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Summer Research Fellowship Programme of India's Science Academies

Characterization and prospecting of manglicolous fungi with special reference to plant growth promoting factors

Unnati Chaudhary

Cellulose and Paper Technology, Forest Research Institute deemed to be University, Dehradun, Uttarakhand-2480006

Dr. Punarbasu Chaudhuri

Department of Environmental Science, University of Calcutta, Kolkata, West Bengal-700019

Abstract

In the present study, fungi isolated from different sampling sites of Indian Sundarbans are morphologically identified and screened for their ability to produce Plant Growth Promoting Factors (PGPF) viz. Indole-3-Acetic Acid (IAA) and 1-aminocyclopropane-1-carboxylate deaminase (ACC Deaminase). The major objectives of the work are screening and morphological identification of plant growth promoting factors (PGPF) producing manglicolous fungi isolated from Indian Sundarbans and molecular characterization of manglicolous fungi as well as monitoring of PGPF. Colony morphology was studied for the fungal cultures and morphological identification of fungal isolates was done by slide culture technique followed by lactophenol-cotton-blue staining. Colony morphology evaluation suggested the presence of predominantly irregular margins, rhizoid forms and umbonate or convex elevations. Microscopic identification was conducted with the aid of a compound light microscope. 17 fungal species out of 20, were microscopically identified which included Alternaria sp., Anthrinium sp., Aspergillus sp., Candida sp., Cladorrhinum sp., Cladosporium sp., Curvularia sp., Fusarium solani, Fusarium sp., Humicola sp., Mucor sp., Nigrosporasacchari, Penicillium sp., Pestalotia sp., Rhizoctonia sp., Trichoderma sp., Trichospermum sp, and 3 were unidentified. All the fungi obtained were screened for production of PGPF viz. IAA and ACC deaminase. 2 fungal species, Penicillium sp. and Aspergillus sp. showed the maximum potential for IAA and ACC deaminase synthesis. Molecular characterization of fungal species comprised DNA extraction, amplification of DNA using Polymerase Chain Reaction (PCR) and gel electrophoresis. The amplified DNA can be further sent for Sanger Sequencing which can help in molecular identification of fungal species. The monitoring of IAA and ACCD was done by Response Surface Methodology (RSM).

Keywords: Plant Growth Promoting Factors, Indole-3-Acetic Acid (IAA), 1-aminocyclopropane-1-carboxylate deaminase (ACC deaminase), manglicolous fungi

Abbreviations

Abbreviations 
                           PGPF Plant Growth Promoting Factors
IAA Indole-3-acetic Acid
ACC Deaminase 1-aminocyclopropane-1-carboxylate deaminase
RSM Response Surface Methodology
CCD Central Composite Design
ANOVA Analysis of Variance
PCR Polymerase Chain Reaction

INTRODUCTION

Mangroves are considered the most fecund coastal ecosystems of the world which harbor diverse biological communities in their habitat (Ramanathan et.al., 2008) and provide us with immense ecosystem services (Donato et.al., 2011).They are found in the wetland areas of the tropics and sub-tropics ( Friess, 2019). They are adapted to withstand harsh conditions such as high salinity, high temperature and intense tides (Giri et al., 2010) and support a massive assortment of biological life. They are considered hotspots of fungi as they provide a detritus based habitat. et.al. (2012) defined mangroves as a dynamic ecotone between terrestrial and marine ecosystems. The Sundarbans are the largest mangrove forests of the world in the deltaic region created at the conflux of the rivers Ganges, Brahmaputra and Meghna in the Bay of Bengal. They cover an approximate area of 10,000 km. The larger part (60%) falls in the territory of Bangladesh while 40% is confined to the Indian subcontinent. They are the major biodiversity hotspots which nurture conglomerations of fauna, flora and microorganisms. Among the microorganisms, a distinguishing class found in the Sundarbans is the manglicolous fungi. Manglicolous fungi are the fungal communities that inhabit mangrove forests. They consist of both marine and terrestrial fungi, the former referring to fungi which occur in submerged plant parts while the latter is present in plant parts above water (Kohlmeyer,1969).Marine fungi is more abundant as compared to their counterpart. They can be divided into saprophytes, parasites or symbionts depending on their mode of obtaining nutrition. Mangrove fungi include lower fungi such as oomycetes and thraustochytrids as well as higher fungi such as ascomycetes and basidiomycetes. The first manglicolous fungus was reported by Cribb and Cribb (1995) from Avicennia marina in Australia. Mangrove fungi are considered to be the second largest group of marine fungi (Sridhar, 2005). They have great ecological and biotechnological potential (Thatoi et.al., 2013) which has been exploited by many scientists in the past decades. They are utilized to exudate out bioactive metabolites which are utilized at an industrial scale for production of various compounds including biopesticides, antibiotics, anti-oxidants, immunosuppressants, anti-cancer, anti-diabetic, anti-viral and insecticidal agents etc. (Nishad et.al.,2018). They also produce plant growth promoting factors such as phytohormones and volatile organic compounds (VOCs).Plant growth promoting fungi are ubiquitous, non-symbiotic, saprophytic fungi which facilitate growth of plants. Most commonly reported plant growth promoting fungi belong to genera Aspergillus, Fusarium, Penicillium, Piriformospora, Phoma, and Trichoderma (Hossain et.al, 2017). There are a wide variety of mechanisms by which PGPF promote plant growth, out of which plant growth promotion by phytohormone secretion will be considered here. A vital role in plant growth is exhibited by phytohormones which are chemical substances that regulate all aspects of plant growth. Phytohormones viz. Indole Acetic Acid, Gibberellins, Auxin etc. (Berg, 2009) are shown to be actively produced by manglicolous fungi (Kumar et.al., 2019). Enhanced plant growth by the fungus Trichoderma in the absence of pathogens has been reported in many plants (Whipps et.al., 2002). Harman et.al. (2004) reported the increased growth in maize and other crops by the presence of a strain T-22 of Trichoderma. Indole-3-acetic acid (IAA), a derivative of indole is a form of Auxin that is biologically active and induces lateral root growth and root hair formation (Mehmood et.al., 2018). Ethylene is a phytohormone which causes ripening of fruits and senescence of leaves in plants. It is although considered to be a senescence hormone and is known to inhibit plant growth but it can lead to plant growth promotion by suppressing the release of ACC which is a precursor of Ethylene and thereby inhibiting senescence and promoting plant growth which can be achieved by the presence of an enzyme ACC Deaminase. ACC Deaminase helps in lowering down the levels of ethylene thereby promoting plant growth (Viterbo et.al., 2010).

LITERATURE REVIEW

Manglicolous fungi are the mycoflora which inhabit the mangrove niche of Indian Sundarbans. The occurrence of fungi in mangroves has been determined and characterized by numerous scientists across the world. Ramanathan et. al. (2008) analysed the fungal diversity in the sediments of Sundarbans at three different sampling locations and identified 10 fungal genera of which Aspergillus and Penicillium were the most common. 22 fungal species belonging to 14 genera were identified from soils of mangroves found in the Mahanadi delta, Orissa first by Behera et.al. (2012) who regarded soil as one of the likely habitat of fungi in addition to decaying woods, submerged plants, decaying plant materials, pneumatophores etc. Sarma and Vittal (2001) gave an account of 88 fungal species from the decaying substrates of mangroves in the Godavari and Krishna deltas (Andhra Pradesh) on the east coast of India. In a study conducted on Pichavaram mangrove forests, southeast India by Kathiresan (2000), the mycoflora identified consisted of 23 fungal species with majority belonging to Deuteromycetes. A total of 19 fungal species with Aspergillus sp. being the predominant were isolated from submerged mangrove leaf litter in India by Rajendran and Kathiresan (2007). The prior studies showed that endophytic fungus Pestalotia sp. excrete antimicrobial substances, xylitol and oxysporone and also reported that endophytic microorganisms from mangrove ecosystem might be a potential source of anti-infective substances (Pramanik et.al., 2019) . A novel marine fungal genus Thyridariella was discovered from decaying wood of Avicennia marina from Muthupet mangroves, Kaveri River Delta, Tamil Nadu, from the southeast coast of India by Devadatha et.al. (2018). The mycoflora in the Godavari and Krishna deltas, on the east coast of India were studied by Sarma et.al. (2001) and found 73 and 67 species from the Godavari and Krishna mangroves respectively, 55 species being common to both while 18 species found exclusively in the Godavari mangroves and 12 found only in Krishna mangroves. The frequency of occurrence of marine fungi on the coast of Maharashtra was reported by Borse (1988) by studying wood samples collected from the intertidal regions of mangroves and a total of 36 species were identified with Massarina valataspora being the most common.

Sharouny et.al. (1998) identified 33 species of fungi collected from decayed twigs of Avicennia marina from intertidal region of red sea, upper Egypt. The predominant among them were Halosphaeri aquadricornuta, Leptosphaeria australiensis and Periconia prolifica. Alias et.al. (1995) studied the biodiversity of fungi in the Malaysian mangroves for which he compared the fungal diversity at three locations (Morib, Kuala Selangor, Port Dickson) and also identified fungi inhabiting common mangrove tree species Avicennia marina and Bruguiera gymnorrhiza. 39 species of intertidal mangrove fungi were specified by Hyde (1989) from the driftwood and decaying wood samples obtained from Kampong Nelayan mangrove near Belawan, north Sumatra.

Characterization of mangrove fungi has made exploration of fungi for pharmaceutical and industrial purposes feasible. The ecological role and biotechnological potential of mangrove fungi has been reported by Thatoi et.al. (2013). Decomposition and recycling of nutrients is the most vital role of fungi occurring in mangroves. They also act as a source of novel bioactive metabolites which have a wide array of uses. The products obtained from manglicolous fungi include antibiotics, antimycotics, immunosuppressants, anti-diabetic and anticancer compounds etc. (Strobel et.al., 2004). In addition to these products having biotechnological prospective, the manglicolous fungi also augment plant growth and fitness (Kumar et.al, 2019). Plant growth can be enhanced by phytohormones like IAA, Gibberellins etc. released by the fungi as reported by Kumar et.al, 2019. The ability of the enzyme 1-aminocyclopropane-1-carboxylate deaminase (ACC deaminase) isolated from strains TSK8 and SKS1 Trichoderma have been shown to have a positive effect on the growth of mangrove seedlings (Saravanakumar et.al., 2018). The levels of ethylene are monitored by the presence of an enzyme called ACC Deaminase, produced by many bacterial (Glick et.al., 2007; Glick 2014) and fungal strains, which carries out or ceases the conversion of ACC, precursor of ethylene, into ethylene (McDonnell et.al., 2009).

Earlier reports have shown that a tremendous amount of work has been conducted successfully on bacteria with respect to plant growth promotion (Saharan and Nehra, 2011; Lugtenberg and Kamilova, 2009; Santoyo et.al., 2016) but the area of fungi with respect to PGPF is yet to be exploited at a magnificent scale. The diversity, distribution and potential applications of mangrove fungi were reported by Sridhar and Maria (2005) but there is no accounted work on manglicolous fungi from Indian Sundarbans with respect to their plant growth promoting factors producing ability.

OBJECTIVES

The aim of the study was characterizing and prospecting manglicolous fungi isolated from Indian Sundarbans with special reference to Plant Growth Promoting Factors. It encompasses-

Screening and morphological identification of plant growth promoting factors (PGPF) producing manglicolous fungi isolated from Indian Sundarbans

Molecular characterization of manglicolous fungi

Monitoring of PGPF

MATERIALS AND METHODS

Chemicals Required

Potato dextrose broth, agar powder, distilled water, ethanol(100% and 70%), milliQ water, chloramphenicol, spirit, lysis buffer, TE extraction buffer, RNAse A, PCI(Phenol, Chloroform, Isoamyl alcohol), Ferric Chloride, Perchloric acid, Sabouraud Dextrose Broth (SDB), L- tryptophan, commercial Indole Acetic Acid, α-ketobutyrate, tris- HCl, 2,4- dinitrophenylhydrazine, sodium hydroxide (NaOH), Synthetic Media, 1X TAE buffer, agarose, Ethidium Bromide, Loading dye

Glasswares Required

Petriplates, test tubes, test tube stand, conical flasks, pipette, pipette tips, falcon tubes, falcon tubes stand, reagent bottles, amber bottles, amber falcons, centrifuge tubes, measuring cylinders, glass slides, cover slips, beakers, trash beakers

Miscellaneous

Absorbent and non- absorbent cotton, cotton plugs, spirit lamp, spatula, forcep, needle, butter paper, match box, marker, transparent nail polish, syringe, glass wool, HPLC water, commercially available DNA Ladder, primer

Instruments Used

Laminar air flow chamber, weighing machine, autoclave, incubator, compound microscope, centrifuge, refrigerator, spectrophotometer, PCR machine, Ultrasonic Cell Crusher Noise Isolating Chamber, transilluminator, hot plate, Rocker, electrophoresis equipment, Thermal Cycler.

Morphological identification of manglicolous fungi

Isolation of fungal samples

The fungal samples were previously isolated from different habitats of Indian Sundarbans. The sources include leaves, soil, water and litter. Isolation of fungi from soil and water was done first by serially diluting the samples and then plating them on media and incubating them at suitable temperature for 2-3 days (Watanabe, 2010). For leaves and litter samples, they were first washed in running water and small portions of leaves were cut and dipped in 70% ethanol for 5 seconds followed by immersing in 4% NaOCl for 1 minute, rinsing with sterile water for 10 seconds and subsequently plating them on PDA media (Rajagopal et.al., 2000).

Culturing of isolated fungal samples

For culturing, first of all Potato Dextrose Agar (PDA) media is prepared and allowed to cool. The agar slants are prepared which are then inoculated with fungi using an inoculating loop. All this is carried out under highly sterile conditions in laminar air flow. The agar slants are incubated for suitable time under optimum temperature.

(a) Potato Dextrose Agar (PDA) media

Preparation

Potato Dextrose Agar (PDA) media is prepared by dissolving 24 gms Potato Dextrose Broth and 1.75% agar in 1000 ml distilled water (subject it to heat if needed) and sterilizing it by autoclaving at 121℃, 15 lbs pressure for 15 minutes.

Ingredients

Potato (peeled) 200 g/L

Dextrose 20 g/L

Agar 15 g/L

Distilled water 1000 ml

Final pH (at 25 ºC) 5.6±2.5.

Sub-culturing of fungal samples

Sub culturing was done from previously cultured fungal samples in order to maintain stock cultures.

Slide culture technique

For slide culture, 1.75% agar was poured into sterile plastic petri plates and allowed to solidify. Small cubical blocks were cut out from the solidified media using a sterilized blade and placed in the petriplates. Inoculation by fungal sample was done on each block and covered with a cover slip. The petriplates were incubated in a rotory incubator until adequate growth takes place (Harris, 1986).

Colony Morphology Evaluation

Colony morphology evaluation was done on the basis of margins, forms and elevation on agar plates as described by Watanabe (2010).

Slide preparation

The slides were prepared using lactophenol-cotton-blue staining technique.

Microscopic observation

The prepared slides were observed under a compound light microscope. The key identification features assessed include spore type, spore shape, type of sporangia and type of mycelium present.

Screening of manglicolous fungi for positive PGPF production

The 20 fungal species identified were screened for their ability to produce PGPFs - IAA and ACC Deaminase. The process of screening was carried out by conducting enzyme assays by procedures explained as under.

Enzyme assays

[a] Auxin Production Assay

For standardization, HPLC water was used as a blank. A standard curve of different concentrations ( 0, 10, 20, 30, 40, 50) of commercial Indole Acetic Acid was used for µg/ml auxin quantification. 0.1% (w/v) L- tryptophan was added to Sabouraud Dextrose Broth (SDB) and the fungal isolates were grown for 7 days. 1.5ml of mycelia were centrifuged at 10,000 for 5 minutes to separate culture filtrate. 2ml of Salkowski Reagent was mixed with 1ml of culture filtrate and was stored for 30 minutes at room temperature. An orange- red colour developed which is quantified using UV-Visible Spectrophotometer at 530 nm. (Aban et.al., 2017)

(i) Sabouraud Dextrose Broth (SDB)

Preparation

30gm SDB is dissolved in 1000ml distilled water and heated if necessary to dissolve the medium completely. Then it is sterilized by autoclaving at 15 lbs pressure (121°C) for 15 minutes.

Ingredients

Dextrose (Glucose) 20 g/L

Peptone 10 g/L

Final pH (at 25°C) 5.6±0.2

(ii) Salkowski Reagent Preparation

For Salkowski Reagent Preparation, 98ml of 35% Perchloric acid is added to 2ml of 0.5M Ferric Chloride solution.

[b] ACC Deaminase Production Assay

(i) Standardization of ACC Deaminase Activity

In order to obtain standards, a solution of 100mM (0.1M) α-ketobutyrate is prepared in 0.1M tris- HCl ( pH 8.5 ) and stored at 4℃. A series of known concentrations are prepared in a volume of 200 µl, to which 300 µl of the 2,4- dinitrophenylhydrazine reagent is added, vortexed and incubated at 30℃ for 30 minutes. A blood red colour appears by the addition of 2N NaOH and mixed which is then measured at 540nm.

(ii) ACC Deaminase Production Assay

20 µl spore suspension is inoculated in 10ml Synthetic Media (SM) and the culture is grown for 48 hours. The washed mycelia is transferred to 5ml SM without ammonium and with 0.3-3 mM ACC. The culture is suspended in half volume of 0.1M Tris- buffer ( pH 8.5 ) after induction period and homogenized. 25 µl toluene was added to a 200 µl aliquat and vortexed vigorously for 30 seconds. 20 µl of 0.5M ACC is added and incubated for 15 minutes at 30℃. After this, 1 ml of 0.56N HCl is added to it and the lysates are centrifuged at 10,000 for 10 minutes. 1 ml of supernatant is mixed with 800 µl of 0.56N HCl and 300 µl of 2,4- dinitrophenylhydrazine ( 0.2 gm in 100 ml of 2N HCl ). The mixture is incubated at 30℃ for 30 minutes and 2ml NaOH is added to each sample following which they are measured at 540nm.

Synthetic Media Composition

Glucose 15 g/L

Magnesium sulfate heptahydrate (MgSO4.7H2O) 0.2 g/L

Dipotassium phosphate (K2HPO4) 0.6 g/L

Potassium Chloride (KCl) 0.15g/L

Ammonium Nitrate (NH4NO3 ) 1g/L

Trace 1ml

Ferrous sulfate heptahydrate (FeSO4.7H2O) 0.005 g/L

Maganese Sulfate monoahydrate (MnSO4.H2O) 0.006 g/L

Zinc Sulfate monohydrate (ZnSO4. H2O) 0.004 g/L

Cobalt chloride (CoCl2) 0.002g/L

Molecular characterization of manglicolous fungi

Molecular characterization encompasses DNA extraction, amplification of DNA using Polymerase Chain Reaction (PCR) and gel electrophoresis.

DNA Extraction

For DNA extraction, the fungi were grown in Potato Dextrose Broth (PDB) culture for 7 days. About 10 ml PDB is transferred to each vial and inoculation of the fungal strains is carried out. After an incubation period of 7 days, the fungal mass so obtained is filtered using glass wool. The fungal mass is subjected further to DNA extraction protocol. The fungal mass is transferred to a 2ml centrifuge tube containing lysis buffer and glass beads. Homogenization is carried out twice using Ultrasonic Cell Crusher Noise Isolating Chamber. The homogenized mass is centrifuged at 13,000 rpm for 10 minutes. Then, 2µl of RNAseA is added and allowed to stand at a temperature of 37℃, following which equal volume of PCI is added and centrifuged at 13,000 rpm for 10 minutes. The upper aqueous layer thus obtained after centrifugation is carefully removed and 100% ethanol is added to it and stored at -20℃ for 30 minutes. After this, it is again centrifuged at 12,000 rpm for 10 minutes and subsequently the DNA pellet is obtained. The DNA pellet is washed using 70% ethanol and centrifuged at 12,000 rpm for 5 minutes. The DNA pellet is then air dried and 1XTE extraction buffer is added to it (Aamir et.al., 2015). Purity of the procured DNA sample is then determined by measuring absorbance at 260 and 280nm in a spectrophotometer.  A 260/A280 ratio will help in evaluating the purity which should fall within a range of 1.7- 2.

[a] Potato Dextrose Broth (PDB)

Preparation

For PDB preparation, 24gms PDB is dissolved in 1000 ml distilled water and sterilized by autoclaving at 121℃, 15 lbs pressure for 15 minutes.

Ingredients

Potatoes 200g/L

Dextrose 20 g/L

Final pH ( at 25°C) 5.1±0.2

DNA Amplification

The obtained DNA can be subjected to Polymerase Chain Reaction (PCR) in order to make multiple copies. The procured DNA samples were amplified in a Thermal Cycler in a process called Touchdown PCR but no satisfactory results could be obtained within the specified time of work.

Gel Electrophoresis

Procured DNA is run on agarose gel in a process called Electrophoresis to determine the molecular weight of the corresponding DNA. In order to run DNA sample on gel, first of all agarose gel is prepared and poured into the gel tray after placing the combs in position. The gel is allowed to solidify by letting it stand at room temperature. Once the gel solidifies, it is placed in the gel box and filled with 1X TAE buffer until the gel is submerged. Loading dye is added to each sample of DNA. A commercially available ladder is loaded into the first well and the samples are loaded in the subsequent wells. The electrodes are connected to the power source and the samples are run for 25 minutes at 100V following which the gel is removed from the gel box. The gel is then placed in TAE buffer containing 5µL Ethidium Bromide and placed on a rocker for about 15 minutes. In order to visualize the bands, the gel is placed in a transilluminator.

[a] Agarose Gel Preparation

To prepare 2% 20mL agarose gel, 0.4 gm of agarose is weighed and added to 20mL 1X TAE buffer in a conical flask. It is then heated on a hot plate with continuous shaking.

Monitoring of PGPF

Monitoring of PGPF is done by optimizing enzyme activity of IAA and ACC deaminase produced by Penicillium sp. and Aspergillus sp. using RSM.

Response Surface Methodology (RSM) is a design tool which is utilized for optimization of analytical data and includes mathematical and statistical techniques on the basis of a polynomial equation fitting the experimental data (Bezerra et.al., 2008). It was developed by  George E. P. Box and K. B. Wilson in 1951. It is an optimization process that has proved to be the most favoured in the past years (Baş and Boyacı, 2007). The input variables also called as factors, independent variables or process variables can be controlled to create a response (performance measures of a process) (Myers et.al., 2016). It is used to construct a relationship between response of interest, y, and input variables denoted by x1x2,…,xk (Khuri and Mukhopadhyay, 2010 )..

Central Composite Design (CCD) is the most commonly used software for RSM. The statistical analysis of the experiment data was done using Design-Expert Version 8.0.5b ( Stat-Ease Inc., Minneapolis, MN, USA). The evaluation of significant terms in the models was done by Analysis of Variance (ANOVA). The adequacy of the models was evaluated using many parameters like best fit test, p value, f value, determination coefficient ( R2), adjusted determination coefficient ( R adj2) and coefficient of variation.

RESULTS AND DISCUSSION

Screening and morphological identification of manglicolous fungi

Microscopic Identification and Colony Morphology Evaluation

Identification features of the 20 fungal species
Fungi nameColony FormColony elevationColony marginColour of ColonyFew Identification Features
Alternaria sp.RhizoidConvexFilamentousGrayish-green or black coloniesPear shaped conidia arising from a conidiophorre which is present on a septate mycelium
Arthrinium sp.RhizoidUmbonateFilamentousWhite coloniesErect conidiophores having one or more conidia at the apex
Aspergillus sp.IrregularUmbonateFilamentousBlack colourConidia present in chains at the end of a bulb like structure present at the end of the conidiophore arising from a septate mycelium
Candida sp.RhizoidConvexFilamentousWhite colonies that turn greenish-blue, black, or brown subsequentlyApical or lateral conidia present directly on the hyphae
Cladorrhinum sp.RhizoidConvexFilamentousBlack colourErect or branched conidiophores with a mass of conidia on them
Cladosporium sp.IrregularConvexFilamentousgreenish-black and powdery coloniesConidia arising from a complex structure of conidiophores
Curvularia sp.RhizoidUmbonateFilamentousPale brown colour coloniesConidiophores are simple or branched with conidia being usually 4 celled
Fusarium solaniIrregularUmbonateEntirepale yellowish brown or pinkish brown Slightly curved conidia present at the apex of the conidiophore
Fusarium sp.RhizoidConvexFilamentouslight pink colony with white marginSlender conidia with 5-6 septa present on conidiophore
Humicola sp.RhizoidConvexEntirecloudy whiteConidia usually oval in shape attached in ones or twos to the hyphae apically or laterally
Mucor sp.RhizoidConvexEntireWhite coloured coloniesSingle sporangiophore emerging from aseptate mycelium and round sporangium presnt
NigrosporasacchariIrregularConvexEntireblakish whiteBlack disc shaped conidia present at the end of conidiophore
Penicillium sp.IrregularUmbonateEntiregreenish or blue-green culturesConidia present in chains at the end of a structure present on conidiophore arising from a septate mycelium
Pestalotia sp.IrregularConvexFilamentousbrownish whiteSpindle shaped conidia with 4-5 cells
Rhizoctonia sp.RhizoidConvexFilamentous white brownBranched hyphae with no conidia
Trichoderma sp.IrregularUmbonateFilamentousBlackish whiteBranched conidiophores having spore masses on top of philaides
Trichospermum sp.IrregularUmbonateFilamentousGreyishApical or lateral conidia present directly on the hyphae
Unidentified Filamentous Fungal species 1RhizoidConvexFilamentousbrownish whiteConidia present in chains at the end of a structure present on conidiophore arising from a septate mycelium
Unidentified Filamentous Fungal species 2RhizoidUmbonateFilamentousblackConidia present in chains at the end of a bulb like structure present at the end of the conidiophore arising from a septate mycelium
Unidentified Filamentous Fungal species 3RhizoidUmbonateFilamentouslight reddishBlack disc shaped conidia present at the end of conidiophore

Predominantly irregular margins, rhizoid forms and umbonate or convex elevations were observed in the agar plates with fungal cultures.20 fungal species were identified morphologically based on features described by Cappuccino and Sherman (2005).

fungi 1_4.tif
    showing a)Alternaria sp. b) Arthrinium sp. c) Aspergillussp.d) Candida sp. e) Cladorrhinumsp f) Cladosporium sp. g) Curvularia sp. h) Fusarium solani i) Fusarium sp.

    Picture1_2.tif
      showing j) Humicola sp. k) Mucor sp. l) Nigrosporasacchari m) Penicillium sp. n) Pestalotia sp. o) Rhizoctoniasp. p) Trichoderma sp. q) Trichospermum sp. r) Unidentified Filamentous Fungal Species 1 s) Unidentified Filamentous Fungal Species 2 t) Unidentified Filamentous Fungal Species 3

      Screening of Manglicolous Fungi

      IAA and ACC deaminase activity of the 20 identified fungal species
      S.No.Fungal isolatesIAA activity (IU/ml)ACC deaminase activity (IU/ml)
      1Alternaria sp.113.85114.86
      2Arthriniumsp .111.52110.39
      3Aspergillus sp.182.12186.52
      4Candida sp.70.4277.53
      5Cladorrhinum sp.44.1540.19
      6Cladosporium sp.19.1612.11
      7Curvulariasp.58.7655.42
      8Fusarium solani88.1388.11
      9Fusarium sp.101.12110.11
      10Humicola sp.109.86105.32
      11Mucor sp.87.1898.13
      12Nigrosporasacchari33.6834.55
      13Penicilliumsp.158.62139.53
      14Pestalotiasp.89.1266.86
      15Rhizoctonia sp.88.1220.55
      16Trichoderma sp.34.1655.59
      17Trichospermum sp.12.1348.18
      18Unidentified Filamentous Fungal species 170.1359.14
      19Unidentified Filamentous Fungal species 296.5590.52
      20Unidentified Filamentous Fungal species 3110.1899.31
      Picture11_1.tif
        Bar graph showing IAA and ACC deaminase activity of the 20 fungal species

        It is clear from the graph that 2 fungal species Penicillium sp. and Aspergillus sp. shows the highest activity for IAA and ACC deaminase. IAA and ACC deaminase activity for Aspergillus spare 182.12 IU/mL and 186.52 IU/mL respectively while for Penicillium sp. the value for IAA activity is 158.62 IU/mL and for ACC deaminase activity is 138.53 IU/ml.

        Molecular characterization of Manglicolous Fungi

        DNA of 20 fungal species was extracted, amplified by PCR and the molecular weight of the 2 most dominating species i.e. Penicillium sp. and Aspergillus sp. was determined through Gel Electrophoresis.

        dna GEL_3.tif
          Picture of the gel run of .Penicillium sp. and Aspergillus sp.

          Monitoring of Plant Growth Promoting Factors by RSM

          Interaction among significant variables through RSM was evaluated using Central Composite Design (CCD). The optimum values of the three variables viz. incubation time, pH and temperature considered in the study were determined in such a way so that IAA and ACC deaminase activity by the fungi could be maximized. Tables depict the responses for IAA and ACC deaminase activity (IU/mL).

          Quadratic Second Order Polynomials in each case in terms of actual and coded units were acquired through the Regression Analysis Technique. The values for Std. Dev., C.V%, R-Squared, Pred R-Squared and Adeq Precision were obtained as well.

          Details of Response Surface Methodology (CCD) Optimization
          NameUnitsLowHigh-alpha+alpha
          Incubation timedays6152.9319318.0681
          pHdays482.636419.36359
          temperature204013.182146.8179

          IAA and ACC deaminase synthesized from Penicillium sp.

          Actual Design

          Table 4: Actual Design of responses in Penicillium sp.

          shows the actual design of IAA and ACC deaminase activity for the variables incubation time, pH and temperature in Penicillium sp.
          Factor 1Factor 2Factor 3Response 1Response 2
          StdA:Incubation timeB: pHC:temperatureIAA ActivityACC deaminase Activity
          DaysIU/mlIU/ml
          1642021.3522.06
          21542051.6652.37
          3682041.4142.12
          41582081.4482.15
          5644034.3835.09
          61544051.3452.05
          7684031.3332.04
          81584061.3462.05
          9663083.3484.05
          1015630111.6112.31
          111023021.4122.12
          121083081.682.31
          131062041.4142.12
          141064041.3442.05
          151063081.6582.36

          Response 1: IAA Activity

          Sequential Model Sum of Squares [Type I]: (Quadratic vs 2FI)

          Analysis of Variance (ANOVA) for the response surface quadratic models (Quadratic vs 2FI) for Penicillium sp.
          ResponsesSum of SquaresdfMean SquareF-valuep-value
          IAA8252.3932750.801125.41< 0.0001

          The highest order polynomial is selected where the additional terms are significant and the model is not aliased.

          ANOVA ANALYSIS:

          Factor coding is Coded. Sum of squares is Type III – Partial. The Model F-value of 566.77 implies the model is significant. There is only a 0.01% chance that an F-value this large could occur due to noise.P-values less than 0.0500 indicate model terms are significant. In this case A, B, C, AB, AC, BC, A², B², C² are significant model terms. Values greater than 0.1000 indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve your model.

          Analysis of Variance (ANOVA) for the response surface quadratic models for Penicillium sp. - IAA activity
          SourceSum of SquaresDfMean SquareF-valuep-value
          Model12467.9591385.33566.77< 0.0001
          A-Incubation time2119.0612119.06866.96< 0.0001
          B- pH617.921617.92252.80< 0.0001
          C-temperature33.10133.1013.540.0042
          AB61.57161.5725.190.0005
          AC70.35170.3528.780.0003
          BC229.941229.9494.08< 0.0001
          650.761650.76266.24< 0.0001
          1564.2211564.22639.96< 0.0001
          5077.6315077.632077.38< 0.0001

          It can be determined from Table 5 that the Predicted R² of 0.9736 is in reasonable agreement with the Adjusted R² of 0.9963; i.e. the difference is less than 0.2. Adeq Precision measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of 83.080 indicates an adequate signal. This model can be used to navigate the design space.

          Modelling statistics through Analysis of Variance (ANOVA) for Penicillium sp- IAA activity
          Std. Dev.1.560.9980
          Mean62.24Adjusted R²0.9963
          C.V. %2.51Predicted R²0.9736
          Adeq Precision83.0799

          Predicted vs Actual:

          The predicted vs actual plot shown in figure shows that there is similarity between experimental data and predicted data by Response Surface Methodology (RSM) for IAA activity.

          Picture5_2.tif
            a) Predicted vs actual plot showing the accordance between predicted status and actual status of IAA activity of Penicillium sp. and Contour Plots showing effect of interaction of b) incubation days and pH c) incubation days and temperature d) pH and temperature on IAA activity in Penicillium sp.

            Response 2: ACC deaminase Activity

            Sequential Model Sum of Squares [Type I]: (Quadratic vs 2FI)

            Analysis of Variance (ANOVA) for the response surface quadratic models(Quadratic vs 2FI) for Penicillium sp.
            ResponsesSum of SquaresdfMean SquareF-valuep-value
            ACC deaminase8252.3932750.801125.41< 0.0001

            The highest order polynomial is selected where the additional terms are significant and the model is not aliased.

            ANOVA ANALYSIS:

            Factor coding is Coded. Sum of squares is Type III – Partial. The Model F-value of 566.77 implies the model is significant. There is only a 0.01% chance that an F-value this large could occur due to noise. P-values less than 0.0500 indicate model terms are significant. In this case A, B, C, AB, AC, BC, A², B², C² are significant model terms. Values greater than 0.1000 indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve your model.

            Analysis of Variance (ANOVA) for the response surface quadratic models for Penicillium sp.- ACC deaminase activity
            SourceSum of SquaresDfMean SquareF-valuep-value
            Model12467.9591385.33566.77< 0.0001
            A-Incubation time2119.0612119.06866.96< 0.0001
            B-pH617.921617.92252.80< 0.0001
            C-temperature33.10133.1013.540.0042
            AB61.57161.5725.190.0005
            AC70.35170.3528.780.0003
            BC229.941229.9494.08< 0.0001
            650.761650.76266.24< 0.0001
            1564.2211564.22639.96< 0.0001
            5077.6315077.632077.38< 0.0001

            It can be determined from Table 8 that the Predicted R² of 0.9736 is in reasonable agreement with the Adjusted R² of 0.9963; i.e. the difference is less than 0.2.

            Adeq Precision measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of 83.080 indicates an adequate signal. This model can be used to navigate the design space.

            Modelling statistics through Analysis of Variance (ANOVA) for Penicillium sp.- ACC deaminase activity
            Std. Dev.1.560.9980
            Mean62.95Adjusted R²0.9963
            C.V. %2.48Predicted R²0.9736
            Adeq Precision83.0799

            Predicted vs Actual:

            The predicted vs actual plot shown in figure shows that there is similarity between experimental data and predicted data by Response Surface Methodology (RSM) for ACC deaminase activity.

            Picture6_1.tif
              a) Predicted vs actual plot showing the accordance between predicted status and actual status of ACC deaminase activity of Penicillium sp. and Contour Plots showing effect of interaction of b) incubation days and pH c) incubation days and temperature d) pH and temperature on ACC deaminase activity in Penicillium sp.

              Desirability function:

              Picture9.tif
                Desirability function showing optimum incubation time, pH and temperature conditions for IAA and ACC deaminase activity in Penicillium sp.

                According to Response Surface Methodology (RSM) analysis, the desirability function is 1 which is just. The optimum incubation time was 14.862, pH was 6.8973 and temperature was 29.4521℃ to obtain maximum values for IAA and ACC deaminase activity which is 113.85 IU/mL and 114.56 IU/mL respectively.

                IAA and ACC deaminase synthesized from Aspergillus sp.

                Actual design

                Actual Design of responses in Aspergillus sp.
                Factor 1Factor 2Factor 3Response 1Response 2
                StdA:Incubation timeB:pHC:temperatureIAA ActivityACC deaminase Activity
                DaysdaysIU/mlIU/ml
                1642020.1317.94
                21542050.4448.25
                3682040.1938.13
                41582080.2278.03
                5644033.1630.97
                61544050.1247.93
                7684030.1127.92
                81584060.1257.93
                9663082.1279.93
                1015630110.38108.19
                111023020.1918.22
                121083080.3878.19
                131062040.1938.33
                141064040.1237.93
                151063080.4378.24

                Table 12 shows the actual design of IAA and ACC deaminase activity for the variables incubation time, pH and temperature in Aspergillus sp.

                Response 1: IAA Activity

                Sequential Model Sum of Squares [Type I]: (Quadratic vs 2FI)

                Analysis of Variance (ANOVA) for the response surface quadratic models (Quadratic vs 2FI) for Aspergillus sp.
                ResponsesSum of SquaresdfMean SquareF-valuep-value
                IAA8252.3932750.801125.41< 0.0001

                The highest order polynomial is selected where the additional terms are significant and the model is not aliased.

                ANOVA ANALYSIS:

                Factor coding is Coded. Sum of squares is Type III – Partial. The Model F-value of 566.77 implies the model is significant. There is only a 0.01% chance that an F-value this large could occur due to noise. P-values less than 0.0500 indicate model terms are significant. In this case A, B, C, AB, AC, BC, A², B², C² are significant model terms. Values greater than 0.1000 indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve your model.

                Analysis of Variance (ANOVA) for the response surface quadratic modelsforAspergillus sp.- IAA activity
                SourceSum of SquaresdfMean SquareF-valuep-value
                Model12467.9591385.33566.77< 0.0001
                A-Incubation time2119.0612119.06866.96< 0.0001
                B- pH617.921617.92252.80< 0.0001
                C-temperature33.10133.1013.540.0042
                AB61.57161.5725.190.0005
                AC70.35170.3528.780.0003
                BC229.941229.9494.08< 0.0001
                650.761650.76266.24< 0.0001
                1564.2211564.22639.96< 0.0001
                5077.6315077.632077.38< 0.0001

                Modelling statistics through Analysis of Variance (ANOVA) for Aspergillus sp.- IAA activity
                Std. Dev.1.560.9980
                Mean61.02Adjusted R²0.9963
                C.V. %2.56Predicted R²0.9736
                Adeq Precision83.0799

                It can be determined from Table 15 that the Predicted R² of 0.9736 is in reasonable agreement with the Adjusted R² of 0.9963; i.e. the difference is less than 0.2. Adeq Precision measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of 83.080 indicates an adequate signal. This model can be used to navigate the design space.

                [Page]

                Predicted vs Actual:

                The predicted vs actual plot shown in figure shows that there is similarity between experimental data and predicted data by Response Surface Methodology (RSM) for IAA activity.

                Picture7_1.tif
                  a) Predicted vs actual plot showing the accordance between predicted status and actual status of IAA activity of Aspergillus sp. and Contour Plots showing effect of interaction of b) incubation days and pH c) incubation days and temperature d) pH and temperature on IAA activity in Aspergillus sp.

                  Response 2: ACC deaminase Activity

                  Sequential Model Sum of Squares [Type I]: (Quadratic vs 2FI)

                  Analysis of Variance (ANOVA) for the response surface quadratic models (Quadratic vs 2FI) for Aspergillus sp.
                  ResponsesSum of SquaresdfMean SquareF-valuep-value
                  ACC deaminase8226.2832742.091201.15< 0.0001

                  The highest order polynomial is selected where the additional terms are significant and the model is not aliased.

                  ANOVA ANALYSIS:

                  Analysis of Variance (ANOVA) for the response surface quadratic models for Aspergillus sp.- ACC deaminase activity
                  SourceSum of SquaresdfMean SquareF-valuep-value
                  Model12432.5891381.40605.11< 0.0001
                  A-Incubation time2115.2812115.28926.58< 0.0001
                  B- pH617.381617.38270.44< 0.0001
                  C-temperature34.78134.7815.230.0029
                  AB60.94160.9426.700.0004
                  AC69.42169.4230.410.0003
                  BC231.341231.34101.34< 0.0001
                  642.351642.35281.38< 0.0001
                  1554.1611554.16680.79< 0.0001
                  5059.8515059.852216.43< 0.0001

                  Factor coding is Coded. Sum of squares is Type III – Partial. The Model F-value of 605.11 implies the model is significant. There is only a 0.01% chance that an F-value this large could occur due to noise. P-values less than 0.0500 indicate model terms are significant. In this case A, B, C, AB, AC, BC, A², B², C² are significant model terms. Values greater than 0.1000 indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve your model.

                  Modelling statistics through Analysis of Variance (ANOVA) for Aspergillus sp.- ACC deaminase activity
                  Std. Dev.1.510.9982
                  Mean58.87Adjusted R²0.9965
                  C.V. %2.57Predicted R²0.9754
                  Adeq Precision85.8066

                  It can be determined from Table 18 that the Predicted R² of 0.9754 is in reasonable agreement with the Adjusted R² of 0.9965; i.e. the difference is less than 0.2.Adeq Precision measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of 85.807 indicates an adequate signal. This model can be used to navigate the design space.

                  Predicted vs Actual:

                  The predicted vs actual plot shown in figure shows that there is similarity between experimental data and predicted data by Response Surface Methodology (RSM) for ACC deaminase activity.

                  Picture8.tif
                    a)Predicted vs actual plot showing the accordance between predicted status and actual status of ACC deaminase activity of Aspergillus sp. and Contour Plots showing effect of interaction of b) incubation days and pH c) incubation days and temperature d) pH and temperature on ACC deaminase activity in Aspergillus sp.

                    Desirability function:

                    Picture10.tif
                      Desirability function showing Optimum incubation time, pH and temperature conditions for IAA and ACC deaminase activity in Aspergillus sp.

                      According to Response Surface Methodology (RSM) analysis, the desirability function is 1 which is just. The optimum incubation time was 14.6987, pH was 6.70303 and temeperature was 29.038℃ to obtain maximum values for IAA and ACC deaminase activity which is 110.891 IU/mL and 108.632 IU/mL respectively.

                      CONCLUSION

                      The isolated manglicolous fungi were identified microscopically as Alternaria sp., Anthrinium sp., Aspergillus sp., Candida sp., Cladorrhinum sp., Cladosporium sp., Curvularia sp., Fusarium solani, Fusarium sp., Humicola sp., Mucor sp., Nigrosporasacchari, Penicillium sp., Pestalotia sp., Rhizoctonia sp., Trichoderma sp. and Trichospermum sp. Plant Growth Promoting Factors (PGPF) studied in this report included IAA and ACC deaminase which help promote plant growth through varied mechanisms. The study can be extended to other PGPFs. In our study, the 20 manglicolous fungi were found to be involved in production of IAA and ACC deaminase. Out of these, 2 species Penicillium sp. and Aspergillus sp. were dominant and showed maximum enzymatic activity as compared to the others. IAA and ACC deaminase activity for Aspergillus sp. are 182.12 IU/mL and 186.52 IU/mL respectively while for Penicillium sp. the value for IAA activity is 158.62 IU/mL and for ACC deaminase activity is 138.53 IU/mL which are more than that of other fungal species considered in the study. The propensity of manglicolous fungi can be further exploited and they can be utilized on a large scale in agricultural practices as a substitute for chemical fertilizers, thereby decreasing the dependency on them and subsequently saving mother Earth from their deleterious effects.

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                      ACKNOWLEDGEMENTS

                      I start my acknowledgement with sincere obeisance to God by whose grace this work has seen the light of the day. I am also grateful to IASc-INSA-NASI for providing me such a great research opportunity which will help me immensely in my future.

                      I express my deep sense of reverence and gratitude to my guide, Dr. Punarbasu Chaudhuri for his kind guidance, valuable suggestions, constant supervision and help during the course of work and preparation of report. I would like to thank Praveen Tudu, Shouvik Mohanty and Shaheen Akhtar, Research Scholars in the lab for constantly guiding and supporting me throughout my tenure.

                      I would also like to thank Department of Environmental Science, Ballygunge Science College, University of Calcutta for hosting me and providing me an ambience worth working. I am also grateful to my college for allowing me to take this prestigious fellowship.

                      I express my deepest appreciation to my family members for supporting me at each and every step, showing commitment to my ambition and giving me constant moral support which helped me complete my work with flying colours.

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