Agarase, Amylase and Xylanase from Halomonas meridiana: A Study on Optimization of Coproduction for Biomass Saccharification

Abstract

Coproduction of multienzymes from single potential microbe has captivated contemplation in industries. Bacterial strain, Halomonas meridiana VITSVRP14, isolated from seaweed was labored to produce amylase, agarase and xylanase conjointly using submerged fermentation. The optimum production conditions clinched by classical optimization were: pH 8; 1.5% inoculum; 24 h incubation, 40 °C; 8% NaCl (sodium chloride); 1% lactose and NaNO₃ (sodium nitrate). The preponderant variables (pH, temperature, lactose) and their interaction effect on enzyme production were studied by Plackett-Burman design and Response Surface Methodology (RSM). There were 3.29, 1.81 and 2.08 fold increase in enzyme activity with respect to agarase, amylase and xylanase after optimization against basal medium. After 24 h of enzymatic treatment, the saccharification rates of the coproduced enzyme mixture were 38.96% on rice bran, 49.85% on wheat bran, 61.2% on cassava bagasse and 57.82% on corn cob. Thus, the coproduced enzyme mixture from a bacterium with halotolerance is plausible in pretreated lignocellulose degradation. The ability of this single microbe Halomonas meridiana VITSVRP14, in coproducing agarase, amylase and xylanase give the nod for its application in biomass saccharification by subsiding cost, energy and time involved in the process.

1. Introduction

Three enzymes of the Glycoside Hydrolase (GH) family: agarase, amylase and xylanase, are the major heed of our research. Enzymatic hydrolysis of polysaccharides is a hot topic among fermentation biologists since environmentally friendly procedures are always preferred [1]. Agarases are the enzyme groups that catalyze the depolymerization of agar and agarose into agaro and neoagaro oligosaccharides (AOS and NAOS) [2]. These oligosaccharide products parade their applications in the food, cosmetic, medical and pharmaceutical industries [3]. Amylases break down starch molecules’ glycosidic linkages and they account for around 30% of the global enzyme market in the industrial sector [4]. Amylases from microbes have wide applications in detergent [4], baking, dairy, textile, leather [5], paper, pulp, pharma, bioethanol [6], waste treatment, starch processing [4] and animal feed industries [7]. Xylanases act on the backbone of xylan and convert it to xylose and xylo oligosaccharides. They are commonly used in the biorefinery, baking, food, feed, pulp, paper, textile and biofuel industries [8]. On account of their excellent specificity, reaction conditions, little activity loss and low production of side products, bacterial enzymes are favored catalysts in polysaccharide hydrolysis. So far, according to the literature survey from our side, the coproduction study on agar, starch and xylan degrading enzymes has not been reported. Thence, we were highly desirable on coproducing these three enzymes from a halotolerant bacterium.

In general, amylases are produced by Bacillus, Lactobacillus, Corynebacterium, Halobacillus, Halomonas, etc. [9]. Organisms that produce agarases are Bacillus, Pseudomonas, Agarivorans, Alteromonas and Cytophaga along with other species [10]. Xylanases are produced by Aspergillus [11], Bacillus, Pseudomonas, Clostridium, Staphylococcus, Thermoactinomyces [12], Pediococcus and a few other organisms [13]. For commercial enzymes of microbial origin, submerged fermentation is the preferable choice. The process parameters (temperature, pH, inoculum size, salt concentration, carbon and nitrogen sources) can be easily monitored and adjusted, which is an edge of submerged fermentation over other fermentation techniques. Furthermore, this fermentation technique can be simply scaled up, and meek microscopic analysis may be employed to monitor contamination, and enzymes can be extracted efficiently [4].

Producing different enzymes from distinct sources is not cost-effective for any industry that needs multiple enzymes [14]. Hence, the concomitant production of two or more enzymes by microbial fermentation is an adroit and putative technique which can reduce time, energy and labor as well [15]. From the literature survey, we found that laccase and lignin peroxidase [16], amylase and xylanase [17], cellulase and xylanase [18], amylase, lipase and protease [19], protease and amylase [5], xylanase and alkaline protease [20], endoglucanase and xylanase [21], amylase and protease [15], pectinase, cellulase and xylanase [22] were few of the coproduced enzymes through bacterial submerged fermentation. Apart from these, quite a few coproduction studies have also been conducted through solid state fermentation using both bacteria and fungi.

The synthesis of enzymes by microorganisms is heavily influenced by nutritional sources and growing conditions [14]. To achieve a high product yield and to make the process more cost-effective, an efficient production medium must be designed and it can be achieved by optimizing the medium of production. One of the recurrently used techniques in scrutinizing the optimum parameters and their accurate effects is the One Variable At a Time (OVAT) approach [23]. Owing to the few other beneficial attributes over classical optimization methods in medium formulation, production conditions and also in new product designing, Response Surface Methodology (RSM) can be used as a statistical approach [24].

Lignocellulosic biomass is amongst the most renewable biomass resources that are prolific, with an estimated annual production of a hundred billion tons [25]. Woody biomass, aquatic biomass, bagasse, crop straws, energy and food crops are examples of commonly obtainable lignocellulose biomass. Contrasted with other biomasses, this lignocellulosic biomass offers conspicuous benefits, particularly in production, availability, cost and the potential to be used without jeopardizing food security [26]. As a result, numerous physical, chemical and biological methods along with combined pretreatments have been tracked down for converting lignocellulosic biomass into high-value-added products [27,28].

Pretreating lignocelluloses for the improvement of lignocellulose monomer production often results in a saline environment which subsequently affects the further processes. It has been reported that bacteria act more efficiently than fungi in degrading lignocellulose [29]. That being the case, consortia of enzymes from single halotolerant bacteria can be cost-effective and can ameliorate lignocellulose degradation. The whole intention of our study is to bring those enzymes to light that could act on and hydrolyze their corresponding substrates under challenging conditions concomitantly. The underexploration of microbially derived co-enzymes that work under salinity stress for lignocellulose degradation made us curious to effectuate this application study. This study can be claimed as novel since there has not been any study on coproducing these three enzymes from Halomonas meridiana to the best of our knowledge.

2. Materials and Methods

2.1. Sample Collection and Bacterial Isolation

Variegated seaweed samples like Padina gymnospora, Sargassum polycystum, Sargassum wightii, Chondrus crispus, Gracilaria corticata, Gracilaria crassa, Gracilaria edulis, Hypnea musiformis, Kappaphycus alvarezii, Ulva lactuca and Ulva fasciata were collected in a sterile container along with sea water from Rameswaram (9.2876° N, 79.3129° E) coastal area and were brought to the laboratory. These seaweeds were homogenized in collected sterile sea water for an hour. After homogenization, the samples were serially diluted up to 10⁻⁸ with sterile sea water and were plated on Zobell marine agar with pH 7.6 ± 0.2 at 37 ℃ for 48 h [30]. The composition of the Zobell marine agar is given in Table S1 of Supplementary Materials. Individual colonies with different morphology were selected and grown on Zobell marine agar plates with pH 7.6 ± 0.2 at 37 ℃ for 48 h. Subsequent quadrant plating was done on nutrient agar (composition given in Supplementary Data S2) plates with pH 7.4 ± 0.2 and was grown at 37 ℃ for 24 h to obtain pure cultures.

2.2. Qualitative Assay

Screening was done on nutrient agar plates supplemented with substrates; starch (2%), agar (2%) and xylan (2%) on different plates for inspecting the amylase [31] agarase [32] and xylanase [33] activities of the organism. After 24 h of incubation at 37 ℃ iodine (amylase and agarase) and Congo red (xylanase) solution was poured onto the plates to visualize the clear zone.

2.3. Biochemical Characterization and Molecular Identification of the Bacterial Isolate

Among all the isolated microbes, the one which had highest zone of clearance for all three enzymes was chosen and by using standard procedures, the biochemical tests like Gram staining, motility test, oxidase activity, catalase production, Methyl Red-Voges Proskauer, sugars utilization and fermentation were performed for understanding its biochemical characteristics [34]. Further, the genomic DNA of the isolated bacteria was extracted using DNA isolation protocol by [35] and amplification of the 16S rRNA gene was done using universal primers 27F and 1492R. The resulting PCR product was purified and sequenced. For the phylogenetic affiliation of the isolated organism with other organisms, the attained gene sequence was submitted to GenBank and subsequently put through BLAST followed by alignment with earlier available sequences in NCBI database [17] by ClustalX and phylogenetic tree construction by neighborhood joining method was also accomplished [5].

2.4. Co-Production

The isolated strain which showed all three enzyme activities was accrued in 2% NaCl supplemented nutrient broth (composition same as nutrient agar excluding agar) media at 37 ℃ for 24 h at 120 rpm with 1% inoculum. To garner the crude enzyme, the culture was centrifuged at 8000 rpm for 20 min at 4 ℃ following modified protocol [21]. Ensuing incubation and biomass separation, the crude enzyme was reaped by collecting the supernatant. With this crude enzyme further quantitative assays were performed. All the forthcoming values in this study are mean ± standard error of triplicates.

2.5. Quantitative Assays

Agarase, amylase and xylanase activities were measured by performing DNS (3,5-dinitrosalicylicacid) assay with 30 min incubation of enzyme substrate mixture at 37 °C followed by heating the mixture in boiling water bath for 10 min after the addition of 3,5-dinitrosalicylicacid. The absorbance was measured spectrophotometrically at 540 nm [15]. One unit activity of the produced enzyme can be defined as the amount of galactose, maltose and xylose released in molar equivalents by the action of enzyme under provided assay conditions. The respective reducing sugars; galactose, maltose and xylose were used for plotting the standard graphs.

2.6. Optimization

2.6.1. Classical Optimization

To beget an enzyme in hoards with better quality, the process of fermentation must be carried out under optimized conditions. In order to acquire those optimized conditions, different optimization techniques have to be performed. One of those preliminary optimization techniques is the classical optimization which is One Variable At a Time (OVAT) method [24]. In OVAT, a single factor is varied to understand the effect of that factor on the response, by maintaining all other factors constant. In this study, the effects of various physicochemical parameters that affect the enzyme production were optimized through classical method of optimization. The optimization of each parameter in the fermentation process plays a vital role in understanding and in enhancing the activity of the produced enzyme [14]. In that way, the major parameters like pH of the production media, inoculum required for optimum enzyme production, fermentation time, temperature, NaCl, different carbon and nitrogen sources were optimized in a sequential manner. All the optimization experiments were conducted in triplicates.

2.6.2. Statistical Optimization

The version 10.0.1 of design expert statistical software (StatEase, Minneapolis, MN, USA) was used to get the working parameters and response surface graphs for statistical experimental data analysis.

Plackett-Burman Design

The identification of pre-eminent variables that has prime effect on enzyme production and its activity is the paramount step in any optimization study [36]. Accordingly, eight variables including coproduction conditions (pH, temperature, rotation rate, incubation time, inoculum size) and medium components (NaCl, Lactose and Sodium nitrate concentration) were selected along with four dummy variables and a set of 12 experiments were conducted at their high and low levels. After the experiment, the enzyme activities were recorded and the significant variables that exhibited confidence level beyond 95% were picked out for further statistical studies.

Response Surface Methodology

With the aim of enhancing the coproduction of agarase, amylase and xylanase enzymes, Response Surface Methodology (RSM) was performed by choosing the most significant independent experimental variables attained from Plackett-Burman experiment. To spot on the precise optimal concentrations of those significant variables that are requisite for this coproduction, a Central Composite Design (CCD) which is typically compatible in fitting a quadratic surface for any process optimization was piloted with five coded levels (as in

Table 1. Experimental variables and their levels employed in RSM study for optimizing coproduction.

Symbol	Experimental Variables	Unit	Levels
			−α	−1	0	+1	+α
A	Temperature	°C	23.18	30	40	50	56.82
B	pH	-	6.32	7	8	9	9.68
C	Lactose Concentration	%	0.16	0.5	1	1.5	1.84

) comprising 20 experimental runs with six axial points and six replications of the center point. These axial points were calculated by α = 2^(k/4), where k is the number of factors used in the experiment [21]. The number of experimental runs to be conducted was calculated with the formula:

$${\mathrm{N}=2}^{\mathrm{n}}+2\times \mathrm{n}+ {\mathrm{n}}_{\mathrm{c}}$$

(1)

where, N: number of experimental runs to be conducted; n: number of experimental variables and n_c: number of center points.

To appraise goodness of fit and the models’ significance statistically, analysis of variance (ANOVA) with 95% confidence intervals was also performed. The second order polynomial equation was fitted to the data obtained from these 20 experimental runs to unearth the interrelationships and relationships between the chosen experimental variables.

$${\mathrm{Y}=\mathsf{\beta }}_{\mathrm{o}}+{\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{k}}{\mathsf{\beta }}_{\mathrm{i}}{\mathrm{X}}_{\mathrm{i}}+{\displaystyle \mathsf{\sum }}_{i=1}^{\mathrm{k}}{\mathsf{\beta }}_{\mathrm{i}\mathrm{i}}{\mathrm{X}}_{\mathrm{i}}^2+\sum {\mathrm{j}\mathsf{\beta }}_{\mathrm{i}\mathrm{j}}{\mathrm{X}}_{\mathrm{i}}{\mathrm{X}}_{\mathrm{j}}$$

(2)

• Y: Predicted Response
• K: Number of factors
• β_o: Model Constant
• β_i: Linear Coefficient
• β_ii: Quadratic Coefficient
• β_ij: Interaction Coefficient
• X_i and X_j: Input variables that influence the response

The working parameters and the response surface graphs were acquired by using Design Expert software version 10 (StatEase, Minneapolis, MN, USA). The fitted second order polynomial equation was signified in three dimensional (3D) plots which elucidate the effect of the independent experimental variables and their interactions on coproduction/enzyme activity. By the possible combination of optimized variables in yielding maximum response, the legitimacy of the model was decided [14].

2.7. Application Study

2.7.1. Substrate Pretreatment

The moisture content of the chosen lignocellulosic biomasses was reduced by sun drying them for 48 h there upon sieving them through 1 mm screen. The alkali treatment was given for 12 h with 1 M sodium hydroxide solution at room temperature and the neutralization of the treated residues was done using distilled water wash and oven drying at 80 °C eventually [37].

2.7.2. Saccharification

The crude enzyme produced by the isolated organism was used to saccharify the pre-treated lignocellulose rich substrates such as rice bran, wheat bran, cassava bagasse and corn cob. The coproduced crude enzyme mixture was incubated at 40 °C with 1% pre-treated biomass in 20 mL basal buffer solution of pH 7.4 along with 0.005% of sodium azide to impede contamination [38]. For a period of 24 h, at every 3 h interval the enzyme substrate mixture was collected and the reducing sugars produced due to enzymatic reaction was measured by DNS method with standard reducing sugars (agarose, glucose, xylose) and the saccharification was calculated by the below specified formula: In order to calculate percentage of saccharification precisely, at every assay timing the reducing sugars in both enzyme and substrate blanks were detracted from the actual enzyme substrate mixture.

$$\mathrm{Saccharification}\left(\%\right)=\frac{\mathrm{Released}\mathrm{reducing}\mathrm{sugar}}{\mathrm{Utilized}\mathrm{substrate}}\times 100$$

(3)

3. Results

3.1. Qualitative Assay

Bacterial strains plated on nutrient agar plates with starch, agar and xylan were checked for maximum zone of clearance around them after 24 h of incubation by the addition of iodine solution onto the plates. The exhibition of single and concurrent enzyme activities of the 78 isolated bacterial strains is demonstrated in Figure 1 for better understanding.

It was observed that 7 out of 78 strains had all three agarase, amylase and xylanase activities. The images of an isolate exhibiting all three enzyme activities are displayed in Figure 2 and that particular isolate was selected for further studies.

Pure cultures of that isolate were maintained on Zobell marine agar plates. Predominantly agarases, amylases and xylanases had been isolated from marine samples like sediments, seawater, seaweeds and microbes [2,13,36].

3.2. Biochemical Characterization and Identification of the Bacterial Isolate

The observations of the performed standard biochemical tests are tabulated in

Table 2. Observations of biochemical tests performed for Halomonas meridiana VITSVRP14.

Characteristic	Observation
Morphology	Long Rod
Pigmentation	White
Gram Staining	Gram Negative
Motility	Motile
Catalase	Positive
Oxidase	Positive
Urease	Positive
Starch Hydrolysis	Positive
Casein Hydrolysis	Negative
Tween 20 hydrolysis	Positive
Tween 80 hydrolysis	Positive
Methyl Red	Negative
Vogues-Proskauer	Positive
Indole	Negative
Carbohydrate Fermentation	Positive
Hydrogen Sulfide production	Positive
Glucose Fermentation	Positive
Lactose Fermentation	Positive
Sucrose Fermentation	Positive

. For bacterial identification, the comparison of the obtained sequence with available sequences in NCBI database and from the constructed phylogenetic tree (Supplementary Materials Figure S1) it was revealed that the organism was Halomonas meridiana since the similarity was 98.29% in the BLAST hit. Under the accession number ON063907 in the GenBank database, the sequence of the isolated microbe Halomonas meridiana VITSVRP14 is available.

3.3. Co-Production

The nutrient broth supplemented with 2% NaCl was used as the basal medium for enzyme production. The agarase, amylase and xylanase activities were analyzed by harvesting the fermented broth at regular time interval of 4 h after microbial inoculation up until stationary phase to savvy the fermentation time needed for the bacteria and the observations are represented in Figure S2 of Supplementary Materials. It was revealed that the enzyme activities were optimum at 24 h incubation time. And hence it was followed in further experiments. The isolated organism, Halomonas meridiana VITSVRP14 was able to produce agarase, amylase and xylanase enzymes with activities of 1.10 U/mL, 4.26 U/mL and 2.79 U/mL respectively in NaCl supplemented nutrient broth.

3.4. Optimization

3.4.1. Classical Optimization

Using One Variable At a Time (OVAT) method, the preliminary experimentations for the investigation of optimum levels of pH, inoculum size, incubation time, temperature, NaCl, different carbon and nitrogen sources were conducted.

Effect of Key Cultural Parameters on Enzyme Production

pH: The initial pH of the production medium conspicuously has an effect on the microbial growth which solidly affects the enzyme production. Hence, the effect of pH on the enzyme production was optimized by varying the pH of the production media from pH 4 to pH 12. The other production conditions were: 24 h incubation, 37 ℃, 2% NaCl and 120 rpm agitation. It was observed that maximum enzyme production of all three enzymes was found at pH 8 with 1.36 U/mL agarase activity, 4.96 U/mL amylase activity and 3.38 U/mL xylanase activity. The observed effect of pH on all three enzyme activities is showed in Figure 3A. As reported by previous authors [36], it is evident that bacteria that hail from marine samples are active in alkaline environments. From this pH optimization study, it was evident that more than 50% of activity was endured for all three enzymes even at pH 10 which will enable the coenzyme mixture to work under alkaline environmental conditions.

To interpret the observed result, it is known that all enzymes are proteins with acidic and basic amino acids at their ends. When pH of the environment in which the enzyme resides change, it automatically affects its ionic state leading to structural changes which in turn affects the activity of that particular enzyme. This change in pH also sways the substrate by affecting its shape and charge which negatively affects the substrate specificity and catalysis. Thus, there will be an optimum pH or pH range for any enzyme to evince its maximum activity. Our results corelated with [39] who has stated that Halomonas species are capable of surviving and producing metabolites in a pH range from 6 to 13.

Inoculum size: The effect of inoculum concentrations ranging from 0.5% to 3% with an interval of 0.5% was investigated and the remarks are displayed in Figure 3B. It was seen that maximum agarase activity of 1.61 U/mL, amylase activity of 5.06 U/mL and xylanase activity of 3.40 U/mL were witnessed at 1.5% inoculum size which gradually decreased when the inoculum size was increased further. The elevation in the enzyme activity on increasing the inoculum size might be due to more interactivity of bacteria with the nutrients of the medium. As asseverated in [40], this interactivity possibly enhances the metabolism and protein synthesis of the bacteria which has positive effect on enzyme activity. The competition between the nutrient and substrate at higher inoculum size percentages might have led to decrease in enzyme activity after optimum inoculum size.

Incubation time: To envisage the time needed to attain maximum enzyme production, the activity of the produced enzyme was monitored at 8, 12, 24, 36 and 48 h of incubation and the graph is patterned (Figure 3C). It could be distinctly seen that the maximum enzyme production was obtained at 24 h of incubation and the activities were 1.65 U/mL, 5.16 U/mL and 3.33 U/mL for agarase, amylase and xylanase activities respectively.

Temperature: The temperature of the medium in which the enzyme is produced has a patent effect on the microbe which affects the enzyme production sternly. Hence, the effect of temperature on production of enzymes was optimized by varying the temperature from 30 °C to 60 °C with an interval of 10 °C. It was spotted that the production of all three enzymes were high at 40 °C with 1.72 U/mL agarase activity, 5.32 U/mL amylase activity and 3.45 U/mL xylanase activity. The observed effect of temperature on all three enzyme activities is presented in Figure 3D. Our results accustomed with few other reports by [41,42,43], where the optimum temperatures were reported between 37 °C to 45 °C. In order to stay in shape and to exhibit their activity, enzymes must be upheld in their tertiary structure by the intermolecular interactions of amino acids of that protein. These intermolecular interactions can primarily be affected by pH and temperature as external factors. Hence the increase or decrease in temperature from its optimum value will affect the enzyme activity in an effectively.

Effect of Media Components on Enzyme Production

NaCl: There exists a very close relation between the enzyme activity and metabolic activity of any microbe. The addition of NaCl at higher concentration usually results in cell lysis due to the osmotic pressure that had been created in the microbial environment. As the bacterium used in this study has been isolated from marine samples, 2% NaCl was provided to the production medium as default. In addition to that, it would be necessary to find the effect of different concentrations of NaCl on the enzyme activity. Hence, by varying the concentration like 2%, 4%, 6%, 8% and 10% in the production medium the activities were determined and plotted in Figure 4. Author of [37] cultured Halomonas meridiana in 8% NaCl broth culture for xylanase production. In our study, we manifested that our organism Halomonas meridiana VITSVRP14 could survive till 12% NaCl concentration and the optimum NaCl concentration for enzyme production was 8% and the maximum activities were 2.30 U/mL, 6.23 U/mL and 4.76 U/mL for agarase, amylase and xylanase as compared to other NaCl concentrations. Earlier reports of [39] enlighten that these Halomonas species are capable of enduring in high saline conditions even with low nutrients by the production of ectoine and polyhydroxybutyrates (PHB) as their survival strategy.

Different carbon and nitrogen sources: To meliorate the enzyme production, 1% of different carbon sources like starch, xylan, sucrose, lactose, maltose, glucose, fructose, galactose, mannitol, agar agar (0.1%) and agarose (0.1%) were appended to the production medium and the activities were determined and represented in Figure 5A. Meticulously, all the carbon sources used for the optimization study boosted the enzyme activities except mannitol which constrained the microbial growth that obviously reduced the enzyme production and activity. Amongst all the carbon sources, the maximum enzyme production was witnessed with 1% lactose. The agarase, amylase and xylanase activities at 1% lactose were 2.81 U/mL, 6.94 U/mL and 5.46 U/mL respectively. Further, the effect of different concentrations of lactose on each enzyme activity was also investigated and represented in Figure 5B.

The effects of organic and inorganic nitrogen sources on the enzyme activity were determined. Comparatively, the organic nitrogen sources had negative effects on the enzyme activity than the inorganic nitrogen sources. Among the inorganic nitrogen sources, 1% of sodium nitrate was found to exhibit agarase activity of 2.94 U/mL, amylase activity of 7.11 U/mL and xylanase activity of 5.48 U/mL and the comprehensive results are signified in Figure 5C. The observation of different sodium nitrate concentrations on enzyme activity is depicted in Figure 5D.

3.4.2. Statistical Optimization

Plackett-Burman Design Evaluation of Significant Production Media Variable

The identification of significant variables that directly affect the coproduction was screened using Plackett-Burman screening two-level factorial design. From this design, it was declared that temperature, pH and lactose concentration play vital role in coproduction. The magnitude of individual variable can be understood by Pareto chart (Figure 6). From the obtained Pareto chart of all three enzymes, the threshold of temperature which was beyond the t-value and Bonferroni limits evidenced its weighty effect on the coproduction.

Response Surface Methodology

In order to provide highly reliable results, the RSM methodology was chosen as a statistical tool. As stated by the authors of [20], to perform RSM experiments, a Central Composite Design had been chosen over a Box-Behnken design for our study since it would allow us to know the effect of the chosen significant factors on the response as we go below or beyond the previously chosen levels. In Box-Behnken design we have only three levels whereas in Central Composite Design the experiment is flexible to five levels including positive and negative axial points (−α and +α).

The experimental variables of the factorial analysis obtained from Plackett Burmann experiments were (A) temperature, (B) pH and (C) lactose concentration. The individual and interactive significant effects of these three independent variables on coproduction were studied at five coded levels. A multiple regression analysis was exploited to acquire predicted response for the attained experimental data. To understand the response-factor correlation, the denoted second order polynomial equations were framed [14]. By comparing the factor coefficients of these coded equations, the relative impact of the factors can be discerned.

Agarase Activity = + 3.38 − 0.22*A − 0.11*B − 0.0089*C + 0.069*AB − 0.091*AC + 0.089*BC − 0.86*A² − 0.60*B² − 0.20*C² − 0.089*ABC + 0.058*A²B + 0.16*A²C − 0.59*AB²

(4)

Amylase Activity = + 7.07 − 0.61*A − 0.27*B + 0.35*C + 0.16*AB − 0.15*AC − 0.12*BC − 1.68*A² − 1.26*B²

(5)

Xylanase Activity = + 5.37 − 0.93*A − 0.29*B + 0.28*C + 0.021*AB − 0.0.094*AC + 0.024*BC − 1.43*A² − 0.77*B² − 0.18*C²

(6)

The significance of this second order polynomial equation was tested by dint of ANOVA (

Table 3. Concise ANOVA table.

	Agarase		Amylase		Xylanase
Source	F Value	p Value Prob > F	F Value	p Value Prob > F	F Value	p Value Prob > F
Model	45.27	<0.0001	15.47	<0.0001	9.94	0.0006
A-Temperature	7.83	0.0312	9.4	0.0107	21.63	0.0009
B-pH	1.85	0.2223	1.86	0.2003	2.05	0.183
C-Lactose	0.013	0.9134	2.98	0.1122	2.01	0.187
AB	1.08	0.3385	0.37	0.5559	0.006	0.9371
AC	1.9	0.2167	0.33	0.5745	0.13	0.7286
BC	1.8	0.228	0.22	0.6518	0.008	0.9297
A square	301.83	<0.0001	75.61	<0.0001	53.39	< 0.0001
B square	148.97	<0.0001	42.33	<0.0001	15.29	0.0029
C square	17.06	0.0061	-	-	0.85	0.3791

). The model F-values of 45.27 for agarase, 15.47 for amylase, 9.94 for xylanase and very low probability values of <00001, <0.0001 and 0.0006 corresponding to agarase, amylase and xylanase activities sternly indicate the significance and fit of the designed experiment in anticipating the response.

The rightness and fit of the obtained regression model could be determined by the co-efficient of determination (R²) which in general should be greater than 0.75 [14]. The R-squared values obtained were 0.9899, 0.9184 and 0.8995 for agarase, amylase and xylanase respectively therein indicating the acceptability of the model. It was also observed that the difference between the predicted and adjusted R-squared values for all three activities were less than 0.2 which expresses that there is no possibility of any problem or block effect with the designed model or the obtained data. Since the differences between the predicted and adjusted R-squared values were in reasonable agreement, there was no necessity for any model reduction or response transformation [44]. In addition to this, the lack of fit was also non-significant for all three enzymes which clearly infer that the model is fit. The signal to noise ratio of the designed model is determined by the resulting adequate precision values. The values of adequate precision above 4 such as 17.836, 12.861 and 10.692 for agarase, amylase and xylanase respectively indicated an adequate signal. The individual and square effects of temperature and also the square effect of pH were observed to be significant in all three enzyme production. The satisfactory degree of precision can be divulged by a lower coefficient of variation (CV) where we observed lower values such as 8.33, 14.6 and 19.82 corresponding to agarase, amylase and xylanase evinced that the model is precise and reliable. The observed and predicted values of the coproduced enzymes; agarase, amylase and xylanase in CCD study are represented in

Table 4. The observed and predicted values for co-production of agarase, amylase and xylanase in CCD with coded units and real values.

Runs	Temperature		pH		Lactose Conc.		Agarase Activity (U/mL)		Amylase Activity (U/mL)		Xylanase Activity (U/mL)
	Actual	Code	Actual	Code	Actual	Code	Observed	Predicted	Observed	Predicted	Observed	Predicted
1	30	−1	7	−1	1.5	+1	2.77	2.82	5.21	5.52	4.13	4.24
2	40	0	9.682	+α	1	0	1.42	1.4	3.63	3.55	3.12	3.04
3	50	+1	9	1	0.5	−1	0.92	1	3.01	3	1.18	1.09
4	30	−1	9	1	0.5	−1	2.05	2	3.75	3.7	3.06	3.24
5	30	−1	7	−1	0.5	−1	2.64	2.58	4.22	4	3.65	3.82
6	30	−1	9	1	1.5	+1	2.89	2.81	4.05	4.17	3.84	3.91
7	40	0	6.318	−α	1	0	1.78	1.77	4.66	4.51	4.51	4.48
8	40	0	8	0	0.159	−α	2.74	2.66	6.33	6.42	4.84	4.92
9	40	0	8	0	1	0	3.46	3.36	7.19	7.54	5.66	5.57
10	40	0	8	0	1	0	3.62	3.36	7.62	7.54	5.37	5.57
11	40	0	8	0	1.841	+α	2.71	2.56	8.32	8.64	6.1	9
12	50	+1	7	−1	0.5	−1	0.88	1	3.05	2.94	1.48	1.39
13	50	+1	9	1	1.5	+1	1.04	1	2.91	2.84	1.38	1.33
14	50	+1	7	−1	1.5	+1	1	1	3.23	3	1.79	1.75
15	40	0	8	0	1	0	3.47	3.36	7.39	7.54	5.39	5.57
16	23.182	−α	8	0	1	0	1.25	1.17	3.94	3.52	3.1	2.88
17	40	0	8	0	1	0	3.18	3.36	7.73	7.54	5.81	5.57
18	40	0	8	0	1	0	3.33	3.36	7.29	7.54	5.51	5.57
19	56.818	+α	8	0	1	0	0.51	0.45	1.95	1.77	0.77	1.1
20	40	0	8	0	1	0	3.54	3.36	7.27	7.54	5.3	5.57

. The minimum and maximum activities of agarase, amylase and xylanase were observed as 0.51 U/mL and 3.62 U/mL, 1.95 U/mL and 8.32 U/mL, 0.77 U/mL and 6.1 U/mL respectively, where it clearly signifies that the elected variables and their varying concentrations have a commendable effect on enzyme production. With similar experimental variables such as pH, temperature and substrate concentration and with other experimental designs of RSM, authors of [45] acheived threefold increase in xylanase yield.

The perceptible effects of variation in the selected parameters on the enzyme activity can be portrayed by the contour plots [46]. The significant interaction between the variables can be confirmed by the acquired elliptical plots (Figure 7). Comparing the contour plots of all three enzymes it can be concluded that the lactose concentration had substantial effect on enzyme production on its interaction with other two parameters. The contour plots showed that the temperature range from 30 °C to 40 °C with a minimum of 0.5% lactose concentration to a maximum of 1.5% exhibited maximum enzyme production for agarase and xylanase. With 1% of glucose concentration and at pH 8, authors of [47] noticed maximum agarase production which is slightly analogous with our results. Other bacterial species isolated from sediment samples exhibited maximum xylanase activity at pH 9 and 40 °C with 48 h of incubation [48]. From the contour plots, for maximum amylase production, the temperature was between 35 °C and 40 °C and the lactose concentration was above 0.9%. Authors of [49] also witnessed that the bacterial amylase activity was decent around neutral pH range. From our study, we could find that the pH range of about 7.5 to 8 was optimal for the coproduction.

The optimal points of the individual variables and the interactions between them have been expounded by the three-dimensional response plots (Figure 8). At a fixed lactose concentration (1%), there was a gradual improvement of agarase activity upon gradual increase of pH (7 to 8) and temperature (30 °C to 50 °C) whereas after its optimum point (pH 8 and 40 °C) decrease in agarase activity was detected at higher pH (above 8) and temperature levels (above 40 °C). For amylase activity, the changes in temperature (30 °C to 50 °C) and pH (7 to 9) affected the enzyme production whereas the lactose concentration did not affect the production which means that even from a lower to higher concentration (0.5% to 1.5%) changes of lactose the enzyme activity remained stable. The authors of [50] reported that the maximum activity of xylanase from a Bacillus species was at 30 °C and pH 7. The changes in temperature deliberately affected the xylanase production fiercely where at an optimum temperature (40 °C) maximum activity was observed even with changes in other two parameters and at lower (30 °C) and higher (50 °C) temperature ranges there was decrease in the enzyme activity.

3.5. Application Study

The magnitude of saccharification capacity of the coenzyme mixture on the selected lignocellulosic substrates: rice bran, wheat bran, cassava bagasse and corn cob after their pretreatment was inspected at 3rd, 6th, 9th, 12th and 24th hour (Figure 9). With increase in the interaction time of the lignocellulosic substrates and the coenzyme mixture, the release of reducing sugars also increased which palpably imply that the selected biomass was inclined to enzymatic saccharification. Due to the subjective pretreatment of lignocellulose biomass, easy accessibility of the substrates to the coenzyme mixture boosted the catalytic activity in turn resulting in high release of reducing sugars [51]. Out of the selected lignocellulosic substrates on comparing 24th hour evaluations, maximum reducing sugars were unfettered from cassava bagasse with 61.2% of saccharification with respect to glucose as it has equivalent proportions of fiber and starch in its dry matter along with ample proportions of proteins, lipids and minerals [52]. Next to cassava bagasse, corn cob exhibited 57.82% of saccharification as it has enormous cellulosic content with linear glucose units [53]. With around two third arabinoxylan of its total composition [54], after alkali [55] and enzymatic treatments wheat bran exhibited nearly 50% saccharification. Rice bran exhibited around 35 to 40% saccharification as maximum in all sugar equivalences. As previously reported, lignocellulolytic enzymes like amylase and xylanase have budding applications in lignocellulosic biomass conversion [56]. To culminate, it can be asserted that the coproduced enzyme mixture from Halomonas meridiana VITSVRP14 is a potential candidate that could be applied in the bioconversion of lignocellulosic substrates for further applications.

4. Conclusions

Enzymes that are produced extracellularly by halotolerant and halophilic microbes are proved to perpetuate their catalytic activity in conjunction with their structural stability over an eclectic salinity. Efficacious execution of classical optimization using OVAT followed by statistical optimization using RSM for coproduction of agarase, amylase and xylanase by submerged fermentation in a minimal nutrient medium engendered the enzyme production. The application of this coproduced enzyme mixture on pretreated lignocellulosic biomass proclaimed reasonable saccharification rates. As three enzymes are coproduced by a single microbe, the cardinal factors like time, cost, manpower and energy load could be lessened in any industrial application. In spite of the fact that the enzymes are coproduced, if any of these enzymes is needed individually, then it can be attained through specific downstream process like chromatographic techniques and can be used for its corresponding application.