ABSTRACT
Poly-γ-glutamic acid (γ-PGA) is an anionic polyamino acid composed of L and D glutamic acid isomers linked via amide bond between the α-amino and γ-carboxylic groups of adjacent monomers largely synthesised by microorganisms of the Genus Bacillus. γ-PGA has been explored in the design of scaffolds, nanosystems, adhesives, hydrogels, thickeners and humectants because of its non-toxicity, non-immunogenicity, water solubility and biodegradability. This study was undertaken to optimize the production of γ-PGA from Bacillus subtilis using rice husk by solid state fermentation. Different Bacillus subtilis were isolated from water, soil and Daddawa samples collected in Samaru, Zaria, Nigeria and used for the production of γ-PGA. The isolates were characterised by biochemical and molecular techniques and then subjected to screening and production optimization by central composite design of response surface methodology. Among the 3 isolates (S1, S4 and S5), S5 has a significantly higher yield of 2.5 g/L of γ-PGA, hence was used in solid state fermentation for the optimization and production of γ-PGA using rice husk as the basal media. Exactly eleven (11) media components and process parameters were screened using definitive screening design (DSD) with citric acid, glycerol and 6-diazo-5-oxo-L- norleucine (DON) having the most significant influence on the yield which were optimized by response surface methodology using central composite design (CCD). A maximum production of γ-PGA of 19.55±5.75 mg/g and molecular weights within the range of 35 kDa to 96 kDa bands by SDS-PAGE were obtained when the concentrations of medium components were set at 25 μg/100g DON, 7.5% citric acid and 9.96% glycerol. Hence, B. subtilis isolate S5 is a promising strain for the production of low molecular weight γ-PGA from rice husk via solid state fermentation.
TABLE OF CONTENTS
Title Page i
Declaration ii
Certification iii
Acknowledgments iv
Abstract v
Table of Contents vi
List of Figures x
List of Tables xii
List of Appendices xii
Abbreviations, Definitions, Glossary and Symbols xiii
CHAPTER ONE 1
1.0 INTRODUCTION 1
1.1 Background of the Study 1
1.2 Statement of Research Problem 4
1.3 Justification 5
1.3 Aim and Objectives 5
1.3.1 Aim 5
1.3.2 Specific Objectives of the Study 6
CHAPTER TWO 7
2.0 LITERATURE REVIEWS 7
2.1 Discovery and History of γ-PGA 7
2.2 Mechanism of Biosynthesis of γ-PGA 9
2.2.1 Racemization 11
2.2.2 Polymerization 12
2.2.3 Regulation of γ-PGA 13
2.2.4 Degradation γ-PGA 14
2.3 Genes Involved in γ-PGA 16
2.4 Recombinant DNA Technology in 𝛾-PGA Production 19
2.5 Nutrients Requirement for 𝛾-PGA Production in Bacillus subtilis 21
2.6 Activity of 𝛾-PGA Degrading Enzyme (𝛾-GTP) 22
2.7 Inhibition Studies on 𝛾-GTP 23
2.8 Production and Optimization of γ-PGA 24
2.9 Uses of γ-PGA 26
2.9.1 Metal chelator 26
2.9.2 Drug carrier/deliverer 27
2.9.3 Tissue engineering 29
2.9.4 Cryoprotectant 30
2.10 6-Diazo-5-oxo-L-Norleucine (DON) 30
2.11 Gamma‐Glutamyl Transpeptidase (γ-GTP) Inhibition by DON 31
2.12 Rice Husks 32
CHAPTER THREE 34
3.0 MATERIALS AND METHODS 34
3.1 Materials 34
3.1.1 Chemicals/reagents 34
3.1.2 Apparatus and equipment 34
3.1.3 Samples and sample collection 35
3.2 Methodology 36
3.2.1 Experimental flowchart 36
3.2.2 Preparation of biomass and proximate analyses 37
3.2.3 Strain and culture condition 37
3.2.4 Molecular characterization of isolated B. subtilis 41
3.2.5 Selection of promising isolate and preservation 42
3.2.6 Isolation and purification of γ-PGA 43
3.2.7 In silico docking of DON on γ-GTP 43
3.2.8 Minimal inhibitory concentration (MIC) DON on B. subtilis 44
3.2.9 Process and medium parameters for solid state fermentation (SSF) 45
3.2.10 Polymer characterization 46
3.3 Experimental Design for the Optimization Process 48
3.3.1 γ-PGA production by solid state fermentation (SSF) 48
3.3.2 Definitive screening design 49
3.3.3 Central composite design (CCD) method 52
3.4 Data Analyses 54
CHAPTER FOUR 55
4.0 RESULTS 55
4.1 Isolates Obtained from Collected Samples of Water, Soil and Daddawa 55
4.1.1 Microscopic and biochemical characteristics of the isolates 55
4.2 Molecular characteristics of 16S rRNA gene of the isolates 59
4.3 Screening and Selection of Best γ-PGA Producing Isolate 59
4.4 Minimum Inhibitory Concentration of DON against B. subtilis 59
4.5 In silico characteristic binding of γ-PGA and DON 59
4.6 Definitive Screening and Optimization of Production of γ-PGA 64
4.6.1 Definitive screening of medium components and conditions 64
4.6.2 Normal analysis of DSD experiments responses 64
4.6.3 One-way ANOVA of selected significant factorial model’s independent factors 67
4.6.4 Response surface optimization by central composite design 67
4.6.5 Summary of fit statistics of developed model 68
4.6.6 Three Dimensional Response Surface Plots of the Optimized Parameters 73
4.7 Validation Production of γ-PGA 73
4.8 Model Equation 73
4.9 Polymer Characterization 76
4.9.1 Amino acid analysis of produced γ-PGA 76
4.9.2 FT-IR spectra of produced γ-PGA 76
4.9.3 Molecular weight of produced γ-PGA 77
CHAPTER FIVE 82
5.0 DISCUSSIONS 82
CHAPTER SIX 89
6.0 CONCLUSIONS AND RECOMMENDATIONS 89
6.1 Conclusions 89
6.2 Recommendations 89
REFERENCES 90
APPENDICES 104
LIST OF FIGURES
Figure 3.1: Flowchart of Experimental Activities Conducted in the Study 38
Figure 4.1: Electropherogram of isolates 16S rRNA gene PCR products… 60
Figure 4.2: γ-PGA yields of Isolates in Submerged Broth… 62
Figure 4.3: Normal plot of effects of independent factors and interactions on γ-PGA yield from the DSD screening… 66
Figure 4.4: Response Surface Plots… 74
Figure 4.5: Absorbance peaks of γ-PGA digest analysis by HPLC 78
Figure 4.6: FT-IR spectrum of produced γ-PGA 79
Figure 4.7: SDS-PAGE Electropherogram of Produced γ-PGA 81
LIST OF TABLES
Table 3.1: Set Ranges and Levels of the Independent Factors for DSD 50
Table 3.2: Design Matrix Code for Definitive Screening Design… 51
Table 3.3: Design Matrix Code for Central Composite Design… 53
Table 4.1: Gram Staining Characteristics of the Isolates… 57
Table 4.2: Biochemical and spore staining characteristics of the isolates… 58
Table 4.3: Sequence BLAST analysis of isolates’ 16S rRNA gene 61
Table 4.4: In silico docking of γ-GTP Inhibition Kinetics by DON 63
Table 4.5: γ-PGA response from DSD of medium components and conditions… 65
Table 4.6: One-way ANOVA of Selected significant Factorial Model’s Independent Factors… 69
Table: 4.7: Response surface optimization by full central composite design 70
Table 4.8: Summary of fit statistics of various possible models… 71
Table: 4.9: One-way ANOVA of Selected FCCD Quadratic Model Terms 72
Table 4.10: Confirmation of designed experiment for the Production of γ-PGA 75
Table 4.11: FT-IR Spectra Analyses of Produced γ-PGA 80
LIST OF APPENDICES
Appendix I: Buffer preparations 104
Appendix II: Proximate content of rice husk used in the study 105
Appendix III: Table of factors for Definitive Screening Design… 106
Appendix IV: Design Matrix for Definitive Screening Design in actual set values… 107
Appendix V: Concentrations of medium components and moisture content adjustment for definitive screening design 108
Appendix VI: Normal plot of medium components and condition from definitive screening design… 109
Appendix VII: Coded Levels of central composite design 110
Appendix VIII: Concentrations of medium components and moisture content adjustment for central composite design 111
Appendix IX: Coefficients of variability in terms of coded factors of independent and interaction terms… 112
Appendix X: Constraints of Independent Factors for validation/confirmation experiment 113
Appendix XI: Minimum inhibitory concentration end point of DON on B. subtilis 114
Appendix XII: Contributions to Knowledge 115
ABBREVIATIONS, DEFINITIONS, GLOSSARY AND SYMBOLS
CFU: Colony forming unit.
CwlO: γ-glutamyl-hydrolase gene.
CwlS: Peptidoglycan hydrolase genes.
DON: 6-Diazo-5-oxo-L-norluecine.
DSD: Definitive screening design.
FCCD: Full central composite design.
FT-IR: Fourier transform-Infra-red spectroscopy.
GGT1: Gamma‐glutamyl Transpeptidase 1.
glpFK: Cytoplasmic membrane transporter of glycerol.
Glr: Glutamate receptor 1 gene.
mg/gds: Milligram per gram of dry substrate.
MOPS: 3-morpholinopropane-1-sulfonic acid.
OU749: is a patented three-ring sulfonated benzene derivative that can competitively inhibit γ-glutamyl transpeptidase.
pgs: poly-γ-glutamic acid synthase gene with the subunits designated as pgsA, pgsB, pgsC and pgsE.
RacE: Glutamate racemase gene.
S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11 and S12 are assigned symbols for the isolates obtained from the sample of soil, water and Daddawa.
SDS-PAGE: Sodium dodecyl sulphate-polyacrylamide gel electrophoresis.
yrpC: Glutamate racemase2 gene.
γ-GTP: Gamma glutamyl transpeptidase.
CHAPTER ONE
1.0 INTRODUCTION
1.1 Background of the Study
The search for better ways to improve the standard of living through development of new and safer technologies in the area of enzymes and industrial biopolymers in the production of useful medical and pharmaceuticals drivers such as hydrogels, drugs carriers, gene delivery vehicles and super absorbents is in the centre stage of modern day research (Das et al., 2018; Ramazan, 2019). Poly γ-glutamic acid (γ-PGA) is one of such biopolymers (Zhang et al., 2018b). Poly γ-glutamic acid is a polyamide, anionic biopolymer composed mainly of D-and L-glutamic acid units connected via amide linkages between α-amino and γ-carboxylic acid groups of the monomers (Hsueh et al., 2017). γ-PGA is classified into; γ- L-PGA (only L-glutamic acid residues), γ-D-PGA (only D-glutamic acid residues), and γ- LD-PGA (both L- and D-glutamic acid residues) based on the enantiomers of glutamic acid residues (Luo et al., 2016).
γ-PGA is water-soluble, nontoxic, non-immunogenic and edible biopolymer. It also has adhesive, film forming, and moisture retention properties making it an interesting material for drug delivery/release, bio-adhesive, cosmetics, food, agriculture, and sewage treatment (Lin et al., 2016). It also has the potential to be used for protein crystallization, as a soft tissue adhesive and a non-viral vector for safe gene delivery (Ogunleye et al., 2015).
There are four methods of γ-PGA production: chemical synthesis, peptide synthesis, biotransformation, and microbial fermentation (Hara et al., 2014). Microbial fermentation is the most cost-effective and has numerous advantages, including use of inexpensive raw materials, minimal environmental pollution, high natural product purity, and mild reaction conditions (Luo et al., 2016). The microbial biosynthesis is comprised of racemization, polymerization, regulation, and degradation (Cai et al., 2017). The racemization is an important step in γ-PGA biosynthesis with D-isomer as either homo (D-Isomer only) or co- polymer (D and L isomers mixed) (Hara et al., 2014).
𝛾-PGA microbial fermentation is exclusively of gram-positive bacteria (genus: Bacillus, class: Bacilli) except for recombinant E. coli. 𝛾-PGA producing microorganisms are classified based on their glutamic acid requirement for 𝛾-PGA biosynthesis; some require exogenous glutamic acid which must be supplied in the growth medium. Members of this class are termed glutamate-dependent, while the other class does not require the supply of glutamic acid in the growth medium due to their ability to synthesize glutamic acid using tricarboxylic acid (TCA) cycle intermediates (Lin et al., 2016). The former predominates most of the known PGA producers, such as Bacillus subtilis chungkookjang, B. licheniformis ATCC9945A, B. subtilis (natto) IFO333 and B. subtilis RKY3. The glutamate independent 𝛾-PGA producers include B. subtilis C1, B. subtilis TAM-4, B. licheniformis A35, and B. licheniformis SAB-26 (Songa et al., 2010).
Unlike most proteinaceous materials, γ-PGA is synthesized in a ribosome-independent manner; thus, substances that inhibit protein translation have no inhibitory effects on the production of γ-PGA. Furthermore, due to the γ-linkage of its component glutamate residues, γ-PGA is resistant to proteases that cleave α-amino linkages (Luo et al., 2016). The bulk of γ-PGA is synthesized at the onset of the stationary growth phase due to nutrient starvation/limitation during this phase (Kimura et al., 2004b; Hsueh et al., 2017). Bacillus subtilis has the property of producing a great number of various industrial enzymes and biopolymers (Gu et al., 2018).
There are five genes identified to be involved in γ-PGA synthesis in B. subtilis. i.e, pgsB, pgsC, pgsA, pgsE and pgdS (Bajaj and Singhal, 2011). pgsBCA operon was identified as the sole machinery handling the polymerizing γ-PGA at the active site of the synthase complex (PgsBCA) which is ATP-dependent (Kimura et al., 2004). Two signal transduction systems have been implicated in the down and up regulation of pgs operon (ComP-ComA regulator and DegS-DegU, DegQ, and SwrA system). Alteration of degQ alters the synthesis of γ- PGA and also down-regulates the production of degradation enzymes. Exo-γ-glutamyl transpeptidase (γ-GTP) as well as endo-γ-glutamyl transpeptidase (α-GTP) are two enzymes identified to be responsible for degrading synthesized γ-PGA. The first enzyme was implicated in the exogenous breakdown of stored γ-PGA in B. subtilis so that it can be used as carbon and nitrogen source (Kobayashi, 2008) usually expressed during the stationary phase under the control of the ComQXPA quorum-sensing system (Mordini et al., 2013). Inhibiting or knocking down γ-PGA hydrolase γ-GTP) can result in better yield with high molecular weight. It has been established that B. subtilis strains deficient in γ- GTP were unable to cleave γ-PGA and they sporulates earlier than wild-type strains (Kobayashi, 2008).
Optimization techniques, especially the response surface methodology are statistical techniques used in the design and simulation of experiments (Kimura and Fujimoto, 2014). A lot of biomass has been used such as Cassava peels (John et al., 2020), cow dung (Yong et al., 2011), Bagasse (Feng et al., 2014b), Sago (Mahanraj et al., 2019), Soy hull (Anju et al., 2018) among others in the production of γ-PGA using different production microorganism.
Rice is a staple food for more than half of the world’s population. It is estimated globally that rice husk constitute more than 20% of the over 500 million tons of paddy produced (Runge et al., 2019). With Nigeria having an estimated production of 4.435 million tons annually and projected to increase at the rate 9.07% annually (Knoema, 2021) As a result of the volume of rice produced annually, husks generated from the milling of rice are eventually becoming a nuisance in major dumping sites (Chauhan et al., 2017). Rice husk is a high potential substrate, which is amenable for value addition. It contains about 34- 62% CHO, 15-20% fat, 11-15% Protein, 7-10% ash 7-11% fibre (Mohammed et al., 2017; Song et al., 2019). Despite the enormous potential that could be tapped, most of the husk from rice milling is either burnt or dumped as waste in open fields and a small amount is used as fuel for boilers, electricity generation and bulking agents for composting of animal manure (Mohammed et al., 2017).
1.2 Statement of Research Problem
Currently used materials for drug and gene delivery (viral particles and liposomes) have significant side effects (Thomas et al., 2003; Hughes, 2017) and are also susceptible to degradation by enzymes (Gaber et al., 2017; Wen et al., 2017). γ-PGA on the other hand promises to be a more suitable candidate, but its application is limited by high cost of production from recombinant DNA technology as well as the raw materials required in the fermentation(Meerak et al., 2008). Although microbial production of γ-PGA has since been established, the cost of production, which ultimately affects the market price (i.e.
₦110,000.00 per 100mg high-purity sodium salt γ-PGA (Sigma Aldrich, 2019)) is still very high and this is one of the major limitations to the widespread application of this polymer. Based on this, most researches on microbial production of γ-PGA focused on the optimization of growth conditions with the hope of obtaining a high yield, specific enantiomeric composition and desired molecular mass of γ-PGA at reduced cost (Ogunleye et al., 2015; Ajayeoba et al., 2019).
1.3 Justification
Considering the cost of fermentation of γ-PGA, there is need to employ the use of cheap and readily available biomass for its microbial fermentation which in a way will serve the dual purpose of bioremediation to minimize environmental pollution at the same time generate microbial products of commercial importance (Bankar et al., 2018).
The knowledge of the enzymes and genes involved in γ-PGA production is important not only in increasing the production with respect to reducing cost, but also helps to understand the mechanism of γ-PGA applications (Ogunleye et al., 2015).
Inhibiting the activity of γ-GTP during γ-PGA biosynthesis could improve the final yield consequent from reports that alteration of degQ gene results in low activity of γ-GTP (Stanley and Lazazzera, 2005). This could result in higher yields of γ-PGA with specific properties in contrast to the normal fermentation (Ogunleye et al., 2015). Also, optimization of γ-PGA with respect to cost of production, molecular weight and its conformational / enantiomeric properties is a major step in making its application practical Sharma et al., 2017).
1.3 Aim and Objectives
1.3.1 Aim
The aim of this study was to optimize the production of γ-PGA from B. subtilis via solid- state fermentation (SSF) using rice husk.
1.3.2 Specific Objectives of the Study
The specific objectives of the study were to:
i. Isolate and characterize B. subtilis from soil, water and Daddawa.
ii. Screen and select promising isolate for γ-PGA production.
iii. Dock 6-diazo-5-oxo-L-norluecine (DON) against γ-GTP and determine the minimal inhibitory concentration (MIC) of DON on B. subtilis.
iv. Optimize the medium and process parameters for the production of γ-PGA by response surface methodology.
v. Characterize the produced γ-PGA.
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