was attempted and both the species of EPNs were pathogenic on cardamom shoot borer (Heterorhabditis indica and Steinernema bicornutum) however H.indica (strain ICRI- EPN 18) was more virulent than S. bicornatum. (ICRI- EPN 35). The per cent mortality caused by H. indica (strain ICRI-EPN 18) was 60-100% at higher doses (4000-6000 ijs) and significantly different from lower doses whereas the mortality was 60-80% only at higher doses (5500-6000) with S.bicornutum strain (ICRI-EPN 35). A new species of EPN s were isolated from ginger and turmeric ecosystem is also pathogenic to C.punctiferalis. Key words

EPNs, cardamom, Shoot Heterorahbditis, biocontrol)

borer,

The cardamom shoot/ capsule borer, Conogethes punctiferalis Guenee’ is one of the major pest of Small cardamom (Elettaria cardamomum Maton.). The early stage larva damages the unopened leaf spindle, raceme, panicle and capsules. At a later stage the larvae bore into the shoots. Peak infestation is noticed in three seasons: January- February, May- June and SeptemberOctober .Chemical insecticide has to be targeted on early stages of the larvae, which are usually seen within 1520 days after adult emergence in the field. Insecticide sprays at late stages of the larvae, which bore in pseudostem, may not give adequate control of the pest. Injection of insecticide/ Bacillus thuringiensis (B.t.,) solution through the bore hole on shoots is effective for controlling larval infestation in pseudostems (Vardarasan, et al., 2010). A local isolates of EPN Heterorhabditis indica (strain ICRI-EPN18) is found to be an effective biocontrol agent on cardamom root grub Basilepta fulvicorne Jacoby, a major pest on small cardamom, (Varadarasan, et al, 2007).

Entomopathogenic Nematodes (EPN) of the genera Steinernema and Heterorhabditis sp. (Nematoda: Rhabditida) have emerged as excellent biological control agents for foliar and soil pests. They are emerging as a potent bio control agent against a variety of insect pests infesting different crops (Shapiro et al. 2002; Pervez et al. 2012). There is a scope of using EPNs in cardamom, which will reduce the chemical pesticide usage. Even though it is soil borne, they are being used to control the foliar pests, (Ali, et.al., 2005). An attempt was made to evaluate the bio control efficacy and cross infectivity of local isolates of EPN viz., H. indica (strain ICRI EPN-18) and S.bicornutum (strain ICRI EPN35) on C.punctiferalis.

MATERIALS AND METHODS The lab study was conducted during July to Nov’ 2013 at Spices Board, Indian cardamom Research Inst.(ICRI), Myladumpara, Idukki, Kerala,India.The native (local) isolates of Heterorhabditis indica (ICRI –EPN 18) and Steinernema bicornutum (ICRI-EPN 35) were isolated from soil by Galleria bait method (Bedding and Akhurst,1975).Infective juveniles of EPNs were obtained by multiplying on late instar larvae of greater wax moth, Galleria mellonella (L.). The larvae of shoot borer were collected from fields and reared in cardamom shoots (tender and matured) under laboratory condition. Infective juveniles in liquid suspension @ 50 to 60,000 ijs per shoot borer were injected through the bore hole on shoots by using a syringe. Each treatment with three replications were maintained. Mortality was recorded at 24, 48 and 72 hours after inoculation. EPN infected shoot borer were collected and put in white trap and ijs were collected. This is to confirm the cross infectivity and entomopathogenicity by EPN on C.punctiferalis. All data were subjected to analysis of variance (ANOVA) and means compared according to Duncan´s multiple range test (DMRT at 1% and 5 % significance).

26


Table 1. Laboratory bioassay of H. indica (ICRI- EPN 18) on C. punctiferalis Treatments

Mortality of shoot borer larvae 24 hours

48 hours

72 hours

Percentage (Mean)

Percentage (Mean)

Percentage (Mean)

50 ijs

0 (25.0)b

0 (25.0)b

20(35.0)c

100 ijs

20(35.0)ab

40(45.0)ab

60(55.0)abc

200 ijs

20(35.0)ab

40(45.0)ab

80(35.0)bcd

300 ijs

40(45.0)ab

40(45.0)ab

80(45.0)bcd

400 ijs

20(35.0)ab

60(55.0)ab

80(55.0)abc

500 ijs

0(25.0)b

20(35.0)ab

40(55.0)abc

1000 ijs

20(35.0)ab

40(45.0)ab

60(65.0)ab

1500 ijs

20(35.0)ab

40(45.0)ab

80(65.0)ab

2000 ijs

20(35.0)ab

60(55.0)ab

100 (65.0)ab

2500 ijs

0(25.0)b

60(55.0)ab

80(55.0)abc

3000 ijs

20(35.0)ab

40(45.0)ab

60(65.0)ab

3500 ijs

40(45.0)ab

60(55.0)ab

60(65.0)ab

4000 ijs

60(55.0)a

80(65.0)a

80(65.0)ab

4500 ijs

40(45.0)ab

80(65.0)a

100(65.0)ab

5000 ijs

20(35.0)ab

60(55.0)ab

100(65.0)ab

5500 ijs

40(45.0)ab

80(65.0)a

80(75.0)a

6000 ijs

40(45.0)ab

80(65.0)a

100(75.0)a

29.99

33.72

29.08

CD @ 1%

(Figures in parantheses are arc sine transformed values) (Mean mortality followed by common alphabets are not significantly different by DMRT @1% significance

RESULTS AND DISCUSSION Eventhough mortality was recorded on shoot borer, caused by EPN, at 24 hours after inoculation, the percentage mortality was higher with higher exposure time. With higher dose of ijs of EPN, the mortality was also higher. Between Heterorhabditis and Steinernema, the former had registered mortality in almost all the doses at 24 hours of exposure whereas Steinernema could register mortality only at 1000 ijs at 24 hours and at 400 ijs at 48 hours and at 200 ijs at 72 hours. The mortality caused by Heterorhabditis indica (strain ICRI-EPN 18) was 60-100% at higher doses (4000-6000 ijs) and significantly different from lower doses whereas the mortality was 60-80% only at higher doses (5500-6000) with Steinernema bicornutum strain (ICRI-EPN 35).

Heterorhabditis appears to be highly virulent and effective in causing mortality on shoot borer at lower dose with lesser exposure time. The efficacy of EPN has been less on foliar pests in field condition than in glass house conditions due to unfavourable environmental condition such as increased temperature, low Relative Humidity and UV radiation (Scroeder, et al., 1996). However the ijs injected inside the pseudostem of cardamom find a favourable condition and are protected from UV and hence recorded higher mortality. The results indicated that both the species of EPN were pathogenic on cardamom shoot borer (Heterorhabditis and Steinernema) however H.indica (strain ICRI- EPN 18) was more virulent than S. bicornatum. (ICRI- EPN 35). Heterorhabditis have a better host finding ability than the Steinernema (Kaya and Gaugler, 1993).

ALI, et al., Entomopathogenicity and Cross Infectivity of EPNs on Cardamom Shoot/Panicle/Capsule Borer

27

Table 2. Laboratory bioassay of S. bicornatum (ICRI- EPN 35) on C.punctiferalis Treatments

Mortality of shoot borer larvae 24 hours

48 hours

72 hours

Percentage (Mean)

Percentage (Mean)

Percentage (Mean)

50 ijs

0 (25.0)a

0 (25.0)b

0(25.0)b

100 ijs

0 (25.0)a

0(25.0)b

0(25.0)ab

200 ijs

0(25.0)a

0(25.0)b

20(35.0)ab

300 ijs

0(25.0)a

0(25.0)b

20(35.0)ab

400 ijs

0(25.0)a

20(35.0)ab

20(35.0)ab

500 ijs

0(25.0)a

0(25.0)b

40(45.0)ab

1000 ijs

20(35.0)a

20(35.0)ab

40(45.0)ab

1500 ijs

20(35.0)a

40(45.0)ab

60(55.0)ab

2000 ijs

0(25.0)a

20(35.0)ab

60 (55.0)ab

2500 ijs

20(35.0)a

20(35.0)ab

60(55.0)ab

3000 ijs

20(35.0)a

60(55.0)a

80(65.0)ab

3500 ijs

20(35.0)a

40(45.0)ab

80(65.0)ab

4000 ijs

40(45.0)a

60(55.0)a

80(65.0)ab

4500 ijs

40(45.0)a

60(55.0)a

80(65.0)ab

5000 ijs

20(35.0)a

40(45.0)ab

80(65.0)ab

5500 ijs

40(45.0)a

60(55.0)a

60(55.0)a

6000 ijs

20(35.0)a

40(45.0)ab

80(65.0)a

24.66

29.08

30.85

CD @ 1%

(Figures in parantheses are arc sine transformed values) (Mean mortality followed by common alphabets are not significantly different by DMRT @1% significance)

A new species of entomopathogenic nematode (EPN) of the genus Oscheius was isolated from the rhizosphere of ginger (Zingiber officinale Rosc.) in Kerala by Parvez, et al., 2012. The efficacies of eight native entomopathogenic nematodes (EPNs) were tested against larvae of hairy caterpillar, Euproctis sp., and larvae and pupae of the shoot borer, C. punctiferalis, and their multiplication and penetration of these nematodes in the shoot borer larvae was also assessed. Of the tested EPNs, all isolates, except IISR 08 of Oscheius sp., caused 100% mortality to larvae of hairy caterpillar. Heterorhabditis sp. (IISR-EPN 01), Steinernema sp. (IISR-EPN 02) and Oscheius sp. (IISREPN 07 and 08) caused 100% mortality also to shoot borer larvae. Oscheius sp. (IISR-EPN 07) was the most virulent against the shoot borer pupae, causing 100% mortality, followed by Steinernema sp. (IISR-EPN 02) and Oscheius sp. (IISR-EPN 05) which killed 67% of the pupae. Although all instars of C.punctiferalis were susceptible to test EPNs, there were differences among these EPNs in their ability to kill the insect. Among test EPNs, Steinernema sp. (IISR-EPN 02) and O. gingeri

appears to be the most promising. The per cent mortality increased with the increase the dosages as well as exposure time. The greatest number of infective juveniles was observed for Steinernema sp. (IISR-EPN 02) followed by Oscheius sp. (IISR-EPN 05), with the fewest found in Steinernema sp. (IISR-EPN 03) and Oscheius sp. (IISR-EPN 07) (10,432 and 14,373 IJs/ larva, respectively). The largest number of IJs that penetrated into shoot borer larvae (15.5 IJs/larva) were of Steinernema sp. (IISR-EPN 03) followed by Heterorhabditis sp. (IISR-EPN 01), and the fewest (2.8 IJs/larva) were of Oscheius sp. (IISR-EPN 08). EPNs have not been previously used for the biological control of leaf feeding larva ( Lema sp.,) infesting turmeric, and this is the first report of their possible use as a biological control agent against insect pest infesting turmeric, (Parvez, et al., 2014). Therefore, more investigations on their mass production, formulation and performance under field conditions should be encouraged with sustainable formulations of EPN for the biological control of major pests of spices.

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ACKNOWLEDGEMENT The first author expressing his sincere thanks to Director of Research, Spices Board of India, Indian Cardamom Research Institute(ICRI), Myladumpara, Kerala for the grant of permission and providing necessary facilities to conduct lab and field studies with native EPN isolates.

LITERATURE CITED Ali,S.S., Ahmed, R., Hussain, M.A and Parvez R.2005.Pest management of pulses through entomopathogenic nematodes. Indian Institute of Pulses Research (IIPR) ICAR, Kanpur, UP,India; pp.59. Bedding, R.A. and Akhurst, R.J. 1975. A simple technique for the detection of insect parasitic rhabditid nematodes in soil. Nematologica 21 : 109-110. Kaya, H.K.and Gaugler, R. 1993. Entomopathogenic Nematodes.Annual Review of Entomology,38: 181-206. Pervez, R., S.J. Eapen, S. Devasahayam and T.K. Jacob 2012. Efficacy of some entomopathogenic nematodes against insect

pests of ginger and their multiplication. Nematol. medit. 40: 39-44. Pervez.R., S. Devasahayam and S.J. Eapen 2014. Determination of LD 50 and’ LT 50 of entomopathogenic nematodes against shoot borer (Conogethes punctiferalis Guen.) infesting ginger (Zingiber officinale Rosc.) Ann. PI. Protec. Sci. 22 (1) : 169173 Pervez, R., T. K. Jacob, S Devasahayam and S. J. Eapen 2014. Penetration and infectivity of entomopathogenic nematodes against Lema sp. (Chrysomelidae: Coleoptera) infesting turmeric (Curcuma longa L.) and their Multiplication, Journal of Spices and Aromatic Crops, 23 (1) : 71–75 Varadarasan, S., Chandrasekar, S.S., Gopakumar, B. and Selvan, M. Tamil 2010. Preliminary evaluation of shoot injection of biocide for borer management in cardamom. Insect Environment, 16(3):111-113 Varadarasan, S., Hafitha, N.M., Sithara, L., Balamurugan, R., Chandrasekar, S.S. and Ali, M.A. Ansar 2011. Entomopathogenic Nematodes-science, Technology and field outreach for biological control of Cardamom root grub Basilepta fulvicorne Jacoby. Journal of Plantation Crops, 39 (1):86-91. Received on 10-09-2014



Growth Performance, Photosynthetic Efficiency and Pigment Concentration of Glycine max (L.) Merr., as affected by Alphamethrin, a Synthetic Pyrethroid Insecticide FOZIA BASHIR1, FAISAL ZAHID2 AND M. IQBAL3 1

Department of Botany, Krishna College of Science and Information Technology, Bijnor, Uttar-Pradesh-246701, India 2 Faculty of Science, Department of Biochemistry, Venkateshwara University Gajroula, U.P., India 3 Department of Botany, Hamdard University, New Delhi-110062, India

ABSTRACT Plants face threats of pesticide toxicity that hampers plant growth and development. Forty-five-day-old plants of soybean (Glycine max) were exposed to several Alphamethrin concentrations (0.00%, 0.05%, 0.10%, 0.15% and 0.20%) through foliar spray in the field conditions. In the treated plants, as observed at the preflowering (10 DAT), flowering (45 DAT) and post-flowering (70 DAT) stages, total plant height, total plant biomass (root + shoot + leaf), content of photosynthetic pigments, photosynthetic rate and stomatal conductance showed enhancement at 0.10% dose of Alphamethrin compared to control and then a decline with increasing concentration of Alphamethrin. The quantity of photosynthetic pigments was maximum at the flowering stage, followed by the preflowering and post-flowering stages. The carotenoid content and intercellular CO2 concentration showed a linear increase with increasing concentrations of Alphamethrin. The changes observed were dose-dependent, showing a strong correlation with the degree of treatment. Key words

Alphamethrin; photosynthetic pigments; photosynthetic rate; stomatal conductance, intercellular CO2.

The long-term ecological hazards associated with the use of organochlorine, organophosphate, and carbamate pesticides have propelled introduction of a new generation of pesticides with a lesser degree of persistence. Consequently, synthetic pyrethroids, which are powerful insecticides but less persistent and less toxic to mammals and birds have emerged as a viable substitute. One such pyrethroid is Alphamethrin [(S)a lpha-cya no-3-phenoxybenz yl(1R,3R)-3-(2,2dichlorovinyl)-2,2-dimethylcyclopropane carboxylate and (R)-alpha-cyano-3-phenoxybenzyl (1S,3S)-3-(2,2dichlorovinyl)-2,2-dimethylcyclopropane carboxylate]. It is widely used in agricultural crops and forestry because of its high activity against a broad spectrum of

insect pests (Villarini et al. 1998). However, it can accumulate to levels 10 times over the initial concentration, if applied repeatedly on the leaf of the experimental plants in a single season at rates higher than the rate at which it is degraded (Mueller-Beilschmidt 1990). The seeds and seedlings of legumes often require to be treated with different pesticides due to their wide susceptibility to a variety of seed- and soil-borne fungi and insects. Insecticides may cause oxidative stress in plant cells, affecting the various metabolic activities and growth components in plants (Toscano, et al., 1982, Jones, et al., 1986). Despite the replacement of most of the old organo-chlorine insecticides by pyrethroids, little is known of the ecological side effects these new materials may have on the soils, and on the overall growth and yield of the plant and the associated soil microorganisms in the agro-ecosystem. The present study therefore investigates whether the use of Alphamethrin may have some bearing on plant growth, biomass accumulation, photosynthetic pigments and photosynthetic efficiency of plants. Glycine max (L.) Merr., a popular pulse crop species, has been used as the test plant.

MATERIAL AND METHODS The seeds of Soybean [Glycine max (L.) Merr.], Fabaceae, were obtained from Division of Genetics (Pulse Research) of the Indian Agricultural Research Institute, New Delhi. Field experiments were conducted in the kharif season (July-October) at the experimental field of Hamdard University, New Delhi. Forty-fiveday-old seedlings were subjected to foliar application of five levels (0.00%, 0.05%, 0.10%, 0.15% and 0.20%) of Alphamethrin (10 % E.C.), prepared by dissolving the required amount of Alphamethrin in double distilled water and designated as A0, A1, A2, A3 and A4

30


respectively. Each experiment was repeated three times with three replicates. Seedlings were collected 10, 45 and 70 days after the treatment (DAT) to analyze the effect of Alphamethrin treatment on plant height, plant biomass, photosynthetic pigment concentration and the photosynthetic efficiency. For biomass estimation, the plants were dried in an oven at 80 ÚC ± 2 ÚC. The dry weights of samples, recorded with the help of electronic top pan balance (Eagle, New Delhi, India), were expressed in g. The chlorophyll and carotenoid contents were determined by extracting them from fresh leaves (0.1 gm) in 7 ml Dimethyl sulphoxide (DMSO) and incubating the extract at 65oC for two hours. To 1 ml aliquot, 3ml DMSO was added and absorbance was measured at 480, 510, 645 and 663 nm wavelength using spectrophotometer (Hiscox and Israelstam 1979). The amounts of chlorophyll a, chl. b, total chlorophyll and carotenoid (mg pigment/g fresh leaves) were calculated with the help of formulae given by Duxbery and Yentsch (1956), Mac-Lachlan and Zalik (1963) and Arnon (1979). Photosynthetic rate, stomatal conductance and

intercellular CO2 of fully expanded leaves were recorded at each sampling time by using a portable Infra Red Gas Analyzer (LI-6400, LICOR Inc., Lincoln, USA) and expressed as µ mol m-2 S-1, µ mol CO2 m-2 S-1 and ppm, respectively. The measurements were taken on sunny days (at 9.00 to 11.00 a.m.) at ambient temperature. Statistical analyses were carried out by two-way classification of ANOVA (Cochran and Cox, 1957) in order to confirm whether the values were significantly different, taking P < 0.05 as significance level.

RESULTS AND DISCUSSION Growth and Biomass The total plant height and plant biomass (root + shoot + leaf) of the control plants increased with increasing plant age, being maximum at post-flowering stage. A significant (p< 0.05) reduction was observed in the above parameters when treated with different concentrations of alphamethrin as observed at 10, 45 and 70 DAT, compared with the control, except with A2 (0.10% solution) at the post-flowering stage, where a sharp increase was observed. Tables 1 (a-d). In treated

Table 1. Variation in Growth and Biomass parameters at various growth stages of Glycine max (L) Merr, treated with different concentration of Alphamethrin. Parameters/ Stages Control (a) Total Plant Height (cm) Pre-flowering 21.60±0.60 (00) Flowering 23.70 ± 0.66(00) Post-flowering 25.50± 0.710(00) (b)Root Dry Weight (g) Pre-flowering 2.66±0.07 (00) Flowering 2.82± 0.07(00) Post-flowering 3.14±0.08 (00) (c) Shoot Dry Weight (g) Pre-flowering 4.10 ± 0.11(00) Flowering 4.62±0.12 (00) Post-flowering 4.90 ± 0.13(00) (d) Leaf Dry Weight (g) Pre-flowering 0.45 ± 0.01 (00) Flowering 0.91 ± 0.02 (00) Post-flowering 1.99 ± 0.05(00)

0.05

Alphamethrin (%) 0.10

0.15

0.20

23.00±0.63 (6) 25.43±0.71 (7) 28.50±0.78 (11)

24.30±0.68 (12) 27.50± 0.75(16) 31.00±0.87 (22)

20.30±0.55 (6) 22.50 ± 0.63 (5) 24.20 ±0.66 (5)

18.00±0.50 (17) 20.20±0.57 (15) 22.80±0.62 (10)

3.02± 0.08 (13) 3.14± 0.08(11) 3.47±0.09 (10)

3.24±0.08 (22) 3.54 ± 0.09 (25) 3.75±0.07 (19)

2.27± 0.06 (14) 2.57±0.07 (8) 2.79±0.07 (11)

2.010±0.05 (24) 2.05±0.06 (27) 2.38 ±0.06 (24)

5.04 ± 0.13 (22) 5.34 ±0.14 (15) 5.74 ± 0.15 (17)

5.74 ± 0.15 (40) 5.98 ± 0.16 (30) 6.10 ± 0.16 (24)

3.86 ± 0.10 (5) 4.10± 0.11 (11) 4.38 ± 0.11(10)

3.39±0.09 (17) 3.950±0.10 (14) 4.14±0.11 (15)

0.53 ± 0.01 (17) 1.29 ± 0.04 (41) 2.13 ± 0.04(7)

0.59 ± 0.02 (31) 1.33 ± 0.03 (46) 2.35 ± 0.07 (18)

0.41 ± 0.01(8) 0.82 ± 0.02 (10) 1.84 ± 0.07 (7)

0.37 ±0.01 (17) 0.70±0.02 (23) 1.63± 0.03(18)

(a) Total Plant Height (cm); (b) Root Dry Weight (g); (c) Shoot Dry Weight (g); and (d) Leaf Dry Weight (g). Values in parenthesis indicate percent variation with reference to respective controls. Mean ± SE (n = 5). Values with different superscripts are significantly (P